Animal identity and health monitoring system based on electronic ear tag block chain data

Abstract

The invention provides an animal identity and health monitoring system based on electronic ear tag block chain data, and relates to the technical field of electronic ear tag data acquisition, and the system comprises an identity creation unit which is used for creating a corresponding electronic identity according to an electronic ear tag corresponding to each animal; the acquisition unit is used for acquiring acquired data corresponding to the electronic ear tag and correspondingly storing the acquired data and the electronic identity, and the acquired data comprises positioning information and animal body monitoring data; and the monitoring unit is used for carrying out data monitoring according to the stored positioning information and the animal body monitoring data. According to the monitoring system provided by the invention, the health of the animal corresponding to each electronic identity can be monitored in real time, and when the animal monitoring data is abnormal, the health risk level of the animal to other animals can be well determined, so that the health management of the animal is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic ear tag data collection, and in particular to an animal identity and health monitoring system based on electronic ear tag blockchain data. Background Art

[0002] ‌Electronic ear tags‌ are electronic tags that use RFID technology to identify animals individually. They have built-in electronic chips and antennas and are usually applied to the ears of livestock to prove the identity of the livestock and carry individual information about the livestock. Electronic ear tags can be used to collect animal temperature data, location data, and weight data corresponding to each electronic ear tag.

[0003] When animals are currently monitored via electronic ear tags, they can only transmit and store the data collected by the electronic ear tags, or simply determine whether the animal has health problems based on its body temperature data. However, it is impossible to determine whether the animal may pose a health threat to other animals around it, and it is impossible to effectively monitor the health of all animals. Summary of the invention

[0004] In view of the shortcomings of the prior art mentioned above, the purpose of the present invention is to provide an animal identity and health monitoring system based on electronic ear tag blockchain data, which is used to solve the problem that in the prior art, when animals are monitored through electronic ear tags, they can only transmit and store the collected data of the electronic ear tags, or simply judge whether the animals have health problems based on the animal's body temperature data, but cannot determine whether the animal may pose a health threat to other animals around it, and cannot well achieve health supervision of all animals.

[0005] To achieve the above-mentioned purpose and other related purposes, the present invention provides an animal identity and health monitoring system based on electronic ear tag blockchain data, which is applied to blockchain nodes in a blockchain network, and includes: an identity creation unit, which is used to create a corresponding electronic identity according to the electronic ear tag corresponding to each animal; an acquisition unit, which is used to acquire the collected data corresponding to the electronic ear tag, and store the collected data in correspondence with the electronic identity, wherein the collected data includes positioning information and animal body monitoring data; a monitoring unit, which is used to monitor data according to the stored positioning information and animal body monitoring data; an analysis unit, which is used to analyze the animal body monitoring data when the animal body monitoring data is abnormal to obtain the health abnormality type; a generation unit, which is used to generate an infection level according to the health abnormality type; a prediction unit, which is used to predict the infection level of all infected animals according to the infection level and the positioning information corresponding to each electronic ear tag; and an evaluation unit, which is used to evaluate the infection level of each infected animal to obtain the health risk level of the infected animal corresponding to each electronic identity.

[0006] In one embodiment of the present invention, the monitoring unit includes: a static monitoring module for classifying and monitoring the animal body according to different types of monitoring data, and analyzing and obtaining the classification sub-monitoring score. , according to the classification sub-monitoring score , and obtain the static monitoring score Dynamic monitoring module, used to monitor simultaneously based on positioning information and animal body monitoring data of preset types, and analyze dynamic monitoring scores under different combinations ; Score statistics module, used to monitor the score based on static and dynamic monitoring scores , and obtain the comprehensive detection scores under different combinations ,in, is the static monitoring weight, Dynamic monitoring weights; and score monitoring module, used to monitor the comprehensive detection scores Perform threshold detection, when the comprehensive detection score When the comprehensive test score is greater than the set threshold, the animal's body monitoring data is normal. When it is less than the set threshold, the animal body monitoring data is abnormal.

[0007] In one embodiment of the present invention, the static monitoring module includes: a first comparison module for comparing the animal body monitoring data With benchmark data Compare and get the data difference between the two ; Positive and negative value monitoring module, used to monitor the data difference Monitor positive and negative values ​​and calculate the difference between the values. The positive and negative values ​​of the animal body are obtained Corresponding data types The classification sub-monitoring score ; and a calculation module for calculating the Corresponding sub-monitoring weight and classification sub-monitoring scores , normalized to obtain the static monitoring score .

[0008] In one embodiment of the present invention, the dynamic monitoring module includes: a first model analysis module for analyzing the real-time positioning information of the animal through a large model to obtain the animal's motion state; and a second model analysis module for analyzing the animal's motion state and a preset number of animal body monitoring data. Conduct large-scale model analysis to obtain body monitoring data of different numbers of animals Corresponding dynamic monitoring score .

[0009] In one embodiment of the present invention, the analysis unit analyzes the animal body monitoring data to obtain the health abnormality type, including: a search module for The dynamic monitoring score of the corresponding combination when it is less than the set threshold , find the preset cycle under the corresponding combination form Physical monitoring data of each animal in ; Trend chart creation module, used to monitor the body data of each animal within a preset period according to the corresponding combination form , generating a corresponding data change trend graph; a second comparison module, used to compare the data change trend graph with the abnormal graph library under the corresponding combination form; and an output module, used to output the healthy abnormality type corresponding to the abnormal change trend graph when there is an abnormal change trend graph corresponding to the data change trend graph in the abnormal graph library.

[0010] In one embodiment of the present invention, the generating unit includes: an abnormality degree query module, which is used to obtain the health abnormality degree of the animal according to the stage of the abnormal change trend diagram of the health abnormality type corresponding to the data change trend diagram corresponding to the abnormal image library; wherein the abnormal image library includes all abnormal change trend diagrams of all stages of health abnormality; and an infection level generating module, which is used to obtain the corresponding infection level by looking up a table according to the health abnormality degree and the health abnormality type. .

[0011] In one embodiment of the present invention, the prediction unit includes: an infection radius determination module for determining the infection radius of the infectious animal according to the infection level of the infectious animal. , get the location coordinates based on the infectious animal The infection radius ; and the infection degree prediction module, which is used to predict the infection level based on the location information of the infected animal and the infection radius , predict the infection level of all infected animals .

[0012] In one embodiment of the present invention, the infection degree prediction module includes: an infection distance analysis module for analyzing the real-time location coordinates of the infected animal. and infection radius , and get the infection radius Electronic IDs of all infected animals in ; Based on electronic identity The corresponding location coordinates of the infected animal , get different moments The transmission distance between infected animals and infectious animals ; and the infection degree calculation module, which is used to calculate the infection degree according to the infection radius Different distances within Corresponding infection degree , get the infection distance The corresponding infection degree ; According to the infection distance The infection time and the corresponding infection , get the infection degree of each infected animal ;in, , where different moments Corresponding to different infection levels and infection radius , different levels of infection and infection radius Corresponding distance Different infection levels .

[0013] In one embodiment of the present invention, the evaluation unit includes: a prediction library creation module for establishing a health risk level prediction library according to the corresponding health abnormality type; a level detection module for evaluating the infection degree of each infected animal; Perform health risk level detection in the health risk level prediction library; and a storage module for detecting the degree of infection When a corresponding health risk level exists in the health risk level prediction library, the corresponding health risk level and the electronic identity are stored in correspondence.

[0014] In one embodiment of the present invention, the evaluation unit also includes: a monitoring library creation module, which is used to establish a monitoring library for health risk levels that exceed the set health risk level; a level handling module, which is used to manually alarm the electronic identity corresponding information when the health risk level exceeds the set health risk level, and transfer the electronic identity corresponding information of the health risk level to the monitoring library; and a recovery monitoring module, which is used to monitor the health risk level in the monitoring library in real time, and when the health risk level is lower than the set health risk level, the electronic identity corresponding information of the health risk level is returned to the original storage library.

[0015] As described above, an animal identity and health monitoring system based on electronic ear tag blockchain data of the present invention has the following beneficial effects: through the corresponding blockchain node in the blockchain network, the electronic identity corresponding to the electronic ear tag and the collected data of the electronic ear tag are stored correspondingly to ensure the traceability of the data. And in the process of data storage, the health status of each animal can be determined by monitoring and analyzing the collected data. When an animal's health is abnormal, the infection level corresponding to the abnormality will be determined to predict the degree of infection of other infected animals, thereby deriving the health risk level of other infected animals that may also be abnormal, so that managers can adjust the management method of animals according to the health risk level of each animal, etc., to optimize the health management of animals. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Shown is a structural architecture diagram of the health monitoring system of the present invention.

[0017] Figure 2 Shown is a schematic diagram of the structure of an electronic ear tag in a preferred embodiment of the present invention.

[0018] Figure 3 Shown is a flow chart of the health monitoring system method of the present invention.

[0019] Component number description Identity creation unit 111; acquisition unit 112; monitoring unit 113; analysis unit 114; generation unit 115; prediction unit 116; evaluation unit 117. DETAILED DESCRIPTION

[0020] See also Figure 1 The present invention provides an animal identity and health monitoring system based on electronic ear tag blockchain data, which is applied to blockchain nodes in a blockchain network, and includes: an identity creation unit 111, which is used to create a corresponding electronic identity according to the electronic ear tag corresponding to each animal; an acquisition unit 112, which is used to acquire collected data corresponding to the electronic ear tag, and store the collected data in correspondence with the electronic identity, wherein the collected data includes positioning information and animal body monitoring data; a monitoring unit 113, which is used to perform data monitoring according to the stored positioning information and animal body monitoring data; an analysis unit 114, which is used to analyze the animal body monitoring data when the animal body monitoring data is abnormal to obtain the health abnormality type; a generation unit 115, which is used to generate an infection level according to the health abnormality type; a prediction unit 116, which is used to predict the infection degree of all infected animals according to the infection level and the positioning information corresponding to each electronic ear tag; and an evaluation unit 117, which is used to evaluate the infection degree of each infected animal to obtain the health risk level of the infected animal corresponding to each electronic identity.

[0021] It is not difficult to find from the above content that in the process of storing the electronic ear tag data in the corresponding blockchain node in the blockchain network, the animal identity creation and corresponding health status monitoring of the stored electronic ear tag blockchain data can be realized. Specifically, the identity creation unit 111 can be used to establish a corresponding electronic identity in the corresponding blockchain node in the blockchain network according to the electronic ear tag corresponding to each animal. The acquisition unit 112 can be used to store the collected data obtained by the electronic ear tag in the corresponding blockchain node in the blockchain network, and the collected data and the electronic identity are stored in correspondence to facilitate data tracing. The monitoring unit 113 can be used to monitor the positioning data and animal body monitoring data in the collected data when the collected data and the electronic identity are stored in correspondence to determine whether the body monitoring data is abnormal. The analysis unit 114 can be used to further analyze the animal body monitoring data corresponding to the abnormality when the monitoring unit 113 data monitors the animal body monitoring data abnormality, so as to determine the health abnormality type corresponding to the abnormality. And the generation unit 115 can be used to obtain the corresponding infection level according to the obtained health abnormality type and the analysis result of the animal body monitoring data. Furthermore, the prediction unit 116 can predict the possible infection degree of other animals according to the infection level and the positioning information corresponding to each electronic ear tag, so as to determine the possibility of abnormal animal body monitoring data of the infected animals according to the predicted infection degree. That is, the infection degree of the infected animals is evaluated by the evaluation unit 117, so as to evaluate the corresponding health risk level, so as to determine the risk intensity of abnormal animal body monitoring data. In the above manner, after determining the animal with abnormal animal body monitoring data, it is possible to analyze its infectiousness to other animals, so as to achieve all-round health management and control of all animals through the predicted health risk level of each infected animal, which not only realizes the storage and utilization of the data collected by the electronic ear tags, but also can well predict the health status of all animals.

[0022] In one embodiment of the present invention, the monitoring unit 113 includes: a static monitoring module, which is used to perform classified monitoring according to the body monitoring data of different types of animals, and analyze and obtain the classified sub-monitoring scores. , according to the classification sub-monitoring score , and obtain the static monitoring score Dynamic monitoring module, used to monitor simultaneously based on positioning information and animal body monitoring data of preset types, and analyze dynamic monitoring scores under different combinations ; Score statistics module, used to monitor the score based on static and dynamic monitoring scores , and obtain the comprehensive detection scores under different combinations ,in, is the static monitoring weight, Dynamic monitoring weights; and score monitoring module, used to monitor the comprehensive detection scores Perform threshold detection, when the comprehensive detection score When the comprehensive test score is greater than the set threshold, the animal's body monitoring data is normal. When it is less than the set threshold, the animal body monitoring data is abnormal.

[0023] When the stored positioning information and animal body monitoring data are monitored, the static monitoring module can be used to analyze the animal body monitoring data corresponding to the static state to obtain the classification sub-monitoring scores under different categories. Furthermore, the classification sub-monitoring scores corresponding to the body monitoring data of different types of animals are calculated. Normalize to get the classification sub-monitoring score All corresponding static monitoring scores The dynamic monitoring module is used to realize dynamic monitoring of animals in a moving state, that is, by simultaneously monitoring the animal's positioning information and the animal's body monitoring data under the preset category to determine the corresponding dynamic monitoring score in the dynamic state. , and the corresponding dynamic monitoring score under each animal body monitoring data combination are different, so different comprehensive detection scores can be obtained through the score statistics module Then, we can use the comprehensive detection score Perform threshold detection to determine the overall detection score Whether it exceeds the set threshold. And in the comprehensive detection score When the comprehensive test score is greater than the set threshold, it means that the animal's body monitoring data is normal; When it is less than the set threshold, it means that the animal body monitoring data is abnormal. Through the above method, it is possible to quickly predict whether the animal body monitoring data is abnormal, so that when it is abnormal, it is possible to quickly determine the degree of infection to other animals.

[0024] In one embodiment of the present invention, the static monitoring module includes: a first comparison module for comparing the animal body monitoring data With benchmark data Compare and get the data difference between the two ; Positive and negative value monitoring module, used to monitor the data difference Monitor positive and negative values ​​and calculate the difference between the values. The positive and negative values ​​of the animal body are obtained Corresponding data types The classification sub-monitoring score ; and a calculation module for calculating the Corresponding sub-monitoring weight and classification sub-monitoring scores , normalized to obtain the static monitoring score .

[0025] Monitoring data on animals When performing static monitoring analysis, the first comparison module is used to compare the animal body monitoring data With benchmark data Compare and compare to get the difference between the two, that is, the data difference Then determine the data difference through the positive and negative value monitoring module The positive and negative values ​​of Is positive and greater than the first set value When the corresponding classification sub-monitoring score is obtained When the data difference Is a negative value and is less than the second set value When the corresponding classification sub-monitoring score is obtained Then the calculation module is used to monitor the scores of the corresponding classification sub-categories. And each data type Corresponding sub-monitoring weight , perform normalization calculation to obtain the static monitoring score Among them, animal body monitoring data The body temperature data, weight data, etc. may be directly or indirectly obtained through the electronic ear tag. For example, the weight data may be obtained by uploading the weight data of the animal corresponding to the electronic ear tag when weighing each animal.

[0026] In one embodiment of the present invention, the dynamic monitoring module includes: a first model analysis module for analyzing the real-time positioning information of the animal through a large model to obtain the animal's motion state; and a second model analysis module for analyzing the animal's motion state and a preset number of animal body monitoring data. Conduct large-scale model analysis to obtain body monitoring data of different numbers of animals Corresponding dynamic monitoring score .

[0027] In the location information and animal body monitoring data When performing dynamic monitoring and analysis, the first model analysis module can be used to analyze the positioning information corresponding to the real-time state of the animal using a large model to determine the animal's movement state, such as stopping, running at a certain speed, etc. Then the second model analysis module can be used to analyze the animal's movement state and the preset number of animal body monitoring data. The large model analysis was performed again to determine the body monitoring data of different numbers of animals Corresponding dynamic monitoring score In this embodiment, the large model in the first model analysis module can be a large model obtained by training the historical positioning information corresponding to each animal and the corresponding animal movement state; the large model in the second model analysis module can be a large model obtained by training the historical animal movement state and a preset number of animal body monitoring data. , and dynamic monitoring scores The large model obtained by model training. Therefore, the body monitoring data of different numbers of animals can be determined relatively quickly through large model analysis. Corresponding dynamic monitoring score .

[0028] In one embodiment of the present invention, the analysis unit 114 analyzes the animal body monitoring data to obtain the health abnormality type, including: a search module for The dynamic monitoring score of the corresponding combination when it is less than the set threshold , find the preset cycle under the corresponding combination form Physical monitoring data of each animal in ; Trend chart creation module, used to monitor the body data of each animal within a preset period according to the corresponding combination form , generating a corresponding data change trend graph; a second comparison module, used to compare the data change trend graph with the abnormal graph library under the corresponding combination form; and an output module, used to output the healthy abnormality type corresponding to the abnormal change trend graph when there is an abnormal change trend graph corresponding to the data change trend graph in the abnormal graph library.

[0029] When the monitoring unit 113 determines that the animal body monitoring data is abnormal, the search module is used to determine the animal body monitoring data corresponding to the abnormal animal body monitoring data. Dynamic monitoring score in combination form . So that the dynamic monitoring score can be Further determine the preset cycle under the corresponding combination form Physical monitoring data of each animal in Then, the corresponding data change trend graph is created by using the trend graph creation module, so as to realize the reflection of the health abnormality type of the animal through the data change trend graph. Specifically, the data change trend graph is compared with the abnormal graph library under the corresponding combination form through the second comparison module to find out whether there is an abnormal change trend graph corresponding to the data change trend graph in the abnormal graph library. The output module is used to output the health abnormality type corresponding to the abnormal change trend graph when there is an abnormal change trend graph corresponding to the data change trend graph in the abnormal graph library, so as to realize the determination of the infection level of the corresponding source animal according to the health abnormality type, and then realize the prediction and evaluation of the health risk level of other infected animals.

[0030] In one embodiment of the present invention, the generation unit 115 includes: an abnormality degree query module, which is used to obtain the health abnormality degree of the animal according to the stage of the abnormal change trend diagram of the health abnormality type corresponding to the data change trend diagram corresponding to the abnormal image library; wherein the abnormal image library includes all abnormal change trend diagrams of all stages of health abnormality; and an infection level generation module, which is used to obtain the corresponding infection level by looking up the table according to the health abnormality degree and the health abnormality type. .

[0031] When the generating unit 115 performs health abnormality type analysis based on the animal body monitoring data, the abnormality degree query module is used to determine the health abnormality degree corresponding to the abnormality change trend diagram according to the stage of the abnormal change trend diagram corresponding to the data change trend diagram in the abnormality library. In addition, the infectious level generating module is used to determine the abnormal transmission of the animal according to the health abnormality degree and health abnormality type, that is, to obtain the corresponding infectious level by looking up the table. .

[0032] In one embodiment of the present invention, the prediction unit 116 includes: an infection radius determination module for determining the infection radius of an infected animal according to the infection level of the infected animal. , get the location coordinates based on the infectious animal The infection radius ; and the infection degree prediction module, which is used to predict the infection level based on the location information of the infected animal and the infection radius , predict the infection level of all infected animals .

[0033] The infection degree of other infected animals is estimated by using the prediction unit 116. When predicting, the infection radius determination module uses the infection level of the corresponding infectious animal Determine the infected animal based on the location coordinates The infection radius And then according to the infection degree prediction module to realize the combination of the location information of the infectious animal and the infection radius , get the infection degree of each infected animal .

[0034] In one embodiment of the present invention, the infection degree prediction module includes: an infection distance analysis module for analyzing the real-time location coordinates of the infected animal. and infection radius , and get the infection radius Electronic IDs of all infected animals in ; Based on electronic identity The corresponding location coordinates of the infected animal , get different moments The transmission distance between infected animals and infectious animals ; and the infection degree calculation module, which is used to calculate the infection degree according to the infection radius Different distances within Corresponding infection degree , get the infection distance The corresponding infection degree ; According to the infection distance The infection time and the corresponding infection , get the infection degree of each infected animal ;in, , where different moments Corresponding to different infection levels and infection radius , different levels of infection and infection radius Corresponding distance Different infection levels .

[0035] The infection degree prediction module is used to calculate the infection degree of each infected animal. During prediction, the transmission distance between the infecting animal and the infected animal is calculated through the transmission distance analysis module. ; Among them, the selection of infected animals also needs to first determine whether their coordinates are based on the positioning coordinates and infection radius If it is within the range, then further determine the electronic identity of the infected animal Corresponding location coordinates of infected animals The infection degree calculation module can be used to calculate the infection radius. Different distances within Corresponding infection degree and at the infectious distance The infection time , predict the infection degree of each infected animal . So as to achieve the goal of Health risks of getting infected animals.

[0036] In one embodiment of the present invention, the evaluation unit 117 includes: a prediction library creation module for establishing a health risk level prediction library according to the corresponding health abnormality type; a level detection module for evaluating the infection degree of each infected animal; Perform health risk level detection in the health risk level prediction library; and a storage module for detecting the degree of infection When a corresponding health risk level exists in the health risk level prediction library, the corresponding health risk level and the electronic identity are stored in correspondence.

[0037] After predicting the infection degree of the infected animal, the evaluation unit 117 first establishes a health risk level prediction library through the prediction library creation module. The health risk level prediction library is a library of corresponding relationships between the infection degree and the health risk level under different health abnormality types. Thus, after knowing the health abnormality type and the infection degree, the level detection module can be used to predict the health risk level according to the infection degree. Health risk level detection is performed in the health risk level prediction library to obtain the corresponding health risk level, and then the corresponding health risk level and the electronic identity are stored in correspondence through the storage module, thereby ensuring the correspondence between the assessment data and the corresponding electronic identity when the data is stored in the blockchain.

[0038] Furthermore, the evaluation unit 117 also includes: a monitoring library creation module, which is used to establish a monitoring library for health risk levels that exceed the set health risk level; a level handling module, which is used to manually alarm the electronic identity corresponding information when the health risk level exceeds the set health risk level, and transfer the electronic identity corresponding information of the health risk level to the monitoring library; and a recovery monitoring module, which is used to monitor the health risk level in the monitoring library in real time, and when the health risk level is lower than the set health risk level, the electronic identity corresponding information of the health risk level is returned to the original storage library.

[0039] In this embodiment, after obtaining the health risk level of the infected animal, a monitoring library with a health risk level exceeding the set health risk level can be established through the monitoring library creation module, and the monitoring library can be used to store the electronic identity and electronic identity related information (that is, the electronic identity corresponding information) whose health risk level exceeds the set health risk level. And through the level disposal module, when the health risk level exceeds the electronic identity corresponding information of the set health risk level, an artificial end alarm can be performed, and the electronic identity corresponding information of the corresponding health risk level can be controlled to be transferred to the monitoring library. The health risk level in the monitoring library can also be monitored in real time by restoring the monitoring module. If the health risk level is lower than the set health risk level, the electronic identity corresponding information of the health risk level is returned to the original storage library. Specifically, for example, after an observation period, no obvious changes occur. As time goes by, the health risk level of the corresponding infected animal can be reduced, and the electronic identity corresponding information of the health risk level can be returned to the original storage library to reduce the monitoring amount in a reasonable way.

[0040] See also Figure 2 ,exist Figure 2 In one embodiment, the electronic ear tag can be passed through the ear of the animal to collect data from the animal. The specific structure of the electronic ear tag can include a main tag 1 and an auxiliary tag 2, wherein the main tag 1 is provided with an ear tag neck 11, and the top of the ear tag neck 11 is provided with an ear tag head; the auxiliary tag 2 is provided with an ear tag lock 21 for locking and installing the ear tag head. The ear tag head is passed through the ear of the animal, and the ear tag neck 11 is arranged along the thickness direction of the animal's ear, and the main tag 1 and the auxiliary tag 2 are arranged on both sides of the animal's ear, respectively, so as to realize the installation of the ear tag on the animal's ear.

[0041] See also Figure 3 The present invention also provides an animal identity and health monitoring method based on electronic ear tag blockchain data, which is applied to blockchain nodes in a blockchain network, including: Step S10: creating a corresponding electronic identity according to the electronic ear tag corresponding to each animal; Step S20: acquiring collected data corresponding to the electronic ear tag, and storing the collected data in correspondence with the electronic identity, wherein the collected data includes positioning information and animal body monitoring data; Step S30: performing data monitoring according to the stored positioning information and animal body monitoring data; Step S40: when the animal body monitoring data is abnormal, the animal body monitoring data is analyzed to obtain the health abnormality type; Step S50: Generate an infection level according to the health abnormality type; Step S60: predicting the infection degree of all infected animals according to the infection level and the positioning information corresponding to each electronic ear tag; Step S70: Evaluate the infection degree of each infected animal to obtain the health risk level of each electronic identity corresponding to the infected animal In summary, the present invention discloses an animal identity and health monitoring system based on electronic ear tag blockchain data, which stores the electronic identity corresponding to the electronic ear tag and the collected data of the electronic ear tag in correspondence through the corresponding blockchain node in the blockchain network to ensure the traceability of the data. And in the process of data storage, the health status of each animal can be determined by monitoring and analyzing the collected data. When an animal's health is abnormal, the infection level corresponding to the abnormality will be determined to predict the degree of infection of other infected animals, thereby deriving the health risk level of other infected animals that may also be abnormal, so that managers can adjust the management method of animals according to the health risk level of each animal, etc., to optimize the health management of animals. Therefore, the present invention effectively overcomes the various shortcomings in the prior art and has a high industrial utilization value.

[0042] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. An animal identity and health monitoring system based on electronic ear tag blockchain data, characterized in that: Blockchain nodes used in blockchain networks include: An identity creation unit, used to create a corresponding electronic identity according to the electronic ear tag corresponding to each animal; An acquisition unit, used to acquire collected data corresponding to the electronic ear tag, and store the collected data in correspondence with the electronic identity, wherein the collected data includes positioning information and animal body monitoring data; A monitoring unit, used for performing data monitoring according to the stored positioning information and the animal body monitoring data; An analysis unit, used for analyzing the animal body monitoring data to obtain a health abnormality type when the animal body monitoring data is abnormal; A generating unit, used for generating an infection level according to the health abnormality type; A prediction unit, used for predicting the infection degree of all infected animals according to the infection level and the positioning information corresponding to each electronic ear tag; and The evaluation unit is used to evaluate the infection degree of each infected animal to obtain the health risk level of the infected animal corresponding to each electronic identity.

2. The animal identity and health monitoring system based on electronic ear tag blockchain data according to claim 1 is characterized in that: The monitoring unit comprises: Static monitoring module, used to classify monitoring according to different types of animal body monitoring data, and analyze and obtain classification sub-monitoring scores , according to the classification sub-monitoring score , and obtain the static monitoring score ; A dynamic monitoring module is used to simultaneously monitor the positioning information and the animal body monitoring data under preset categories, and analyze and obtain dynamic monitoring scores under different combinations. ; A score statistics module is used to monitor the static scores according to the and the dynamic monitoring score , and obtain the comprehensive detection scores under different combinations ,in, is the static monitoring weight, To dynamically monitor weights; and Score monitoring module, used to monitor the comprehensive test scores Perform threshold detection, when the comprehensive detection score When the comprehensive test score is greater than the set threshold, the animal's body monitoring data is normal. When it is less than the set threshold, the animal body monitoring data is abnormal.

3. The animal identity and health monitoring system based on electronic ear tag blockchain data according to claim 2 is characterized in that: The static monitoring module comprises: The first comparison module is used to compare the animal body monitoring data With benchmark data Compare and get the data difference between the two ; Positive and negative value monitoring module, used to monitor data difference Monitor positive and negative values ​​and calculate the difference between the values. The positive and negative values ​​of the animal body are obtained Corresponding data types The classification sub-monitoring score ;as well as Calculation module for each data type Corresponding sub-monitoring weight and classification sub-monitoring scores , normalized to obtain the static monitoring score .

4. The animal identity and health monitoring system based on electronic ear tag blockchain data according to claim 2 is characterized in that: The dynamic monitoring module comprises: A first model analysis module is used to analyze the real-time positioning information of the animal through a large model to obtain the animal's movement state; and The second model analysis module is used to analyze the animal's movement state and a preset amount of animal body monitoring data. Conduct large-scale model analysis to obtain body monitoring data of different numbers of animals Corresponding dynamic monitoring score .

5. The animal identity and health monitoring system based on electronic ear tag blockchain data according to claim 2 is characterized in that: The analysis unit analyzes the animal body monitoring data to obtain health abnormality types, including: Search module, used to find The dynamic monitoring score of the corresponding combination when it is less than the set threshold , find the preset cycle under the corresponding combination form Physical monitoring data of each animal ; Trend chart creation module, used to monitor the body data of each animal within a preset period according to the corresponding combination form , generate the corresponding data change trend chart; A second comparison module is used to compare the data change trend graph with the abnormal graph library in the corresponding combination form; and The output module is used to output the health anomaly type corresponding to the abnormal change trend graph when there is an abnormal change trend graph corresponding to the data change trend graph in the abnormal graph library.

6. The animal identity and health monitoring system based on electronic ear tag blockchain data according to claim 5 is characterized in that: The generating unit comprises: An abnormality degree query module, used to obtain the degree of abnormal health of the animal according to the stage of the abnormal change trend diagram of the health abnormality type corresponding to the data change trend diagram corresponding to the abnormal picture library; wherein the abnormal picture library includes all abnormal change trend diagrams of all stages of health abnormality; and The infection level generation module is used to obtain the corresponding infection level by looking up the table according to the health abnormality degree and health abnormality type. .

7. The animal identity and health monitoring system based on electronic ear tag blockchain data according to claim 1 is characterized in that: The prediction unit comprises: The infection radius determination module is used to determine the infection level of the infected animal , get the location coordinates based on the infectious animal The infection radius ;as well as The infection degree prediction module is used to predict the infection level based on the location information of the infected animal and the infection radius. , predict the infection level of all infected animals .

8. The animal identity and health monitoring system based on electronic ear tag blockchain data according to claim 7 is characterized in that: The infection degree prediction module comprises: Infection distance analysis module, based on the real-time location coordinates of infectious animals and infection radius , and get the infection radius Electronic IDs of all infected animals in ; Based on electronic identity The corresponding location coordinates of the infected animal , get different moments The transmission distance between infected animals and infectious animals ;as well as The infection degree calculation module is used to calculate the infection degree based on the infection radius. Different distances within Corresponding infection degree , get the infection distance The corresponding infection degree ; According to the infection distance The infection time and the corresponding infection , get the infection degree of each infected animal ;in, , where different moments Corresponding to different infection levels and infection radius , different levels of infection and infection radius Corresponding distance Different infection levels .

9. The animal identity and health monitoring system based on electronic ear tag blockchain data according to claim 1, characterized in that: The evaluation unit comprises: A prediction library creation module is used to establish a health risk level prediction library according to the corresponding health abnormality type; Level detection module, used to determine the infection level of each infected animal Perform health risk level detection in the health risk level prediction database; and Storage module, used when the infection level is detected When a corresponding health risk level exists in the health risk level prediction library, the corresponding health risk level is stored in correspondence with the electronic identity.

10. The animal identity and health monitoring system based on electronic ear tag blockchain data according to claim 1 is characterized in that: The evaluation unit further comprises: A monitoring library creation module is used to establish a monitoring library for health risk levels exceeding the set health risk level; A level handling module, used to manually alarm the electronic identity corresponding information whose health risk level exceeds the set health risk level, and transfer the electronic identity corresponding information of the health risk level to the monitoring library; and The recovery monitoring module is used to monitor the health risk level in the monitoring library in real time. When the health risk level is lower than the set health risk level, the electronic identity corresponding information of the health risk level is returned to the original storage library.

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