Guowei Wang

Yu Sun

Jing Chen

Yang Jiao

Chuanhong Zhang

Haijiao Yu

Chan Lin

Guogang Zhao

Quinoa traceable system based on Internet of things

Keywords: IOT, Quinoa, Traceability system

Quinoa is known as the lost ancient "gold" nutrition, along with the increase of domestic demand, in recent years at home also growing acreage, in order to solve the quinoa products traceability of quinoa planting, pesticides fertilizer use and processing data information acquisition, storage and processing, and other issues. This article USES the Internet of things technology, ZigBee development technology and space technology such as fuzzy data mining, based on the Internet of things of quinoa traceability system, realized the quinoa cultivation, production and transportation, warehousing and other real-time data acquisition, transmission, processing, and text messages warning and quinoa disease quinoa products full traceability, etc. This project research has completed a preliminary test in the experimental base, basic functions required to complete the project, can achieve the goal of the quinoa products traceability.

Introduction

With the improvement of people's living standards, people have set higher demands on food. Quinoa is not only rich in nutrients of all kinds for human needs, but also possesses high-quality proteins close to the amino acid composition of human body. It is an important function to increase the nutritional value of food for the population food. In the meantime [START_REF] Popkov | Ethnosocial processes and ethnosocialpolicy[END_REF], the development of the quinoa industry can reduce the human demand for animal protein to a certain extent, thereby reducing the input of livestock, pens and human resources in the breeding industry, the occupation of cultivated land in the breeding grounds, and the pollution of animal excrement to the environment [START_REF] Liu | Design and implementation of traceability system for stichopusjaponicus products[J][END_REF].

Based on this, this project designs and develops a set of traceability system of quinoa based on Internet of Things. The system core technology and equipment for independent development, the overall high level of technology, and complete sets of equipment costs and prices much lower than similar foreign products, with the support of specialized software, the monitoring data can be quantified, scientific decision-making [START_REF] Guowei | The comprehensive evaluation of the spatial difference ofsoil nutrients by variable rate [J][END_REF]. Provide a typical demonstration reference for the traditional agricultural production manual mode to the agricultural informationization, intelligent and large-scale production mode transformation.

The research status

LIN Fengsheng and FU Xiang introduce through the analysis on the whole circulation process for imported food, a quality safety traceability system based on quality safety critical control point analysis was proposed [START_REF] Lin Fengsheng | Quality Traceability System for Imported Food Based on Critical Control Point Analysis[END_REF] in《Quality Traceability System for Imported Food Based on Critical Control Point Analysis》,FENG Chun-wei and GONG You-hui introduce a fruit and vegetable traceability system on the strength of the Internet of things (IoT) technology and the GPS and 3G/4G technology was designed [START_REF] Feng Chun-Wei | FENG Chun-wei and GONG You-hui[END_REF]in《Design on IoT-based Fruit and Vegetable Traceability System》.

Main research content

Based on the Internet of Things technology, this project has perfected a set of traceability system for quality monitoring records analysis of quinoa planting, processing, transporting and warehousing processes. The project focuses more on the planting and processing of quinoa, collecting, transmitting and storing the data of the key processes such as quinoa planting and processing through the Internet of Things technology so that the products have a unique identification throughout the process and are added The early warning system to ensure the controllable operation of product quality. The main technologies of this project include the following aspects

planting process can be controlled

In quinoa is adopting the data entry of planting stage, using the mature technology of Internet of things will quinoa grown stage of resource allocation and other important information into the database, the data in the process of planting and processing realtime monitoring, calculated by the system science, irrigation, fertilization and pesticide dosage, promoting the scientific and standardized planting quinoa production [START_REF] Lin | The research on Internet of things application architecture based on web[C]// Advanced Research and Technology in Industry Applications[END_REF][START_REF] Haiming | Comparison of the structure and implementation of the Internet of things system[END_REF].

Use at present relatively mature and stable ZigBee transmission technology in the field of network communication, at the same time establish a wireless sensor network to complete the quinoa growth environment of soil temperature, soil humidity, illumination, temperature, air temperature, air humidity, and crop status and automatic data acquisition of agricultural information. Field workbench USES solar power systems provide energy, the use of GPRS network communication to transmit data through the network to the server, the server for data receiving, parsing, storage, forwarding, etc.

The whole process of data monitoring

The traceability system supported by the database, using the unique identification code, by marking a design and labeled data interaction to complete the underlying structures of the traceability system. In logistics link to use automatic qr code identification record all data information, realize the traceability of the product, at the same time, using a large amount of data to support the warning system for the whole digital early warning information.

Key information traceability

Establish a traceability website, use the database to organize the above information storage, through the product above the unique identification code can be queried on the network quinoa products in the planting, processing and transportation links and other important information, making quinoa food safety is not It is an empty talk..

Stem design

The system mainly includes hardware layer, network layer, data layer and display layer, a total of five aspects. Among them, the hardware layer mainly includes various types of sensors during the quinoa growing stage and RFID and two-dimensional code scanning equipment installed on per processing equipment during the quinoa processing. The hardware layer is mainly responsible for all aspects of the basic data acquisition. The network layer mainly includes zigbee transmission network and GPRS transmission gateway, which is mainly responsible for transmitting the basic data stably and efficiently to the server. The data layer mainly includes quinoa traceability server and database, receives a large amount of basic data through the server, parses, organizes and analyzes and finally stores it into the database, supplemented by a small amount of manual data entry to provide complete data support for the entire traceability system . The display layer mainly includes the data display set up by the quinoa cultivation site and the quinoa traceability website. Data display real-time display of the basic data, but also for quinoa planting workers to provide real-time warning information. The Traceability Web site provides a stable and secure check-in point for end consumers who purchase quinoa products. Quercetin can be found on the website using the unique identification code on the quinoa bag to find out where the quinoa is grown, produced, and shipped Of the important information.

The overall design of the system as shown below:

Hardware layer

The main role of the hardware layer is responsible for the detection of quinoa planting and other related aspects of the data. It consists of soil sensors, air sensors, light sensors, water temperature sensors, several smart switches, several environmental monitoring cameras placed in quinoa fields and several processing monitoring cameras placed in the processing sites, sensors for collecting other parameters, Sensor and data transmission port one by one corresponding to the Zigbee nodes together. Some of these data acquisition ranges are shown in the following table

Network layer

The main function of the network layer is to collect the data collected by the hardware layer and send it to the server quickly and stably. Through Zigbee node selforganizing network will be carried by each node data aggregated to the Zigbee gateway, the Zigbee gateway will be aggregated data sent to the GPRS gateway. The format that the GPRS gateway converts the data into the byte array is sent to the server via the network

Data layer

The main function of the data layer is to receive, organize, analyze, store and forward data. Data layer by the server and database composed of two parts. After the server receives the data sent by the GPRS gateway, the server analyzes the data, analyzes it and stores it in the database. The main function of the database is to store all the data generated from all traceability systems and provide data support for early warning and intelligent control of traceability systems.

The server

Server is mainly composed of two modules, respectively is data reception processing module and data sending module. Data receiving and processing module of the main workflow: First, the server to open a dedicated Socket monitoring function, the establishment of a data transmission between the TCP / IP data transmission connection. Then, the data observing node collects the data to its designated GPRS gateway through the wireless network, and finally sends the collected environment data to the specified IP address (ie, the IP address of the server) [START_REF] Zhihong | Overview of wireless sensor networks for the Internet of things[END_REF][START_REF] Ng | On Spectral Clustering: Analysis and an algorithm[END_REF]. In order to achieve the function of sending data sent by the data sending gateway at any time. Finally, the accepted data is processed and stored in the database, and displayed on the server side. So as to achieve the remote monitoring of quinoa growing environment-related data results.

Some of these data processing rules are shown in the following table: The air temperature sent by the 0x00158d00:0x001c6615 node is 20 ℃ and the humidity is about 32%。 B18.6#0x00158d00:0x001c6615

The water temperature sent by 0x00158d00:0x001c6615 node is 20℃。 C23054.1#0x00158d00:0x001c6615

The light intensity sent by 0x00158d00:0x001c6615 node is 23054.1lx。 Data sending module: in order to reach the timely detection of environmental data, data needs to be sent to the corresponding screen at the same time for technical personnel in time to see. So the server will also need a data interface, through the Socket and TCP/TP protocol to establish server and display the transport connection, according to a specified transport protocol to forward the server receives the data to the display.

Server history data query results as shown below:

Fig. 2 Historical data query

Display layer

Display layer mainly by the work of the display screen and quinoa traceability site consists of two parts. Work display and then receive the server to send the relevant parameters of the data and warning information after the data is displayed on the led display for staff and farmers to view; quinoa traceability site is mainly used to display information related to quinoa, such as quinoa nutrition Value, quinoa cultivation methods, quinoa food practices and other relevant information. One of the more important functions is to provide consumers and other quinoa users with a traceability query that can be queried through the identification code on the quinoa bag to find that quinoa reaches the consumer's hands from planting to growth to production and delivery.

The traceability of query result display as shown in the figure below:

Fig. 3 The query result 4 Summary

The main achievement of this article

The system uses GPS, sensor technology and wireless network technology to set up a wireless sensor network for the production monitoring of quinoa to realize the realtime collection and storage of quinoa planting production environment information; establish the early warning model of quinoa disease based on data mining; Real-time collection of data, historical data and models and other data to establish relevant database 4; integrated WebGIS, GPS, data mining, expert systems and Internet of Things technology, developed a quinoa traceability system based on the Internet of Things.

The next step

• The equipment cost is higher, higher energy consumption wireless sensor network (WSN), the next step "towards low cost and high reliability, energysaving, environmentally friendly and intelligent five direction.

•

The precision of forecasting model is not very high, the next step according to the data, the improved model, improve the model accuracy.

•

The system functions and drawbacks, as further application, constantly improve and improve the system.

Fig.1System main architecture diagram

  Fig.1System main architecture diagram

Table 1 .

 1 Measurement data sheet

	Measurement items	Measuring range	Measurement mode
	air temperature	temperature 0-50℃	Continuous measurement
	air humidity	humidity 0-100%	Continuous measurement
	water temperature	temperature 0-60℃	Continuous measurement
	illuminance	1-65532lux	Continuous measurement
	soil temperature	temperature 0-50℃	Continuous measurement
	soil moisture	humidity 0-100%	Continuous measurement
	Environmental surveillance cameras	Quinoa field	Continuous measurement
	Process monitoring cameras	Quinoa processing area	Continuous measurement

Table 2 .

 2 Data analysis table

	String format	Post recognition data
	A20.0#32.0#0x00158d00:0x001c6615

Acknowledgment

The study was conducted by 2016 jilin province rural special project supported by the modern agricultural development 《 Demonstration and Application of Traceable System of Quinoa Products Based on Internet of Things and 3S Technology》.


Duo Wang

Ying Xu

Qifeng Liu

Yue Lou

Chaorong Luo

Changji Wen

Abnormal Identification of Swine Flu Clinical Characteristics Based on Body Temperature and Behavior

Keywords: Swine flu, Activity recognition, Jilin landrace, body temperature

The pathology and virus isolation are the mainly diagnostic approaches for swine flu currently. Although the diagnosis rate is high, it is not conducive to detecting and intervening the infected pigs timely because of the serious time delay. Therefore, this paper proposed a novel framework method for the early detection and warned of swine influenza. The Jilin landrace are as the subjects of the experiment in this paper. Firstly, the body temperature changes were monitored compared between healthy and infected pigs respectively. And then the machine vision method was used to identify the basic predefined behavior of the landrace. Afterward, the behavior of the healthy and infected pigs were determined the abnormality or not. In the experiments, the results showed that the temperature of the infected pigs increased from 1 ~ 2 h to 40.3 ~ 41.5 ℃ and the lying status of the sick pigs was significantly increased compared to other activities such as feeding and drinking water. The experimental results showed that this method waseffective for early detection of swine flu.

Introduction

Swine flu is a type of acute respiratory disease. It is caused by influenza A virus (influenza A virus). With the continuous expansion of large-scale pigs, swine flu poses a serious threat in recent years. It not only affects the quality of pork, but also affects the market supply seriously and demands to result in large swings in the price of pork.

At the same time, some viruses are similar to those of human which are zoonoses viruses. The swine flu is seriously harm to the national economy and the health of residents.

The study of swine influenza has attracted much attention. How to study the rapid, scientific and accurate diagnostic method is of great significance to the surveillance and prevention of swine influenza. The study found that the clinical features of swine influenza generally manifested as several hours to several days, and the average time of onset was 4 days. At the beginning of the disease, the body temperature suddenly rise to 40.3 ~ 41.5 ℃, the external behavior of anorexia or loss of appetite, often lying, shortness of breath, abdominal breathing, paroxysmal cough. Sick pigs squeezed together, it is difficult to move [START_REF] Zhang | Isolation and identification of H1N1 subtpye of SIV and development of diagnostic method and immunogenicity of M2e recombinant plasmid[END_REF][START_REF] Lu | Progress on Swine influenza virus[END_REF]. The current diagnosis of swine influenza in addition to conventional clinical symptoms and pathological changes in diagnosis, mainly rely on laboratory diagnostic methods, by detecting the blood coagulation contained in the virus contains, and then determine the blood coagulation virus exclusion to achieve the diagnosis of swine flu .The swine influenza virus H1N1, H1N2 and H3N2 subtype multiple PCR diagnostic method is a more efficient and specific diagnostic method. It can be used for clinical rapid diagnosis and classification of samples [START_REF] Yang | An Overview on Swine Influenza Viruses[END_REF][START_REF] Gong | Prokaryotic Expression ,Purification and Identification of Pa-x-specific Sequence of Swine Influenza Virus[END_REF][START_REF] Peng | The Rescue of H1N1 Subtype Swine Influenza Virus[END_REF][START_REF] Xiong | Review on application of Internet of Things technology in animal husbandry in China[END_REF]. The diagnostic methods such as pathology and virus isolation have higher diagnostic rate, but they have serious lag time, which is not conducive to timely detection and early intervention of infected pigs. Therefore, the use of abnormal changes in body temperature and the behavior of the clinical characteristics of live pigs have important research significance.

Clinical features were mainly through manual monitoring, but the traditional method of manual monitoring have a strong subjectivity, high labor costs, work intensity, fatigue and misdiagnosis rate and other issues. In order to overcome the drawbacks of manual monitoring methods, the rapid development and industrialization of the Internet of things what based on "perception" has gradually penetrated into all walks of life, and the development of the Internet of things included animal husbandry is also very rapid [START_REF] Cornou | carriers sows' activity types from acceleration patterns: An application of the Multi-Process Kalman Filter[END_REF]. Domestic and foreign scholars use the Internet of things technology in the field of livestock monitoring to carry out extensive research. The method achieved satisfactory results which analyze the life signs of livestock specific stages and the recorded data of behavior by using data mining method [START_REF] Cornou | Classification of sows' activity types from acceleration patterns using univariate and multivariate models[END_REF][START_REF] Escalante | Sow-activity classification from acceleration patterns: A machine learning approach[END_REF][START_REF] Liu | Acquisition and Analysis of Sow Behavior before Farrowing Based on Acceleration Sensor[END_REF][START_REF] Zion | The use of computer vision technologies in aquaculture -A review[END_REF][START_REF] Wen | Image segmentationg method for maize diseases based on pulse coupled neural network with modified artificial bee algorithm[END_REF]. The acquired data is through the three axis accelerometer and Bluetooth device sensors placed in different parts of the body parts of livestock. Other methods use the machine vision technology, the use of external camera device to obtain the external control of the target shape and movement of information flow, combine with artificial intelligence technology to achieve the semantic analysis and understand of image information. Machine vision technology has real-time, non-invasive, non-intrusive and so on, so in the field of agricultural cultivation and breeding have been sustained attention .It gradually become a hot spot in recent years [START_REF] Peng | Image segmentation algorithm for Overlapping Fruits Based on Disparity Map[END_REF][START_REF] Xue | Feature fusion for basic behavior unit segmentation from video[END_REF][START_REF] Lao | Behavior recognition method for individual laying hen based on computer vision[END_REF][START_REF] Wen | Visual Dictionary for Cows Sow Behavior Recognition[END_REF][START_REF] Laptev | On space-time interest points. IJCV [J[END_REF][START_REF] Dollár | Behavior recognition via sparse spatio-temporal features[C]//Visual Surveillance and Performance Evaluation of Tracking and Surveillance[END_REF].

In summary, through the analysis of relevant research reports at home and abroad concluded that the domestic and foreign research reports are very few by using Internet of things "perception" technology, monitoring the body temperature and detecting abnormal behavioral for early detection and warning of swine influenza. Therefore, this paper proposed a novel method by combining monitoring temperature and recognizing behavior on the basis of previous work, in order to achieve the temperature of infected pigs and abnormal behavior and summarize warning rules of the swine flu early.

2

Materials and methods

Experimental materials

Experimental subjects

The samples, the Jilin Landrace, collected from Jifeng Pig Farm and COFCO Songyuan Pig Breeding Base in Jilin Province. The temperature was recorded in the period from 2013 to 2016 in the spring and autumn. Record the normal pigs 100 times, sick pigs only 70 times. Video collection of normal pigs only have 70 times, sick pigs have 20 times.

Experimental environment

Video capture platform includes, Panasonic WV-CP240 / G color CCD camera with a resolution of 752 pixels (horizontal) × 582 pixels (vertical), Dahua DAHUADH-DVR0804LE-AS8 hard disk burner, back-end processor for ThinkStationP710 high performance Workstation, M4000 professional graphics, 64G memory, 512G Xeon E5-2630V4 dual CPU, the system software module using IMAQ Vision, the algorithm development platform for OpenCV. Body temperature monitoring equipment using a self-test developed portable Bluetooth thermometer, including components, Sic -506F chip single-bus high-precision temperature sensor (0 ~ 50 ℃ range with ± 0.01 ℃ accuracy, 0.004 ~ 0.034 ℃ signal resolution rate). HM06A Bluetooth module, using CSR BlueCore, TI CC2540 chip, configure 6 ~ 8 MB software storage space, support AT (attention) instruction. Lower machine STC15F2K60s microcontroller as the core processor, the host computer using VB programming. From the long-term video interception of the basic behavior of the video segment, the basic behavior, including lying, walking, arch, feeding and eating, behavior video capture single frame diagram shown in Figure 1.

Basic methods

According to the expert's experience and observation, the clinical characteristics of swine flu showed that the temperature of the swine was suddenly increased to 40.3 ~ 41.5 ℃, the external behavior was anorexia or loss of appetite, frequent drinking water, continuous lying, Together, it is difficult to move. Therefore, monitoring the abnormal changes of body temperature and behavior of pigs are important bases for timely and effective detection of infected pigs. First, this study uses a self-developed Bluetooth body temperature measurement instrument, by wearing a home to monitor the ear root of pigs to record changes in the temperature of pigs, the analysis of normal pigs' body temperature compared with the sick pig in the whole course of abnormal changes in body temperature, and then combined with our previous works to use machine vision technology to achieve livestock behavior identification and understanding [START_REF] Dollár | Behavior recognition via sparse spatio-temporal features[C]//Visual Surveillance and Performance Evaluation of Tracking and Surveillance[END_REF], by comparing the basic behavior of healthy pigs in the video revealed and finding abnormal behavior of diseased pigs. In order to judge and predict the prevalence of swine, it is on the basis of monitoring the abnormality of body temperature, behavior and its occurrence.

Bluetooth body temperature recorder

The Bluetooth body temperature recorder is made up of temperature sensors, microcontrollers, communication and storage units. It can be real-time monitoring of the temperature through the signal stored in the storage unit, set the abnormal body temperature duration exceeds a certain threshold through the wireless Bluetooth way to monitor the temperature data sent to the PC side for recording analysis. Temperature sensor used TSic -506F chip, which the temperature sensor for a single bus high precision (0 ~ 50 ℃ range with ± 0.01 ℃ accuracy, 0.004 ~ 0.034 ℃ signal resolution).

The host computer uses VC ++ programming, can carry out wireless real-time data transmission and inquiry through Bluetooth. Temperature sensor chip with T092 package, the temperature signal through the microstrip line and the motherboard link, the package after the temperature measurement parts into the rectangular rubber module, the package is soft and strong, the motherboard and the battery moisture proofing treatment, not only ensure good Heat conduction, but also cannot wear the skin pressure.

Video behavior recognition and understanding

We used the video recognition and understanding method, in order to realize the abnormal behavior discovery and recognition of the cow in the early work [START_REF] Dollár | Behavior recognition via sparse spatio-temporal features[C]//Visual Surveillance and Performance Evaluation of Tracking and Surveillance[END_REF]. On the basis of this method, we also applied to the abnormal behavior identification and regularity of the pig. The implementation steps are as follows: The basic steps are as follows: (1) Video preprocessing, equal length segmentation of a given video segment (7 frames in this paper), using the training video segment (lying, walking, arching, feeding and eating) / Segment); ② in the video segment to extract the spatial and temporal points of interest, the use of an improved space-time local binary model feature description of the spatial and temporal points of interest to extract the characteristics of the description [START_REF] Zhao | Dynamic texture recognition using local binary patterns with an application to facial expressions[END_REF][START_REF] Glaser | Incorporating temporal context in bag-of-words models[END_REF][START_REF] Liu | Learning human actions via information maximization[END_REF]; ③ visual dictionary to achieve the video segment Feature description and representation, and use the trained classifier to achieve basic behavior recognition. Then, on the basis of accurate identification of the basic behavior, the specific test set is identified and understood. The following steps are as follows: (1) Video preprocessing, equal segmentation of a given video segment (7 frames / segment) Video segment feature description and representation, and the same steps in the previous step in the same way; ③ using the training classifier on the video segment semantic annotation, and according to the principle of video segment merging to achieve the recognition and understanding of the test video segment. One of the key technologies involves the following two points:

① to improve the space-time local binary model feature description operator, the specific expression is as follows:

) , ( ) , , ( , 1 0 ∈ • + =    0 ≥ 1 = a std a mean threshold else threshold t y x g TOP MLBP q j , - (1)
p t y x g t y x g std p t y x g w mean p q q j p q q j q j p q

q j ∑ ∑ , ) - ( - 0 = 1 0 = 2 0 = = = ⋅ = ∑ ) , , ( ) , , ( ) , , ( , , , , µ µ (2)
② video segment merging operator, the specific calculation of expression As follows:

         ≠ ∧ ≠ ∧ ≠ = ∧ ≠ ⊕ = ∧ ≠ ∧ ≠ ⊕ = ⊕ = 1 + 1 - 1 + 1 - 1 - 1 + 1 - 1 + 1 + 1 - 1 + 1 - 1 - 1 - 1 - 1 - 1 + 1 - ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) | , ( i i i i i i i i i i i i i i i i i i i i i i i i i i i i v L v L v L v L v L v L v v L v L v L v L v v v L v L v L v L v L v L v v v L v L v v v v v Merge (3)
Here, i v the first segment of the initial segmentation video segment, said video segment belongs to the behavior category label, said video frame merge operation.The detailed algorithm implementation is described ` [START_REF] Dollár | Behavior recognition via sparse spatio-temporal features[C]//Visual Surveillance and Performance Evaluation of Tracking and Surveillance[END_REF].

Experimental results and analysis

Experimental part have three groups of experiments, the first and second groups of experiments combined with expert knowledge, in which the experimental use of a Bluetooth thermometer to achieve healthy pigs and infected pigs in the body temperature monitoring and regular detection, experimental two use of pig basic behavior sample set. In the training literature [START_REF] Dollár | Behavior recognition via sparse spatio-temporal features[C]//Visual Surveillance and Performance Evaluation of Tracking and Surveillance[END_REF], the behavior recognition model is used to realize the accurate identification of the basic behavior of live pigs. On the basis of the above methods, experiment 3 found that the abnormal expression of body temperature and behavior of diseased pigs was obtained, and the clinical characteristics of the disease were obtained and the feasibility of the early identification of swine flu was verified.

3.1

Experiment I: Monitoring abnormal body temperature

Experiment setting

The temperature of the swine was high in the spring and autumn of 2013-2020. The temperature of the thermometer was the position of the pig ear, and the non-contact infrared thermometer FLUKE-F59 was used in the ear root of the pig. During the recording method, the manual recording and the combination of the Bluetooth thermometer were recorded. During the period, 100 pigs were recorded for normal pigs.

The time interval of normal recording time was 6h, and the time interval of recording was 0.5h. Sick pigs only 70 times. Manual recording time interval of 2h, Bluetooth thermometer recorder recording time interval of 0.5h.

Experimental results and analysis

First, to effectively verify the validity of the temperature measurement of the Bluetooth thermometer, we make two groups of comparative experiments, experimental (1) healthy pig temperature record comparison, due to the use of thermometer temperature recording time interval of 0.5h, manual records For the 6h, so in order to ensure the validity of the record data comparison, that is, manual mode and thermometer automatic recording method whether there is significant difference, the thermometer data selection corresponding to the manual recording time points before and after the four groups of data to take the mean comparison, Through ANOVA analysis and DUNCAN verification, it can be seen from Table 1 that there is no significant difference between thermometer measurement and manual measurement( 05 0 > 96 0 = . . P

). Experiment [START_REF] Lu | Progress on Swine influenza virus[END_REF] We examined the body temperature of diseased pigs with the same test method. The results of the test were shown in Table 2, and there was no significant difference between the two methods. In addition, no significant difference in the measurement results under different temperature measurement experiments verify the effectiveness of the temperature measurement of the Bluetooth thermometer. On this basis, the normal pig 100 times, and the sick pigs 70 times, using a thermometer to measure the normal and diseased pig body temperature contrast records shown in Figure 3, where the corresponding time point temperature data for the record data Take the mean.

Fig. 3. Comparison of normal and diseased pigs with body temperature

The results showed that the temperature of healthy pigs was maintained between 38 ℃ and 39 ℃ during the period of temperature measurement, and the body temperature of the infected pigs jumped to above 40 ℃ after 1-2 hours after the incubation period of swine influenza virus, And in the onset cycle basically maintained at 40-42 ℃ . Therefore, when the pig body temperature abnormal fluctuations occur, you can use the thermometer to record the body temperature and make a warning based on fluctuations in body temperature.

3.2

Experiment II: Basic behavior recognition

Experiment setting

Video collection of healthy pigs 70 times, sick pigs 20 times, the two groups were the control group, the video collection were used alone feeding, sick pigs were isolated feeding. Video capture time is 6:00 -8:00 pm, during this period of live pigs are more active, and to ensure that the video capture scene in the light of attention, sooner or later or cloudy artificial light source to fill the light operation, video capture effective time is 1400 hours. And then use the artificial pre-processing of the collected video, including the basic behavior of lying, walking, arching, feeding and drinking water, including the front, side and back of the video segmentation and artificial categories marked, respectively, to obtain five basic behavior In the front, side, back and random view training test samples of the 60 groups, a total of 1200 groups marked video samples, each video segment length of about 2 minutes, for the behavior identification model training.

Experimental results and analysis

First of all, for the 1200 group of training video, using 5 fold cross validation to take the mean way, the behavior recognition model of training and testing, specific training see the relevant literature [START_REF] Dollár | Behavior recognition via sparse spatio-temporal features[C]//Visual Surveillance and Performance Evaluation of Tracking and Surveillance[END_REF]. Identify the results set up to confuse the matrix to illustrate, as shown in Figure 4.

Fig. 4. The results of basic behavior recognition

The experimental results showed that the average recognition rate of lying and walking is 92.8%, which means that there are significant differences between the behavior and the performance of arch, feeding and drinking water. The average recognition rate of arch, feeding and drinking water was 83.2%, which was relatively low. The reason for the analysis was that the external morphological difference of the above behaviors was small and the misclassification was obvious. The results show that the average accuracy of the recognition model is 88.6% in the positive, lateral and posterior view.

Experiment III: Abnormal rules of temperature and behavior

On the basis of experiment one and two, we further found that the diseased pigs were abnormal in their body temperature and behavior.

Experiment setting

According to the field of animal medicine knowledge, Swine flu disease course are divided into latency, disease period, development period, peak period and recovery period, which latency clinical manifestations of the performance is less obvious implicit performance, the period in a non-infectious state. Sick 20 long white pigs the final diagnosis is still using the PCR method. From the experimental point of view specific screening basis for the selection and diagnosis of swine flu pigs with the feeding of pigs using PCR method for effective screening isolation feeding. During the incubation period, the temperature was recorded at 0.5 m intervals and the body temperature was recorded by effective screening. At the same time on the sick pig every day from 6: 00-18: 00 continuous video capture, sooner or later or cloudy artifi-cial light source to fill the light operation, select 7 of which effective video total length of 588 hours.

Experimental results and analysis (i) abnormal rules of body temperature

First, the body temperature data of the pigs were recorded using a thermometer. We intercepted the virus infected swine flu pigs from health to illness and then to cure the body temperature data, the trend of body temperature changes shown in Figure 5.

Healthy pigs are virus carriers after being infected by the virus, during which the incubation period, the onset period, the development period, the peak period and the full expectation. During the incubation period, the temperature of the body temperature is maintained at 38 ℃ -39 ℃ normal body temperature, the incubation period is about 1 day. After the rapid increase in body temperature to 40 ℃ -42 ℃, that is, the duration of onset in 1-2 hours or so on. This body temperature lasts until about 4 to 6 days of healing. When the sick pig is cured, the body temperature will gradually decrease to normal body temperature. So accurate and accurate monitoring of abnormal temperature is important for pig disease early warning and further manual intervention.

Fig. 6. Abnormal behavior frequency statistics results

The experimental results show that the results of this method are consistent with the results of artificial identification in the data trend of sick pig and normal pig lying, arch, walking, eating and drinking water, that is, after the disease is developed, Lying frequency significantly increased, arch, walking, eating and drinking behavior significantly reduced the number of times. The data show regular irregularity. Combined with artificial identification, sick pig lying behavior frequency has been increased. Arch, walking, eating and drinking water frequency has been reduced.

Conclusion

In this paper, the use of body temperature thermometer and machine vision method to achieve sick pig body temperature and behavior abnormalities of the discovery. First of all, the effectiveness of abnormal temperature monitoring of the thermometer was verified. Firstly, the temperature of the pigs was recorded by artificial and body temperature thermometer, and the validity of the thermometer was verified by statistical method. Secondly, the sick pig only to record the temperature and found abnormal changes in the prevalence of body temperature. Thirdly, through the basic behavior video training set training behavior recognition model, in order to further realize the disease in the video surveillance abnormal behavior to provide model method. Finally, combined with body temperature and behavior monitoring methods to achieve the abnormal rule of diseased pigs found. According to the temperature data found in the trend of changes in body temperature. That is in the incubation period and the onset of short-term temperature .Suddenly it jumped from the normal body temperature quickly increased to 40 ℃ -41 ℃. Using the behavioral identification model, it was found that the prevalence of sick pigs in the video . It was significantly increased, and the other activities such as feeding and drinking water were significantly reduced. The experimental results show that the method can effectively realize swine influenza surveillance, and the automatic identification of swine flu clinical features is a useful supplement to the existing pathology analysis methods. This method is one of great significance for the monitoring and early warning of swine flu and the timely provision of human intervention.

Fig. 1 .

 1 Fig. 1. Schematic diagram of the basic behavior Note: Behavior Description:(a) lying on the front (b) lying on the side (c) lying on the back (d) walking front (e) walking side (f) walking behind (g) arch front (h) arch side (i) arch back (j) Feeding front (k) Feeding side (l) Feeding back (m) drinking water front (n) drinking water side (o) drinking water behind

Fig. 2 .

 2 Fig. 2. Physical map of the Bluetooth body temperature recorder

Fig. 5 .

 5 Fig. 5. course of body temperature trend chart

Table 1 .

 1 different temperature measurement of significant significance

	method	Average number	Standard deviation	Sample size	DUNCAN
	thermometer	38.75	1.021	5000	A
	artificial	38.74	1.043	5000	A

Table 2 .

 2 different temperature measurement of significant significance

	method	Average number	Standard deviation	Sample size	DUNCAN
	thermometer	40.95	1.041	5000	A
	artificial	40.81	1.023	5000	A


Leilei Deng

Zhenghao Wang

Hui Zhou

Application of image segmentation technology in crop disease detection and recognition

Keywords: Image segmentation, Regional location, Crop, Recognition processing

Computer vision technology and image processing technology are applied in the field of agriculture gradually. How to diagnose crop diseases quickly and effectively has become a research hotspot. In this paper, we combine edge detection and fuzzy clustering algorithm to get the new algorithm through the experiment of more than 1500 pictures. The different kinds of diseases and insect pests of the 5 different crop leaves are used as the research object. Through the gray processing of the images, the removal of the unrelated background, the image segmentation, and the filling of the pixels of the crop disease and insect pests, the final calculation is final. The degree of crop leaf diseases and insect pests is calculated. From the experimental data, the degree of crop damage can be accurately reflected, and the degree of crop leaf disease and insect damage can be calculated, and the automatic batch operation of image segmentation can be realized.

Introduction

Agriculture is the cornerstore for development of our economy. Along with the improvement of living standards, demands of high reliable crop have grown in people's mind. In the practical production, the crop disease is an important factor limiting the growth of farmers, which causes huge loss and a direct influence on the crop market. Farmers mostly depend on its own experience to restore the crop disease. And having gained some effect, but due to farmers are not experts and recognition capability is limited, the crop disease is often invisible to the naked eye cannot figure out "atcs appropriately the situatio" and timely preventing. So depend on visual system and rich experience( subjectivity, limitation, fuzzy) that is not enough to say the science of the disease identify. The traditional disease detection method not only time consuming, but also impacts the forecasted accuracy of the crop disease. With the rapidly developing of computer image processing and identification technology, it is possible for the crop disease to accurately identify, timely detection, adopt right measures and reduce losses. Therefore, it is endowed with great significance.

*Deng Leilei, in Changchun City, Jilin Province, Master, Associate professor Wang Zhenghao, in Changchun City, Jilin Province, Postgraduate student Zhou Hui, in Changchun City, Jilin Province, Postgraduate student

The Definition of Image Segmentation

Image segmentation technology has been developing for many years, scholars have made the expressing method of different interpretations and forms. In common parlance, the image segmentation call the propect that is the separation of useful and worthless content to reserve useful image information, which is the first step to the image processing and what to leave off is the background. Only after completion of the operation, the higher order operation can go on in the following image processing. For more abstract statement, the image segmentation means that the pixels in the image broken into separate blocks according to characteristics. The pixel in block has the same attributes where a different block is used for the value of output pixels is quite different. The computer programing technique might prefer the image segmentation, which provides the service locators to describe. R is set to the image to be split in a set. If R1, R2, R3 and RN are the correct segmentation, the following five conditions will be satisfied.

=R

(1) i, j, i j, Ri Rj= (2) i=1, 2, ……, N, P(Ri)=T

i, j, i j, P(Ri Rj)=F (4) i=1, 2, ……, N, Ri is not empty (5)

Description (1) says that the subset combination is equal to the collection of image. Description(2) says that the two of these subsets are neccessary to be disjointed, which the each segmentation is independent. Description (3) shows that all elements of the each subset are connected [START_REF] Shuifa | Algorithm for Damaged Leaf Segmentation Based on Block Mark[END_REF] . Description(4) says that all elements of the different subset are disconnected. Description (5) says that the each subset must be non-null. The image can split by defining and the defined way of expressiveness has great significance. The image segmentation is extracted and the useful region is marked, and the usefulness of regions completely be defined subjectively. It is truly successful when the useful region can be extracted [START_REF] Jing | Image Detecting for Hazard Levels of Corn Spots[END_REF][START_REF] Fang | Recognition of Cucumber Leaf Disease Based on Support Vector Machine[END_REF][START_REF] Zhihua | Segmentation Method for Cotton Mite Disease Image under Complex Background[END_REF] .

Different Identification Methods

Threshold Segmentation Method

The threshold segmentation method is a simple and efficient image segmentation method, which useing one or more thresholds to the image grayscale is divided into several parts, the same part of the pixel is a whole. The threshold segmentation method is the earliest method to study the image identification of the crop disease [START_REF] Jinwei | A Otsu Threshold Segmentation Method Based on Rebuilding and Dimension Reduction of the Two-Dimensional Histogram[END_REF] . In 1989, the threshold segmentation method is first proposed by the Japanese to divide the sagittifolia disease leaf of micronutrients such as calcium, magnesium and iron. Because the extracted RGB bar chart features and location characteristics are not representative, which resulting in segmentation effect is not ideal [START_REF] Xiuli | SAR Image Segmentation Based on Watershed and Spectral Clustering[END_REF] . On the basis of it, the domestic Chen Jiajuan try to use the local threshold method to extract the bar chart feature and adopt local threshold segmentation to segment the cotton disease image from the background in a laboratory environment. In 2009, the British scholar in the plant disease recognition identification system, taking the banana and plantain black leaf streak as an example, and using the optimal threshold of the bar chart segmentation method, the lesion and the background of the leaves are successfully separated, and the impact is positive. The characteristic of the threshold segmentation method are that is both simple and space-efficient, the difficulty lies in the selection of threshold. The texture and color of the pest area are different from the pest-free area, and based on analyzing the gray bar chart can be more intuitive to find the threshold to achieve the image segmentation.

Edge Detection Method

Much of the information of the image exists in the edge that the basic characteristic of the edge can effectively solve the problem of the image segmentation. The edge detection method is classical image segmentation theory, which is widely used in image segmentation. In 2011, Baumt uses the Soble edge detection algorithm to separate the wheat plaque from the background. And in 2012, He Dandan uses the algorithm is designed by combining the minimum external rectangle algorithm with the median filter and the Canny operator to propose the rice leaf edge detection algorithm based on multi-strategy fusion technology, which is effcient and accurate. The edge detection method is based on the change of the gray value of the pixel on the edge in between the different region. In the crop disease image, there are many mutations in the edge gray value of the disease. Although the edge detection method has such features as easy operation, the disadvantage of the segmentation efficiency depends on the edge detection operator and the robustness is poor [START_REF] Jiaming | Connectivity Constrained Graph-Cut for Fast Interactive Image Segmentation[END_REF][START_REF] Jia | A Graph Cuts Based Interactive Image Segmentation Method[END_REF] .

People will notice the place where objects intersect when they see a picture, and people can select the main useful information automatically in the brain. And the place where objects intersect is also the area where the gray value of the image changes obviously. The regional information provides important information for the main location of the image, which provides the important basis for the segmentation of the target object. The study found that the region where the gray value of the image violently would normally be the edge of the object. If people can determine the edge of the object, through variance of the gray value, which can improve efficiency and accuracy of the image segmentation. The edge of the image is the important and useful information in the image segmentation. The characteristic of the pixel of the image along the edge becomes incontinuous, which can be a great help by finding the edge of the object in the image [START_REF] Shoudong | Gaussian Super-Pixel Based Fast Image Segmentation Using Graph Cuts[END_REF] .The researchers found that the signal presented along the edge of the form can be roughly classified into three ways. The three types are the stepped edge, the roof-edge and the linear edge. The characteristic of the stepped edge is that the gray value of changing abruptly up and down in the steady state, and the balance is maintained after it complete the change. The roof-edge is characterized by the fact that the gray value in the state is slow rising and slow descending, however, there is a turning point, the gray value of the sudden change in the state of slow descending and slow rising. And the characteristic of the linear edge is that the gray value in the steady state suddenly upward and downward after the dramatic change, however, the gray value of the state has gone through a series of changes can return to the original state soon [START_REF] Shuanqiang | Liver Tumor Segmentation Using Graph Cuts Based on CUDA[END_REF] . The three forms of edge should look like the Figure . 2 below.

Figure. 2. Three forms of the edge

There are recognizable and unrecognizable diseases in the image areas. In order to achieve higher recognition accuracy of the disease area, we can set the initial image of the initial position in the image, where the length and width of the rectangular box represent a certain percentage of the length and breadth of the image, respectively. The two experiments are carried out through a large number of the image measurements. The average value where = 0.7 is the long scale factor of the image and = 0.8 as the image of the wide scale factor [START_REF] Xiaodan | Research Progress on Image Segmentation[END_REF] .

Accurate Location Method

Combined with the edgelocation method, the image of the target region of the position box to further refine and make the four sides closer to the edge of the disease area. Such as outward gradually widening. For example, taking the border outward a distance, and the original frame will form a new expansion region after the distance of the border and the original. as this Figure . 3 shows, the region is set to D. In this region, the number of the edge information is calculated by the edge detection algorithm iterative computation [START_REF] Chen | An Interactive Object Cutout Algorithm Based on Graph-Cut and Generalized Shape Prior[END_REF] . This process can be carried out several times and kept it. Compare the completion to the amount of information within specified time limits. Of course, the optimal solution contains the most edge information to determine the final precise border.

The stepped edge

The roof-edge

The linear edge (1) Specifies that the vertex of the lower left corner of the original image is at the origin of the coordinate axis. The left edge of the original image coincides with the ordinate axis and the lower edge coincides with the abscissa axis. The original image length is L and the width is t W.

(2) If the rectangle length and width of the disease area are a*b and the center coordinates are (x0, y0), then the size of the rectangular box of the crop is 4a*3b and the abscissa of the center coordinates of the rectangular box remains unchanged. The vertical coordinates of b/2+2b is that the crop coordinates of the rectangular box center coordinates (x0, y0-5a/2). The distance from the left frame to the ordinate axis is wL=x0-3b/2, the distance from the right frame to the right edge of the original image is wR=W-x0-3b/2, the distance from the lower edge to the abscissa axis is wB=y0-9a/2 and the number of expansion is 3 times, which you can calculate the expansion time.

The three borders of the respective steps, set the left side of the block step dL, the right border step dR, the lower frame step dB, then dL= (x0-3b/2) / 3 and dR=(W-x0-3b/2)/3, dB=(y0-9a/2)/3 [START_REF] Xiaobin | Image Segmentation based on Weighted Cut[END_REF][START_REF] Li Yuchuan | Multiscale Image Segmentation Based on Graph Weighted Kernel K-means[END_REF][START_REF] Peng | A Novel Dynamic Shape Based Infrared Object Subtraction Method[END_REF] .

Fuzzy Clustering Method

Ruspin first proposed the fuzzy clustering theory in 1969 based on the unsupervised learning of pattern recognition [START_REF] Mei | Analysis and Comparison of Common Image Segmentation Techniques[END_REF] . In 2009, Wang Shouzhi adopts the Mercer kernel based on K-means clustering algorithm to complete the maize leaf disease spot segmentation, and the correctly in 82%. In 2012, JawareTH utilizes the Otsus algorithm to calculate, and the edge of the infected image is eliminated by using the optimized K-means clustering method [START_REF] Wenbing | A New Image Thresholding Method Based on Graph Spectral Theory[END_REF] . The algorithm is proved to be an efficient and high-precision segmentation algorithm. In 2012, Li Zhen uses the model of Lab color based on k-means clustering method to identify the color image of the red The fuzzy clustering method dictates the parent-child relationship of the each pixel of image has fuzziness, and the fuzzy clustering method has been used abroad in recently years. A massive amount of the crop disease image data will get the feature of ambiguity and uncertainty. The membership function of the fuzzy clustering will be modeled with the feature of ambiguity and uncertainty, therefore it is not only suitable for still the image segmentation. The fuzzy clustering method also has flaws, the sensitivity of the noise and initialization data and the calculation of algorithm are the practical application of the agricultural production to become further improvement and optimization [START_REF] Yixiu | An Enhanced Scheme for Interactive Image Segmentation[END_REF] .

3 Image Segmentation Algorithm

The Flow Diagram of Segmentation Algorithm

We will illustrate the image segmentation algorithm by considering the simple process shown in figure. 4 [START_REF] Qing | Discriminatory Technology of Necessity for Image Segmentation Based on Complexity Index[END_REF] .

Segmentation Result

After the algorithmimplementation, the paper experiments with the 1500 images to meet the scientific and extensive nature of the experimental samples and choose the disease image in the actual marketing network system consist of the disease image, not diseased image, simple background image, complex background image and the color of the black and gray [START_REF] Xiaoli | Image Segmentation Based on Pulse-Coupled Neural Networks[END_REF] . In order to The maximum between-cluster method of the divisions, shown in the diagram as below. The crop disease is calculated. Firstly, the size of the segmented image (unfilled) for calculation. Then, the target area is filled with the segmented image and the disease is filled as shown below. The degree of crop disease can be calculated by the corresponding image pixel spot, where M and N are the width of the image in pixel and the height of the image in pixel, and the maximum number of the black and white pixels in the binary image is divided by the maximum between-cluster method [START_REF] Yongyi | Entropy Based Bi-region Image Segmentation[END_REF] . By way of example, the X2 represents the total number of white pixels in the A area (including the disease region of the target leaf), the X2 represents the total number of black pixels, and according to the demands to the target object. Therefore, the binary image of the disease in the black pixel filled with the black pixel, and called the binary image in Matlab. After the disease area has been filled, the white pixel of the disease area is filled again and the full black pixel is filled after the statistical filling. The X3 represents the number of the black pixel i where is the number of the disease spot. Finally, can obtain the percentage of the leaf disease is L = (X1 -X2) / X3 × 100%.

The different types of crop diseases are taken as the research subjects. The size of the crop disease is calculated by the gray processing of the image, the removal of the background quantity, the image segmentation, the filling of the pixels in the crop disease area [START_REF] Fu | Research on Application of Image Segmentation Method[END_REF] . Experimental results show that the average danger degree of tomato, pepper, rice, citrus and maize disease is 9.36%, 3.174%, 7.393%, 3.723%, 11.306%, respectively. And the results of the miss rate of tomato, pepper, rice, citrus and maize disease area is 0.767%, 0.549%, 0.922%, 0.253%, 0.902% [START_REF] Dan | Image Segmentation Methods and its Applications[END_REF] , respectively, and can be control at a lower percentage. The error rate reflects the difference of the effect of the different sample segmentation, and the experimental data can reflect the degree of crop disease accurately. According to the segmentation results, the maximum between-cluster is not ideal for the segmentationeffect [START_REF] Fei | A Survey of Image Segmentation[END_REF] . As the lesion area to deal with, so the algorithm optimization needs to be further improved. The percentage of crop disease can effectively control the dose of pesticide with reduced environmental pollution. The studied result will provide a certain scientific basis for the quality of the crop disease.

Conclusions

The paper consider finding the available method of image processing to resolve the problem by measuring the damaged degree of crop leaves. Firstly, the sample image is executed to pre-process. Then, using the maximum between-cluster segmentation algorithm to catch the pest and pest-free areas of crop leaves. Finally, a method to the pest area is used to filling of picture element, and the crop disease degree percentage is calculated. The method is suitable for the determination of many kinds of crop diseases, but the leaf disease spot is still indivisible, which need to be futher improved and perfected.

Figure. 1 .

 1 Figure.1. The simple process of Threshold Segmentation

Figure. 3 .

 3 Figure. 3. Diagram of the Accurate Location MethodLeft, right, and bottom three borders of the calculation of the step size, as shown in Figure.3.(1) Specifies that the vertex of the lower left corner of the original image is at the origin of the coordinate axis. The left edge of the original image coincides with the ordinate axis and the lower edge coincides with the abscissa axis. The original image length is L and the width is t W.(2) If the rectangle length and width of the disease area are a*b and the center coordinates are (x0, y0), then the size of the rectangular box of the crop is 4a*3b and the abscissa of the center coordinates of the rectangular box remains unchanged. The vertical coordinates of b/2+2b is that the crop coordinates of the rectangular box center coordinates (x0, y0-5a/2). The distance from the left frame to the ordinate axis is wL=x0-3b/2, the distance from the right frame to the right edge of the original image is wR=W-x0-3b/2, the distance from the lower edge to the abscissa axis is wB=y0-9a/2 and the number of expansion is 3 times, which you can calculate the expansion time. The three borders of the respective steps, set the left side of the block step dL, the right border step dR, the lower frame step dB, then dL= (x0-3b/2) / 3 and dR=(W-x0-3b/2)/3, dB=(y0-9a/2)/3[START_REF] Xiaobin | Image Segmentation based on Weighted Cut[END_REF][START_REF] Li Yuchuan | Multiscale Image Segmentation Based on Graph Weighted Kernel K-means[END_REF][START_REF] Peng | A Novel Dynamic Shape Based Infrared Object Subtraction Method[END_REF] .

Fig. 4 .

 4 Fig. 4. Flowchart of the diseased Image Segmentation Algorithm based on pre-detection

  obtain the experimental results of the new algorithm and be convenient for the reference and comparison based on the classical Crabcut algorithm.


Haifeng Jia

Guifen Chen
email: guifchen@163.com

Research and Application of Safety Management for Virtual Desktop in

Keywords: Cloud desktop, Virtualization, Sharing, Security, Management

In the view that the traditional PC desktop management model can not meet the demand of teaching resources in colleges and universities, this paper introduces the cloud desktop technology into the teaching resource management of colleges and universities to improve the utilization rate of the teaching resources and the management level of the staff. In this paper, the author builds a 900-point cloud desktop system platform by using the cloud server and thin terminal of Huawei. Based on the virtualization technology and cloud computing security model, it realizes the unity of cloud desktop, resource sharing and ensures the security and remote control of data. The test application was carried out in the teaching and management of the training center on the cloud. Result of application indicates that cloud desktop is more efficient, convenient and safer than traditional PC in resource utilization, network requirements, hardware configuration, energy saving management and use of the model, and it also has a huge development advantages.

Introduction

The cloud desktop is a solution which is used to the replace PC. Desktop computing terminals are no longer using computer hosts, instead of computing power. The cloud desktop realizes the unity of the desktop and the sharing of resources through the cloud computing software. Therefore, the use of display and cloud terminal devices can achieve login, office, teaching and etc. [START_REF] Wu Yuanyuan | Cloud desktop technology in the computer room management application[END_REF] .In the view of virtual desktop overall system point, the client, transmission network, server, storage and other aspects, will have a security risk. Ignoring any details can lead to information loopholes in the entire system. As cloud computing provides maximum convenience for users and enterprises to use storage resources, software resources, and computing resources at a low cost, the biggest challenge to it, or the problem of it, comes from security. Security issues such as security boundaries, application security, cloud computing abuse, abuse, denial of service attacks, insecure interfaces and APs, shared technology issues, data leakage, account numbers, and service hijacking are particularly important.

The desktop cloud platform provides desktop services to end users through the virtual desktop architecture [START_REF] Tao | Desktop cloud in the university computer laboratory application[END_REF]. VDI is a combination of remote desktop connectivity and virtualization technology. Through server virtualization technology, it runs multiple virtualized hosts on a physical server. Each virtualized host has independent computing, network, software and other resources, installing and running their own client operating system. Such as Windows7, Windows XP or Linux. Each virtual machine is independent of each other without affecting.

Users connect to the virtual machine remotely via the desktop transport protocol and get their desktop environment users local terminals or PCs only by running a remote desktop connection client program to get complete computer software and systems and other resources [START_REF] Jiang Fan Jun | Cloud desktop technology in the application of the computer teaching management in colleges and universities[END_REF] . Cloud desktop in the performance of centralized management, resource sharing, high efficiency operating advantages at the same time, its complex system management problems, the problem of the resources are difficult to effectively use, security issues, also plagued IT administrators. The author analyzes the construction, operation management and maintenance of cloud desktop in the virtual simulation training center of Changchun University of Finance and Economics, summarizes the mainstream cloud desktop security management technology at home and abroad and designs the corresponding cloud management model according to the unit's own situation [START_REF] Rui | Cloud desktop platform based on virtual technology in the teaching the application[END_REF] .

The key technologies of cloud desktop management

Technology of cloud desktop management involves in Web technology, cloud computing virtualization technology, data mining and other technologies. Therefore, the cloud desktop management technology has many problems to study, the most basic is the platform monitoring technology, resource optimization technology and security technology. The following is a brief introduction to the three aspects combined with the Institute of practical training center [START_REF] Xu Yingying | New Cloud Security Solution and Its Key Technology[END_REF] .

2.1monitoring the desktop virtual terminal, simplifying the management model

The desktop system is more complex and consists of several components, such as server, storage, and network. There are multiple management terminals, each of which is difficult to have a clear understanding of the system; Fault location is difficult, and it is necessary to analyze multiple systems to solve the problem.

The virtualization technology of cloud desktop uses the background cloud server to perform the monitoring and management of the performance of the CPU, memory, terminal, network and other aspects of the virtual terminal. Through detailed data monitoring, it can effectively manage the resource utilization problems existing in terminal use, ensure that the cloud desktop platform has the maximum limit and optimal use system resources. when using the CPU resource footprint, insufficient memory and the virtual hard disk space in the platform virtual desktop, it can quickly make the intuitive response to the purpose of the warning. Platform monitoring interface shown in Figure1.

Monitoring the virtual desktop environment to improve utilization of resource

It is difficult to management the desktop cloud resource: resource allocation is difficult to do effective management. With a long time running of the cloud desktop system, virtual resources can not be effectively used. For example: If cloud desktop does not shut down for a long time, virtual resources can not been recycled, resources are not enough to be allocated for a large part of other virtual desktops, resulting in a serious resource idle and waste.

Monitoring the usage of the virtual host, storage, virtual machine through the background management terminal and analyzing statistics, resource recycling is carried out by the virtual machine that distributes the resources in excess, the resource allocation is increased to the virtual machine with insufficient resource allocation, and the overall environment and the user experience is improved.Virtualization resource management interface is shown in Figure2.

Figure. 2. Virtualization Resource Management of the Institute training center

Auditing the user behavior of the virtual desktop, ensuring system security

It is difficult for desktop cloud security audit: It is difficult to audit illegal login behavior,as virtual desktop login behavior is lack of control Virtual desktop operating behavior lacks control, it is difficult to audit illegal operation behavior.

When the operator operates the key equipment (server, secret machine), he will record the operation of the screen image recording;while the screen element information is also recorded at the same time; Auditors are audited on the server by looking at audit records.Log audit management records for system run are shown in Figure3.

The overall architecture and planning of the platform

Based on the Center's teaching and office planning, we will plan to build the cloud desktop system platform of the 900-points in the center. The platform adopts Huawei's cloud server and cloud thin terminal, the user goes through the terminal and protocol ofthe desktop display to accessaddress or portal of the cloud desktop, the access address or portal generally preload the load balancing device [START_REF] Xiangyu | Design and Implementation of Cloud Desktop Management Platform Based on Virtualization[END_REF] . The user manage server, going through management system of the user,to verify the user identityusually by Microsoft's AD (Active Directory), the request is directed to a random or custom cloud desktop after the authentication. Then the user logs in the cloud desktop, he or she visits the business system as if he logged on to the local desktop [START_REF] Wu | Analyze the cloud desktop technology application in university training rooms management[END_REF] . According to the above plan described the infrastructure shown in Figure4.

The IP address design of overall network planning and information.

Based on the number of the above-mentioned architecture and information points to plan the network topology of the planning center, for each information thin terminal allocation of resources and fixed IP address so that the background cloud server-side seamless docking. Network topology planning specific description: (1). All terminals can access the Internet, going through core switch of the campus network leads to an interface to connect the export firewallof the training center,all the address of the training center will be interconnected with campus network and Internet by the default route.

(2) The uplink of internal core switch of the training center is connected with the firewall, and the downlink is connected to the access switch of each terminal directly through the multimode fiber link. At the same time, the CE5855 switch is connected with the 10GE port, and the CE5855 switch is connected withthe service interfaceof each server.

(3). Two CE5855 switches to be stacked,are interconnected with the core switch at the same time, and configure the link aggregation. (4). Network management software to manage all the network equipment. The network topology scheme is shown in Figure 5.

Thedesign ofplatform system IP address

According to the network topology planning program, processing IP address design, andinformation and IP address are allocatedfor the thin terminal client .The VLAN number only valid in the training building; is forwarded by three-way routing after arriving on the campus network. The specific allocationplanin Table 1. The number of terminals to accommodate more than 120 cost 400,000 yuan , which saves the cost and greatly enhance the efficiency of use.In the traditional room each room needs an administrator,while in the training room the entire building needs two administrators to meet the management requirements. Table 2 gives a detailed comparison. Table 2. The comparison of advantage by cloud desktop and traditional desktop

Table 1. Training center IP address allocation tableChangchun University of Finance and Economics

Set up a cloud desktop

Virtual Resource of Cloud Desktop Management

The key technologies of cloud data center Virtual resource management : resource virtualization, the virtual machine deployment provided by resource , resource scheduling, and virtualized migration. The structure of its resource management is shown in Figure7

The allocation strategy of cloud resource

Cloud computing technology enables the allocation of resources on demand and increases the system utilization and overall performance. Resource allocation strategies need to meet the various parameters, such as throughput, delay, response time and so on. Due to the limited resources, heterogeneity of resources, geographical constraints, the dynamics of the environment and resource requirements, we need an effective resource allocation system to provide users with the best resources. To this end, we propose a model of cloud resource allocation [START_REF] Zhang | An Optimal Resource Allocation Method for Aircraft Subassembly System Based on Graphic Ant Colony Algorithm[END_REF] . For the free resource there are N user requests, the t (1 <= t <= n) user requests the cloud system to provide K (t) VMs so that R (t) is the maximum of network distance between the virtual machines for the t user, the network diameter R (t) = max {| eij (t) |: 1 <= i, j <= K (t)} is optimized to minimize the sum of the virtual machine network diameters of each user. The objectives are as follows:

Cloud resource allocation algorithm

Here we study a cloud resource allocation method based on "ant colony algorithm" [19]. Ant colony algorithm is a kind of bionic algorithm which is similar to the path of the natural ants. The ant colony algorithm illustrates the optimal allocation strategy of resources by describing the question how the ants find the optimal route . The optimal allocation model of resource allocation is constructed by using the optimal allocation solution of the ant colony algorithm. The algorithm is applied to the problem of cloud service resource allocation.The effective allocation of terminal resources is solved effectively by the cloud server , avoiding the waste of resources.

Construction and Application of Cloud Security Management Model

Building a Management Model of Cloud Security

The relationship between cloud computing models depends on its three basic core architectures: IaaS virtualization technology, PaaS distributed technology, and SaaS online software technology. These three core architectures together constitute the basic cloud computing security reference model, at all levels play an important role. The relationship between them is shown in Figure 8 [START_REF] Wenchao | Simulation of cloud intrusion detection model in cloud computing network under large data environment[END_REF] .

Figure. 8. The security reference model of cloud computing

IaaS covers hardware platforms such as servers, networks, storage, and infrastructure. PaaS is located above IaaS and is provided to current users through the Internet using distributed software development, testing, and deployment environments as services. [START_REF] Vinothkumar Muthurajan | An Elliptic Curve Based Schnorr Cloud Security Model in Distributed Environment[END_REF] PaaS can be built on virtualized resource pools in IaaS and also can be built on physical infrastructure in data centers. SaaS is based on the Internet to provide a software service application model that passes the server software to users through the browser, which can be used by users online. [START_REF] Yimu | A cloud computing security model and architecture design research[END_REF] The cloud security architecture is important at all levels, and its core needs remain the protection of the confidentiality, integrity, availability, and privacy of applications and data [START_REF] Chuang | Cloud computing security: architecture, mechanism and model evaluation[END_REF] . The content of cloud computing in the three security layers of data, application and virtualization (see table 3), and its corresponding coping strategies and technologies are worthy of in-depth study [START_REF] Xue Haiwei | A Multilevel Security Model for Private Cloud[END_REF] . In the future, the research should focus on remote control management of cloud servers, cloud network security, dynamic allocation and recycling of cloud resources. Apply cloud security protection to campus network security management, and make efforts to become more intelligent and platform.

Figure. 1 .

 1 Figure.1. The cloud desktop platform monitoring of the Institute training center

Figure. 3 .

 3 Figure. 3. The user behavior audit logmanagement of the Institute training center 3. The build and design scheme of cloud desktop platform.

Figure. 4 .

 4 Figure. 4. Schematic diagram of the cloud desktop infrastructure

Figure. 5 .

 5 Figure.5. The cloud desktop platform network topology diagram of Changchun University of Financeand Economics

  According to the above program for the IP and network and user configuration, to build the cloud desktop landing interface of the college training center and compared with the traditional PC desktop, as shownin Figure6

Figure. 6 .

 6 Figure. 6. Cloud desktop and traditional PC login interface in college training center

Figure. 7 .

 7 Figure. 7. The virtual resource management structure of cloud desktop

5. 2

 2 The cloud applications of teaching and management in the training centerCombined with the training center operation of Changchun University of Finance and Economics, through the cloud terminal management platform, teachers and students can undertake courses and make an appointment through the management platform, such as training room use appointment and opening time booking and other functions. The teacher can also conduct student attendance in the platform, which greatly facilitates the development of teaching and meets the needs of the management of the training room. In the security management, cloud server unified security configuration and risk prevention through cloud server, avoiding the problem of low efficiency of traditional single investigation and killing. Cloud server by customizing all kinds of templates compared with the traditional way of computer room clones to be more efficient, for example: for the paperless examination system template issued to cloud desktop, on the normal teaching system template issued back to the state of the class. The use of teaching software greatly improves students' practical ability and manipulative ability, such as: cloud server saves a lot of time for software installation; there are dozens or more of the teaching software installed on the server side; it is more intuitive to access online teaching through the cloud desktop in B/S mode. In summary, cloud desktop is widely used in both teaching and management, which makes up for some disadvantages of traditional PC desktop. See Figure9for details.

Figure. 9 .

 9 Figure. 9. The applications of cloud laboratory management platform and simulation teaching software 6. Summary and outlook Through the construction of the actual training room and the research of some problems that may exist during the use of virtual simulation training center in colleges and universities, the paper give practice from cloud desktop platform construction, the key technology of cloud desktop management, cloud desktop management strategy, algorithm and scheduling and the construction of cloud security model from several aspects, the test application was carried out through the cloud in the teaching and management of the training center. Research and test results show that the use of cloud desktop technology can effectively solve the traditional PC mode to the disadvantages of the teaching and management, cloud desktop technology for teaching and curriculum design provides a great convenience in resource management, risk monitoring, behavior management etc. By introducing a cloud desktop technology, to engage in a variety of training rooms B/S software teaching makes the university keep ahead in the same level of colleges, at the same time to save cost and improve the quality of teaching play an important role.In the future, the research should focus on remote control management of cloud servers, cloud network security, dynamic allocation and recycling of cloud resources. Apply cloud security protection to campus network security management, and make efforts to become more intelligent and platform.

Table 3 .

 3 The Content Matrix of Cloud Security

	Cloud security level	Cloud Content
	Data Security	Data Transmission, Data Isolation, Data Retention
	Application Security	End User Security, SaaS Security, PaaS Security, IaaS Security
	Virtualization of Security	Virtualization Software,Virtual Server

Acknowledgement

The authors wish to express their gratitude to the national spark program project: Precise operation technology integration and demonstration of corn (No.2015GA660004) for its generous support of this work.


Zeng-Hui Wang

Yan-Jun Zhao

Yang Liu

Dong-Yan Huang

The study of the work parameters of the corn harvester cutter

Keywords: Chopped knife, Feeding speed, Analysis of variance, Regression analysis, Length of stems, The toss distance of stalks

In this paper, the analytical method of multivariate variance is applied to study the function of the various performance factors of the straight-edge cylindrical cutter. Research results indicate that the main factor impacts the model type surface straight blade cylinder type chopped knife is the speed of the blade, and the application of regression analysis method establishes the regression equation of the blade of stem cutting speed and cutting length, cutting speed and stem throwing distance, speed and power consumption. This paper introduces how to choose chopped knife work parameters with the application of these regression equations.

Introduction

Chop model type surface straight blade cylinder type knife is according to the vertical roll type corn harvester pick ears designed the working characteristics of roller, it can simultaneously complete stem chopped recovery without setting up recycling equipment, if we use returning corn stalks as fertilizer, cutting length shorter as well, making green fodder as recycling corn stalks, and the stem cutting length should be in 30 mm advisable, because a cut stem is too long or too short are adverse to the digestion of the livestock. Therefore, to control the cut length of the stalk, we need to find out the factors that affect the length of the stalk. If the stem is recycled as a feed, you will also know the rules of the cutting stem. We have to find out the factors that affect the distance of the stalk.

Based on the multivariate analysis of variance was carried out on the test data, found out the impacts like a cut stem length and stem toss distance are the main factors of chopped knife rotation speed, establish the equation of the stem cutting length and cutting knife rotating speed with the application of these regression equations. Applying the approximate function relation can provide the reference for research of corn harvester and users

Experimental condition

The experiment was carried out on the laboratory bench, and the cutter was installed on the top of the picking part, which was driven separately by the adjustable-speed motor. Plants are fed to a roller which cut by sickle on the clamping. The stem from picking rolls back after discharge, cut by knife chop, and then was thrown. Test the power consumption.

Experimental and experimental results

The experimental scheme adopts orthogonal test method, take the orthogonal table .The purpose of this experiment is to look at the factor A, factor B, and the correlation of A with B. These factors will cause the influence of the three indexes for the length of the stem ) 1 ( y , the distance of the stem ) 2 ( y and the power consumption y .Due to investigate whether the interaction between A and B is significant to the index, we have arranged three repeated trials under the same condition.The experimental and experimental results are listed in table 1.

) ( 5

Table 1 test plan and experimental results

Analysis of variance of experimental results

From the data in table 1, With the increase of feeding speed, the rotational speed of the cutter for increased power consumption is quite obvious. Without considering the influence of factor A and factor B power consumption therefore in the multivariate analysis of variance, we only consider factor A and B of binary indicators.

Τ = ) , ( ) 2 ( ) 1 ( y y y ( ) 1 ( y -The length of the stems, ) 2 ( y -The throwing distance of stalks) ijk y ( 4 , 3 , 2 , 1 = i , 4 , 3 , 2 , 1 = j ) -The test result of the i level of factor A( 4 , 3 , 2 , 1 = i
)and the j level of factor B.We make By analyzing the formula of variances, the dispersion matrix is obtained (1.5m/s) (5)Interaction with A and B of the difference matrix-

ijk k j i ij y y 3 1 4 1 4 1 3 1 = = = ⋅ Σ Σ Σ = , ∑∑∑ = = = × × =
(1)Dispersion matrix-Q :         - - = - - = ∑∑∑ = = = Τ 51 .
Q : A Q         - - = - - × = ∑ = Τ ⋅ ⋅ ⋅ ⋅ 105 . 0 3 . 0 3 . 0 075 . 27 ) )( ( 4 3 4 1 i i i y y y y (3)Group alienation matrix about factor B-B Q : B Q         - - = - - × = ∑ = Τ ⋅ ⋅ ⋅ ⋅
B A Q × : B A Q ×         = - - - = 69 . 0 706 . 2 706 . 2 18 . 3 R B A Q Q Q Q
Factor A , factor B, and A cross B have no significant effect on the indicators study, if so

y ijk ~) , ( 2 Σ µ N . Τ ) , ( 2 1 µ µ µ is the mean vector,         = Σ 2 2 2 1 2 1 2 1 σ ρ σ σ ρ σ σ σ
is the covariance matrix. We can get respectively distributions of the sum of the squares and the matrix,

) , 47 ( ~2 Σ W Q ; ) , 3 ( ~2 Σ W Q A ; ) , 3 ( ~2 Σ W Q B ) , 2 ( ~2 Σ × W Q B A ; ) , 32 ( ~2 Σ W Q R
Whether Factor A, factor B and A cross B have significant impact on dual index. Test the above factors and we get the respectively distributions of statistics.

) 3 , 32 , 2 ( Ã A Λ + = Λ Q Q Q R R ) 3 , 32 , 2 ( B B Λ + = Λ Q Q Q R R ) 9 , 32 , 2 ( B A B A Λ + = Λ × × Q Q Q R R
Firstly, check the significance of factor A, is not significant,The rate of feeding is not significantly affected by the length of the stalk and the distance of the cast.

The regression equation of the cutting speed and the length of the stem, the distance of the stem and the power consumption 4.1The sorting of the experimental data

Multivariate analysis of variance shows that the main factor that influence the cut stem length, stem toss distance and power consumption is the speed of the blade. But the correlation of the feeding speed with stem cut length and toss distance can be neglected, and the cutting speed and feeding speed and power consumption has obvious relevance. So we can take the knife speed of four different levels that matching with stem feeding speed of the collocation of four different levels as the cutting speed for different level 4 times in repeated trials, coupled with the test of each level 3 repetitions, is considered to be a four levels on cutting speed for 12 times. Use x for cutting speed, )

The regression equation of the length of the stalk section and the cutting speed

As the data in table 2 shows, the length of the stem cut is decreasing as the cutting speed increasing.Therefore, there are two kinds of functional relations between the cutting speed and the length of the stem to be chosen.

x y

1 0 ) 1 ( β β + = ) 0 ( 1 < β ) 1 ( bx ae y - = ) 1 ( ) 0 , 0 ( > > b a ) 2 (
But with the increase of stem cutting length are getting smaller, the cut stem length is not negative, so it is not reasonable to use the linear regression equations [START_REF] Zeng-Hui | Probability theory and mathematical statistics[END_REF].Therefore we choose and the regression equation between x with ) 1 ( y as the index regression model [START_REF] Shou-Zheng | Multivariate statistical analysis method[END_REF].

Estimate the two parameters of the regression equation [START_REF] Shou-Zheng | Multivariate statistical analysis method[END_REF].

First, take the natural log of both ends. In the beginning, by the data in table 2 can be seen that with the increase in cutting speed. The distance of Stem began to increase, but when the cutting speed increased to 2100 RPM, toss distance began to decline.This reason is, with the increase of cutting speed, the stem get shorter and the quality lighter, so the throwing distance get smaller, Suppose that the original regression equation of toss distance with cutting speed is a quadratic function. Multiple linear regression analysis method can be used to estimate for the parameter estimates in type [START_REF] Khaleghian | Terrain classification using intelligent tire[END_REF].Only need to make the type [START_REF] Khaleghian | Terrain classification using intelligent tire[END_REF] x x = 1 2 2

x x = , at this moment, the type ( 7) is converted into a binary linear regression equation.

The regression equation of cutting speed with power consumption.

By the data in table 2 can be seen, with the increase of cutting speed power ,the power consumption is increasing, we can also find that power consumption has the obvious linear relationship with cutting speed, thus we have the regression equation of power consumption and the cutting speed of cutter

The precision analysis of each regression equation

The estimated values about the From the estimates in table 3, the accuracy of each regression equation is higher, and the correlation index of each regression equation is calculated.

The correlation index is defined as

Fund 2 Experimental conditions and experimental results 1 . 1

 211 project: national key research and development project funded by National Science and Technology department (project number: 2016YFD0700300) Author profile: Wang Zeng hui (1956 -), male, research direction: statistics and optimization method; Corresponding author: Yan-Jun Zhao (1979 -), male,research direction: partial differential equation and its application, the application of time series analysis and data mining, E-mail:yjzchsnenu@163.com Definite parametersWe have known the value of the blade radius(r) is 145mm, the turning radius of the cutter(R) is 210mm.The number of blade is 4, the spike roller speeds n =1000 r/min, Hold the feed chain speed-

  not significant,The rate of feeding is not significantly affected by the length of the stalk and the distance of the cast.Secondly, check the significance of factor B, significant. The rate of feeding is significantly affected by the length of the stalk and the distance of the cast.Finally, check the significance of the interaction of A cross B,

1 。, 3

 13 case, the (2) is converted into a linear regression model. regression equation (4), the parameter A and the parameter B are estimated. Take the data in table 2 into type (5) ,we estimate 0011856 And we get the regression equation of the cutting length ) The regression equation of the throwing distance and the cutting speed.

  analysis of multivariate linear regression, and then the regression equation of the distance and the cutting speed of the stem is obtained.

  in the type[START_REF] Zhang | Design of depth-control planting unit with single-side gauge wheel for no-till maize precision planter[END_REF] and evaluate the data in table 2 into the formula (5),we have,

  type[START_REF] Igathinathane | Corn stalk orientation effect on mechanical cutting[END_REF],[START_REF] Stefanello | Functional structure of a human-powered seeder[END_REF] and[START_REF] Zhao | Analysis and experiment on cutting performances of high-stubble maize stalks[END_REF] are listed in chart 3.The estimated values of the regression equation are compared to the actual value x

  is the average of the test power consumption of the test results of 12 repetitions. Work up the data in table 1 for the following table 2.

														1 ( y is the average of the test
	results in 12 repetitions,	) y is the average of the toss distance of the test results of 12 repetitions, 2 (	y	(	3	)
	Chart 2 The relation of the cutting speed with	y	) 1 (	,	y	(	2	)	,	y	(	3	)
	x	y	) 1 (											y	(	2	)	y	(	3	)
	Cutting speed (r/min)	Cutting length (mm)				Toss distance (m)	Power consumption
	1800	39.33											8.4	9.65
	1900	31.95											8.83	10.27
	2000	30.18											9.23	11.15
	2100	27.0											9.19	12.64

The value is more close to 1, the better the regression equation is, the value is more close to 0, the worse the regression equation is.

We conclude indexes of correlation in respectively regression equation are

The choice of the chopping knife work parameter

In the last section,we set up the regression equation of the cutting speed and the cutting length, toss distance and power consumption of the stem. We are able to use the regression equation to forecast and control.

The selection of the cutting length of the stem

Regression equation ( 6) can be used to predict cutting length of stem, For example, take knife speed = 1700, we get Using the regression equation ( 6). We only need to control the cutting speed if we control the cutting speed.

For example, if you want to control the value of cut stem length is , we can work out the result is x =1870.

The choice of the throwing distance of the stalks

The application regression equation ( 9) can be applied to predict the toss distance of the stem with the definite value.

For example, if you take the value of cutting speed is 1970, then we can predict the toss distance of the stem by type(9) is

, if you take the value is 2200, then we have the value of the distance is

Forecast power consumption

The regression equation ( 11) can be used to predict the power consumption when the cutting speed is determined.

For example, take the value of cutting speed is 1700, we get