Electronic ear tag-based large-scale fattening pig identification and breeding management method and system
Patent Information
- Application Number
- CN202611022626.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-10
- Publication Date
- 2026-09-25
AI Technical Summary
[0004]本发明的目的就在于解决当移动猪只的耳标信号即将脱离识别范围时，设备无法及时中断当前轮询进程，进而难以捕捉对应的动态耳标数据，导致移动猪出现识别漏检的问题，而提出基于电子耳标的规模化育肥猪标识与养殖管理方法及系统
[0021]本发明的有益效果：采用动态优先级轮询算法，通过信号强度变化率判定猪只移动趋势，优先读取快速远离天线的电子耳标，降低移动猪只的漏检率，完整采集个体身份与传感健康数据；再利用遗传算法优化健康参数标准，结合环境与生猪活动数据得到最优健康养殖方案。
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Figure CN122804707A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of livestock breeding management technology, specifically relating to a method and system for large-scale fattening pig identification and breeding management based on electronic ear tags. Background Technology
[0002] Currently, large-scale fattening pig farms generally rely on RFID electronic ear tags and reading / writing devices to achieve individual pig identification, growth data collection, feeding control, and full-process information traceability. Through the built-in sensor module of the electronic ear tag, vital signs data such as body temperature and activity level can be collected in real time. Combined with the reading / writing device, breeding data such as weight and feed intake can be obtained. It is the core technical support for the refined and digitalized breeding of pigs.
[0003] Current technology uses a traditional FIFO (First-In, First-Out) static polling mechanism to collect data from electronic ear tags in a fixed queue order, following a first-in, first-out principle. However, in dynamic operation scenarios such as pig farm regrouping and pen separation, pigs are constantly running within the aisles, limiting the effective radiation range of the read / write antenna. Dynamically moving pigs quickly enter and exit the recognition area. When the ear tag signal of a moving pig rapidly attenuates and is about to leave the recognition range, the device cannot interrupt the current polling process in time, making it difficult to capture the corresponding dynamic ear tag data and resulting in missed detections of moving pigs. Summary of the Invention
[0004] The purpose of this invention is to solve the problem that when the ear tag signal of a moving pig is about to leave the recognition range, the device cannot interrupt the current polling process in time, making it difficult to capture the corresponding dynamic ear tag data, resulting in missed detection of moving pigs. Therefore, this invention proposes a method and system for large-scale identification and breeding management of fattening pigs based on electronic ear tags.
[0005] In a first aspect of this invention, a method for large-scale identification and breeding management of fattening pigs based on electronic ear tags is first proposed, the method comprising: Each pig entering the fattening stage is assigned a unique electronic ear tag; the electronic ear tag has a built-in RFID radio frequency chip, temperature sensor and motion sensor; Multiple fixed RFID readers were deployed at key entrances and exits of the pigsty. When a preset number of pigs wearing electronic ear tags pass through a key entrance or exit, the RFID reader will activate a dynamic priority polling algorithm to identify multiple electronic ear tags. Obtain the health status parameters corresponding to the successfully identified electronic ear tag; The optimal health status parameters are obtained by dynamic optimization based on the health status parameters. The optimal health status parameters are input into the backend to generate the optimal healthy breeding plan.
[0006] Optionally, the specific steps of the dynamic priority polling algorithm include: Obtain the signal strength value of each electronic ear tag. The rate of change of signal intensity is calculated based on the signal intensity value of each electronic ear tag; The specific calculation process for the rate of change of signal strength is as follows: The ratio of the difference between the signal strength value of the electronic ear tag at the current moment and the signal strength value of the electronic ear tag at the previous moment to the time interval is recorded as the rate of change of signal strength. Based on the signal strength change rate sorted from smallest to largest, and the electronic ear tags were prioritized, the electronic ear tags in the key entrances and exits of the activity were sorted to generate a dynamic polling queue. The RFID reader establishes communication with each electronic ear tag sequentially according to the dynamic polling queue, reads the identification code, tag, and health status parameters stored in the electronic ear tag; when the high-priority electronic ear tag at the front of the dynamic polling queue is read, the RFID reader updates the signal strength change rate of the remaining electronic ear tags in real time and dynamically adjusts the order of the dynamic polling queue.
[0007] By dynamically adjusting the recognition queue based on the signal strength change rate, ear tags that are rapidly moving away from the antenna and about to leave the recognition area are prioritized for reading. This overcomes the shortcomings of traditional fixed-order polling, significantly reducing the problem of missed detection of pigs in dynamic scenarios such as group transfers and weighing, and improving the recognition success rate when multiple pigs are passing in parallel.
[0008] Optionally, after the RFID reader initiates a dynamic priority polling algorithm to identify multiple electronic ear tags, it further includes: All electronic ear tags within the key entrances and exits of the activity are identified and their corresponding reading tags are used; the reading tags indicate the identified state of the electronic ear tags, namely, an identified state and an unidentified state. If the current electronic ear tag is displayed as unrecognized, then the health status parameters stored in the current electronic ear tag are read.
[0009] The system distinguishes between ear tag recognition and unrecognized states, and collects health parameters only for unrecognized ear tags. This avoids repeatedly reading the same ear tag data, reduces the number of RFID radio frequency communications, lowers the energy consumption and computing pressure of the reader, and shortens the overall identification time for batches of pigs.
[0010] Optionally, the step of dynamically optimizing the health status parameters to obtain the optimal health status parameters includes: Step 1: Use the health status parameters as chromosomes and randomly generate multiple chromosomes; integrate the multiple chromosomes to obtain an initial population; the health status parameters include body temperature parameters and activity level parameters; Step 2: Calculate the fitness value of each chromosome in the current population using the fitness formula, and select the chromosome with the largest fitness value as the optimal chromosome for the current population; Step 3: Iteratively update the optimal chromosome of the current population to obtain an intermediate population, and then execute Step 2; Step 4: If the preset maximum number of iterations is reached, the optimal chromosome is output, which is the optimal health status parameter.
[0011] Using body temperature and activity level as parameters for chromosome iteration optimization, this method is not limited to a uniform and fixed health threshold. It can adapt to different pig house environments and fattening pigs at different growth stages, and dynamically output standard health parameters suitable for the population.
[0012] Optionally, the calculation process of the fitness formula includes:
[0013] in, Indicates the fitness value. This is the optimal body temperature for pigs. This is the optimal body temperature for pigs. These are the unit parameters of the sport numbered i, where i = 1, 2, ..., N; N represents the total number of parameter numbers for the sport. It is the optimal safety value for sport number i; It is the numerical value of the sport numbered i.
[0014] Simultaneously incorporating environmental temperature and various exercise indicators of pigs to comprehensively calculate adaptability, while taking into account differences in the breeding environment and individual physical characteristics, the optimized health benchmark is made to fit the real working conditions of the pig farm, and the precision feeding and health early warning programs generated in the backend are more in line with actual breeding needs.
[0015] In a second aspect of this invention, a large-scale fattening pig identification and breeding management device based on electronic ear tags is proposed, comprising: Allocation module: Each pig entering the fattening stage is assigned an electronic ear tag with a unique code; the electronic ear tag has a built-in RFID radio frequency chip, temperature sensor and motion sensor, and the unique code of the electronic ear tag is bound to the basic information of the pig; Deployment module: Deploy multiple fixed RFID readers at key entrances and exits of the pigsty; Polling identification module: When a preset number of pigs wearing electronic ear tags enter or exit a key entrance, the RFID reader starts a dynamic priority polling algorithm to identify multiple electronic ear tags; Data acquisition module: Acquires the health status parameters corresponding to the successfully recognized electronic ear tags; Dynamic optimization module: Dynamically optimizes health status parameters to obtain the optimal health status parameters; Healthy Farming Solution Module: Generates the optimal healthy farming solution based on the input of optimal health status parameters to the backend.
[0016] Optionally, the polling identification module includes: a first step module, a second step module, a third step module, and a fourth step module. The first step module is used to obtain the signal strength value of each electronic ear tag; The second step module is used to calculate the rate of change of signal strength based on the signal strength value of each electronic ear tag; The specific calculation process for the rate of change of signal strength is as follows: The ratio of the difference between the signal strength value of the electronic ear tag at the current moment and the signal strength value of the electronic ear tag at the previous moment to the time interval is recorded as the rate of change of signal strength. The third step module is used to sort the electronic ear tags according to the signal strength change rate from small to large, set priorities for the electronic ear tags, sort the electronic ear tags in the key entrances and exits of the activity, and generate a dynamic polling queue. The fourth step module is used to establish communication with each electronic ear tag sequentially according to the dynamic polling queue, and read the identification code, tag, and health status parameters stored in the electronic ear tag; when the high-priority electronic ear tag at the front of the dynamic polling queue is read, the RFID reader updates the signal strength change rate of the remaining electronic ear tags in real time and dynamically adjusts the order of the dynamic polling queue.
[0017] Optionally, after the RFID reader initiates a dynamic priority polling algorithm to identify multiple electronic ear tags, it further includes: All electronic ear tags within the key entrances and exits of the activity are identified and their corresponding reading tags are used; the reading tags indicate the identified state of the electronic ear tags, namely, an identified state and an unidentified state. If the current electronic ear tag is displayed as unrecognized, then the health status parameters stored in the current electronic ear tag are read.
[0018] Optionally, the dynamic optimization module is further used for: Step 1: Use the health status parameters as chromosomes and randomly generate multiple chromosomes; integrate the multiple chromosomes to obtain an initial population; the health status parameters include body temperature parameters and activity level parameters; Step 2: Calculate the fitness value of each chromosome in the current population using the fitness formula, and select the chromosome with the largest fitness value as the optimal chromosome for the current population; Step 3: Iteratively update the optimal chromosome of the current population to obtain an intermediate population, and then execute Step 2; Step 4: If the preset maximum number of iterations is reached, the optimal chromosome is output, which is the optimal health status parameter.
[0019] Optionally, the calculation process of the fitness formula includes:
[0020] in, Indicates the fitness value. This is the optimal body temperature for pigs. This is the optimal body temperature for pigs. These are the unit parameters of the sport numbered i, where i = 1, 2, ..., N; N represents the total number of parameter numbers for the sport. It is the optimal safety value for sport number i; It is the numerical value of the sport numbered i.
[0021] The beneficial effects of this invention are as follows: It adopts a dynamic priority polling algorithm to determine the movement trend of pigs by the rate of change of signal strength, prioritizes reading electronic ear tags that are moving away from the antenna, reduces the false negative rate of moving pigs, and collects complete individual identity and sensor health data; then it uses a genetic algorithm to optimize health parameter standards and combines environmental and pig activity data to obtain the optimal healthy breeding plan. Attached Figure Description
[0022] The invention will now be further described with reference to the accompanying drawings.
[0023] Figure 1 A flowchart illustrating a method for large-scale identification and breeding management of fattening pigs based on electronic ear tags, provided in an embodiment of the present invention; Figure 2 A framework diagram of a large-scale fattening pig identification and breeding management device based on electronic ear tags provided in an embodiment of the present invention; Detailed Implementation
[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0025] It should be noted that all formula calculations in the scheme are purely numerical calculations.
[0026] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0027] This invention provides a method for large-scale identification and management of fattening pigs based on electronic ear tags. See also... Figure 1 The method includes the following steps: S101: When a preset number of pigs wearing electronic ear tags enter or exit a key entrance, the RFID reader will activate a dynamic priority polling algorithm to identify the multiple electronic ear tags. S102: Obtain the health status parameters corresponding to the successfully identified electronic ear tag; S103: Optimal health status parameters are obtained through dynamic optimization based on health status parameters; S104: Based on the optimal health status parameters input to the backend, generate the optimal healthy breeding plan.
[0028] In one implementation, the workflow before step S101 is as follows: each pig entering the fattening stage is assigned an electronic ear tag with a unique code; the electronic ear tag has a built-in RFID radio frequency chip, temperature sensor and motion sensor, and the unique code of the electronic ear tag is bound to the basic information of the pig; multiple fixed RFID readers are deployed at the key entrances and exits of the pig house.
[0029] In one implementation, after the RFID reader communicates with the RFID radio frequency chip, the temperature sensor and motion sensor collect data and communicate with the backend.
[0030] Specifically, key entry and exit points for the activity include the transfer channel, weighing area, and feeding station; NTC thermistors can be used for temperature sensors, and triaxial accelerometers can be used for motion sensors; the optimal healthy breeding plan includes precise adjustment of feed amount and recording of health status assessment; the preset number of pigs wearing electronic ear tags is determined based on the specific scenario.
[0031] In one implementation, the specific steps of the dynamic priority polling algorithm include: Obtain the signal strength value for each electronic ear tag; The rate of change of signal intensity is calculated based on the signal intensity value of each electronic ear tag; The specific calculation process for the rate of change of signal strength: The ratio of the difference between the signal strength value of the electronic ear tag at the current moment and the signal strength value of the electronic ear tag at the previous moment to the time interval is recorded as the rate of change of signal strength. The electronic ear tags are sorted from smallest to largest according to the rate of change of signal strength, and a priority is set for each electronic ear tag. The electronic ear tags in the key entrances and exits of the activity are sorted to generate a dynamic polling queue. The RFID reader establishes communication with each electronic ear tag sequentially according to the dynamic polling queue, reads the identification code, tag, and health status parameters stored in the electronic ear tag; when the high-priority electronic ear tag at the front of the dynamic polling queue is read, the RFID reader updates the signal strength change rate of the remaining electronic ear tags in real time and dynamically adjusts the order of the dynamic polling queue.
[0032] Specifically, the signal strength value is mapped to the spatial distance between the pig and the antenna, which, strictly speaking, is the power of the signal returned by the electronic ear tag received by the reader, usually in dBm; the rate of change of signal strength reflects the movement trend and speed of the electronic ear tag relative to the antenna.
[0033] In one implementation, the signal strength values of each electronic ear tag are acquired in real time, and their rate of change is calculated. The ear tags are then sorted from smallest to largest rate of change, assigning higher priority to tags that are rapidly moving away from the antenna, thus generating a dynamic polling queue. This breaks the rigid first-in, first-out (FIFO) mechanism, allowing the reader to prioritize identifying moving pigs about to leave the signal radiation field, effectively avoiding missed ear tag readings due to polling lag. Furthermore, in step S4, after each high-priority ear tag is read, the system immediately updates the signal strength rate of change of the remaining ear tags and dynamically adjusts the queue order, ensuring that the polling order is always synchronized with the real-time movement of the pigs. This further enhances the algorithm's adaptability to dynamic operation scenarios such as group transfers and weighing. In addition, the dynamic queue adjustment mechanism reduces invalid polling and redundant communication, shortens the overall identification time for batches of pigs, and improves the integrity of data acquisition and system operating efficiency.
[0034] In one implementation, electronic ear tags with a negative rate of change in signal strength and a larger absolute value indicate that they are moving away from the antenna faster and have a shorter time to leave the signal radiation field, and are therefore assigned a higher polling priority. Electronic ear tags with a positive rate of change in signal strength or close to zero indicate that they are approaching the antenna or are in a relatively stationary state, and are assigned a lower polling priority.
[0035] In one implementation, after the RFID reader initiates a dynamic priority polling algorithm to identify multiple electronic ear tags, it also includes: All electronic ear tags within the key entrances and exits of the event are identified and their corresponding reading tags are displayed. The reading tag indicates the status of the electronic ear tag being identified, namely, the identified status and the unidentified status. If the current electronic ear tag is displayed as unrecognized, then the health status parameters stored in the current electronic ear tag are read.
[0036] In one implementation, the system maintains a corresponding reading tag for each electronic ear tag within key entrances and exits. This tag clearly distinguishes between identified and unidentified states, and only performs a health status parameter reading operation when the tag indicates unidentified. This mechanism brings significant optimization effects: First, by using status marking, it avoids repeated readings of the same electronic ear tag, effectively reducing the number of redundant communication interactions between the RFID reader and the ear tag, thereby reducing the occupancy rate of the radio frequency channel and the overall power consumption of the reader; second, since it eliminates repeated polling of identified ear tags, the system can concentrate communication time slots on unidentified ear tags, accelerating the traversal and identification process of all pigs within the entrance and exit, and significantly shortening the overall collection time when batches of pigs pass through.
[0037] In one implementation, the optimal health state parameters are obtained through dynamic optimization based on the health state parameters, including: Step 1: Use health status parameters as chromosomes and randomly generate multiple chromosomes; integrate multiple chromosomes to obtain the initial population; health status parameters include body temperature parameters and activity level parameters; Step 2: Calculate the fitness value of each chromosome in the current population using the fitness formula, and select the chromosome with the largest fitness value as the optimal chromosome for the current population; Step 3: Iteratively update the optimal chromosome of the current population to obtain an intermediate population, and then execute Step 2; Step 4: If the preset maximum number of iterations is reached, output the optimal chromosome, which represents the optimal health status parameters.
[0038] In one implementation, health status parameters (including body temperature and activity level parameters) are encoded into chromosomes, and a genetic algorithm is used for iterative optimization, breaking through the static evaluation mode that relies on fixed health thresholds in traditional breeding management. Based on the actual collected health data, the optimal health parameter benchmark that is adapted to the current pig house environment and pig herd status is dynamically output, avoiding the misjudgment problem of fixed thresholds in different seasons and different growth stages, and providing an adaptive decision-making basis for precise feeding and health early warning in the back end.
[0039] In one implementation, the calculation process of the fitness formula includes:
[0040] in, Indicates the fitness value. This is the optimal body temperature for pigs. This is the optimal body temperature for pigs. These are the unit parameters of the sport numbered i, where i = 1, 2, ..., N; N represents the total number of parameter numbers for the sport. It is the optimal safety value for sport number i; It is the numerical value of the sport numbered i.
[0041] In one implementation, the first term of the formula For pig body temperature factors, the closer the measured body temperature is to the optimum body temperature, the larger this value becomes. This allows the system to adaptively favor a combination of health parameters that matches the pig's ideal thermophysiological state, effectively capturing potential health risks reflected by abnormal body temperature fluctuations, and avoiding the limitations of assessing other vital signs in isolation from the individual's physiological baseline; the second term of the formula... The exponential function amplifies the cumulative deviation of multiple motion parameters from their optimal safe values. This means that even small deviations in individual motion indicators can produce significant differences in fitness values after accumulation, enhancing the algorithm's sensitivity and ability to identify abnormal motion states. The integrated architecture of temperature and motion parameters means that fitness evaluation no longer relies on a single indicator, but simultaneously considers both environmental comfort and individual behavioral performance. This guides the genetic algorithm to iteratively optimize in a direction that both conforms to the pig house environment and closely reflects the actual physiological state of the pigs, ultimately outputting the optimal health state parameters.
[0042] This invention also provides a device for large-scale identification and management of fattening pigs based on electronic ear tags. See also Figure 2 The device includes the following modules: Allocation module (201): Allocates an electronic ear tag with a unique code to each pig entering the fattening stage; the electronic ear tag has a built-in RFID radio frequency chip, temperature sensor and motion sensor, and binds the unique code of the electronic ear tag with the basic information of the pig; Deployment module (202): Deploy multiple fixed RFID readers at key activity entrances and exits of the pigsty; Polling identification module (203): When a preset number of pigs wearing electronic ear tags enter the key entrance and exit, the RFID reader starts the dynamic priority polling algorithm to identify multiple electronic ear tags; Data acquisition module (204): Acquires the health status parameters corresponding to the successfully identified electronic ear tags; Dynamic optimization module (205): Dynamically optimizes the health status parameters to obtain the optimal health status parameters; Healthy Aquaculture Solution Module (206): Generates the optimal healthy aquaculture solution based on the input of optimal health status parameters to the backend.
[0043] The foregoing has described one embodiment of the present invention in detail, but this content is merely a preferred embodiment and should not be considered as limiting the scope of the present invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the scope of the claims of this invention.
Claims
1. A method for large-scale identification and management of fattening pigs based on electronic ear tags, wherein each pig entering the fattening stage is assigned an electronic ear tag with a unique code; the electronic ear tag incorporates an RFID radio frequency chip, a temperature sensor, and a motion sensor; and multiple fixed RFID readers are deployed at key entrances and exits of the pigsty, characterized in that... The method includes: When a preset number of pigs wearing electronic ear tags pass through a key entrance or exit, the RFID reader will activate a dynamic priority polling algorithm to identify multiple electronic ear tags. Obtain the health status parameters corresponding to the successfully identified electronic ear tag; The optimal health status parameters are obtained by dynamic optimization based on the health status parameters. The optimal health status parameters are input into the backend to generate the optimal healthy breeding plan.
2. The method for large-scale identification and breeding management of fattening pigs based on electronic ear tags according to claim 1, characterized in that, The specific steps of the dynamic priority polling algorithm include: Obtain the signal strength value for each electronic ear tag; The rate of change of signal intensity is calculated based on the signal intensity value of each electronic ear tag; The specific calculation process for the rate of change of signal strength is as follows: The ratio of the difference between the signal strength value of the electronic ear tag at the current moment and the signal strength value of the electronic ear tag at the previous moment to the time interval is recorded as the rate of change of signal strength. Based on the signal strength change rate sorted from smallest to largest, and the electronic ear tags were prioritized, the electronic ear tags in the key entrances and exits of the activity were sorted to generate a dynamic polling queue. The RFID reader establishes communication with each electronic ear tag sequentially according to the dynamic polling queue, reads the identification code, tag, and health status parameters stored in the electronic ear tag; when the high-priority electronic ear tag at the front of the dynamic polling queue is read, the RFID reader updates the signal strength change rate of the remaining electronic ear tags in real time and dynamically adjusts the order of the dynamic polling queue.
3. The method for large-scale identification and breeding management of fattening pigs based on electronic ear tags according to claim 1, characterized in that, After the RFID reader initiates a dynamic priority polling algorithm to identify multiple electronic ear tags, it also includes: All electronic ear tags within the key entrances and exits of the activity are identified and their corresponding reading tags are used; the reading tags indicate the identified state of the electronic ear tags, namely, an identified state and an unidentified state. If the current electronic ear tag is displayed as unrecognized, then the health status parameters stored in the current electronic ear tag are read.
4. The method for large-scale identification and breeding management of fattening pigs based on electronic ear tags according to claim 1, characterized in that, The process of dynamically optimizing health status parameters to obtain optimal health status parameters includes: Step 1: Use the health status parameters as chromosomes and randomly generate multiple chromosomes; integrate the multiple chromosomes to obtain an initial population; the health status parameters include body temperature parameters and activity level parameters; Step 2: Calculate the fitness value of each chromosome in the current population using the fitness formula, and select the chromosome with the largest fitness value as the optimal chromosome for the current population; Step 3: Iteratively update the optimal chromosome of the current population to obtain an intermediate population, and then execute Step 2; Step 4: If the preset maximum number of iterations is reached, the optimal chromosome is output, which is the optimal health status parameter.
5. The method for large-scale identification and breeding management of fattening pigs based on electronic ear tags according to claim 4, characterized in that, The calculation process of the fitness formula includes: in, Indicates the fitness value. This is the optimal body temperature for pigs. This is the optimal body temperature for pigs. These are the unit parameters of the sport numbered i, where i = 1, 2, ..., N; N represents the total number of parameter numbers for the sport. It is the optimal safety value for sport number i; It is the numerical value of the sport numbered i.
6. A large-scale fattening pig identification and breeding management device based on electronic ear tags, characterized in that, The device includes: Distribution module: Each pig entering the fattening stage is assigned an electronic ear tag with a unique code; the electronic ear tag has a built-in RFID radio frequency chip, temperature sensor and motion sensor; Deployment module: Deploy multiple fixed RFID readers at key entrances and exits of the pigsty; Polling identification module: When a preset number of pigs wearing electronic ear tags enter or exit a key entrance, the RFID reader starts a dynamic priority polling algorithm to identify multiple electronic ear tags; Data acquisition module: Acquires the health status parameters corresponding to the successfully recognized electronic ear tags; Dynamic optimization module: Dynamically optimizes health status parameters to obtain the optimal health status parameters; Healthy Farming Solution Module: Generates the optimal healthy farming solution based on the input of optimal health status parameters to the backend.
7. The large-scale fattening pig identification and breeding management device based on electronic ear tags according to claim 6, characterized in that, The polling identification module includes: a first-step module, a second-step module, a third-step module, and a fourth-step module. The first step module is used to obtain the signal strength value of each electronic ear tag; The second step module is used to calculate the rate of change of signal strength based on the signal strength value of each electronic ear tag; The specific calculation process for the rate of change of signal strength is as follows: The ratio of the difference between the signal strength value of the electronic ear tag at the current moment and the signal strength value of the electronic ear tag at the previous moment to the time interval is recorded as the rate of change of signal strength. The third step module is used to sort the electronic ear tags according to the signal strength change rate from small to large, set priorities for the electronic ear tags, sort the electronic ear tags in the key entrances and exits of the activity, and generate a dynamic polling queue. The fourth step module is used to establish communication with each electronic ear tag sequentially according to the dynamic polling queue, and read the identification code, tag, and health status parameters stored in the electronic ear tag; when the high-priority electronic ear tag at the front of the dynamic polling queue is read, the RFID reader updates the signal strength change rate of the remaining electronic ear tags in real time and dynamically adjusts the order of the dynamic polling queue.
8. The large-scale fattening pig identification and breeding management device based on electronic ear tags according to claim 6, characterized in that, After the RFID reader initiates a dynamic priority polling algorithm to identify multiple electronic ear tags, it also includes: All electronic ear tags within the key entrances and exits of the activity are identified and their corresponding reading tags are used; the reading tags indicate the identified state of the electronic ear tags, namely, an identified state and an unidentified state. If the current electronic ear tag is displayed as unrecognized, then the health status parameters stored in the current electronic ear tag are read.
9. The large-scale fattening pig identification and breeding management device based on electronic ear tags according to claim 6, characterized in that, The dynamic optimization module is also used for: Step 1: Use the health status parameters as chromosomes and randomly generate multiple chromosomes; integrate the multiple chromosomes to obtain an initial population; the health status parameters include body temperature parameters and activity level parameters; Step 2: Calculate the fitness value of each chromosome in the current population using the fitness formula, and select the chromosome with the largest fitness value as the optimal chromosome for the current population; Step 3: Iteratively update the optimal chromosome of the current population to obtain an intermediate population, and then execute Step 2; Step 4: If the preset maximum number of iterations is reached, the optimal chromosome is output, which is the optimal health status parameter.
10. The large-scale fattening pig identification and breeding management device based on electronic ear tags according to claim 9, characterized in that, The calculation process of the fitness formula includes: in, Indicates the fitness value. This is the optimal body temperature for pigs. This is the optimal body temperature for pigs. These are the unit parameters of the sport numbered i, where i = 1, 2, ..., N; N represents the total number of parameter numbers for the sport. It is the optimal safety value for sport number i; It is the numerical value of the sport numbered i.