CN113507491A

CN113507491A - Method and system for uploading all-chain information of clean egg production in real time

Info

Publication number: CN113507491A
Application number: CN202110486291.XA
Authority: CN
Inventors: 尹涛; 黄汉英; 孙若文; 李鹏飞; 赵思明; 熊善柏
Original assignee: Huazhong Agricultural University
Current assignee: Huazhong Agricultural University
Priority date: 2021-04-30
Filing date: 2021-04-30
Publication date: 2021-10-15
Anticipated expiration: 2041-04-30
Also published as: CN113507491B

Abstract

The invention relates to a method and a system for uploading whole chain information of clean egg production in real time, wherein the method comprises the following steps: collecting a plurality of node parameters; generating uploading tasks according to the data amount corresponding to each node parameter, setting corresponding priority for each uploading task, determining corresponding distribution weight according to each uploading task and the corresponding priority, distributing corresponding distribution bandwidth and transmission delay according to the distribution weight and the total bandwidth of data transmission, and sequencing and uploading each uploading task based on the distribution bandwidth and the transmission delay; and processing data according to each node parameter in the uploading task uploaded by the edge server to generate corresponding tracing information, determining a production node with risk according to the node parameter, generating corresponding early warning information and issuing the early warning information to the production node. According to the method, the parameters of each node are effectively sorted by weight distribution, so that quick and effective information uploading and accurate information tracing are achieved, and the safety of clean egg production is guaranteed.

Description

Method and system for uploading all-chain information of clean egg production in real time

Technical Field

The invention relates to the technical field of agricultural information, in particular to a method and a system for uploading all-chain information in clean egg production in real time.

Background

Egg products are the main food for people in most areas of China, and the yield and quality of the produced products are very important for the people. The existing egg production industry chain comprises four nodes of cultivation, storage, processing and circulation, wherein each node can influence the yield and quality of produced eggs. In the prior art, a certain node or a certain production factor of an egg product is often monitored respectively, the monitoring form and the monitoring content are single, and the whole industrial chain cannot be completely monitored and judged. In addition, in the prior art, the monitoring information is processed by adopting a traditional data processing mode, and the data processing process lacks high efficiency and rapidity. Therefore, how to efficiently and comprehensively monitor the industrial chain of egg production is an urgent problem to be solved.

Disclosure of Invention

In view of the above, a need exists for a method and a system for uploading information of a whole chain of clean egg production in real time, so as to solve the problem of how to efficiently and comprehensively monitor the industrial chain of clean egg production.

The invention provides a method for uploading whole chain information of clean egg production in real time, which comprises the following steps:

collecting a plurality of node parameters, wherein the node parameters comprise parameters correspondingly monitored by a sensor arranged at each production node in the process of producing the clean eggs;

generating uploading tasks according to the data volume corresponding to each node parameter, setting corresponding priority for each uploading task, determining corresponding distribution weight according to each uploading task and the corresponding priority, distributing corresponding distribution bandwidth and transmission delay according to the distribution weight and the total bandwidth of data transmission, and sequencing each uploading task based on the distribution bandwidth and the transmission delay and uploading to the cloud platform;

and performing data processing according to each node parameter in the uploading task to generate corresponding tracing information for recording the whole chain information of clean egg production, determining the production node with risk according to the node parameter, and generating corresponding early warning information to be issued to the production node.

Further, the generating an upload task according to the data volume corresponding to each node parameter, and setting a corresponding priority for each upload task includes:

generating corresponding uploading tasks according to the data quantity corresponding to the node parameters which are uploaded by the sensor of each production node every time;

setting the priority for the corresponding uploading task according to the feedback time length after the task of different sensors is uploaded, wherein the corresponding priority is sequentially increased along with the sequential decrease of the feedback time length of the sensors.

Further, the determining a corresponding distribution weight according to each uploading task and the corresponding priority, and distributing a corresponding distribution bandwidth and transmission delay according to the distribution weight and the total bandwidth of data transmission includes:

determining a corresponding first proportional quantity according to the ratio of the data quantity of each uploading task to the sum of the data quantities of all the uploading tasks;

determining a corresponding second proportional quantity according to the ratio of the priority of each uploading task to the sum of the priorities of all the uploading tasks;

multiplying the first proportional quantity by a preset first influence factor to determine a first product value, and multiplying the second proportional quantity by a preset second influence factor to determine a second product value;

determining the corresponding distribution weight according to the sum of the first product value and the second product value;

multiplying the distribution weight by the total bandwidth of the transmission data and the total calculation resource of the edge server respectively to determine the distribution bandwidth and the distribution resource corresponding to each uploading task;

and determining the corresponding transmission time delay according to the data volume of each uploading task and the allocated resources.

Further, the sum of the first influence factor and the second influence factor is a predetermined preset value.

Further, the node parameters include operation images of the respective production nodes, the operation images are obtained by intercepting in an operation video stream, and the data processing process of the operation video stream includes:

dividing the operation video stream into a plurality of video frame sequence groups, calling an API (application programming interface) algorithm, and counting target identification numbers corresponding to the video frame sequence groups;

determining a frame filtering model according to the time delay and the bandwidth of the edge node uploaded to the cloud platform and the target identification number, removing redundant video frame sequence groups according to the frame filtering model, and determining a first frame sequence group;

extracting key frames in the first frame sequence group according to the image information entropy of the first frame sequence group, and determining a second frame sequence group;

distributing virtual machine resources according to the target identification numbers and the data volume of the second frame sequence groups, and determining an uploading sequence of the second frame sequence groups to the cloud platform;

estimating the posture of each frame of image in the second frame sequence group, and determining the position coordinates of a plurality of joint points;

determining a joint point distance variable quantity matrix according to the position coordinate variable quantity of the same joint point between two adjacent frames of images in the second frame sequence group;

equally dividing the second frame sequence group, and performing matrix addition on the joint point distance variable quantities generated by two adjacent frames in each section of video to obtain a cumulative distance variable quantity matrix of each section as a feature vector of the second frame sequence group;

inputting the characteristic vectors into a well-trained deep learning model for classification, and determining corresponding operation specification indexes;

and comparing the operation specification index with a corresponding prestored index library, and carrying out early warning on the corresponding processing node according to a comparison result.

The invention provides a clean egg production full-chain information real-time uploading system, which comprises:

the node parameter acquisition device is used for acquiring a plurality of node parameters, wherein the node parameters comprise parameters correspondingly monitored by a sensor arranged at each production node in the clean egg production process;

the edge server is used for generating uploading tasks according to the data volume corresponding to each node parameter and setting corresponding priority for each uploading task; the system is also used for determining corresponding distribution weight according to each uploading task and the corresponding priority; the device is also used for determining corresponding distribution bandwidth and transmission time delay according to the distribution weight and the total bandwidth of data transmission; the system is also used for sequencing and uploading each uploading task to the cloud platform according to the allocated bandwidth and the transmission delay;

the cloud platform is used for carrying out data processing according to each node parameter in the uploading task uploaded by the edge server to generate corresponding traceability information and recording the whole chain information of clean egg production; and the early warning system is also used for determining the production nodes with risks according to the node parameters, generating corresponding early warning information and sending the early warning information to the production nodes.

Further, the node parameters include a breeding main node parameter, and the breeding node parameters sequentially include the following sub-node parameters: hatching egg parameter, hatching parameter, breed parameter and laying eggs parameter, the breed owner node monitoring facilities who corresponds includes:

the hatching egg monitoring equipment is used for monitoring the hatching egg acceptance parameters to feed back the quality condition in the hatching egg acceptance process, wherein the hatching egg acceptance parameters comprise at least one of the quality of eggs, the environment quality of a farm and the pollutant content during the hatching egg acceptance;

the hatching monitoring device is used for monitoring the hatching parameters to feed back the breeding condition in the hatching process, wherein the hatching parameters comprise at least one of egg quality during hatching and environment quality of a hatching image hatchery;

the breeding monitoring equipment is used for monitoring the breeding parameters to feed back the breeding condition, wherein the breeding parameters comprise at least one of the health condition of the chickens during breeding, the water quality, the feed quality and the environment quality of a farm;

and the egg laying monitoring equipment is used for monitoring the egg laying parameters so as to feed back egg laying conditions and eggshell breakage conditions, wherein the egg laying parameters comprise at least one of egg product images, egg product pollutant content and egg product nutrition content during egg laying.

Further, the node parameters include storage main node parameters, and the storage main node parameters sequentially include the following sub-node parameters: the system comprises warehousing parameters, storage parameters and ex-warehouse parameters, wherein the corresponding storage main node monitoring equipment comprises:

the warehousing monitoring equipment is used for monitoring the warehousing parameters to feed back the quality of the produced eggs in the warehousing process, wherein the warehousing parameters comprise at least one of the quality of the eggs, the content of pollutants in the eggs and the nutrient content of the eggs during warehousing;

the storage monitoring equipment is used for monitoring storage parameters to feed back the environmental condition in the storage process, wherein the storage parameters comprise at least one of warehouse temperature and humidity, warehouse cleanliness and warehouse carbon dioxide content;

and the ex-warehouse monitoring equipment is used for monitoring ex-warehouse parameters so as to feed back the quality of the ex-warehouse eggs in the ex-warehouse process, wherein the ex-warehouse parameters comprise at least one of the quality of the ex-warehouse eggs, the content of pollutants in the egg products and the content of nutrients in the egg products.

Further, the node parameters include a processing main node parameter, and the processing node parameters sequentially include the following sub-node parameters: receive parameter, washing parameter, disinfection parameter, drying parameter, film coating parameter, hierarchical parameter and packing parameter, corresponding processing main node monitoring facilities includes:

the receiving monitoring equipment is used for monitoring the receiving parameters so as to feed back the quality of the produced eggs when the eggs are received in the processing process and the operation specifications when the eggs are received, wherein the receiving parameters comprise at least one of the quality of the eggs, the pollutant content of the eggs, the nutrient content of the eggs and the receiving operation images when the eggs are received in the processing process;

the cleaning monitoring equipment is used for monitoring the cleaning parameters so as to feed back the cleanliness of the egg products during cleaning and the operation specifications during cleaning, wherein the cleaning parameters comprise at least one of the cleanliness of the egg products during cleaning and the cleaning operation images;

the disinfection monitoring device is used for monitoring the disinfection parameters so as to feed back the microorganism content of the produced eggs during disinfection and the operation specification during disinfection, wherein the disinfection parameters comprise at least one of the microorganism content of the eggs during disinfection and the disinfection operation image;

the drying monitoring device is used for monitoring the drying parameters so as to feed back the drying degree of the produced eggs during drying and the operation specification during drying, wherein the drying parameters comprise at least one of the surface moisture content of the eggs during drying and the drying operation image:

the film coating monitoring equipment is used for monitoring the film coating parameters to feed back the film forming degree of the egg product during film coating and the operation specification during film coating, wherein the film coating parameters comprise at least one of film coating speed, egg product film coating area and film coating operation images during film coating:

the grading monitoring device is used for monitoring the grading parameter so as to feed back the grading grade of the produced eggs during grading and the operation specification during grading, wherein the grading parameter comprises at least one of the egg size grade and the grading operation image:

and the package monitoring equipment is used for monitoring the package parameters so as to feed back the package condition of the egg products during packaging and the operation specification during packaging, wherein the package parameters comprise at least one of an egg product code spraying rate, an egg product packaging rate and a package operation image.

Further, the node parameters include a circulation master node parameter, and the circulation master node parameter includes the following child node parameters: receiving parameters, transportation parameters and delivery parameters, wherein the corresponding circulation node monitoring equipment comprises:

the receiving monitoring equipment is used for monitoring the receiving parameters so as to feed back the quality of the produced eggs when receiving in the circulation process and the operation specification when receiving, wherein the receiving parameters comprise at least one of the quality of the eggs when receiving in the circulation process, the content of egg pollutants, the content of egg nutrients and the receiving operation images;

the transportation monitoring equipment is used for monitoring the transportation parameters to feed back the track condition during transportation, wherein the transportation parameters comprise at least one of a transportation route, a transportation track and transportation time;

and the delivery detection equipment is used for monitoring the delivery parameters so as to feed back the quality of the eggs during delivery and the operation specification during delivery, wherein the delivery parameters comprise at least one of the quality of the eggs, the content of contaminants in the eggs, the content of nutrients in the eggs and the delivery operation images during delivery in the circulation process.

Compared with the prior art, the invention has the beneficial effects that: firstly, comprehensively and effectively collecting node parameters of each production main node and each production sub node in the production process of clean eggs; then, converting each node parameter to be uploaded into an uploading task, setting corresponding priority for each node parameter according to actual requirements, determining corresponding distribution weight jointly by combining the data volume and the priority of the uploading task to be uploaded, and distributing total bandwidth of data transmission and total computing resources of an edge server based on the distribution weight, thereby fully considering the influence of the priority (namely actual transmission requirement) and the data volume on network resource distribution, effectively distributing virtual machine resources and efficiently uploading the virtual machine resources to a cloud platform in real time; and finally, the cloud platform performs data processing according to the uploaded node parameters to generate corresponding tracing information and early warning information, so that the whole production chain is comprehensively and efficiently monitored. In summary, the invention comprehensively collects the information of each node in the node industrial chain, performs weight distribution by using the data volume and priority of the node parameters, thereby performing resource distribution, effectively sequences the uploading of each node parameter through the edge manager, performs data processing on various node parameters through the cloud platform, achieves effective information management and information tracing, ensures the safety of clean egg production, ensures the rapid processing of each node parameter in the monitoring process, further realizes the high efficiency and accuracy of clean egg production monitoring, is beneficial to performing timely feedback and early warning, improves the safety of clean egg production, and in addition, ensures that the transmission time delay of all tasks is close to the average time delay, the mean variance of the transmission time delay is small, can rapidly transmit all tasks at the same time, and has good synchronism.

Drawings

FIG. 1 is a schematic flow chart of a method for uploading information of a whole chain for clean egg production in real time according to the present invention;

fig. 2 is a first flowchart of step S2 provided in the present invention;

FIG. 3 is a second flowchart illustrating step S2 according to the present invention;

fig. 4 is a schematic structural diagram of a clean egg production full-chain information real-time uploading system provided by the invention.

Detailed Description

The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.

Example 1

The embodiment of the invention provides a method for uploading clean egg production full chain information in real time, and as seen in fig. 1, fig. 1 is a schematic flow chart of the method for uploading clean egg production full chain information in real time provided by the invention, and the method for uploading clean egg production full chain information in real time comprises steps S1 to S3, wherein:

in step S1, collecting a plurality of node parameters, wherein the node parameters include parameters correspondingly monitored by a sensor arranged in each production node in the clean egg production process;

in step S2, generating upload tasks according to the data amount corresponding to each node parameter, setting a corresponding priority for each upload task, determining a corresponding allocation weight according to each upload task and the corresponding priority, allocating a corresponding allocation bandwidth and transmission delay according to the allocation weight and a total bandwidth of data transmission, and ordering and uploading each upload task to the cloud platform based on the allocation bandwidth and the transmission delay;

in step S3, data processing is performed according to each node parameter in the upload task uploaded by the edge server, corresponding traceability information is generated for recording the clean egg production full-chain information, the production node with risk is determined according to the node parameter, and corresponding early warning information is generated and sent to the production node.

In the embodiment of the invention, firstly, node parameters of each production node in the production process of the clean eggs are comprehensively and effectively acquired; then, converting each node parameter to be uploaded into an uploading task, setting corresponding priority for each node parameter according to actual requirements, determining corresponding distribution weight jointly by combining the data volume and the priority of the uploading task to be uploaded, and distributing total bandwidth of data transmission and total computing resources of an edge server based on the distribution weight, thereby fully considering the influence of the priority (namely actual transmission requirement) and the data volume on network resource distribution, effectively distributing virtual machine resources and efficiently uploading the virtual machine resources to a cloud platform in real time; and finally, the cloud platform performs data processing according to the uploaded node parameters to generate corresponding tracing information and early warning information, so that the monitoring of the whole production chain in the universities is achieved.

Preferably, referring to fig. 2, fig. 2 is a first schematic flow chart of step S2 provided by the present invention, and the step S2 includes steps S21 to S22, where:

in step S21, generating the corresponding upload task according to the data amount corresponding to the node parameter uploaded by the sensor of each production node each time;

in step S22, the priorities are set for the corresponding uploading tasks according to the feedback durations after the tasks of different sensors are uploaded, wherein the priorities are sequentially increased as the feedback durations of the sensors are sequentially decreased.

As a specific embodiment, the embodiment of the invention sets reasonable priority to meet different data transmission requirements.

In a specific embodiment of the present invention, different priorities are set for each task according to the response requirement of each task, and the priorities are classified into 5 levels, specifically as follows:

R_i∈{1，2，...，5}，i＝1，2，...，20

wherein, R-1 is the lowest priority, R-5 is the highest priority, and the priorities are increased in sequence from R-1 to R-5. The priority setting principle is that a high priority is set for a sensor task needing quick feedback to be processed, a notification is fed back in time to protect and rescue, and the priority R is set to be 5.

Preferably, referring to fig. 3, fig. 3 is a schematic flow chart illustrating the step S2 provided in the present invention, wherein the step S2 includes steps S23 to S28, wherein:

in step S23, determining a corresponding first proportional quantity according to a ratio of the data quantity of each upload task to the sum of the data quantities of all upload tasks;

in step S24, determining a corresponding second proportional quantity according to a ratio of the priority of each upload task to the sum of the priorities of all the upload tasks;

in step S25, multiplying the first proportional quantity by a preset first influence factor to determine a first product value, and multiplying the second proportional quantity by a preset second influence factor to determine a second product value;

in step S26, determining the corresponding assigned weight according to the sum of the first product value and the second product value;

in step S27, the allocation weight is multiplied by the total bandwidth of the transmission data and the total resource calculated by the edge server, and the allocation bandwidth and allocation resource corresponding to each upload task are determined;

in step S28, the corresponding transmission delay is determined according to the data amount of each upload task and the allocated resource.

As a specific embodiment, the embodiment of the present invention determines the corresponding allocation weight together with the data volume and the priority of the upload task to be uploaded, and allocates the total bandwidth of data transmission and the total computing resource of the edge server based on the allocation weight, thereby fully considering the influence of the priority (i.e., the actual transmission requirement) and the data volume on the network resource allocation.

Preferably, the sum of the first influence factor and the second influence factor is a predetermined preset value. As a specific embodiment, the preset value described in the embodiment of the present invention is preferably 1.

In one embodiment of the present invention, the bandwidth and resource allocation weight are calculated as follows:

wherein D is_iThe data volume of the task i, sigma D, alpha data volume ratio, and R_iThe priority of the task i, the sum of the priorities of the tasks, and the influence coefficient of the 1-alpha priority on the distribution weight parameter.

According to the distribution weight, the distribution scheme of the bandwidth and the computing resource can be calculated

B_i＝Q_iB

Wherein, B_iThe communication bandwidth acquired during transmission for task i, the total bandwidth of the wireless communication link transmitting data,

computing power allocated for edge servers, f^EThe computing power of the edge server.

The following describes the calculation method of weight distribution in detail by taking bandwidth optimization as an example, and the transmission delay can be calculated according to the data volume and the bandwidth, as shown in table 1 below:

TABLE 1

Data volume (MB)	＜1	1～10	10～20	20～30
					Bandwidth (MB)	4	40	70	100
Transmission delay(s)	＜0.83	＜0.83	＜0.949	＜0.997

Calculation of the amount of transmission data: task 1 is data collected by a farm temperature sensor, each data size is 2 bytes, the data is collected once per second, the data volume collected in one hour is 7200 bytes, which is approximately equal to 7KB, namely D₁The remaining 19 tasks, with data size 15KB for task 2 to task 5, 20KB for task 6 to task 15, 25KB for task 16 to task 20, 392KB for total task data, less than 1MB, are set as 7KB, and the transmission bandwidth is selected as shown in table 34 MB. The data transmission amount of each task is specifically shown in table 2 below:

TABLE 2

Task numbering	Data volume (KB)	Task numbering	Data volume (KB)
				1	7	11	20
2	15	12	20
				3	15	13	20
4	15	14	20
				5	15	15	20
6	20	16	25
				7	20	17	25
8	20	18	25
				9	20	19	25
10	20	20	25

Two different methods are respectively adopted to allocate the bandwidth, which specifically comprises the following steps:

the method comprises the following steps: the priority of task 1 is set to 1, the priority of task 2 to task 5 is set to 2, the priority of task 6 to task 15 is set to 3, and the priority of task 16 to task 20 is set to 5. The influence coefficient α of the data amount ratio on the distribution weight parameter is set to 0.7, the influence coefficient 1- α of the priority ratio on the distribution weight parameter is set to 0.3, and the bandwidth distribution and the transmission delay can be calculated according to the above formula, as shown in table 3.

Average distribution method: the total amount of twenty task data is 392KB, the allocated bandwidth is 4M, the bandwidth is equally allocated according to the number of tasks, the bandwidth allocated to each task is 0.2M, and the bandwidth allocation and transmission delay of each task are shown in table 3.

The results of two different bandwidth allocation methods are compared as follows:

TABLE 3

As can be seen from the above table, tasks 1 to 5 have a small data size and a low priority, the transmission delay of the weight optimization method is greater than that of the average distribution method, tasks 6 to 15 have a transmission delay approximately equal to that of the average distribution method, tasks 16 to 20 have a large data size and a high priority, and the transmission delay of the weight optimization method is reduced by 0.1073s compared with the average distribution method. The average transmission time delay distributed by adopting the weight optimization method and the average distribution is 0.3211s and 0.3179s respectively and is approximately equal, the mean variance of the transmission time delay is 0.0142 and 0.0736 respectively, and the mean variance of the transmission time delay of the weight optimization method is smaller than that of the average distribution method, so that the performance of the weight optimization method is better. In summary, the weight optimization method allocates bandwidth according to the transmission data volume and the priority, the task with small data volume and low priority has small allocated bandwidth and increased time delay, but the time delay still can meet the response requirement, the task with large data volume and high priority has more allocated bandwidth, so that the transmission time delay is smaller, and the requirement of quick transmission is met, thereby the transmission time delay of all tasks is close to the average time delay, the mean square error of the transmission time delay is smaller, all tasks can be quickly transmitted at the same time, and the method has good synchronism.

Preferably, the method further comprises: and comparing the plurality of breeding node parameters, the plurality of collecting and storing node parameters, the plurality of processing node parameters and the plurality of transportation node parameters with the corresponding pre-stored parameter index library respectively, and early warning the corresponding nodes according to parameter comparison results. Therefore, early warning is carried out through effective data comparison.

Preferably, the intelligent detection method for quality information of the whole chain of the clean eggs based on the internet of things further comprises the following steps: and converting the production information of the produced clean eggs into corresponding RFID labels. Therefore, direct information tracing of the consumer is facilitated through the arrangement of the RFID tag.

In a specific embodiment of the present invention, the plurality of process node parameters comprises an operational video stream during the process, wherein:

processing edge nodes corresponding to the node parameters, specifically dividing the operation video stream into a plurality of video frame sequence groups, calling an API (application programming interface) algorithm, and counting target identification numbers corresponding to the plurality of video frame sequence groups; the frame filtering model is determined according to the time delay, the bandwidth and the target identification number uploaded to the cloud platform by the edge node, redundant video frame sequence groups are removed according to the frame filtering model, and a first frame sequence group is determined; the image processing device is also used for extracting key frames in the first frame sequence group according to the image information entropy of the first frame sequence group and determining a second frame sequence group;

the edge node manager is specifically used for being in communication connection with the plurality of edge nodes, and is used for allocating virtual machine resources according to the target identification numbers and the data volume of the plurality of second frame sequence groups and determining an uploading sequence of the second frame sequence groups to the cloud platform;

the cloud platform is specifically used for carrying out human body posture estimation on each frame of image in the second frame sequence group and determining position coordinates of a plurality of joint points; the joint distance variation matrix is also used for determining a joint distance variation matrix according to the position coordinate variation of the same joint between two adjacent frames of images in the second frame sequence group; the second frame sequence group is further used for carrying out average division on the second frame sequence group, and matrix addition is carried out on the joint point distance variable quantities generated by two adjacent frames in each section of video to obtain a cumulative distance variable quantity matrix of each section as a feature vector of the second frame sequence group; and the method is also used for inputting the feature vectors into the completely trained deep learning model for classification and determining the corresponding operation specification index.

In a specific embodiment of the present invention, the processing node is specifically divided into a plurality of sub-nodes, and 4 paths of cameras are respectively set up, and a total of 24 paths of cameras are set up. The bandwidth and the storage space required by the 24 paths of cameras for full-video monitoring in the processing process comprise an uplink bandwidth for transmitting video data to the edge node by the cameras, a downlink bandwidth for receiving the video data by the edge node, a storage space for locally storing the video data for one month by the cameras, a storage space for cleaning the video data once a month by the cameras and the storage space of the edge node.

The method for uploading the video images of the 24-channel camera by using the edge calculation comprises the following steps:

the first step is as follows: detecting a moving target object in the operation video stream, identifying the video data of the operation video stream, judging whether a moving target exists or not, extracting an extracted video segment with the moving target, and then carrying out next processing on the extracted video data;

the method comprises the following steps of extracting a video clip with a moving target by adopting a three-frame difference method, wherein a specific formula is described as follows:

wherein G (x, y) is G₁(x, y) and g₂(x, y) carrying out logical AND, judging that the continuous three frames of images change, indicating that a moving object exists, and extracting the video clip with the moving object. Therefore, before the identification of the operation behavior specification is carried out, whether a moving target exists needs to be identified, namely an operator has the significance of further carrying out the identification of the operation behavior specification, a video segment with the moving target is extracted through a frame difference method, redundant video segments are removed, and the video segment with the moving target is processed in the next step.

The second step is that: preprocessing with a frame filtering model, wherein the formula of the frame filtering model is as follows:

wherein, O_i，sFor the identification number of the object of the i-th frame in the video stream S, D_upUploading video frame data volume for ECN, D_maxFor the maximum amount of data allowed to be transmitted per unit time of the network, t_dFor task completion time, τ_dMaximum processing time allowed for completion of the task, T_eIs the total time delay, T, of S sent to the cloud computing center_cThe time delay of the direct transmission cloud platform is adopted. Therefore, after the transmission capacity and the total time delay of the current network both meet the conditions, the ECN Controller allocates an uploading channel and starts task scheduling. When a plurality of cameras are used for collecting data, if all video data are processed, the processing time is longer, resources are wasted, and for repeated video content, namely the same scene content collected by different cameras, the video with the most identified target objects is selected through frame filtering, and is identified and analyzed, so that the data processing time is reduced, and the problem of video data redundancy is solved;

the third step: extracting the key frame, wherein the specific flow is as follows:

the joint histogram represents two images I of the same size_iAnd I_jWith the frequency of occurrence of the gray combinations of the pixel pairs at their corresponding locations. For image I of same MxN_i(x，y)、I_j(x, y), the joint probability of the corresponding pixel value pair (p, q) is expressed as:

wherein the content of the first and second substances,

from the above equation, it can be seen that the image I can be obtained by finding the F (p, q) value for all possible pixel value pairs (p, q)_i(x, y) and I_jJoint histogram of (x, y) Joint histogram symmetryIs defined as

Where α is the weight on the diagonal of the joint histogram, here the smaller than normal amount, and β ═ p-qⁿRepresenting the weight of elements far away from the diagonal line, wherein n is an integer in the formula, δ more intuitively represents the similarity between two frames, as δ approaches to 1, the more symmetrical the joint histogram is, that is, the greater the similarity between two images is, when the video content of the target rapidly appears, the brightness and the like is obviously changed, the similarity between frames is correspondingly changed, and generally the similarity δ between adjacent frames belongs to [0, 1]To avoid missing key frames, the threshold T' is set to 0.9.

According to the continuous characteristic of the monitoring video, in a continuously changing video sequence, the characteristic value of the continuous front and back video frames is gradually changed, namely the change of the image information value of the adjacent frames is not large. In order to reduce the redundancy of data, a frame with the largest information entropy value of an image is selected from a video sequence with closer intervals as a key frame, and the calculation formula of entropy of the image information is as follows:

in the formula: n denotes the number of gray levels of an image, x_iIndicates the gray value of the pixel (x, y), p (x)_i) Is the probability of each gray level occurring. In order to prevent redundancy of key frames caused by illumination change and the like in the extracted key frames, one frame with the largest information entropy is selected from adjacent and nearer candidate frames as the key frame. Non-adjacent inter-frame entropy differences of 20 frames apart can be clearly distinguished. Therefore, the key frame is extracted based on the joint histogram, when the interval of the key candidate frame sequence is less than 20, a frame with the largest information entropy is selected as the key frameThe frames can effectively reflect the main content of the continuous video sequence and reduce the redundancy of data.

The fourth step: and (3) scheduling the tasks, wherein the specific flow is as follows:

after the ECN Controller allocates the uploading channel, the ECN feeds back the queue information of the uploading video frame. Under different network environments, parameters of uploaded video streams are different after ECN clusters are dynamically adjusted, and data volume of video frame groups is represented as D for convenience of unified scheduling management of ECN controllers_e，i. The ECN average identification target number may be expressed as:

wherein the content of the first and second substances,

for ECN average identification of target number, D_e，iAmount of data for a group of video frames, B_cFor link capacity, O_iIs the number of identification targets of the ith frame. Thus, the ECN Controller assigns a uniform metric to the scheduling of each upload task

At time T, N ECN tasks in the cluster wait to be scheduled, and the completion time of each task after r rounds of scheduling is recorded as T_iThe validity of the ECN uploading task can be reached only by scheduling and completing before the deadline, and the completion time meets t_i＜τ_iIn which τ is_iIs the latest completion time of the ith ECN upload task. The task scheduling needs to consider the limited available resources of the system, can not exceed a threshold value, have

Wherein r is the number of scheduling rounds,

for the ith frame data amount, M_tScheduling an upper threshold of resources for the task;

under the condition that the allocation condition of system resources and the ECN uploading meeting the deadline are considered, the task scheduling time model is as follows:

wherein, t_iFor scheduled completion time, r is the number of scheduled rounds, D_iFor the ith frame data amount, M_tScheduling an upper threshold, τ, of resources for a task_iThe latest completion time of the ith ECN uploading task is the latest completion time of the ith ECN uploading task, so that the video stream data collected by the multiple cameras cannot be uploaded together, and the uploading resources and sequence need to be allocated, so that efficient transmission is realized.

The fifth step: identifying operation behaviors, wherein the specific flow is as follows:

firstly, extracting the position coordinates of each frame of human body joint points in a video: carrying out posture estimation on each frame of human body in the video by utilizing an Open-position method to obtain position coordinates of 15 joint points of the neck, the chest, the head, the right shoulder, the left shoulder, the right hip, the left hip, the right elbow, the left elbow, the right knee, the left knee, the right wrist, the left wrist, the right ankle and the left ankle of the human body, wherein the coordinate of the kth joint point is expressed as L_k＝(x_k，y_k) K is from 1 to 15;

then, the position coordinates of each joint point are normalized, a coordinate matrix P is formed by the position coordinates of the 15 joint points after normalization,

wherein (x)_k，y_k) Representing the coordinates after the k-th joint point normalization;

further, two adjacent frames of human body are calculatedThe distance variation matrix of the joint points: according to the coordinate matrix P of two adjacent frames_nAnd P_n-1Calculating a joint point position coordinate variation matrix of two adjacent frames, and calculating a joint point distance variation matrix D according to the joint point position coordinate variation matrix;

further, generating video characteristics, averagely dividing the video into 4 sections according to the time length of the video, adding distance variation matrixes D generated by two adjacent frames in each section of the video to obtain accumulated distance variation matrixes Di, wherein i ranges from 1 to 4, carrying out L2 normalization on Di to obtain normalized Di ', and connecting the accumulated distance variation matrixes Di' in series to serve as the characteristics of the whole video: f ═ D1', D2', D3', D4';

then, the videos are classified using neural networks: dividing video data into a training set and a testing set, inputting the characteristics of a training set video into a neural network for training to obtain a trained neural network classification model, and inputting the characteristics of a testing set video into the trained neural network classification model to obtain a classification result.

Preferably, the moving target is detected, when the video data of the moving object is acquired by the camera, whether the moving target exists is judged according to algorithm detection and analysis, the video data with the moving target is uploaded, all the video data are not required to be uploaded, only the video data with the moving target object are uploaded, and therefore the data transmission amount and the data transmission time are reduced, and the bandwidth is saved. In the target detection stage, the inter-frame difference method is used for detecting the moving target, so that whether the moving target exists or not is judged through the inter-frame image difference, the moving target is detected and extracted to obtain the video clip with the moving target, and useless video clips are not processed.

Example 2

The embodiment of the invention provides a clean egg production full-chain information real-time uploading system, and when being seen in combination with fig. 4, fig. 4 is a schematic structural diagram of the clean egg production full-chain information real-time uploading system provided by the invention, and the clean egg production full-chain information real-time uploading system comprises:

the node parameter acquisition device 1 is used for acquiring a plurality of node parameters, wherein the node parameters comprise parameters correspondingly monitored by a sensor arranged at each production node in the clean egg production process;

the edge server 2 is used for generating uploading tasks according to the data volume corresponding to each node parameter and setting corresponding priority for each uploading task; the system is also used for determining corresponding distribution weight according to each uploading task and the corresponding priority; the device is also used for determining corresponding distribution bandwidth and transmission time delay according to the distribution weight and the total bandwidth of data transmission; the system is also used for sequencing and uploading each uploading task to the cloud platform according to the allocated bandwidth and the transmission delay;

the cloud platform 3 is used for carrying out data processing according to each node parameter in the uploading task uploaded by the edge server to generate corresponding traceability information and recording the whole chain information of the clean egg production; and the early warning system is also used for determining the production nodes with risks according to the node parameters, generating corresponding early warning information and sending the early warning information to the production nodes.

In the embodiment of the invention, the node parameter acquisition device is arranged to comprehensively and effectively acquire the node parameter of each production node in the clean egg production process; by setting edge services, converting each node parameter to be uploaded into an uploading task, setting corresponding priority for each node parameter according to actual requirements, determining corresponding distribution weight jointly by combining the data volume and the priority of the uploading task to be uploaded, and distributing total bandwidth of data transmission and total computing resources of an edge server based on the distribution weight, so that the influence of the priority (namely actual transmission requirements) and the data volume on network resource distribution is fully considered, effective virtual machine resource distribution is carried out, and the data are uploaded to a cloud platform efficiently in real time; through setting up the cloud platform, carry out data processing according to the node parameter of uploading, generate corresponding traceability information and early warning information to this reaches the control to the comprehensive colleges and universities of whole production chain.

Preferably, the node parameters include a breeding main node parameter, and the breeding node parameters sequentially include the following sub-node parameters: hatching egg parameter, hatching parameter, breed parameter and laying eggs parameter, the breed owner node monitoring facilities who corresponds includes: the hatching egg monitoring equipment is used for monitoring the hatching egg parameters to feed back the quality condition in the hatching egg acceptance process, wherein the hatching egg parameters comprise at least one of the quality of eggs, the environment quality of a farm and the pollutant content in the hatching egg acceptance process; the hatching monitoring device is used for monitoring the hatching parameters to feed back the breeding condition in the hatching process, wherein the hatching parameters comprise at least one of egg quality, hatching images and environment quality of a hatching place during hatching; the breeding monitoring equipment is used for monitoring the breeding parameters to feed back the breeding condition, wherein the breeding parameters comprise at least one of egg quality, chicken health condition, water quality, feed quality and farm environment quality during breeding; and the egg laying monitoring equipment is used for monitoring the egg laying parameters so as to feed back egg laying conditions and eggshell breakage conditions, wherein the egg laying parameters comprise at least one of egg product images, egg product pollutant content and egg product nutrition content during egg laying. As a specific embodiment, the embodiment of the invention sets the cultivation main node and the corresponding sub-node thereof, thereby ensuring effective monitoring in the cultivation process.

It should be noted that the main cultivation node includes seed nodes for hatching eggs, hatching, cultivating, laying eggs, and the like. The egg seed node uses a full-automatic multifunctional egg product detection instrument to detect the quality of the hatching eggs, measures the yolk height, the weight, the albumen height, the yolk color and the quality of the hatching eggs, uses a microorganism rapid detection system to detect the microorganism quantity on the surface of the hatching eggs, uses a PCR method to detect avian influenza virus, and uses an environment monitoring device to monitor the temperature, the humidity, the illumination and the like of a workshop. The hatching child node uses a video monitor to monitor hatching conditions, such as hatching rate and death rate, an environment monitoring system is used for detecting the hatching conditions in the hatching process, and temperature, humidity, wind speed, sunlight and the like are measured. The breeding sub-nodes use a video monitor to monitor the breeding conditions, such as growth speed, disease conditions and the like, an environment monitoring system is used for detecting environmental factors such as temperature, humidity, wind speed, sunlight and the like in the breeding process, a water body BOD5, CODCr, suspended matters, water temperature, pH value and temperature are measured by a water quality monitoring system, and feed antibiotics, heavy metals, pesticide residues and the like are detected by a feed monitoring system. The egg laying sub-node uses a video monitor to detect egg laying conditions and eggshell breakage conditions, uses a heavy metal rapid detection system to detect heavy metal pollutants (such as cadmium), uses a mycotoxin rapid detection system to detect toxins (such as aureomycin), uses a pesticide residue rapid detector to detect pesticide residues (such as hexachloro-cyclohexane), and uses an online near-infrared spectrum analyzer to detect protein, moisture, fat and the like.

Preferably, the node parameters include storage master node parameters, and the storage master node parameters sequentially include the following child node parameters: warehousing parameters, storage parameters and ex-warehouse parameters, wherein the corresponding storage node monitoring equipment comprises: the warehousing monitoring equipment is used for monitoring the warehousing parameters to feed back the quality of the produced eggs in the warehousing process, wherein the warehousing parameters comprise at least one of the quality of the eggs, the content of pollutants in the eggs and the nutrient content of the eggs during warehousing; the storage monitoring equipment is used for monitoring storage parameters to feed back the environmental condition in the storage process, wherein the storage parameters comprise at least one of warehouse temperature and humidity, warehouse cleanliness and warehouse carbon dioxide content; and the ex-warehouse monitoring equipment is used for monitoring ex-warehouse parameters so as to feed back the quality of the ex-warehouse eggs in the ex-warehouse process, wherein the ex-warehouse parameters comprise at least one of the quality of the ex-warehouse eggs, the content of pollutants in the egg products and the content of nutrients in the egg products. As a specific embodiment, the embodiment of the present invention sets the storage node and the corresponding child node thereof, thereby ensuring effective monitoring during the storage process.

It should be noted that the storage master node includes child nodes such as warehousing, storage, and ex-warehouse. The warehousing sub-node detects the weight of the hatching eggs, the height of the egg white, the color of the yolk, the height of the yolk and the quality of the hatching eggs by using a full-automatic multifunctional egg product detecting instrument, detects the quality of the egg products, detects heavy metal pollutants (such as cadmium) by using a heavy metal rapid detection system, detects toxins (such as aureomycin) by using a mycotoxin rapid detection system, detects pesticide residues (such as hexachloro cyclohexane) by using a pesticide residue rapid detector, and detects protein, water, fat and the like by using an online near-infrared spectrum analyzer. The storage sub-node uses a video monitor to monitor storage conditions in the warehouse, whether other organisms (such as mice and bugs) exist or not, PM2.5 is used for monitoring dust content in the warehouse, an electronic nose is used for monitoring musty and odor in the warehouse and gas (CO2 and PH3), a temperature and humidity sensor is used for monitoring temperature and humidity of the warehouse, and the storage environment is guaranteed to be appropriate. The ex-warehouse subnode uses a full-automatic multifunctional egg product detecting instrument to detect the weight of the hatching eggs, the height of the egg white, the color of the egg yolk, the height of the egg yolk and the quality of the hatching eggs, detects the quality of the egg products, detects heavy metal pollutants (such as cadmium) by using a heavy metal rapid detection system, detects toxins (such as aureomycin) by using a mycotoxin rapid detection system, detects pesticide residues (such as hexachloro cyclohexane) by using a pesticide residue rapid detector, and detects protein, water, fat and the like by using an online near-infrared spectrum analyzer.

Preferably, the node parameters include a processing master node parameter, and the processing node parameters sequentially include the following sub-node parameters: receive parameter, washing parameter, disinfection parameter, drying parameter, film coating parameter, hierarchical parameter and packing parameter, corresponding processing node monitoring facilities includes: the receiving monitoring equipment is used for monitoring the receiving parameters so as to feed back the quality of the produced eggs when the eggs are received in the processing process and the operation specifications when the eggs are received, wherein the receiving parameters comprise at least one of the quality of the eggs, the pollutant content of the eggs, the nutrient content of the eggs and the receiving operation images when the eggs are received in the processing process; the cleaning monitoring equipment is used for monitoring the cleaning parameters so as to feed back the cleanliness of the egg products during cleaning and the operation specifications during cleaning, wherein the cleaning parameters comprise at least one of the cleanliness of the egg products during cleaning and the cleaning operation images; the disinfection monitoring device is used for monitoring the disinfection parameters so as to feed back the microorganism content of the produced eggs during disinfection and the operation specification during disinfection, wherein the disinfection parameters comprise at least one of the microorganism content of the eggs during disinfection and the disinfection operation image; the drying monitoring device is used for monitoring the drying parameters so as to feed back the drying degree of the produced eggs during drying and the operation specification during drying, wherein the drying parameters comprise at least one of the surface moisture content of the eggs during drying and the drying operation image: the film coating monitoring equipment is used for monitoring the film coating parameters to feed back the film forming degree of the egg product during film coating and the operation specification during film coating, wherein the film coating parameters comprise at least one of film coating speed, egg product film coating area and film coating operation images during film coating: the grading monitoring device is used for monitoring the grading parameter so as to feed back the grading grade of the produced eggs during grading and the operation specification during grading, wherein the grading parameter comprises at least one of the egg size grade and the grading operation image: and the package monitoring equipment is used for monitoring the package parameters so as to feed back the package condition of the egg products during packaging and the operation specification during packaging, wherein the package parameters comprise at least one of an egg product code spraying rate, an egg product packaging rate and a package operation image. As a specific embodiment, the embodiment of the present invention sets a processing node and a child node corresponding to the processing node, thereby ensuring effective monitoring during a processing process.

The processing main node comprises sub-nodes of receiving, cleaning, disinfecting, drying, coating, grading, packaging and the like. The receiving sub-node uses a full-automatic multifunctional egg product detecting instrument to detect the weight of the hatching eggs, the height of the egg white, the color of the yolk, the height of the yolk and the quality of the hatching eggs, detects the quality of the eggs, uses a heavy metal rapid detection system to detect heavy metal pollutants (such as cadmium), uses a mycotoxin rapid detection system to detect toxins (such as chlortetracycline), uses a pesticide residue rapid detector to detect pesticide residues (such as hexachloro cyclohexane), and uses an online near-infrared spectrum analyzer to detect protein, water, fat and the like. The cleaning sub-node uses a full-automatic egg washing machine to remove impurities on the surface of the egg product, detects the total amount of the impurities, and uses a video monitor to monitor the operation specification of an operator. The sterilizer node uses an ultraviolet sterilizer to detect the sterilization rate, removes harmful strains such as escherichia coli, salmonella and the like on the surface of the eggshell, and uses a video monitor to monitor the operation specification of an operator. The drying sub-node detects the moisture content on the surface of the eggshell by using a dryer and monitors the operation specification of an operator by using a video monitor. The film coating sub-nodes are coated with films by using an automatic film coating machine, and the operation specifications of operators are monitored by using a video monitor. The grading sub-node uses a full-automatic egg sorting machine to grade eggs with different sizes and qualities, and uses a video monitor to monitor the operation specification of an operator. And the packaging sub-node monitors the operation specification and the packaging effect of an operator by using a video monitor.

Preferably, the node parameter includes a circulation master node parameter, and the circulation master node parameter includes the following child node parameters: circulation receiving parameter, transportation parameter, shipment parameter, the circulation node monitoring facilities who corresponds includes: the circulation receiving monitoring equipment is used for monitoring the circulation receiving parameters so as to feed back the quality of the produced eggs when receiving in the circulation process and the operation specification when receiving, wherein the receiving parameters comprise at least one of the quality of the eggs when receiving in the circulation process, the content of pollutants in the eggs, the content of nutrients in the eggs and the receiving operation images; the transportation monitoring equipment is used for monitoring the transportation parameters to feed back the track condition during transportation, wherein the transportation parameters comprise at least one of a transportation route, a transportation track and transportation time; and the delivery monitoring equipment is used for monitoring delivery parameters so as to feed back the quality of the eggs during delivery and the operation specification during delivery, wherein the delivery parameters comprise at least one of the quality of the eggs, the content of pollutants in the eggs, the content of nutrients in the eggs and the delivery operation images during delivery in the circulation process. As a specific embodiment, the embodiment of the present invention sets a circulation node and a child node corresponding to the circulation node, thereby ensuring effective monitoring during a circulation process.

It should be noted that the circulation main node includes receiving, transporting and discharging sub-nodes. The receiving sub-node uses the RFID to record node codes and clean egg production full-chain food safety data, a full-automatic multifunctional egg product detecting instrument is used for detecting impurity content of clean eggs, weight of the clean eggs, height of egg white, color of yolk, height of yolk and quality of the clean eggs, a heavy metal rapid detection system is used for detecting heavy metal pollutants (such as cadmium), a mycotoxin rapid detection system is used for detecting toxin (aureomycin), a pesticide residue rapid detector is used for detecting pesticide residues (such as hexachloro), an online near infrared spectrum analyzer is used for detecting protein, moisture and fat, quality of finished products is guaranteed, the finished products are uploaded to a food safety big data platform, the data are compared with data of the processed finished product sub-node and national standard data, and if the products meet quality and food safety requirements, the next transport sub-node can be entered. The transport sub-node records the transport route, the transport track and the transport time by using Beidou navigation, and the tracking and positioning of the transport process are realized. The lower cargo node uses a full-automatic multifunctional egg product detection instrument to detect the impurity content of clean eggs, the weight of the clean eggs, the height of egg white, the color of egg yolk, the height of egg yolk and the quality of the clean eggs, uses a heavy metal rapid detection system to detect heavy metal pollutants (such as cadmium), uses a mycotoxin rapid detection system to detect toxins (such as aureomycin), uses a pesticide residue rapid detection instrument to detect pesticide residues (such as hexachloro cyclohexane), uses an online near-infrared spectrum analyzer to detect protein, water and fat, and ensures the quality of the clean eggs after transportation.

It should be noted that the transportation sub-node monitoring system comprises a video monitor and a Beidou navigator. The Beidou navigation monitoring system is used for recording the whole transportation route, track and time, and the video monitor is used for monitoring the state of the whole transportation process. The video safety big data platform is uploaded to with above-mentioned monitoring signal in real time, managers can look over the transportation situation of clean egg through equipment in real time, can look over the starting point of clean egg transportation, the terminal point of transportation, the planning route of transportation, the actual route of transportation, plan transport time, current transit time, transportation personnel information, the node code of clean egg, the realization is tracked clean egg transportation process transparentization, clean egg transportation efficiency can be guaranteed, prevent that transportation personnel from stealing and trading clean egg, the conspiracy is privately, accomplish effective supervision.

Specifically, monitoring facilities among the above-mentioned node monitoring process includes the video monitor, the video data transmission that the video monitor will gather is to the edge server, the edge server passes through AI intelligent analysis, whether analysis operating personnel's wearing accords with the standard requirement, if not according with the requirement, send alarm signal, and simultaneously, still real time monitoring operating personnel's action standardization, compare with corresponding operation standard, the discovery has the action that is not according with the standard requirement, will send the police dispatch newspaper for managers, managers in time looks over and handles. Such as whether the operator has actions which do not meet the standard requirements, such as eating, smoking, calling and the like in the workshop.

Preferably, when finished products are accepted at the finished product sub-node in the processing node, the RFID label is attached to the package of the clean egg products according with the requirements, and the product code, the production enterprise, the production place, the production date, the data detected by the full chain and the like are written into the RFID label for future reference.

Preferably, the cloud platform 3 includes a big data acquisition module, a big data collection module, a big data sorting module, a big data analysis module, a big data display module, a big data application module, and a big data service module. As a specific embodiment, the cloud platform is arranged in the embodiment of the invention, various big data processing modes are adopted to process the monitoring data of each node, and the state of each link is analyzed, so that effective early warning is performed on each link.

Preferably, the big data application module comprises a risk analysis unit, wherein: and the risk analysis unit is used for comparing the plurality of breeding node parameters, the plurality of collecting and storing node parameters, the plurality of processing node parameters and the plurality of transportation node parameters with the corresponding pre-stored parameter index libraries respectively and carrying out early warning according to parameter comparison results. As a specific embodiment, the risk analysis unit is arranged in the embodiment of the invention, and the monitoring parameters are effectively compared and processed, so that the abnormal state is quickly positioned, the effective early warning is carried out, and the safety of each link of the clean egg production is ensured.

Preferably, the plurality of processing node parameters include an operation image, the risk analysis unit is specifically configured to match the operation image with a corresponding pre-stored operation image standard library, and if the operation image is not matched with the pre-stored operation image standard library, an early warning is performed. As a specific embodiment, the embodiment of the invention adopts an image recognition mode, and utilizes data processing modes such as deep learning and pattern recognition to recognize irregular operation scenes, so as to prevent the damage of misoperation of an operator to the quality of the clean eggs.

The invention discloses a method and a system for uploading full-chain information of clean egg production in real time, wherein firstly, node parameters of each production node in the clean egg production process are comprehensively and effectively acquired; then, converting each node parameter to be uploaded into an uploading task, setting corresponding priority for each node parameter according to actual requirements, determining corresponding distribution weight jointly by combining the data volume and the priority of the uploading task to be uploaded, and distributing total bandwidth of data transmission and total computing resources of an edge server based on the distribution weight, thereby fully considering the influence of the priority (namely actual transmission requirement) and the data volume on network resource distribution, effectively distributing virtual machine resources and efficiently uploading the virtual machine resources to a cloud platform in real time; and finally, the cloud platform performs data processing according to the uploaded node parameters to generate corresponding tracing information and early warning information, so that the whole production chain is comprehensively and efficiently monitored.

According to the technical scheme, the information of each node in the node industrial chain is comprehensively acquired, the weight distribution is performed by using the data volume and the priority of the node parameters, so that the resource distribution is performed, the uploading of each node parameter is effectively sequenced through the edge manager, and various node parameters are subjected to data processing through the cloud platform, so that the effective information management and information tracing are achieved, the safety of clean egg production is guaranteed, the rapid processing of each node parameter in the monitoring process is guaranteed, the high efficiency and the accuracy of the clean egg production monitoring are further realized, the timely feedback and early warning are facilitated, the safety of the clean egg production is improved, in addition, the transmission delay of all tasks is close to the average delay, the mean variance of the transmission delay is small, all tasks can be rapidly transmitted at the same time, and the good synchronism is achieved.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims

1. A method for uploading all-chain information of clean egg production in real time is characterized by comprising the following steps:

2. The method for uploading the whole chain information of the clean egg production in real time according to claim 1, wherein the generating an uploading task according to the data amount corresponding to each node parameter and setting a corresponding priority for each uploading task comprises:

3. The clean egg production full-chain information real-time uploading method according to claim 2, wherein the determining a corresponding distribution weight according to each uploading task and the corresponding priority, and the distributing a corresponding distribution bandwidth and transmission delay according to the distribution weight and a total bandwidth of data transmission comprises:

4. The clean egg production full chain information real-time uploading method as claimed in claim 3, wherein the sum of the first influence factor and the second influence factor is a predetermined preset value.

5. The clean egg production full-chain information real-time uploading method as claimed in claim 1, wherein the node parameters comprise operation images of each production node, the operation images are obtained by being intercepted in an operation video stream, and the data processing process of the operation video stream comprises:

6. The utility model provides a clean egg production full chain information real-time upload system which characterized in that includes:

7. The clean egg production full-chain information real-time uploading system according to claim 6, wherein the node parameters comprise a main cultivation node parameter, which in turn comprises the following sub-node parameters: hatching egg parameter, hatching parameter, breed parameter and laying eggs parameter, the breed owner node monitoring facilities who corresponds includes:

the hatching monitoring device is used for monitoring the hatching parameters to feed back the breeding condition in the hatching process, wherein the hatching parameters comprise at least one of egg quality, hatching images and environment quality of a hatching place during hatching;

8. The clean egg production full-chain information real-time uploading system according to claim 6, wherein the node parameters comprise storage master node parameters which in turn comprise the following sub-node parameters: the system comprises warehousing parameters, storage parameters and ex-warehouse parameters, wherein the corresponding storage main node monitoring equipment comprises:

9. The clean egg production full chain information real-time uploading system as claimed in claim 6, wherein the node parameters include processing master node parameters, which in turn include the following sub-node parameters: receive parameter, washing parameter, disinfection parameter, drying parameter, film coating parameter, hierarchical parameter and packing parameter, corresponding processing main node monitoring facilities includes:

10. The clean egg production full chain information real-time uploading system as claimed in claim 6, wherein the node parameters comprise circulation master node parameters, the circulation master node parameters comprise the following sub-node parameters: receiving parameters, transportation parameters and delivery parameters, wherein the corresponding circulation node monitoring equipment comprises: