1. The crayfish full-chain data acquisition and intelligent detection method is applied to a cloud platform and comprises the following steps:

acquiring a lobster image, a node operation image and a node numerical parameter, wherein the node operation image is a monitoring image of each operation process of the lobster production full-chain node, and the node numerical parameter is a numerical parameter of each production process of the lobster production full-chain node;

performing image processing on the lobster image, determining corresponding morphological characteristics and color characteristics, and determining the quality grade of the lobsters according to the morphological characteristics and the color characteristics;

identifying the node operation image, outputting a corresponding abnormal area and an operator number, generating abnormal operation early warning information according to the abnormal area and the operator number, issuing the abnormal operation early warning information to a corresponding node, counting the issuing frequency of the abnormal operation early warning information, and determining an operation standard grade for reflecting the operation standard of personnel;

inputting the node numerical parameters into a corresponding standard library for matching, judging whether the node numerical parameters are in a qualified range, if not, generating parameter abnormity early warning information and issuing the parameter abnormity early warning information to corresponding nodes, meanwhile, counting the issuing frequency of the parameter abnormity early warning information, and determining the quality grade of a production link for reflecting the production safety of the nodes, wherein the node numerical parameters comprise the breeding environment parameters and the lobster quality parameters of each production node;

classifying the lobsters into different grade batches according to the quality grade of the lobsters, and early warning and managing the production environment and personnel operation of each node according to the operation specification grade and the quality grade of the production link.

2. The crayfish full-chain data acquisition and intelligent detection method as claimed in claim 1, wherein the image processing of the crayfish image, the determination of the corresponding morphological feature and color feature, and the determination of the size quality grade according to the morphological feature and the color feature comprises:

acquiring image data of the crayfish by using a camera, and transmitting the image data to an edge server;

preprocessing image data of the crayfish, extracting RGB components, binarizing, denoising, correcting inclination, and binarizing the image by adopting a maximum inter-class variance method;

dividing the image by an object part and a main body part, and recording the ratio of pixel points owned by the object main body to the whole image as k₁Mean value ofThe ratio of the image background pixel points to the whole picture is k₂Mean value of

The image mean should be

Setting a threshold objective function of

Removing speckle noise in the image by using a Gaussian denoising method, wherein a Gaussian filtering template scans each pixel in the image, and the weighted average gray value of the pixels in the neighborhood determined by the Gaussian filtering template is used for replacing the value of the central pixel point of the template;

the template coefficient formula is as follows:

in the above formula, (x, y) is the coordinate of any point in the mask, and (ux, uy) is the coordinate of the center point in the mask;

identifying the freshness of the crayfishes through the color characteristics of the crayfishes, screening the crayfishes with reddish colors according to the RGB color characteristics of the crayfishes through artificial intelligence, and removing dead crayfishes through a sorting device;

calculating the average value of the RGB three channelsDefining the ratio L of the R component on the shrimp body as follows:

identifying the limb integrity of the crayfish through morphological characteristics of the crayfish, and grading the crayfish according to the limb defect condition of the crayfish through artificial intelligence;

the image data of the crayfish is analyzed and processed through artificial intelligence, the size data of the crayfish is obtained, and crayfish grading is carried out according to the size data of the crayfish;

monitoring the smell of the crayfish by using an electronic nose, and ensuring the freshness of the crayfish if abnormal smell such as rotten odor exists;

the crayfish is subjected to spot inspection by using a texture analyzer, the elasticity of the muscle tissue of the crayfish is measured, if the texture analyzer measuring method is invalid, the crayfish is subjected to spot inspection by using a boiling test, and whether the muscle tissue of the crayfish is tight and elastic or not is measured in an artificial sensory tasting mode.

3. The crayfish full-chain data acquisition and intelligent detection method according to claim 1, wherein the node numerical parameters include a breeding node parameter, a storage node parameter, a processing node parameter, and a circulation node parameter, wherein the breeding node parameters include a pond-completion parameter, a water intake parameter, a fry-placing parameter, a grass planting parameter, a fertilizer water parameter, a feeding parameter, and a handover parameter in sequence; the storage node parameters comprise receiving parameters, classification parameters, warehousing parameters, stacking parameters, inventory parameters, warehousing parameters and ex-warehouse parameters; the processing node parameters sequentially comprise a receiving parameter, a primary screening parameter, a cleaning parameter, a grading parameter, a finishing parameter, a packaging parameter, a precooling parameter and an output parameter; the circulation node parameters comprise a goods feeding parameter, a storage parameter, a goods picking parameter, a goods distribution parameter, a transportation parameter and a sales parameter.

4. The crayfish full-chain data acquisition and intelligent detection method as claimed in claim 1, further comprising:

performing data filtering on the breeding node parameters, the storage node parameters, the processing node parameters and the circulation node parameters, and determining filtered parameters to be uploaded;

sequencing the processing time delay of each parameter to be uploaded in an ascending order to form a first sequence, and placing each newly added parameter to be uploaded at the edge node at the tail end of the first sequence;

adjusting the first sequence according to the transmission delay of each edge node, and determining the uploading sequence of the parameters to be uploaded according to the adjusted first sequence;

and sequentially carrying out data processing on the uploaded parameters to be uploaded, and visualizing the data processing result.

5. The crayfish full-chain data collection and intelligent detection method as claimed in claim 3, wherein the breeding node parameters comprise weather change condition data, and the corresponding data processing process comprises:

acquiring the weather change condition data;

and comparing the weather change condition data with a numerical standard library prestored in the cloud platform, and if the weather change condition data exceeds a preset standard range, generating weather early warning information and transmitting the weather early warning information to a corresponding node so as to remind related workers to process the weather early warning information.

6. The crayfish full-chain data acquisition and intelligent detection method as claimed in claim 4, further comprising:

generating corresponding first burning information according to the breeding node parameters, the storage node parameters, the processing node parameters and the circulation node parameters;

generating corresponding second burning information according to the lobster production enterprises, the lobster production and production places and the lobster storage and collection dates;

and burning the RFID electronic tag according to the first burning information and the second burning information, wherein the burning information corresponds to different lobster batches and is used for autonomous query of consumers.

7. The crayfish full-chain data acquisition and intelligent detection method as claimed in claim 1, further comprising:

determining optimal weights corresponding to the lobster quality grade, the operation specification grade and the production link quality grade by utilizing a cooperative game algorithm;

and performing data fusion on the lobster quality grade, the operation specification grade and the production link quality grade through the optimal weight to determine the batch quality grade corresponding to the crayfish.

8. The crayfish full-chain data collection and intelligent detection method as claimed in claim 7, further comprising: and monitoring the lobster image at each node again, comparing the lobster image with the lobster image of the previous node, if the lobster image is consistent with the lobster image of the previous node, allowing the lobster image to flow into the next node, and if the lobster image is inconsistent with the lobster image of the previous node, giving an alarm.

9. The crayfish full-chain data collection and intelligent detection method as claimed in claim 7, further comprising: the method comprises the steps of collecting an activity video of the lobsters, judging the activity frequency of each batch of lobsters according to the frame-dividing comparison of the activity video, and sending out notification information to notify a manager to check in time if the activity frequency is lower than a preset frequency value.

10. A crawfish full-chain data acquisition and intelligent detection device, comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the crawfish full-chain data acquisition and intelligent detection method as claimed in any one of claims 1-9 is realized.

Background

The industrial chain of the existing lobster production comprises four nodes of cultivation, storage, processing and circulation, wherein each node can influence the yield and the quality of the produced lobsters. In the prior art, a certain node or a certain production factor of the produced lobsters 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 lobster production is an urgent problem to be solved.

Disclosure of Invention

In view of the above, a method and an apparatus for full-chain data acquisition and intelligent detection of crayfish are needed to solve the problem of how to efficiently and comprehensively monitor the industrial chain of lobster production.

The invention provides a crayfish full-chain data acquisition and intelligent detection method, which is applied to a cloud platform and comprises the following steps:

acquiring a lobster image, a node operation image and a node numerical parameter, wherein the node operation image is a monitoring image of each operation process of the lobster production full-chain node, and the node numerical parameter is a numerical parameter of each production process of the lobster production full-chain node;

performing image processing on the lobster image, determining corresponding morphological characteristics and color characteristics, and determining the quality grade of the lobsters according to the morphological characteristics and the color characteristics;

identifying the node operation image, outputting a corresponding abnormal area and an operator number, generating abnormal operation early warning information according to the abnormal area and the operator number, issuing the abnormal operation early warning information to a corresponding node, counting the issuing frequency of the abnormal operation early warning information, and determining an operation standard grade for reflecting the operation standard of personnel;

inputting the node numerical parameters into a corresponding standard library for matching, judging whether the node numerical parameters are in a qualified range, if not, generating parameter abnormity early warning information and issuing the parameter abnormity early warning information to corresponding nodes, meanwhile, counting the issuing frequency of the parameter abnormity early warning information, and determining the quality grade of a production link for reflecting the production safety of the nodes, wherein the node numerical parameters comprise the breeding environment parameters and the lobster quality parameters of each production node;

classifying the lobsters into different grade batches according to the quality grade of the lobsters, and early warning and managing the production environment and personnel operation of each node according to the operation specification grade and the quality grade of the production link.

Further, the image processing of the lobster image, determining corresponding morphological features and color features, and determining a size quality grade according to the morphological features and the color features comprises:

acquiring image data of the crayfish by using a camera, and transmitting the image data to an edge server;

preprocessing image data of the crayfish, extracting RGB components, binarizing, denoising, correcting inclination, and binarizing the image by adopting a maximum inter-class variance method;

dividing the image by an object part and a main body part, and recording the ratio of pixel points owned by the object main body to the whole image as k₁Mean value ofThe ratio of the image background pixel points to the whole picture is k₂Mean value of

The image mean should be

Setting a threshold objective function of

Removing speckle noise in the image by using a Gaussian denoising method, wherein a Gaussian filtering template scans each pixel in the image, and the weighted average gray value of the pixels in the neighborhood determined by the Gaussian filtering template is used for replacing the value of the central pixel point of the template;

the template coefficient formula is as follows:

in the above formula, (x, y) is the coordinate of any point in the mask, and (ux, uy) is the coordinate of the center point in the mask;

identifying the freshness of the crayfishes through the color characteristics of the crayfishes, screening the crayfishes with reddish colors according to the RGB color characteristics of the crayfishes through artificial intelligence, and removing dead crayfishes through a sorting device;

calculating the average value of the RGB three channelsDefining the ratio L of the R component on the shrimp body as follows:

identifying the limb integrity of the crayfish through morphological characteristics of the crayfish, and grading the crayfish according to the limb defect condition of the crayfish through artificial intelligence;

the image data of the crayfish is analyzed and processed through artificial intelligence, the size data of the crayfish is obtained, and crayfish grading is carried out according to the size data of the crayfish;

monitoring the smell of the crayfish by using an electronic nose, and ensuring the freshness of the crayfish if abnormal smell such as rotten odor exists;

the crayfish is subjected to spot inspection by using a texture analyzer, the elasticity of the muscle tissue of the crayfish is measured, if the texture analyzer measuring method is invalid, the crayfish is subjected to spot inspection by using a boiling test, and whether the muscle tissue of the crayfish is tight and elastic or not is measured in an artificial sensory tasting mode.

Further, the node numerical parameters comprise cultivation node parameters, storage node parameters, processing node parameters and circulation node parameters, wherein the cultivation node parameters sequentially comprise pond-arranging parameters, water inlet parameters, seedling releasing parameters, grass planting parameters, fertilizer water parameters, feeding parameters and handover parameters; the storage node parameters comprise receiving parameters, classification parameters, warehousing parameters, stacking parameters, inventory parameters, warehousing parameters and ex-warehouse parameters; the processing node parameters sequentially comprise a receiving parameter, a primary screening parameter, a cleaning parameter, a grading parameter, a finishing parameter, a packaging parameter, a precooling parameter and an output parameter; the circulation node parameters comprise a goods feeding parameter, a storage parameter, a goods picking parameter, a goods distribution parameter, a transportation parameter and a sales parameter.

Further, still include:

performing data filtering on the breeding node parameters, the storage node parameters, the processing node parameters and the circulation node parameters, and determining filtered parameters to be uploaded;

sequencing the processing time delay of each parameter to be uploaded in an ascending order to form a first sequence, and placing each newly added parameter to be uploaded at the edge node at the tail end of the first sequence;

adjusting the first sequence according to the transmission delay of each edge node, and determining the uploading sequence of the parameters to be uploaded according to the adjusted first sequence;

and sequentially carrying out data processing on the uploaded parameters to be uploaded, and visualizing the data processing result.

Further, the breeding node parameters include weather change condition data, and the corresponding data processing process includes:

acquiring the weather change condition data;

and comparing the weather change condition data with a numerical standard library prestored in the cloud platform, and if the weather change condition data exceeds a preset standard range, generating weather early warning information and transmitting the weather early warning information to a corresponding node so as to remind related workers to process the weather early warning information.

Further, still include:

generating corresponding first burning information according to the breeding node parameters, the storage node parameters, the processing node parameters and the circulation node parameters;

generating corresponding second burning information according to the lobster production enterprises, the lobster production and production places and the lobster storage and collection dates;

and burning the RFID electronic tag according to the first burning information and the second burning information, wherein the burning information corresponds to different lobster batches and is used for autonomous query of consumers.

Further, the node operation images comprise operation images of different nodes, the operation images are obtained by intercepting operation video streams of different nodes, and the data processing process of the operation video streams comprises the following steps:

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.

Further, still include:

determining optimal weights corresponding to the lobster quality grade, the operation specification grade and the production link quality grade by utilizing a cooperative game algorithm;

and performing data fusion on the lobster quality grade, the operation specification grade and the production link quality grade through the optimal weight to determine the quality grade.

Further, still include: and monitoring the lobster image at each node again, comparing the lobster image with the lobster image of the previous node, if the lobster image is consistent with the lobster image of the previous node, allowing the lobster image to flow into the next node, and if the lobster image is inconsistent with the lobster image of the previous node, giving an alarm.

The invention also provides a crawfish full-chain data acquisition and intelligent detection device which comprises a processor and a memory, wherein the memory is stored with a computer program, and when the computer program is executed by the processor, the crawfish full-chain data acquisition and intelligent detection method is realized.

Compared with the prior art, the invention has the beneficial effects that: firstly, acquiring a lobster image, effectively feeding back the qualification degree of the lobster, acquiring a node operation image, effectively feeding back the operation normalization of the whole lobster production flow, acquiring node numerical parameters, and effectively feeding back the influence of external factors such as the breeding production environment of the whole lobster production flow and the quality of the lobsters of each node; then, effectively identifying the lobster image, and extracting relevant characteristics of the lobster image, so that the freshness and integrity of the lobster image are judged efficiently, unqualified products are prevented from flowing to the market, and the complexity of manual judgment is avoided; furthermore, effective identification is carried out according to the node operation image, an abnormal area (namely irregular behavior of calibration operation) is identified in the image, meanwhile, the serial number of an operator is identified in the image so as to trace to the source, so that abnormal operation early warning information is generated in time and is sent to a corresponding node, error correction is carried out on the operation, and the standard operation in the whole process is ensured; further, the node numerical parameters are matched in a standard library, if the node numerical parameters exceed the range, the node numerical parameters are unqualified, parameter abnormity early warning information is also generated and sent to the corresponding nodes, environmental factors, circulation factors and processing factors are regulated and controlled, meanwhile, the crayfish of each node is monitored, and the production safety is guaranteed in time; and finally, carrying out batch processing on the lobsters by combining a plurality of grade parameters, wherein different batches correspond to different prices, monitoring and managing each production node comprehensively, and realizing efficient and intelligent lobster production. In conclusion, the invention comprehensively collects the information of each node on the lobster industrial chain, efficiently and intelligently monitors the lobster quality by using the lobster image, simultaneously, comprehensively monitors the operation specification, the breeding environment factor, the processing environment factor and the circulation environment factor in the lobster full-chain production line by combining the node operation image and the node numerical parameter in the production process, reasonably and efficiently generates early warning information, fully considers various factors of the whole production chain, and determines the existing risk level according to the frequency of issuing the early warning information, generally speaking, for a certain production link, the higher the issuing frequency of the early warning information is, the greater the existing risk is, the timely management and early warning are required, the rapid processing of each node parameter in the monitoring process is ensured, the high efficiency and the accuracy of the lobster generation monitoring are further realized, and the timely feedback and early warning are facilitated, the safety of lobster production is improved.

Drawings

FIG. 1 is a schematic flow chart of a crayfish full-chain data acquisition and intelligent detection method provided by the invention;

fig. 2 is a schematic structural diagram of a crayfish full-chain data acquisition and intelligent detection 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 crayfish full-chain data acquisition and intelligent detection method, and when being seen in combination with fig. 1, fig. 1 is a schematic flow chart of the crayfish full-chain data acquisition and intelligent detection method provided by the invention, the crayfish full-chain data acquisition and intelligent detection method comprises steps S1 to S5, wherein:

in step S1, acquiring a lobster image, a node operation image and a node numerical parameter, wherein the node operation image is a monitoring image of each operation process of the lobster production full-chain node, and the node numerical parameter is a numerical parameter of each production process of the lobster production full-chain node;

in step S2, performing image processing on the lobster image, determining corresponding morphological features and color features, and determining a lobster quality grade according to the morphological features and the color features;

in step S3, identifying the node operation image, outputting a corresponding abnormal region and an operator number, generating operation abnormality warning information according to the abnormal region and the operator number, issuing the operation abnormality warning information to a corresponding node, counting an issuing frequency of the operation abnormality warning information, and determining an operation specification level for reflecting an operation specification of a person;

in step S4, inputting the node numerical parameters into a corresponding standard library for matching, determining whether the node numerical parameters are within a qualified range, if not, generating parameter abnormality warning information and issuing the parameter abnormality warning information to a corresponding node, and meanwhile, counting the issuing frequency of the parameter abnormality warning information to determine a production link quality level for reflecting the production safety of the node, wherein the node numerical parameters include breeding environment parameters and lobster quality parameters of each production node;

in step S5, classifying lobsters into different grades according to the quality grades of the lobsters, and performing early warning and management on the production environment and the personnel operation of each node according to the operation specification grade and the production link quality grade.

In the embodiment of the invention, firstly, a lobster image is obtained, so that the qualification degree of the lobsters is effectively fed back, a node operation image is obtained, the operation normalization of the whole lobster production flow is effectively fed back, the node numerical parameters are obtained, and the influences of external factors such as the breeding production environment of the whole lobster production flow and the quality of the crayfishes at all nodes are effectively fed back; then, effectively identifying the lobster image, and extracting relevant characteristics of the lobster image, so that the freshness and integrity of the lobster image are judged efficiently, unqualified products are prevented from flowing to the market, and the complexity of manual judgment is avoided; furthermore, effective identification is carried out according to the node operation image, an abnormal area (namely irregular behavior of calibration operation) is identified in the image, meanwhile, the serial number of an operator is identified in the image so as to trace to the source, so that abnormal operation early warning information is generated in time and is sent to a corresponding node, error correction is carried out on the operation, and the standard operation in the whole process is ensured; further, the node numerical parameters are matched in a standard library, if the node numerical parameters exceed the range, the node numerical parameters are unqualified, parameter abnormity early warning information is also generated and sent to the corresponding nodes, environmental factors, circulation factors and processing factors are regulated and controlled, meanwhile, the crayfish of each node is monitored, and the production safety is guaranteed in time; and finally, carrying out batch processing on the lobsters by combining a plurality of grade parameters, wherein different batches correspond to different prices, monitoring and managing each production node comprehensively, and realizing efficient and intelligent lobster production.

Preferably, the image processing the lobster image, determining corresponding morphological characteristics and color characteristics, and determining the size quality grade according to the morphological characteristics and the color characteristics comprises:

acquiring image data of the crayfish by using a camera, and transmitting the image data to an edge server;

preprocessing image data of the crayfish, extracting RGB components, binarizing, denoising, correcting inclination, and binarizing the image by adopting a maximum inter-class variance method;

dividing the image by an object part and a main body part, and recording the ratio of pixel points owned by the object main body to the whole image as k₁Mean value ofThe ratio of the image background pixel points to the whole picture is k₂Mean value of

The image mean should be

Setting a threshold objective function of

Removing speckle noise in the image by using a Gaussian denoising method, wherein a Gaussian filtering template scans each pixel in the image, and the weighted average gray value of the pixels in the neighborhood determined by the Gaussian filtering template is used for replacing the value of the central pixel point of the template;

the template coefficient formula is as follows:

in the above formula, (x, y) is the coordinate of any point in the mask, and (ux, uy) is the coordinate of the center point in the mask;

identifying the freshness of the crayfishes through the color characteristics of the crayfishes, screening the crayfishes with reddish colors according to the RGB color characteristics of the crayfishes through artificial intelligence, and removing dead crayfishes through a sorting device;

calculating the average value of the RGB three channelsDefining the ratio L of the R component on the shrimp body as follows:

identifying the limb integrity of the crayfish through morphological characteristics of the crayfish, and grading the crayfish according to the limb defect condition of the crayfish through artificial intelligence;

the image data of the crayfish is analyzed and processed through artificial intelligence, the size data of the crayfish is obtained, and crayfish grading is carried out according to the size data of the crayfish;

monitoring the smell of the crayfish by using an electronic nose, and ensuring the freshness of the crayfish if abnormal smell such as rotten odor exists;

the crayfish is subjected to spot inspection by using a texture analyzer, the elasticity of the muscle tissue of the crayfish is measured, if the texture analyzer measuring method is invalid, the crayfish is subjected to spot inspection by using a boiling test, and whether the muscle tissue of the crayfish is tight and elastic or not is measured in an artificial sensory tasting mode.

Preferably, the node numerical parameters comprise cultivation node parameters, storage node parameters, processing node parameters and circulation node parameters, wherein the cultivation node parameters sequentially comprise pond-arranging parameters, water inlet parameters, seedling releasing parameters, grass planting parameters, fertilizer water parameters, feeding parameters and handover parameters; the storage node parameters comprise receiving parameters, classification parameters, warehousing parameters, stacking parameters, inventory parameters, warehousing parameters and ex-warehouse parameters; the processing node parameters sequentially comprise a receiving parameter, a primary screening parameter, a cleaning parameter, a grading parameter, a finishing parameter, a packaging parameter, a precooling parameter and an output parameter; the circulation node parameters comprise a goods feeding parameter, a storage parameter, a goods picking parameter, a goods distribution parameter, a transportation parameter and a sales parameter. As a specific embodiment, the embodiment of the invention reasonably sets a plurality of nodes, and ensures the comprehensive monitoring of the whole production chain.

Preferably, the uploading process of the breeding node parameters, the storage node parameters, the processing node parameters and the circulation node parameters includes:

performing data filtering on the breeding node parameters, the storage node parameters, the processing node parameters and the circulation node parameters, and determining filtered parameters to be uploaded;

sequencing the processing time delay of each parameter to be uploaded in an ascending order to form a first sequence, and placing each newly added parameter to be uploaded at the edge node at the tail end of the first sequence;

adjusting the first sequence according to the transmission delay of each edge node, and determining the uploading sequence of the parameters to be uploaded according to the adjusted first sequence;

and sequentially carrying out data processing on the uploaded parameters to be uploaded, and visualizing the data processing result.

As a specific embodiment, the embodiment of the present invention performs sequencing on data uploading by using transmission delays of different nodes, so as to ensure timeliness of data uploading and rationality of network resource allocation.

In a specific embodiment of the invention, the sensors of the internet of things transmit in parallel, and the bandwidth is adjusted by a target planning method to transmit the tasks to the edge server. When a task is transmitted to a cloud server from an edge server, the tasks on the edge server are sequenced, the tasks are arranged in an ascending order according to the processing delay, a new task is added and placed at the end of the sequence, the transmission delay of each task is calculated, the queuing delay of each task is calculated, the two tasks are added, the task with the largest sum of the transmission delay and the queuing delay is preferentially transmitted according to the descending order of the sum of the two tasks, when the new task is added into the queue, the transmission delay and the queuing delay of each current task are recalculated, the tasks are arranged in a descending order, the task with the largest sum of the transmission delay and the queuing delay is preferentially transmitted, each task with the largest sum of the transmission delay and the queuing delay can be preferentially transmitted, and the queuing delay is reduced.

The specific task transmission method comprises the following steps:

method for transmitting task to edge server by sensor of Internet of things

(1) Calculating transmission time delay

According to Shannon's theorem

C＝Blog₂(1+S/N)

Wherein, C is the maximum speed supported by the channel or the capacity of the channel, B is the bandwidth of the channel, S is the average signal power, and N is the average noise power; S/N is the signal to noise ratio.

The transmission delay can be expressed as

Wherein D is_iIs the amount of data for task i, B_iAnd S is the average signal power, namely the product of the transmission power provided when the mobile terminal where the ith task is located sends the task i to the edge server and the channel gain of the channel used for transmission, and N is the average noise power in the channel.

(2) Using a target plan, the model of which is

The target is as follows:

s.t.C1：∑B_i≤B

the constraint conditions are as follows: c2: b is_op

Wherein, B_opIs the optimal transmission channel resource allocation scheme, and B is the total bandwidth of the wireless communication link for transmitting data.

In this way, the task is transmitted to the edge server.

Method for transmitting task from edge server to cloud server

(1) Calculating the processing delay d of each task_proc

In the formula, the data amount D of each task_iComputing power f of the upper edge server_i ^eProcessing delay d for each task_proc。

(2) Sequencing each task according to the sequence of the processing time delay from small to large to form an ascending queue q

q＝(D₁,D₂,...，D_i，...，D_n)

Wherein, the first task of the queue is uploaded preferentially, and the new task is added to the tail end of the queue.

Defining a set before as a set of all tasks arranged in front of the task i, and M is the number of the tasks in the set.

After the task processing is finished, the queue is not needed to be queued, and the transmission is directly carried out according to the ascending queue of the processing time delay, namely, under an ideal condition, the transmission of the previous task is finished, and the processing of the next task is just finished. When a task is queued, it is transmitted as follows.

(3) Calculating the transmission time delay d of each task_trans

In the formula, the data amount D of each task_iThe transmission delay d of each task being greater than the transmission rate C_trans。

(4) Calculating queuing delay d of each task_q

In the formula, j belongs to a set before, the set is a set of all tasks arranged in front of the task i, and the sum of the transmission delays of all tasks arranged in front of the task i is the queuing delay.

(6) And adding the transmission delay and the queuing delay of each task, performing descending arrangement, and uploading the task with the maximum sum of the transmission delay and the queuing delay.

(7) And when a new task is added into the queue, recalculating the queuing delay of each task, performing descending arrangement according to the sum of the new transmission delay and the queuing delay, and preferentially uploading the task with the maximum sum of the transmission delay and the queuing delay.

And the sum of the transmission delay and the queuing delay of the task is calculated, and the priority transmission with the maximum sum of the transmission delay and the queuing delay is carried out to reduce the queuing delay of the task and enable the task to be transmitted quickly.

The task is transmitted to the cloud server through the method.

Preferably, the storage node parameters include lobster images, and the data processing process on the lobster images includes:

inputting the lobster image into a deep learning network model stored in a cloud platform, and outputting corresponding morphological characteristics and color characteristics;

inquiring in a pre-stored characteristic standard library according to the morphological characteristics and the color characteristics, determining a numerical range to which the lobster belongs, and determining the quality grade of the lobster according to the numerical range;

and if the quality grade of the lobsters is lower than a preset standard, generating early warning information and transmitting the early warning information to a corresponding node so as to remind related workers to process the lobsters.

As a specific embodiment, the collected video data is transmitted to the edge server, the edge server analyzes whether the color form of the crayfish is in a fresh state or not through AI intelligent analysis, analyzes whether the crayfish has good activity or not, and accordingly judges the freshness of the crayfish, and if the color variation of the crayfish is found, the activity of the crayfish is poor, namely, the crayfish has high death rate, a manager is notified to check the crayfish in time, and the problem is solved.

Preferably, the breeding node parameters include weather change condition data, and the corresponding data processing process includes:

acquiring the weather change condition data;

and comparing the weather change condition data with a numerical standard library prestored in the cloud platform, and if the weather change condition data exceeds a preset standard range, generating weather early warning information and transmitting the weather early warning information to a corresponding node so as to remind related workers to process the weather early warning information.

As a specific embodiment, the embodiment of the invention monitors weather change conditions such as atmospheric temperature, humidity, atmospheric pressure, wind speed, wind direction, illumination intensity, sunshine duration, carbon dioxide and the like in real time, uploads data to a food safety big data platform in real time, and is used for analyzing the influence of the weather conditions on crayfish breeding, such as overhigh atmospheric temperature or overhigh illumination intensity in time, and needs to cool or shade a crayfish breeding water body.

Preferably, the method further comprises:

generating corresponding first burning information according to the breeding node parameters, the storage node parameters, the processing node parameters and the circulation node parameters;

generating corresponding second burning information according to the lobster production enterprises, the lobster production and production places and the lobster storage and collection dates;

and burning the RFID electronic tag according to the first burning information and the second burning information, wherein the burning information corresponds to different lobster batches and is used for autonomous query of consumers.

As a specific embodiment, the embodiment of the present invention facilitates direct information tracing of a consumer through the setting of an RFID tag.

Preferably, the operational video stream processing provided by the present invention includes steps S001 to S009, wherein:

in step S001, dividing the operation video stream into a plurality of video frame sequence groups, calling an API algorithm, and counting target identification numbers corresponding to the plurality of video frame sequence groups;

in step S002, determining a frame filtering model according to the time delay, bandwidth and target identification number uploaded to the cloud platform by the edge node, removing redundant video frame sequence groups according to the frame filtering model, and determining a first frame sequence group;

in step S003, extracting a key frame in the first frame-sequence group according to the entropy of the image information of the first frame-sequence group, and determining a second frame-sequence group;

in step S004, allocating virtual machine resources according to the target identification numbers and the data amount of the plurality of second frame sequence groups, and determining an uploading sequence of the second frame sequence groups to the cloud platform;

in step S005, performing human body posture estimation on each frame image in the second frame sequence group, and determining position coordinates of a plurality of joint points;

in step S006, a joint distance variation matrix is determined according to the amount of variation in the position coordinates of the same joint between two adjacent images in the second frame sequence group;

in step S007, the second frame sequence group is divided equally, and the joint point distance variation generated by two adjacent frames in each video segment is subjected to matrix addition to obtain each segment of accumulated distance variation matrix as a feature vector of the second frame sequence group;

in step S008, the feature vectors are input into a well-trained deep learning model for classification, and a corresponding operation specification index is determined;

in step S009, the operation specification index is compared with the corresponding pre-stored index library, and is directed to the corresponding processing node according to the comparison result.

As a specific embodiment, the embodiment of the present invention obtains an operation video stream in a processing node monitoring device, effectively avoids data redundancy by using a frame filtering model in an edge node and extracting key frame operations, and at the same time, sets an edge node manager to perform effective virtual machine resource allocation according to an uploading state of a processing node, reasonably plans an uploading sequence, and finally, a cloud platform receives a second frame sequence group, extracts a feature vector therein, and determines whether a corresponding operation is normal by using a deep learning model, thereby ensuring efficient and fast data processing and data uploading, monitoring a processing process in time, comprehensively improving rapidity and efficiency of data uploading and data processing of a large food safety data platform, and realizing comprehensive and fast early warning and monitoring

Example 2

The embodiment of the invention provides a crayfish full-chain data acquisition and intelligent detection system, and as seen in combination with fig. 2, fig. 2 is a schematic structural diagram of the crayfish qualified intelligent detection system provided by the invention, the crayfish qualified intelligent detection system comprises a plurality of monitoring devices and information tracing devices, the monitoring devices comprise breeding node monitoring devices 1, storage node monitoring devices 2, processing node monitoring devices 3 and transportation node monitoring devices 4 and are used for monitoring different node parameters, the node parameters comprise breeding node parameters, storage node parameters, processing node parameters and transportation node parameters, and the internet of things-based crayfish production full-chain information intelligent detection system specifically comprises:

the breeding node monitoring equipment 1 is used for monitoring a plurality of breeding node parameters under the breeding nodes and transmitting the parameters to the information tracing equipment so as to feed back the environmental quality of the breeding place of the lobsters;

the storage node monitoring equipment 2 is used for monitoring a plurality of storage node parameters under the storage nodes and transmitting the storage node parameters to the information tracing equipment so as to feed back whether the lobster storage process is standard or not;

the processing node monitoring equipment 3 is used for monitoring a plurality of processing node parameters under the processing nodes and transmitting the processing node parameters to the information tracing equipment so as to feed back whether the processing process of the lobsters is standard or not;

the circulation node monitoring equipment 4 is used for monitoring a plurality of circulation node parameters under the circulation nodes and transmitting the circulation node parameters to the information tracing equipment so as to feed back whether the lobster circulation process is standard or not;

the information traceability device 5 comprises a food safety big data platform and an electronic tag device, wherein the food safety big data platform is used for carrying out big data processing on the cultivation node parameter, the slaughter node parameter, the processing node parameter, the storage node parameter and the circulation node parameter to form traceability information and visualize a big data processing result; the electronic label equipment is used for converting the tracing information into a corresponding RFID label;

the food safety big data platform comprises a cloud platform, an edge node manager and a plurality of edge nodes corresponding to the monitoring devices respectively, wherein:

the edge node is used for receiving the corresponding node parameters, performing data filtering on the node parameters and determining filtered parameters to be uploaded;

the edge node manager is used for sequencing the processing time delay of each parameter to be uploaded in an ascending order to form a first sequence, and each newly added parameter to be uploaded of the edge node is placed at the tail end of the first sequence; the first sequence is further used for adjusting the first sequence according to the transmission delay of each edge node, and the uploading sequence of the parameters to be uploaded is determined according to the adjusted first sequence;

and the cloud platform is used for sequentially carrying out data processing on the uploaded parameters to be uploaded and visualizing the data processing result.

In the embodiment of the invention, the breeding node monitoring equipment is arranged to acquire a plurality of breeding node parameters of the lobster breeding place, so that the breeding state of the lobster breeding nodes is effectively monitored; the lobster collecting and storing node monitoring method comprises the steps that collecting and storing node monitoring equipment is arranged to obtain a plurality of collecting and storing node parameters in the processing process of a produced lobster, so that the collecting and storing states of the lobster collecting and storing nodes are effectively monitored; the processing node monitoring equipment is arranged to obtain processing node parameters of the lobster processing nodes, so that the processing quality state of the lobster processing nodes is effectively monitored; the method comprises the steps that transportation node monitoring equipment is arranged to obtain transportation node parameters of lobster transportation nodes, so that the transportation state of the lobster transportation nodes is effectively monitored; based on the monitoring data of each node, centralized processing is carried out through a food safety big data platform in the information tracing equipment so as to comprehensively monitor the state of each node on the lobster production chain, the big data platform is utilized to realize rapid processing on various monitoring data, the processing result is visually operated and displayed to related personnel, and the product control and management of lobster production are facilitated; in addition, the production information of the lobsters produced in the production process is burnt to the corresponding RFID tags through the electronic tag equipment in the information tracing equipment, so that a consumer can quickly master the production information (batch number, manufacturer, production place and the like) of the lobsters through the way of scanning the RFID tags, the public opening degree and transparency of the lobster production information are comprehensively guaranteed, the selection and supervision of the consumer are facilitated, and the safety of lobster production is further enhanced; in addition, a plurality of node parameters are obtained, data redundancy is effectively avoided by using data filtering operation, meanwhile, ascending sequencing is carried out according to processing time delay, effective virtual machine resource allocation is carried out, a first sequence is reasonably planned, finally, the first sequence is adjusted according to transmission time delay, and finally the uploading sequence of each parameter to be uploaded is determined, so that efficient and rapid data processing and data uploading are guaranteed, the uploading sequence of each node parameter is reasonably allocated, the full production process of lobsters is monitored in time, the rapidness and the high efficiency of data uploading of a large food safety data platform and data processing are comprehensively improved, and comprehensive and rapid early warning and early warning monitoring are realized.

Preferably, the breeding node parameters sequentially include a whole pond parameter, a water inlet parameter, a seedling releasing parameter, a grass planting parameter, a fertilizer water parameter, a feeding parameter and a handover parameter, and the breeding node monitoring device includes:

the whole pond monitoring equipment is used for monitoring parameters of the whole pond so as to feed back the culture condition in the whole pond process, wherein the parameters of the whole pond comprise at least one of illumination intensity, illumination time and pond scale;

the water inlet monitoring device is used for monitoring the water inlet parameters so as to feed back the quality of the pond water in the water inlet process, wherein the water inlet parameters comprise at least one of water quality suspended substances, water quality floating substances, water quality dissolved oxygen, water quality biochemical oxygen demand, water quality PH value, water quality heavy metals and water quality pesticide residues;

the fry releasing monitoring equipment is used for monitoring the fry releasing parameters so as to feed back the quality of crayfish fries in the fry releasing process, wherein the fry releasing parameters comprise at least one of the quality of the fries, the fry releasing environment, the water temperature, water quality suspended substances, water quality floating substances, water quality dissolved oxygen, water quality biochemical oxygen demand, the water quality PH value, water quality heavy metals and water quality pesticide residues;

the grass planting monitoring equipment is used for monitoring grass planting parameters to feed back the growth condition of the aquatic grass in the grass planting process, wherein the grass planting parameters comprise at least one of grass planting density, atmospheric temperature, humidity, air pressure, atmospheric pressure, wind speed, wind direction, illumination intensity, sunshine hours, water temperature, water quality suspended substances, water quality floating substances, water quality dissolved oxygen, water quality biochemical oxygen demand, water quality PH value, water quality heavy metals and water quality pesticide residues;

the water fertilizing monitoring equipment is used for monitoring the water fertilizing parameters to feed back the growth condition of the waterweeds in the water fertilizing process, wherein the water fertilizing parameters comprise at least one of the density of the waterweeds, the water temperature, water quality suspended substances, water quality floating substances, water quality dissolved oxygen, water quality biochemical oxygen demand, water quality PH value, water quality heavy metals and water quality pesticide residues;

the feeding monitoring equipment is used for monitoring the feeding parameters so as to feed back the pond culture condition in the feeding process, wherein the feeding parameters comprise at least one of water temperature, water quality floating substances, water quality dissolved oxygen, water quality biochemical oxygen demand, water quality PH value, water quality heavy metals and water quality pesticide residues;

the fishing monitoring equipment is used for monitoring the feeding parameters to feed back the pond culture environment in the fishing process, wherein the feeding parameters comprise at least one of atmospheric temperature, humidity, air pressure, atmospheric pressure, wind speed, wind direction, illumination intensity and sunshine duration;

handing-over monitoring facilities is used for monitoring handing-over parameter to the lobster quality of feedback handing-over in-process, wherein, the handing-over parameter includes at least one in lobster qualification standard, lobster mortality, lobster specification, lobster handing-over record, lobster transport license plate number.

As a specific embodiment, the method and the system provided by the embodiment of the invention sequentially carry out omnibearing detection on the environment for breeding the lobsters on the breeding nodes, and ensure the quality of the lobsters from the source.

Preferably, the storage node parameters include a receiving parameter, a classification parameter, a warehousing parameter, a stacking parameter, an inventory parameter, a warehousing parameter, and a delivery parameter, and the storage node monitoring device includes:

receiving monitoring equipment for monitoring the receiving parameters to feed back the lobster quality in the receiving process, wherein the receiving parameters comprise at least one of lobster qualification standard, lobster mortality, lobster specification and lobster handover records;

a classification monitoring device for monitoring the classification parameters to feed back the environment during the classification process

A condition, wherein the classification parameters include at least one of a lobster classification level, lobster cleanliness;

the warehousing monitoring equipment is used for monitoring the warehousing parameters to feed back the operation specifications in the warehousing process, wherein the warehousing parameters comprise at least one of the warehousing environment temperature and humidity, the warehousing environment cleanliness and the lobster cleanliness;

the stacking monitoring equipment is used for monitoring the stacking parameters to feed back the operation specification in the stacking process, wherein the stacking parameters comprise at least one of the temperature and the humidity of a stacking environment, the cleanliness of the stacking environment and the cleanliness of lobsters;

the checking monitoring equipment is used for monitoring the checking parameters to feed back the operation specification in the checking process, wherein the checking parameters comprise at least one of checking environment temperature and humidity, checking environment cleanliness and lobster cleanliness;

the storage monitoring equipment is used for monitoring the storage parameters to feed back the operation specification in the storage process, wherein the storage parameters comprise at least one of storage environment temperature and humidity, storage environment cleanliness and lobster cleanliness;

and the ex-warehouse monitoring equipment is used for monitoring the ex-warehouse parameters so as to feed back the operation specifications in the ex-warehouse process, wherein the ex-warehouse parameters comprise at least one of the lobster qualification standard, the lobster death rate, the lobster specification and the lobster handover record.

As a specific embodiment, the embodiment of the invention sequentially carries out omnibearing detection on the lobster storage process on the storage nodes, thereby ensuring the quality of the lobsters.

Preferably, the processing node parameters sequentially include a receiving parameter, a preliminary screening parameter, a cleaning parameter, a grading parameter, a trimming parameter, a packaging parameter, a precooling parameter, and an output parameter, and the processing node monitoring apparatus includes:

the receiving monitoring equipment is used for monitoring the receiving parameters so as to feed back the quality of the lobsters during receiving in the processing process and the operation specifications during receiving, wherein the receiving parameters comprise at least one of the heavy metal residues of the lobsters, the drug residues of the lobsters and the fish drugs, the pesticide residues of the lobsters and the weight of the lobsters;

the primary screening monitoring equipment is used for monitoring the primary screening parameters so as to feed back the quality of the lobsters in the screening process and the operation specification during screening, wherein the primary screening parameters comprise at least one of lobster images, ambient temperature, ambient humidity and ambient illumination;

the cleaning monitoring equipment is used for monitoring the cleaning parameters so as to feed back the operation specification of the cleaning process, wherein the cleaning parameters comprise at least one of water quality disinfectant content, water quality pH value, water quality water temperature and water quality salinity;

the grading monitoring equipment is used for monitoring the grading parameters to feed back the operation specifications in the grading process, wherein the grading parameters comprise at least one of the air condition of a workshop, the sanitation level of the workshop and the temperature and humidity of the workshop;

the trimming monitoring equipment is used for monitoring the trimming parameters to feed back the operation specifications in the trimming process, wherein the trimming parameters comprise at least one of workshop air condition, workshop sanitation level and workshop temperature and humidity;

the packaging monitoring equipment is used for monitoring the packaging parameters so as to feed back the operation specifications in the packaging process, wherein the packaging parameters comprise at least one of the air condition of a workshop, the sanitation level of the workshop and the temperature and humidity of the workshop;

the precooling monitoring equipment is used for monitoring the precooling parameters to feed back the operation specification in the precooling process, wherein the precooling parameters comprise at least one of precooling temperature and precooling humidity and precooling time;

and the output monitoring equipment is used for monitoring the output parameters so as to feed back the lobster quality in the output process, wherein the output parameters comprise at least one of the lobster cleanliness, the lobster freshness, the lobster grade, the lobster price, the output record, the output time, the output place and the refrigeration temperature and humidity.

As a specific embodiment, the embodiment of the invention sequentially carries out omnibearing detection on the processing process of the lobsters on the processing nodes, thereby ensuring the quality of the lobsters.

Preferably, the circulation node parameters include a stock parameter, a storage parameter, a pick-up parameter, a distribution parameter, a transportation parameter, and a sales parameter, and the circulation node monitoring device includes:

the feeding monitoring equipment is used for monitoring the feeding parameters to feed back the operation specification of feeding handover, wherein the feeding parameters comprise at least one of lobster cleanliness, lobster freshness, lobster grade, lobster price, output record, output time, output place and refrigeration temperature and humidity;

the storage monitoring equipment is used for monitoring the storage parameters to feed back the operation specification of the storage process, wherein the storage parameters comprise at least one of the cleanliness of the lobsters, the freshness of the lobsters, the class of the lobsters, the storage records of the lobsters, the storage time of the lobsters and the storage temperature and humidity;

the picking monitoring device is used for monitoring the picking parameters to feed back the operation specification in the picking process, wherein the picking parameters comprise at least one of lobster cleanliness, lobster freshness, lobster breakage rate, picking record, picking time, picking place and refrigerating temperature and humidity;

the distribution monitoring equipment is used for monitoring the distribution parameters to feed back the operation specification of distribution, wherein the distribution parameters comprise at least one of lobster cleanliness, lobster freshness, lobster breakage rate, distribution record, distribution time, distribution place and refrigeration temperature and humidity;

the transportation monitoring equipment is used for monitoring the transportation parameters to feed back the transportation operation specification, wherein the transportation parameters comprise at least one of lobster cleanliness, lobster freshness, lobster breakage rate, delivery record, transportation time, transportation place and transportation environment temperature and humidity;

and the sales monitoring equipment is used for monitoring the sales parameters so as to feed back the operation specification of the sales process, wherein the sales parameters comprise at least one of the cleanliness of the lobsters, the freshness of the lobsters, the damage rate of the lobsters, the delivery record, the sales time, the sales place and the temperature and humidity of the sales environment.

As a specific embodiment, the embodiment of the invention sequentially carries out omnibearing detection on the circulation process of the lobsters on the circulation nodes, thereby ensuring the quality of the lobsters.

In a specific embodiment of the invention, taking the monitoring of the heavy metal content of crayfish as an example, the heavy metal content needs to be monitored from a source to ensure that the final crayfish heavy metal content is less than the limit value causing damage to human body. The heavy metals of the crayfish mainly come from the young crayfish, the cultured water quality, the stored water quality, the processing and cleaning water quality, the pollution in the processing process and the artificial intention (adding or adjusting the package), so the heavy metals are monitored in several links to ensure that the heavy metal content of the crayfish is smaller than the standard value finally.

Taking the monitoring of the content of heavy metal mercury in crayfish as an example:

detecting the total mercury content of a pond water inlet water body at a water inlet sub-node of a culture node by using a water quality heavy metal detection instrument, detecting the content of mercury in the water body, transmitting the detected mercury content data to an edge server, analyzing the data by the edge server, comparing the data with the national standard for fishery water quality (GB11607 + 1989), wherein the mercury content of the crayfish culture water is less than 0.0005mg/L, if the water inlet mercury content is judged to be more than 0.0005mg/L at the moment, indicating that the water quality is seriously polluted by heavy metal and is unsuitable for culture, generating an alarm, informing a manager to treat a water source, and if the water quality is less than 0.0005mg/L, indicating that the water quality meets the standard and being capable of culturing crayfish.

Monitoring the total mercury content of the crayfish fries by using an atomic fluorescence spectrometer at the fry releasing node, transmitting the detected mercury content data to an edge server, analyzing the data by the edge server, comparing the data with standard NY 5185 plus 2005, wherein the mercury content needs to be less than 0.5mg/kg, if the mercury content of the crayfish fries is judged to be more than 0.5mg/kg at the moment, the crayfish fries are seriously polluted and can not be used for cultivation, generating an alarm, informing a manager to check the source and the cultivation condition of the crayfish fries, and processing in time, if the mercury content is less than 0.5mg/kg, the crayfish fries are in accordance with the standard and are suitable for cultivation.

In the grass planting, water fertilizing and feeding sub-node, a water quality heavy metal detection instrument is used for monitoring the total mercury content of the water body of the pond, the influence of the cultivated water grass, the added fertilizer and the added feed on the water quality is judged, the content of the mercury in the water body is detected, the detected mercury content data is transmitted to an edge server, the edge server analyzes the data, the data is compared with the national standard for fishery water quality (GB11607-1989), the mercury content of the water quality for cultivating the crayfish is less than 0.0005mg/L, if the mercury content after grass planting, water fertilizing and feeding is judged to be more than 0.0005mg/L, the water quality is seriously polluted and is not suitable for cultivation, an alarm is generated, a manager is informed to check and process the water grass, the fertilizer and the feed, if the mercury content is less than 0.0005mg/L, the water quality accords with the standard, the water grass, the fertilizer and the feed have no great influence on the water quality and the heavy metal content accords with the standard, the crayfish can be bred.

Monitoring the total mercury content of the crayfish at a breeding node handover sub-node, a storage node receiving sub-node and a ex-warehouse sub-node, a processing node receiving sub-node and a circulation node receiving sub-node by using a Beijing Hai-Guang AFS9350 type atomic fluorescence spectrometer, transmitting the detected mercury content data to an edge server, analyzing the data by the edge server, comparing the data with a standard NY 5185 plus 2005, wherein the mercury content needs to be less than 0.5mg/kg, if the mercury content of the crayfish at the moment is judged to be more than 0.5mg/kg, the crayfish is seriously polluted, the crayfish cannot be continuously circulated and eaten, an alarm is generated, a manager is informed to check the crayfish tracing source and breeding conditions, the crayfish is timely processed, and if the mercury content is less than 0.5mg/kg, the crayfish is judged to be in line with the standard and can be circulated and eaten.

Detecting the total mercury content of a water body for storing crayfishes by using a water quality heavy metal detection instrument at a storage sub-node and a processing node storage sub-node, detecting the content of mercury in the water body, transmitting the detected mercury content data to an edge server, analyzing the data by the edge server, comparing the data with a water quality standard NY5051-2001 for temporary crayfish storage water, wherein the mercury content of the crayfish storage water is less than 0.005mg/L, if the mercury content of the water body is judged to be more than 0.005mg/L, the water quality is seriously polluted by heavy metal at the moment and is not suitable for storing crayfishes, generating an alarm, informing a manager to treat a water source, and if the mercury content of the water body is less than 0.005mg/L, the water quality is in accordance with the standard and the temporary crayfish storage can be carried out.

Cleaning the child nodes at the processing nodes, detecting the total mercury content of a water body for cleaning crayfish by using a water quality heavy metal detection instrument, detecting the content of mercury in the water body, transmitting the detected mercury content data to an edge server, analyzing the data by the edge server, comparing the data with a conventional index and a limit value GB5749-2006 of the water quality of drinking water, wherein the mercury content of the water for cleaning the crayfish is less than 0.001mg/L, if the mercury content of the water body is judged to be more than 0.001mg/L, the water quality heavy metal pollution is serious, the crayfish is not suitable for cleaning, generating an alarm, informing a manager to process a water source, and if the mercury content of the water body is less than 0.001mg/L, the water quality accords with the standard, and the crayfish can be cleaned.

Through the quality of water to the breed, the quality of water of storing, processing abluent quality of water to and crayfish heavy metal content constantly detects, finally guarantee to flow into market, the crayfish heavy metal content that is eaten accords with national standard, guarantees crayfish food security.

Preferably, the food safety big data platform comprises 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 embodiment of the invention is provided with a food safety big data platform, adopts various big data processing modes to process the monitoring data of each node and analyze the state of each link so as to effectively warn each link.

Example 3

The embodiment of the invention provides a crawfish full-chain data acquisition and intelligent detection device which comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the crawfish full-chain data acquisition and intelligent detection method is realized.

The invention discloses a crayfish full-chain data acquisition and intelligent detection method and device, which comprises the steps of firstly, acquiring a crayfish image, effectively feeding back the qualification degree of the crayfish, acquiring a node operation image, effectively feeding back the operation normalization of the whole crayfish production flow, acquiring node numerical parameters, and effectively feeding back the influence of external factors such as the breeding production environment of the whole crayfish production flow and the quality of each node crayfish; then, effectively identifying the lobster image, and extracting relevant characteristics of the lobster image, so that the freshness and integrity of the lobster image are judged efficiently, unqualified products are prevented from flowing to the market, and the complexity of manual judgment is avoided; furthermore, effective identification is carried out according to the node operation image, an abnormal area (namely irregular behavior of calibration operation) is identified in the image, meanwhile, the serial number of an operator is identified in the image so as to trace to the source, so that abnormal operation early warning information is generated in time and is sent to a corresponding node, error correction is carried out on the operation, and the standard operation in the whole process is ensured; further, the node numerical parameters are matched in a standard library, if the node numerical parameters exceed the range, the node numerical parameters are unqualified, parameter abnormity early warning information is also generated and sent to the corresponding nodes, environmental factors, circulation factors and processing factors are regulated and controlled, meanwhile, the crayfish of each node is monitored, and the production safety is guaranteed in time; and finally, carrying out batch processing on the lobsters by combining a plurality of grade parameters, wherein different batches correspond to different prices, monitoring and managing each production node comprehensively, and realizing efficient and intelligent lobster production.

The technical scheme of the invention comprehensively collects the information of each node on the lobster industrial chain, efficiently and intelligently monitors the lobster quality by using the lobster image, simultaneously, comprehensively monitors the operation specification, the breeding environment factor, the processing environment factor and the circulation environment factor in the lobster full-chain production line by combining the node operation image and the node numerical parameter in the production process, reasonably and efficiently generates early warning information, fully considers various factors of the whole production chain, and determines the existing risk grade according to the frequency of issuing the early warning information, generally speaking, for a certain production link, the higher the issuing frequency of the early warning information is, the higher the existing risk is, the more the existing risk is, the timely management and early warning are needed, the rapid processing of each node parameter in the monitoring process is ensured, the high efficiency and the accuracy of the lobster generation monitoring are further realized, and the timely feedback and early warning are facilitated, the safety of lobster production is improved.

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.