Livestock breeding whole-course traceable intelligent electronic ear tag and method for live pigs

Abstract

The invention relates to the technical field of electronic tracking marks, in particular to a whole-course traceable pig intelligent electronic ear tag and method for livestock breeding. The live pig intelligent electronic ear tag comprises a nonlinear characteristic analysis module, a behavior trend modeling module, a coding strategy adjustment module, a compression efficiency evaluation module, an abnormal mode monitoring module, a resource adjustment strategy module and a behavior early warning feedback module. And the nonlinear characteristic analysis module processes time sequence data by adopting a chaos theory analysis method based on live pig monitoring data. According to the invention, by integrating nonlinear characteristic analysis, behavior trend modeling, coding strategy adjustment, compression efficiency evaluation, abnormal mode monitoring, resource adjustment strategy and behavior early warning feedback modules, the efficiency of livestock breeding management is improved. The application of a chaos theory analysis method and a dynamic modeling technology makes prediction of live pig behaviors and identification of abnormal modes more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic tracking labels, and in particular to an intelligent electronic ear tag and method for pigs that can be traced throughout the entire animal husbandry process. Background Art

[0002] The pig smart electronic ear tag is a high-tech means of monitoring and managing animals from birth to slaughter using electronic equipment and information technology. It involves the comprehensive application of multiple technologies such as electronic identification (RFID), biometrics, remote transmission, data analysis and cloud computing, aiming to improve the management efficiency of animal husbandry, ensure food safety, promote the quality traceability of livestock products, and improve animal welfare.

[0003] Among them, the pig intelligent electronic ear tag used for full traceability of animal husbandry is an intelligent device that integrates multiple monitoring modules. It is installed on the pig's ear to collect real-time data such as the pig's behavior, health status, and environmental interaction. Its purpose is to conduct traceability of the entire pig farming process, including health status, growth environment, and behavior patterns, through accurate and real-time data monitoring, so as to optimize farming management, improve production efficiency, ensure animal health and welfare, and enhance consumers' confidence in the safety and quality of livestock products.

[0004] However, traditional systems are deficient in terms of accuracy, data processing efficiency, and real-time performance in dynamically monitoring the health status and behavior patterns of pigs. Traditional systems rely on static model data and are difficult to capture changes in pig behavior or respond to sudden changes in pig health status in a timely manner, resulting in delays and errors in monitoring information and affecting breeding management decisions. In addition, data processing and transmission efficiency is low, and transmission congestion or information delays often occur due to large amounts of data, reducing management efficiency. This not only increases breeding costs, but also increases breeding risks, reducing the overall quality and market competitiveness of livestock products. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent electronic ear tag and method for pigs that can be traced throughout the entire animal husbandry process.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a pig intelligent electronic ear tag for full traceability of animal husbandry, the pig intelligent electronic ear tag comprising a nonlinear characteristic analysis module, a behavior trend modeling module, a coding strategy adjustment module, a compression efficiency evaluation module, an abnormal mode monitoring module, a resource adjustment strategy module, and a behavior early warning feedback module; The nonlinear characteristic analysis module is based on pig monitoring data and uses chaos theory analysis method to process time series data. By performing attractor reconstruction and Lyapunov exponent calculation, it analyzes the nonlinear dynamic characteristics of the data stream and generates chaos characteristic indicators. The behavior trend modeling module is based on chaotic characteristic indicators and adopts a dynamic modeling method to construct a differential equation model, simulate the pig behavior data generation process, predict the behavior trend and identify the current abnormal mode, and generate a behavior pattern model; The coding strategy adjustment module uses dynamic coding technology to automatically adjust the coding strategy of data based on the behavior pattern model, automatically adjusts the coding strategy according to the needs of the pig behavior pattern, and generates coding optimization data; The compression efficiency evaluation module compresses the coding optimization data based on the coding optimization data using an adaptive data compression algorithm, evaluates the efficiency of multiple compression strategies according to the real-time requirements of the data, adjusts the compression rate and optimizes the transmission strategy, and generates a compression strategy evaluation result; The abnormal pattern monitoring module uses similarity search and track divergence analysis methods based on the compression strategy evaluation results to monitor the behavior data of pigs, identify potential abnormal behaviors, and generate abnormal behavior monitoring results; The resource adjustment strategy module uses a dynamic resource allocation algorithm based on abnormal behavior monitoring results to optimize computing resources and network bandwidth allocation for abnormal data through the dynamic resource allocation algorithm to generate a resource setting strategy; The behavior early warning feedback module is based on resource setting strategies and abnormal behavior monitoring results, uses abnormal behavior monitoring results to generate early warning signals, provides instant feedback on detected abnormal pig behaviors, provides intervention measures, and generates optimized response strategies.

[0007] The present invention has the following improvements: the chaotic characteristic index includes chaotic characteristics of pig activity frequency, dynamic change index of pig behavior diversity, and quantitative evaluation of synchronization of pig group activity; the behavior pattern model includes trend model of pig daily activity, initial recognition pattern of abnormal behavior, and behavior pattern of pig social interaction; the coding optimization data includes coding parameter set of pig behavior pattern, optimized coding scheme of data transmission, and coding strategy of abnormal pig behavior; the compression strategy evaluation result includes compression efficiency result of real-time behavior monitoring data, optimized compression strategy of behavior data, and storage compression scheme of behavior pattern data; the abnormal behavior monitoring result includes abnormal behavior index of pig breeding environment adaptability, abnormal behavior signal of health status change, and recognition result of abnormal group behavior pattern; the resource setting strategy includes computing resource allocation ratio, network bandwidth adjustment strategy, and energy consumption optimization measure; the optimization response strategy includes real-time early warning system configuration of abnormal pig behavior, automatic response scheme of intervention measures, and optimization strategy of pig behavior management.

[0008] The present invention is improved in that the nonlinear characteristic analysis module includes a sequence reconstruction submodule, a stability index analysis submodule, and a characteristic index generation submodule; The sequence reconstruction submodule performs attractor reconstruction based on pig monitoring data, including pig activity frequency, pig body temperature changes, pig feed intake, and pig weight gain rate, and adopts Takens embedding theorem to adjust embedding dimension and delay time parameters, convert one-dimensional time series into multi-dimensional phase space, and generate reconstructed phase space; The stability index analysis submodule is based on the reconstruction of phase space, uses the Wolf algorithm to calculate the Lyapunov index, adjusts the neighborhood radius and evolution time parameters, analyzes the separation rate of the sequence, and generates a Lyapunov index set; The characteristic index generation submodule is based on the Lyapunov index set, uses the number of positive Lyapunov indexes to determine the chaotic state, determines the nonlinear dynamic characteristics in the pig behavior data stream through the analysis of the index value, and generates the chaotic characteristic index.

[0009] The present invention is improved in that the behavior trend modeling module includes a dynamic system modeling submodule, a behavior trend prediction submodule, and an abnormal pattern recognition submodule; The dynamic system modeling submodule is based on the chaotic characteristic index, and the initial condition parameter value is set to the initial state of the pig activity, simulating the pig behavior data generation process to generate a differential equation model; The behavior trend prediction submodule predicts the behavior trend based on the differential equation model, adopts the chaotic time series prediction method, adjusts the prediction step number and the embedding dimension parameter of the phase space reconstruction, and generates the behavior trend prediction result; The abnormal pattern recognition submodule recognizes abnormal patterns based on the behavior trend prediction results, uses a method based on track divergence analysis, adjusts the track divergence threshold to identify mutation points in the behavior data, and identifies abnormal behaviors by analyzing the deviation between the behavior pattern in the prediction results and the normal pattern, and generates a behavior pattern model.

[0010] The present invention is improved in that the coding strategy adjustment module includes a coding strategy adjustment submodule, an abnormal data coding optimization submodule, and a coding efficiency evaluation submodule; The coding strategy adjustment submodule is based on the behavior pattern model, uses a dynamic threshold setting method to distinguish the weight level of the data, adjusts the coding rate parameter value according to the data requirements, and uses multiple compression rates to adjust the coding strategy for emergency data and non-emergency data. The emergency data includes sudden health abnormality alarms and abnormal behavior rapid change indicators, and the non-emergency data includes daily activity monitoring data, weight gain records, and feed intake, to generate an adjusted coding strategy; The abnormal data encoding optimization submodule uses the AES encryption algorithm to encrypt the abnormal behavior data based on the adjusted encoding strategy, adjusts the window size parameter of the LZ77 algorithm to match the abnormal data, optimizes the data volume with the LZ77 compression algorithm, and generates an optimized encoding strategy for the abnormal data; The coding efficiency evaluation submodule performs coding efficiency and data recovery evaluation based on the optimized coding strategy of abnormal data, simulates the data transmission and storage process using the Monte Carlo simulation method, analyzes the transmission efficiency of the encoded data and the integrity of the decoded data under various network conditions, adjusts the window size of the LZ77 compression algorithm, and generates coding optimization data.

[0011] The present invention is improved in that the compression efficiency evaluation module includes a compression strategy optimization submodule, a data importance evaluation submodule, and a compression rate adjustment submodule; The compression strategy optimization submodule performs strategy analysis of the adaptive data compression algorithm based on the coding optimization data, evaluates the compression efficiency and data recovery integrity of various compression strategies by simulating comparative experiments of various compression strategies, selects a compression strategy for compression, and generates compression strategy efficiency analysis results; The data importance assessment submodule uses a data weight allocation algorithm based on the compression strategy efficiency analysis results to assess the real-time requirements, change frequency and access frequency indicators of pig behavior data, classify the data, and generate data importance assessment results; The compression rate adjustment submodule dynamically adjusts the compression rate based on the data importance evaluation result, adopts a dynamic compression rate adjustment algorithm, dynamically adjusts the compression parameters according to the pig behavior data, performs dynamic compression after balancing the compression efficiency and data integrity, and generates a compression strategy evaluation result.

[0012] The present invention is improved in that the abnormal pattern monitoring module includes a similarity analysis submodule, a behavior stability assessment submodule, and an abnormal monitoring feedback submodule; The similarity analysis submodule performs similarity search of time series data based on the compression strategy evaluation results, uses a dynamic time warping algorithm to compare the similarity between pig behavior data and known normal pig behavior patterns, and generates an initial abnormal behavior pattern; The behavior stability assessment submodule performs track divergence analysis based on the initial abnormal behavior pattern, uses Lyapunov exponent calculation to analyze the dynamic stability of pig behavior data and predict changes in pig behavior patterns, and generates improved abnormal behavior patterns; The abnormal monitoring feedback submodule performs feedback adjustment of abnormal pig behavior monitoring results based on improved abnormal behavior patterns, adopts a feedback-based adaptive adjustment algorithm, automatically adjusts monitoring parameters according to the severity and frequency of abnormal pig behavior, and generates abnormal behavior monitoring results.

[0013] The present invention is improved in that the resource adjustment strategy module includes a computing resource optimization submodule, a network bandwidth adjustment submodule, and an energy efficiency management submodule; The computing resource optimization submodule uses priority queuing and resource demand prediction based on the abnormal behavior monitoring results to allocate more computing resources to the detected abnormal data to speed up the processing speed, and adjusts the CPU allocation and memory usage accordingly to generate a computing resource optimization plan; The network bandwidth adjustment submodule is based on the computing resource optimization scheme, adopts real-time network monitoring and bandwidth demand analysis, automatically adjusts the allocation of network bandwidth according to the priority and transmission requirements of abnormal data processing, and generates a network bandwidth adjustment strategy; The energy efficiency management submodule adjusts energy consumption based on the network bandwidth adjustment strategy, adopts the energy efficiency optimization algorithm, monitors the operation status in real time, combines the usage of computing resources and network resources, dynamically adjusts the operation mode and energy allocation strategy, and generates a resource setting strategy.

[0014] The present invention is improved in that the behavior warning feedback module includes a warning signal generation submodule, an intervention measure generation submodule, and a management response optimization submodule; The early warning signal generation submodule uses time series analysis and pattern recognition methods to identify pig behavior patterns based on resource setting strategies and abnormal behavior monitoring results, and generates an optimized management response plan based on defined abnormal pig behavior thresholds; The intervention measure generation submodule is based on the optimization management response plan, adopts the decision tree algorithm, analyzes the severity and cause of the early warning signal, combines the breeding management knowledge base, proposes intervention measures, and generates an intervention measure plan; The management response optimization submodule uses linear programming or genetic algorithms based on the intervention measure plan to adjust the farm management strategy, adjust and optimize resource allocation, and generate an optimized response strategy.

[0015] A method for using a pig intelligent electronic ear tag for full traceability of animal husbandry, the method for using a pig intelligent electronic ear tag for full traceability of animal husbandry is based on the above-mentioned pig intelligent electronic ear tag for full traceability of animal husbandry, and comprises the following steps: S1: Based on pig monitoring data, the phase space reconstruction method and Lyapunov exponent calculation method are used to perform nonlinear analysis of time series. The one-dimensional time series is transformed into a multidimensional phase space by determining the embedding dimension and delay time, and the dynamic stability of the sequence is calculated to generate chaotic feature analysis results. S2: Based on the chaotic feature analysis results, differential equation modeling is used to simulate the pig behavior data generation process, and the model is used to predict the behavior trend and identify abnormal patterns to generate a behavior pattern model result; S3: Based on the behavior pattern model result, a dynamic coding adjustment algorithm is used to adjust the coding rate and compression level of the data according to the complexity and real-time requirements of the behavior pattern, and generate a coding strategy optimization result; S4: Based on the coding strategy optimization result, an adaptive compression efficiency evaluation algorithm is used to evaluate the efficiency of the compression strategy according to the real-time requirements of the data and the network conditions, the compression rate and the transmission strategy are determined, and a compression efficiency evaluation result is generated; S5: Based on the compression efficiency evaluation results, a time series analysis based on similarity search and a dynamic stability analysis based on trajectory divergence are used to identify potential abnormal behavior patterns in pig behavior and generate abnormal pattern monitoring results; S6: Based on the abnormal pattern monitoring result, a dynamic resource allocation algorithm is used to adjust the allocation of computing resources and network bandwidth, optimize resource allocation for the detected abnormal data, optimize processing and transmission efficiency, and generate a resource adjustment strategy result; S7: Based on the resource regulation strategy results, a behavioral early warning algorithm is used to generate early warning signals using the identified abnormal behavior patterns, and targeted intervention measures are provided to promote timely response of breeding management and generate behavioral early warning signal results; S8: Based on the behavioral warning signal results, a response optimization algorithm is used to analyze the severity and cause of the warning signal, formulate management response and intervention strategies, and generate an optimized response strategy.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the efficiency of animal husbandry management is improved by integrating nonlinear characteristic analysis, behavior trend modeling, coding strategy adjustment, compression efficiency evaluation, abnormal pattern monitoring, resource adjustment strategy and behavior early warning feedback module. The application of chaos theory analysis method and dynamic modeling technology makes the prediction of pig behavior and the identification of abnormal patterns more accurate. In addition, by optimizing data transmission through dynamic coding technology and adaptive data compression algorithm, the data processing efficiency and transmission rate are improved, and the real-time and accuracy of data are improved. The adoption of resource adjustment strategy optimizes the allocation of computing resources and network bandwidth, reduces the system operation cost, and improves the overall performance of the system. The implementation of the behavior early warning feedback module provides a basis for timely intervention and handling of abnormal pig behavior, improves food safety assurance, and ultimately enhances consumer confidence. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention proposes a module diagram of a pig intelligent electronic ear tag for full traceability of animal husbandry; Figure 2 The present invention proposes a framework diagram of a pig intelligent electronic ear tag for full traceability of animal husbandry; Figure 3 The present invention proposes a schematic diagram of a nonlinear characteristic analysis module in a pig intelligent electronic ear tag that can be traced throughout the entire livestock breeding process; Figure 4 The present invention proposes a schematic diagram of a behavioral trend modeling module in a pig intelligent electronic ear tag that can be traced throughout the entire livestock breeding process; Figure 5 The present invention proposes a schematic diagram of a coding strategy adjustment module in a pig intelligent electronic ear tag that can be traced throughout the entire livestock breeding process; Figure 6 The present invention proposes a schematic diagram of a compression efficiency evaluation module in a pig intelligent electronic ear tag that can be traced throughout the entire livestock breeding process; Figure 7 The present invention proposes a schematic diagram of an abnormal mode monitoring module in a pig intelligent electronic ear tag that can be traced throughout the entire livestock breeding process; Figure 8 The present invention proposes a schematic diagram of a resource regulation strategy module in a pig intelligent electronic ear tag that can be traced throughout the entire livestock breeding process; Fig. 9 The present invention proposes a schematic diagram of a behavior warning feedback module in a pig intelligent electronic ear tag that can be traced throughout the entire livestock breeding process; Fig.10 The present invention proposes a flow chart of a method for using an intelligent electronic ear tag for pigs that can be traced throughout the entire livestock breeding process. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0020] Example See also Figure 1, the present invention provides a technical solution: a pig intelligent electronic ear tag for full traceability of animal husbandry, the pig intelligent electronic ear tag includes a nonlinear characteristic analysis module, a behavior trend modeling module, a coding strategy adjustment module, a compression efficiency evaluation module, an abnormal mode monitoring module, a resource adjustment strategy module, and a behavior warning feedback module; The nonlinear characteristic analysis module is based on pig monitoring data and uses chaos theory analysis methods to process time series data. By performing attractor reconstruction and Lyapunov exponent calculation, it analyzes the nonlinear dynamic characteristics of the data stream and generates chaos characteristic indicators. The behavior trend modeling module is based on chaotic characteristic indicators and adopts dynamic modeling methods to construct a differential equation model to simulate the pig behavior data generation process, predict behavior trends, identify current abnormal patterns, and generate a behavior pattern model; The coding strategy adjustment module is based on the behavior pattern model and uses dynamic coding technology to automatically adjust the data coding strategy. It automatically adjusts the coding strategy according to the needs of pig behavior patterns and generates coding optimization data. The compression efficiency evaluation module compresses the coding optimization data based on the coding optimization data using an adaptive data compression algorithm, evaluates the efficiency of multiple compression strategies according to the real-time requirements of the data, adjusts the compression rate and optimizes the transmission strategy, and generates compression strategy evaluation results; The abnormal pattern monitoring module uses similarity search and trajectory divergence analysis methods based on the compression strategy evaluation results to monitor the behavior data of pigs, identify potential abnormal behaviors, and generate abnormal behavior monitoring results; The resource adjustment strategy module uses a dynamic resource allocation algorithm based on abnormal behavior monitoring results to optimize computing resources and network bandwidth allocation for abnormal data and generate resource setting strategies; The behavior warning feedback module is based on resource setting strategies and abnormal behavior monitoring results. It uses abnormal behavior monitoring results to generate warning signals, provide instant feedback on detected abnormal pig behaviors, provide intervention measures, and generate optimized response strategies.

[0021] Chaotic characteristic indicators include chaotic characteristics of pig activity frequency, dynamic change indicators of pig behavior diversity, and quantitative evaluation of the synchronization of pig group activities; behavioral pattern models include trend models of pig daily activities, initial identification patterns of abnormal behaviors, and behavioral patterns of pig social interactions; coding optimization data include coding parameter sets of pig behavior patterns, optimized coding schemes for data transmission, and coding strategies for abnormal pig behaviors; compression strategy evaluation results include compression efficiency results of real-time behavior monitoring data, optimized compression strategies for behavior data, and storage compression schemes for behavior pattern data; abnormal behavior monitoring results include abnormal behavior indicators of pig breeding environment adaptability, abnormal behavior signals of changes in health status, and identification results of abnormal group behavior patterns; resource setting strategies include computing resource allocation ratios, network bandwidth adjustment strategies, and energy consumption optimization measures; optimization response strategies include real-time early warning system configuration for abnormal pig behavior, automated response plans for intervention measures, and optimization strategies for pig behavior management.

[0022] In the nonlinear characteristic analysis module, the pig monitoring data is processed by the analysis method of chaos theory, and the time series data processing technology is used to perform the reconstruction of the attractor and the calculation of the Lyapunov exponent. Such analysis helps to reveal the nonlinear dynamic characteristics of the data and generate indicators that reflect the chaotic characteristics of pig activity frequency, dynamic changes in behavioral diversity, and synchronization of group activities. The execution result of this module is a deep understanding of the complexity and predictability of pig behavior, providing key chaos characteristic indicators for subsequent modules.

[0023] In the behavior trend modeling module, based on the chaotic characteristic indicators generated by the previous module, the modeling method of dynamic systems, including differential equation models, is used to simulate the pig behavior data generation process. By establishing and analyzing differential equations, this module can predict the trend of pig behavior, identify the current abnormal pattern, and build a behavior pattern model that reflects the daily activity trend of pigs, abnormal behavior identification, and social interaction.

[0024] In the coding strategy adjustment module, dynamic coding technology is used to automatically adjust the data coding strategy based on the behavior pattern model. According to the specific needs of pig behavior patterns, the optimal set of coding parameters and the optimized coding scheme for data transmission are automatically selected using data coding and information theory principles. It can effectively compress the data volume, optimize storage and transmission efficiency, and ensure the accuracy and integrity of key behavior data, providing an efficient coding optimization strategy for data compression and transmission.

[0025] The compression efficiency evaluation module processes the encoding optimization data through an adaptive data compression algorithm and evaluates the efficiency of different compression strategies. In this module, combined with the real-time requirements of data, the most suitable compression strategy is optimized by comparing and analyzing different data compression rates and compressed data quality. It includes the compression efficiency evaluation of real-time behavior monitoring data, the optimization compression strategy selection of behavior data and pattern data, and finally generates the compression strategy evaluation results.

[0026] The abnormal pattern monitoring module uses similarity search and trajectory divergence analysis methods to monitor pig behavior data and identify potential abnormal behaviors. This includes continuous monitoring of pigs' adaptability to the breeding environment, changes in health status, and group behavior, and identifying abnormal patterns by analyzing the similarities and changing trends of behavioral data. The abnormal behavior monitoring results generated by this process can not only timely discover the abnormal status of pigs, but also provide a scientific basis for taking corresponding management measures.

[0027] The resource adjustment strategy module uses a dynamic resource allocation algorithm to optimize the allocation of computing resources and network bandwidth based on the abnormal behavior monitoring results. By analyzing the urgency and importance of the abnormal behavior monitoring results, the module dynamically adjusts the resource allocation ratio, network bandwidth strategy and energy consumption measures to ensure that the system can respond quickly and effectively when mining abnormal behaviors.

[0028] The behavior early warning feedback module uses resource setting strategies and abnormal behavior monitoring results to generate early warning signals for abnormal pig behavior and provide immediate feedback. By analyzing abnormal patterns and resource adjustment strategies, the module develops targeted intervention measures and optimized response strategies, including the configuration of real-time early warning systems and the implementation of automated response plans. This makes pig behavior management more proactive, enabling timely intervention in potential problems and improving the level of intelligent breeding management.

[0029] See also Figure 2 and Figure 3 ,The nonlinear characteristic analysis module includes a sequence reconstruction submodule, a stability index analysis submodule, and a characteristic index generation submodule; The sequence reconstruction submodule performs attractor reconstruction based on pig monitoring data, including pig activity frequency, pig body temperature changes, pig feed intake, and pig weight gain rate. It also uses Takens embedding theorem to adjust the embedding dimension and delay time parameters, convert the one-dimensional time series into a multi-dimensional phase space, and generate a reconstructed phase space. The stability index analysis submodule is based on the reconstruction of phase space, uses the Wolf algorithm to calculate the Lyapunov index, adjusts the neighborhood radius and evolution time parameters, analyzes the separation rate of the sequence, and generates a Lyapunov index set; The characteristic index generation submodule is based on the Lyapunov index set. It uses the number of positive Lyapunov exponents to determine the chaotic state, and determines the nonlinear dynamic characteristics in the pig behavior data stream through the analysis of the index value to generate chaotic characteristic indicators.

[0030] In the sequence reconstruction submodule, the data format used is time series, including pig activity frequency, body temperature changes, feed intake, and weight gain rate. This module uses the attractor reconstruction method and Takens embedding theorem to adjust the embedding dimension and delay time parameters. The specific execution process involves mapping the one-dimensional time series data to the multidimensional phase space to reveal the potential dynamic structure in the data. By selecting the appropriate embedding dimension and delay time, it is ensured that the trajectory in the multidimensional phase space can faithfully reflect the characteristics of the original dynamic system.

[0031] The stability index analysis submodule then uses the reconstructed phase space and the Wolf algorithm to calculate the Lyapunov index, and analyzes the separation rate of the sequence by adjusting the neighborhood radius and evolution time parameters. The calculation results of the Lyapunov index not only reflect the stability of the system and the feasibility of predicting future states, but also can judge whether the system has chaotic behavior, providing a key indicator for analyzing the dynamic characteristics of pig behavior.

[0032] The characteristic index generation submodule is based on the Lyapunov index set. It uses the number of positive Lyapunov exponents to determine the chaotic state of the system and determines the nonlinear dynamic characteristics in the pig behavior data stream through the analysis of the index value. Through in-depth analysis of the Lyapunov index value, the complex dynamic pattern of pig behavior can be revealed and chaotic characteristic indicators can be generated.

[0033] See also Figure 2 and Figure 4 ,The behavior trend modeling module includes a dynamic system modeling submodule, a behavior trend prediction submodule, and anomaly pattern recognition submodule; The dynamic system modeling submodule is based on the chaotic characteristic index, and the initial condition parameter value is set to the initial state of pig activity, simulating the pig behavior data generation process to generate a differential equation model; The behavior trend prediction submodule predicts the behavior trend based on the differential equation model, adopts the chaotic time series prediction method, adjusts the prediction step number and the embedding dimension parameters of the phase space reconstruction, and generates the behavior trend prediction results; The abnormal pattern recognition submodule identifies abnormal patterns based on the behavior trend prediction results. It uses a method based on track divergence analysis to adjust the track divergence threshold to identify mutation points in the behavior data. It also identifies abnormal behaviors and generates a behavior pattern model by analyzing the deviation between the behavior pattern in the prediction results and the normal pattern.

[0034] In the dynamic system modeling submodule, a data format based on chaotic characteristic indicators is used to simulate the pig behavior data generation process by constructing a differential equation model. The specific process involves selecting chaotic characteristic indicators as model variables. The initial condition parameter values ​​of the differential equation model are set to the initial state of the pig activity to ensure that the model starts the simulation from a biologically real starting point. The differential equation is solved by numerical solutions, such as the Runge-Kutta method, to generate a time series representing the pig behavior data. This operation effectively generates a differential equation model, which provides a basis for subsequent prediction and abnormal pattern recognition.

[0035] In the behavior trend prediction submodule, the behavior trend is predicted by using the chaotic time series prediction method based on the differential equation model. The operation includes adjusting the prediction dimension and the embedding dimension parameters of the phase space reconstruction. Specifically, the embedding dimension is determined by calculating the maximum Lyapunov exponent of the time series, and the one-dimensional time series is converted into a multi-dimensional phase space using the phase space reconstruction method, and then the future behavior trend is predicted through the same trajectory in chaos theory. The behavior trend prediction results generated by this process can reveal the dynamic changes in pig behavior and provide decision support for behavior management and intervention.

[0036] In the abnormal pattern recognition submodule, the abnormal pattern is identified based on the behavior trend prediction results, using the orbital divergence analysis method. By adjusting the orbital divergence threshold, the module identifies the mutation points in the time series and then analyzes the deviation of the behavior pattern in the prediction results from the normal pattern. The operation involves calculating the local divergence rate of the time series and comparing it with the set threshold. Points exceeding the threshold are marked as abnormal.

[0037] See also Figure 2 and Figure 5 ,The coding strategy adjustment module includes a coding strategy adjustment submodule, an abnormal data coding optimization submodule, and a coding efficiency evaluation submodule; The coding strategy adjustment submodule is based on the behavior pattern model and uses a dynamic threshold setting method to distinguish the weight level of the data. It adjusts the coding rate parameter value according to the data requirements and uses a variety of compression rates to adjust the coding strategy for emergency data and non-emergency data. Emergency data includes sudden health abnormality alarms and abnormal behavior rapid change indicators. Non-emergency data includes daily activity monitoring data, weight gain records, and feed intake. The adjusted coding strategy is generated; The abnormal data encoding optimization submodule uses the AES encryption algorithm to encrypt the abnormal behavior data based on the adjusted encoding strategy, adjusts the window size parameters of the LZ77 algorithm to match the abnormal data, optimizes the data volume with the LZ77 compression algorithm, and generates an optimized encoding strategy for the abnormal data; The coding efficiency evaluation submodule performs coding efficiency and data recovery evaluation based on the optimized coding strategy of abnormal data, simulates the data transmission and storage process using the Monte Carlo simulation method, analyzes the transmission efficiency of encoded data and the integrity of decoded data under various network conditions, adjusts the window size of the LZ77 compression algorithm, and generates coding optimization data.

[0038] In the coding strategy adjustment submodule, based on the behavior pattern model and dynamic threshold setting method, the data weight level is distinguished, and the coding strategy is adjusted by setting different compression rates for emergency data such as sudden health abnormality alarms, abnormal behavior rapid change indicators and non-urgent data such as daily activity monitoring data, weight gain records, and feed intake. This submodule adopts a method of dynamically adjusting the coding rate parameter value, including adjusting the compression rate according to the urgency of the data to meet different data processing requirements, ensure the rapid transmission of emergency data and optimize the storage space of non-urgent data, generate specific coding strategy files, and improve data processing efficiency and response speed.

[0039] The abnormal data encoding optimization submodule performs encryption and compression optimization on abnormal behavior data based on the adjusted encoding strategy by adopting the AES encryption algorithm and adjusting the LZ77 algorithm window size parameters. The AES encryption algorithm ensures data security, while the LZ77 algorithm optimizes the data volume by adjusting the window size to match the abnormal data characteristics. In this process, the algorithm execution includes data encryption and compression, which aims to reduce the resources required for abnormal data transmission and storage, while ensuring data integrity and security, generating an optimized abnormal data encoding strategy, and effectively improving the efficiency and security of abnormal data processing.

[0040] The coding efficiency evaluation submodule uses the Monte Carlo simulation method to evaluate coding efficiency and data recovery capabilities based on the optimized abnormal data coding strategy. By simulating the data transmission and storage process under different network conditions, the transmission efficiency of the encoded data and the integrity of the decoded data are analyzed, and then the window size of the LZ77 compression algorithm is adjusted to optimize the encoding process. The execution result of this submodule is the coding optimized data that maintains data transmission efficiency and integrity under various network environments, ensuring the efficiency of the data transmission process and the quality of the data after recovery, and supporting efficient data management and application.

[0041] See also Figure 2 and Figure 6 ,The compression efficiency evaluation module includes a compression strategy optimization submodule, a data importance evaluation submodule, and a compression ratio adjustment submodule; The compression strategy optimization submodule performs strategy analysis of the adaptive data compression algorithm based on the coding optimization data. By simulating a variety of compression strategy comparison experiments, it evaluates the compression efficiency and data recovery integrity of various compression strategies, selects a compression strategy for compression, and generates compression strategy efficiency analysis results. The data importance assessment submodule uses a data weight allocation algorithm based on the compression strategy efficiency analysis results to assess the real-time requirements, change frequency, and access frequency indicators of pig behavior data, classify the data, and generate data importance assessment results; The compression rate adjustment submodule dynamically adjusts the compression rate based on the data importance assessment results. It adopts a dynamic compression rate adjustment algorithm to dynamically adjust the compression parameters according to the pig behavior data, performs dynamic compression after balancing the compression efficiency and data integrity, and generates the compression strategy evaluation results.

[0042] In the compression strategy optimization submodule, the operation is performed through the strategy analysis of the adaptive data compression algorithm. First, the data is optimized based on the encoding. Then, the data is compressed using different compression algorithms (such as LZ77, Huffman coding, etc.), and the compression efficiency and data recovery integrity of each compression strategy are recorded. By evaluating the performance of each strategy, the optimal compression strategy is determined and executed. This process generates the compression strategy efficiency analysis results. This result file records the performance indicators of each compression strategy in detail, including compression ratio, compression speed, and integrity of recovered data. Such analysis results are crucial for selecting the compression strategy that best suits the current data characteristics, ensuring that the data can save space and quickly and accurately restore the original information during storage and transmission.

[0043] In the data importance assessment submodule, the importance of pig behavior data is assessed through a data weight allocation algorithm. This submodule uses the results of compression strategy efficiency analysis as input and evaluates the data based on indicators such as the real-time requirements, change frequency, and access frequency of pig behavior data. The algorithm used is based on decision trees, information gain and other technologies to classify data and identify which data is critical data and requires priority or more efficient compression strategies. The generated data importance assessment result is a structured file that includes data classification and priority indicators. The file guides the subsequent adjustment of the compression rate to ensure that critical data can receive more attention during the compression process, thereby optimizing the efficiency and effectiveness of the entire data processing process.

[0044] In the compression rate adjustment submodule, the compression rate is dynamically adjusted. Based on the data importance assessment results, this submodule uses a dynamic compression rate adjustment algorithm to dynamically adjust the compression parameters according to the classification and importance of the data. Specifically, for data that is assessed as highly important, a lower compression rate is selected to reduce information loss; for data of ordinary or lower importance, a higher compression rate can be used to improve storage and transmission efficiency. The final compression strategy evaluation result is a comprehensive result, which records in detail the compression rate used for each type of data and the expected compression effect.

[0045] See also Figure 2 and Figure 7 ,The abnormal pattern monitoring module includes a similarity analysis submodule, a behavior stability evaluation submodule, and an abnormal monitoring feedback submodule; The similarity analysis submodule performs similarity search of time series data based on the compression strategy evaluation results, and uses a dynamic time warping algorithm to compare the similarity between pig behavior data and known normal pig behavior patterns to generate initial abnormal behavior patterns; The behavior stability assessment submodule performs trajectory divergence analysis based on the initial abnormal behavior pattern, uses Lyapunov exponent calculation to analyze the dynamic stability of pig behavior data and predict changes in pig behavior patterns, and generates improved abnormal behavior patterns; The abnormal monitoring feedback submodule performs feedback adjustment of abnormal pig behavior monitoring results based on improved abnormal behavior patterns. It adopts a feedback-based adaptive adjustment algorithm to automatically adjust monitoring parameters according to the severity and frequency of abnormal pig behavior to generate abnormal behavior monitoring results.

[0046] In the similarity analysis submodule, the data format used is time series data, including pig behavior data obtained through compression strategy evaluation. This module applies the dynamic time warping algorithm, which is refined to include calculating and comparing the time series similarity between pig behavior data and known normal pig behavior patterns. The dynamic time warping algorithm captures the best match between the two sequences by bending the time axis of the time series, thereby determining the similarity between the time series. This process can effectively identify the initial abnormal behavior pattern that is significantly different from the normal behavior pattern, providing a basis for subsequent behavior analysis. The generated initial abnormal behavior pattern file is crucial for early identification of abnormal behavior of pigs, and can trigger the early warning mechanism in time to improve the response speed and accuracy of breeding management.

[0047] The behavioral stability assessment submodule then analyzes the dynamic stability and change trend of pig behavior data based on the initial abnormal behavior pattern through orbital divergence analysis and calculation of Lyapunov exponents. The calculation of Lyapunov exponents involves the quantitative evaluation of the dynamic stability of the system, which is measured by analyzing the separation rate of the sequence evolution over time. This process not only reveals the stability of pig behavior patterns, but also predicts changes in behavior patterns. The generated improved abnormal behavior patterns can more accurately reflect the behavioral abnormalities of pigs, providing a basis for taking appropriate intervention measures.

[0048] The abnormal monitoring feedback submodule is based on improving the abnormal behavior pattern and adopts a feedback-based adaptive adjustment algorithm to adjust the abnormal behavior monitoring results. This module automatically adjusts the monitoring parameters according to the severity and frequency of abnormal behavior and optimizes the monitoring process. The adaptive adjustment algorithm dynamically adjusts the monitoring strategy according to the real-time monitored behavior data to ensure that the monitoring system can effectively respond to the actual changes in pig behavior. The generated abnormal behavior monitoring results not only improve the accuracy and efficiency of abnormal detection, but also provide farmers with real-time and accurate behavioral abnormality information, thereby maintaining the health of pigs and the overall safety of the farm.

[0049] See also Figure 2 and Figure 8 ,The resource adjustment strategy module includes a computing resource optimization submodule, a network bandwidth adjustment submodule, and an energy efficiency management submodule; The computing resource optimization submodule uses priority queuing and resource demand prediction based on the abnormal behavior monitoring results to allocate more computing resources to the detected abnormal data to speed up the processing, and adjusts the CPU allocation and memory usage accordingly to generate a computing resource optimization plan; The network bandwidth adjustment submodule is based on the computing resource optimization solution, adopts real-time network monitoring and bandwidth demand analysis, automatically adjusts the allocation of network bandwidth according to the priority and transmission requirements of abnormal data processing, and generates a network bandwidth adjustment strategy; The energy efficiency management submodule uses energy efficiency optimization algorithms to adjust energy consumption based on network bandwidth adjustment strategies, real-time monitoring of operating status, and combined with the usage of computing resources and network resources to dynamically adjust the operating mode and energy allocation strategy and generate resource setting strategies.

[0050] In the computing resource optimization submodule, computing resources are allocated based on the abnormal behavior monitoring results through priority queuing and resource demand prediction algorithms to achieve rapid processing of abnormal data. This includes allocating priorities based on the urgency of the monitored abnormal behavior data, and using machine learning models based on historical data to predict future resource requirements. Based on this, the system dynamically adjusts the CPU allocation ratio and memory usage to optimize the allocation of computing resources.

[0051] In the network bandwidth adjustment submodule, based on the computing resource optimization scheme, the real-time network monitoring and bandwidth demand analysis methods are used to automatically adjust the allocation of network bandwidth. This process involves monitoring the current network bandwidth usage, analyzing the abnormal data processing requirements sorted by priority, and determining the bandwidth requirements of each task. This strategy effectively guarantees the communication requirements of key tasks and optimizes the overall efficiency of network resource utilization.

[0052] In the energy efficiency management submodule, based on the network bandwidth adjustment strategy, the energy efficiency optimization algorithm is used to dynamically adjust the system's operating mode and energy allocation in combination with the real-time usage of computing resources and network resources. Through real-time monitoring of the system's operating status, the algorithm evaluates the current energy efficiency and adjusts the energy supply strategy based on the actual usage of computing and network resources, such as reducing the energy quota of non-critical tasks and increasing the energy efficiency priority of critical tasks. This operation generates a resource setting strategy that aims to reduce the total energy consumption of the system while ensuring that system performance is not affected and optimizing energy usage.

[0053] See also Figure 2 and Fig. 9 ,The behavior warning feedback module includes the warning signal generation submodule, the ,intervention measures generation submodule, and the management response optimization submodule; The early warning signal generation submodule uses time series analysis and pattern recognition methods to identify pig behavior patterns based on resource setting strategies and abnormal behavior monitoring results, and generates optimized management response plans based on defined abnormal pig behavior thresholds; The intervention measures generation submodule is based on the optimization management response plan, adopts the decision tree algorithm, analyzes the severity and cause of the warning signal, combines the aquaculture management knowledge base, proposes intervention measures, and generates intervention measures plans; The management response optimization submodule uses linear programming or genetic algorithms to adjust farm management strategies, adjust and optimize resource allocation, and generate optimized response strategies based on intervention measures.

[0054] In the early warning signal generation submodule, time series analysis and pattern recognition techniques are used to identify pig behavior patterns based on resource setting strategies and abnormal behavior monitoring results. This process involves serializing pig behavior data, identifying data trends and seasonal changes through time series analysis, and analyzing behavior patterns in combination with pattern recognition methods to identify patterns that match defined abnormal pig behavior thresholds. This operation generates early warning signals based on pig behavior data and provides optimized management response solutions, aiming to identify potential health or behavior problems at an early stage, thereby enabling more effective pig farming management.

[0055] The intervention measures generation submodule uses a decision tree algorithm to analyze the severity and cause of the warning signal according to the optimized management response plan. This submodule takes the warning signal and its associated factors as input, classifies and analyzes the data using the decision tree algorithm, and proposes specific intervention measures based on the farming management knowledge base. Each branch of the decision tree represents a cause of the warning signal, and the leaf node corresponds to the corresponding intervention measure. In this way, the submodule is able to generate intervention measures for different warning signals, assisting managers to take appropriate actions to prevent or mitigate potential problems, and improve farming efficiency and animal welfare.

[0056] The management response optimization submodule uses linear programming or genetic algorithms to adjust farm management strategies based on intervention measures and adjust and optimize resource allocation. This submodule optimizes the overall operational efficiency of the farm by determining the best resource allocation and scheduling plan. If linear programming is used, a mathematical model of resource utilization is established to seek solutions that minimize costs or maximize benefits. If a genetic algorithm is used, the optimal or near-optimal solution is captured iteratively by simulating natural selection and genetic mechanisms. The result of this operation is the generation of an optimized response strategy, which guides the farm on how to effectively allocate and adjust resources when facing early warning signals, achieve the best intervention effect at the lowest cost, and ensure the flexibility and responsiveness of farm management.

[0057] See also Fig.10 A method for using a pig intelligent electronic ear tag for full traceability of animal husbandry is provided. The method for using a pig intelligent electronic ear tag for full traceability of animal husbandry is based on the above-mentioned pig intelligent electronic ear tag for full traceability of animal husbandry, and comprises the following steps: S1: Based on pig monitoring data, the phase space reconstruction method and Lyapunov exponent calculation method are used to perform nonlinear analysis of time series. The one-dimensional time series is transformed into a multidimensional phase space by determining the embedding dimension and delay time, and the dynamic stability of the sequence is calculated to generate chaotic feature analysis results. S2: Based on the results of chaotic feature analysis, differential equation modeling is used to simulate the process of pig behavior data generation, and the model is used to predict behavior trends and identify abnormal patterns to generate behavior pattern model results; S3: Based on the behavior pattern model results, a dynamic coding adjustment algorithm is used to adjust the data encoding rate and compression level according to the complexity of the behavior pattern and real-time requirements, and generate coding strategy optimization results; S4: Based on the coding strategy optimization result, an adaptive compression efficiency evaluation algorithm is used to evaluate the efficiency of the compression strategy according to the real-time requirements of the data and network conditions, determine the compression rate and transmission strategy, and generate a compression efficiency evaluation result; S5: Based on the compression efficiency evaluation results, time series analysis based on similarity search and dynamic stability analysis based on trajectory divergence are used to identify potential abnormal behavior patterns in pig behavior and generate abnormal pattern monitoring results; S6: Based on the abnormal pattern monitoring results, a dynamic resource allocation algorithm is used to adjust the allocation of computing resources and network bandwidth, optimize resource allocation for the detected abnormal data, optimize processing and transmission efficiency, and generate resource adjustment strategy results; S7: Based on the results of the resource regulation strategy, a behavioral early warning algorithm is used to generate early warning signals using the identified abnormal behavior patterns, and targeted intervention measures are provided to promote timely response of aquaculture management and generate behavioral early warning signal results; S8: Based on the behavioral warning signal results, a response optimization algorithm is used to analyze the severity and cause of the warning signal, formulate management response and intervention strategies, and generate an optimized response strategy.

[0058] The method of using the pig intelligent electronic ear tag has brought significant beneficial effects to the livestock breeding field through detailed data processing and analysis steps. First, through nonlinear analysis and extraction of chaotic features, this method can deeply analyze the inherent dynamic characteristics of pig behavior and lay the foundation for accurate modeling of behavior patterns.

[0059] Subsequently, through differential equation modeling and behavioral pattern recognition, the analysis of pig behavior patterns was further deepened, and potential abnormal behavior patterns were discovered in advance. This capability is of great significance for early prevention of pig health problems and improvement of breeding conditions. By dynamically adjusting the data encoding rate and compression level, and adaptively evaluating the efficiency of the compression strategy, this method can ensure the accurate transmission of key information while ensuring data transmission efficiency, which is crucial for the efficient operation of the real-time monitoring system.

[0060] The monitoring of abnormal patterns and the resource adjustment strategies based on them further improve the system's response speed and processing capabilities to emergencies, ensuring that key data can be processed and transmitted with priority when resources are limited. In addition, the generation of behavioral warning signals and the formulation of response optimization strategies can not only promptly remind aquaculture managers to take appropriate intervention measures, but also formulate the most effective management response strategy based on the severity and cause of abnormal events.

[0061] In general, the method of using the pig intelligent electronic ear tag provides a comprehensive, efficient and intelligent management solution for the animal husbandry industry by improving the depth and accuracy of pig behavior analysis, optimizing data processing and transmission processes, and enhancing abnormal monitoring and response capabilities. It can not only improve breeding efficiency and pig health, but also provide strong data support for breeders and promote the sustainable development of animal husbandry.

[0062] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A pig intelligent electronic ear tag for full traceability of animal husbandry, characterized in that: The pig intelligent electronic ear tag includes a nonlinear characteristic analysis module, a behavior trend modeling module, a coding strategy adjustment module, a compression efficiency evaluation module, an abnormal mode monitoring module, a resource adjustment strategy module, and a behavior early warning feedback module; The nonlinear characteristic analysis module is based on pig monitoring data and uses chaos theory analysis method to process time series data. By performing attractor reconstruction and Lyapunov exponent calculation, it analyzes the nonlinear dynamic characteristics of the data stream and generates chaos characteristic indicators. The behavior trend modeling module is based on chaotic characteristic indicators and adopts a dynamic modeling method to construct a differential equation model, simulate the pig behavior data generation process, predict the behavior trend and identify the current abnormal mode, and generate a behavior pattern model; The coding strategy adjustment module uses dynamic coding technology to automatically adjust the coding strategy of data based on the behavior pattern model, automatically adjusts the coding strategy according to the needs of the pig behavior pattern, and generates coding optimization data; The compression efficiency evaluation module compresses the coding optimization data based on the coding optimization data using an adaptive data compression algorithm, evaluates the efficiency of multiple compression strategies according to the real-time requirements of the data, adjusts the compression rate and optimizes the transmission strategy, and generates a compression strategy evaluation result; The abnormal pattern monitoring module uses similarity search and track divergence analysis methods based on the compression strategy evaluation results to monitor the behavior data of pigs, identify potential abnormal behaviors, and generate abnormal behavior monitoring results; The resource adjustment strategy module uses a dynamic resource allocation algorithm based on abnormal behavior monitoring results to optimize computing resources and network bandwidth allocation for abnormal data through the dynamic resource allocation algorithm to generate a resource setting strategy; The behavior early warning feedback module is based on resource setting strategies and abnormal behavior monitoring results, uses abnormal behavior monitoring results to generate early warning signals, provides instant feedback on detected abnormal behaviors of pigs, provides intervention measures, and generates optimized response strategies.

2. The intelligent electronic pig ear tag for full traceability of animal husbandry according to claim 1 is characterized by: The chaotic characteristic indicators include chaotic characteristics of pig activity frequency, dynamic change indicators of pig behavior diversity, and quantitative evaluation of the synchronization of pig group activities; the behavioral pattern model includes a trend model of pig daily activities, an initial identification pattern of abnormal behavior, and a behavioral pattern of pig social interaction; the coding optimization data includes a coding parameter set of pig behavior patterns, an optimized coding scheme for data transmission, and a coding strategy for abnormal pig behavior; the compression strategy evaluation results include compression efficiency results of real-time behavior monitoring data, an optimized compression strategy for behavior data, and a storage compression scheme for behavior pattern data; the abnormal behavior monitoring results include abnormal behavior indicators of pig breeding environment adaptability, abnormal behavior signals of changes in health status, and identification results of abnormal group behavior patterns; the resource setting strategy includes computing resource allocation ratios, network bandwidth adjustment strategies, and energy consumption optimization measures; the optimized response strategy includes real-time early warning system configuration for abnormal pig behavior, automated response plans for intervention measures, and optimized strategies for pig behavior management.

3. The intelligent electronic pig ear tag for full traceability of animal husbandry according to claim 1 is characterized by: The nonlinear characteristic analysis module includes a sequence reconstruction submodule, a stability index analysis submodule, and a characteristic index generation submodule; The sequence reconstruction submodule performs attractor reconstruction based on pig monitoring data, including pig activity frequency, pig body temperature changes, pig feed intake, and pig weight gain rate, and adopts Takens embedding theorem to adjust embedding dimension and delay time parameters, convert one-dimensional time series into multi-dimensional phase space, and generate reconstructed phase space; The stability index analysis submodule is based on the reconstruction of phase space, uses the Wolf algorithm to calculate the Lyapunov index, adjusts the neighborhood radius and evolution time parameters, analyzes the separation rate of the sequence, and generates a Lyapunov index set; The characteristic index generation submodule is based on the Lyapunov index set, uses the number of positive Lyapunov indexes to determine the chaotic state, determines the nonlinear dynamic characteristics in the pig behavior data stream through the analysis of the index value, and generates the chaotic characteristic index.

4. The intelligent electronic pig ear tag for full traceability of animal husbandry according to claim 1 is characterized by: The behavior trend modeling module includes a dynamic system modeling submodule, a behavior trend prediction submodule, and an abnormal pattern recognition submodule; The dynamic system modeling submodule is based on the chaotic characteristic index, and the initial condition parameter value is set to the initial state of the pig activity, simulating the pig behavior data generation process to generate a differential equation model; The behavior trend prediction submodule predicts the behavior trend based on the differential equation model, adopts the chaotic time series prediction method, adjusts the prediction step number and the embedding dimension parameter of the phase space reconstruction, and generates the behavior trend prediction result; The abnormal pattern recognition submodule recognizes abnormal patterns based on the behavior trend prediction results, uses a method based on track divergence analysis, adjusts the track divergence threshold to identify mutation points in the behavior data, and identifies abnormal behaviors by analyzing the deviation between the behavior pattern in the prediction results and the normal pattern, and generates a behavior pattern model.

5. The intelligent electronic pig ear tag for full traceability of animal husbandry according to claim 1 is characterized by: The coding strategy adjustment module includes a coding strategy adjustment submodule, an abnormal data coding optimization submodule, and a coding efficiency evaluation submodule; The coding strategy adjustment submodule is based on the behavior pattern model, uses a dynamic threshold setting method to distinguish the weight level of the data, adjusts the coding rate parameter value according to the data requirements, and uses multiple compression rates to adjust the coding strategy for emergency data and non-emergency data. The emergency data includes sudden health abnormality alarms and abnormal behavior rapid change indicators, and the non-emergency data includes daily activity monitoring data, weight gain records, and feed intake, to generate an adjusted coding strategy; The abnormal data encoding optimization submodule uses the AES encryption algorithm to encrypt the abnormal behavior data based on the adjusted encoding strategy, adjusts the window size parameter of the LZ77 algorithm to match the abnormal data, optimizes the data volume with the LZ77 compression algorithm, and generates an optimized encoding strategy for the abnormal data; The coding efficiency evaluation submodule performs coding efficiency and data recovery evaluation based on the optimized coding strategy of abnormal data, simulates the data transmission and storage process using the Monte Carlo simulation method, analyzes the transmission efficiency of the encoded data and the integrity of the decoded data under various network conditions, adjusts the window size of the LZ77 compression algorithm, and generates coding optimization data.

6. The intelligent electronic pig ear tag for full traceability of animal husbandry according to claim 1 is characterized by: The compression efficiency evaluation module includes a compression strategy optimization submodule, a data importance evaluation submodule, and a compression rate adjustment submodule; The compression strategy optimization submodule performs strategy analysis of the adaptive data compression algorithm based on the coding optimization data, evaluates the compression efficiency and data recovery integrity of various compression strategies by simulating comparative experiments of various compression strategies, selects a compression strategy for compression, and generates compression strategy efficiency analysis results; The data importance assessment submodule uses a data weight allocation algorithm based on the compression strategy efficiency analysis results to assess the real-time requirements, change frequency and access frequency indicators of pig behavior data, classify the data, and generate data importance assessment results; The compression rate adjustment submodule dynamically adjusts the compression rate based on the data importance evaluation result, adopts a dynamic compression rate adjustment algorithm, dynamically adjusts the compression parameters according to the pig behavior data, performs dynamic compression after balancing the compression efficiency and data integrity, and generates a compression strategy evaluation result.

7. The intelligent electronic pig ear tag for full traceability of animal husbandry according to claim 1 is characterized by: The abnormal pattern monitoring module includes a similarity analysis submodule, a behavior stability assessment submodule, and an abnormal monitoring feedback submodule; The similarity analysis submodule performs similarity search of time series data based on the compression strategy evaluation results, uses a dynamic time warping algorithm to compare the similarity between pig behavior data and known normal pig behavior patterns, and generates an initial abnormal behavior pattern; The behavior stability assessment submodule performs track divergence analysis based on the initial abnormal behavior pattern, uses Lyapunov exponent calculation to analyze the dynamic stability of pig behavior data and predict changes in pig behavior patterns, and generates improved abnormal behavior patterns; The abnormal monitoring feedback submodule performs feedback adjustment of abnormal pig behavior monitoring results based on improved abnormal behavior patterns, adopts a feedback-based adaptive adjustment algorithm, automatically adjusts monitoring parameters according to the severity and frequency of abnormal pig behavior, and generates abnormal behavior monitoring results.

8. The intelligent electronic pig ear tag for full traceability of animal husbandry according to claim 1 is characterized by: The resource adjustment strategy module includes a computing resource optimization submodule, a network bandwidth adjustment submodule, and an energy efficiency management submodule; The computing resource optimization submodule uses priority queuing and resource demand prediction based on the abnormal behavior monitoring results to allocate more computing resources to the detected abnormal data to speed up the processing speed, and adjusts the CPU allocation and memory usage accordingly to generate a computing resource optimization plan; The network bandwidth adjustment submodule is based on the computing resource optimization scheme, adopts real-time network monitoring and bandwidth demand analysis, automatically adjusts the allocation of network bandwidth according to the priority and transmission requirements of abnormal data processing, and generates a network bandwidth adjustment strategy; The energy efficiency management submodule adjusts energy consumption based on the network bandwidth adjustment strategy, adopts the energy efficiency optimization algorithm, monitors the operation status in real time, combines the usage of computing resources and network resources, dynamically adjusts the operation mode and energy allocation strategy, and generates a resource setting strategy.

9. The intelligent electronic pig ear tag for full traceability of animal husbandry according to claim 1 is characterized by: The behavior warning feedback module includes a warning signal generation submodule, an intervention measure generation submodule, and a management response optimization submodule; The early warning signal generation submodule uses time series analysis and pattern recognition methods to identify pig behavior patterns based on resource setting strategies and abnormal behavior monitoring results, and generates an optimized management response plan based on defined abnormal pig behavior thresholds; The intervention measure generation submodule is based on the optimization management response plan, adopts the decision tree algorithm, analyzes the severity and cause of the early warning signal, combines the breeding management knowledge base, proposes intervention measures, and generates an intervention measure plan; The management response optimization submodule uses linear programming or genetic algorithms based on the intervention measure plan to adjust the farm management strategy, adjust and optimize resource allocation, and generate an optimized response strategy.

10. A method for using a pig intelligent electronic ear tag that can be traced throughout the entire livestock breeding process, characterized in that: The implementation of the intelligent electronic ear tag for pigs with full traceability of animal husbandry according to any one of claims 1 to 9 comprises the following steps: Based on pig monitoring data, the phase space reconstruction method and Lyapunov exponent calculation method are used to perform nonlinear analysis of time series. The one-dimensional time series is transformed into a multi-dimensional phase space by determining the embedding dimension and delay time, and the dynamic stability of the sequence is calculated to generate chaotic characteristic analysis results. Based on the chaotic characteristic analysis results, differential equation modeling is used to simulate the pig behavior data generation process, and the model is used to predict the behavior trend and identify the abnormal pattern to generate the behavior pattern model result; Based on the behavior pattern model results, a dynamic coding adjustment algorithm is used to adjust the coding rate and compression level of the data according to the complexity and real-time requirements of the behavior pattern, and generate a coding strategy optimization result; Based on the coding strategy optimization result, an adaptive compression efficiency evaluation algorithm is used to evaluate the efficiency of the compression strategy according to the real-time requirements of the data and the network conditions, the compression rate and the transmission strategy are determined, and a compression efficiency evaluation result is generated; Based on the compression efficiency evaluation results, a time series analysis based on similarity search and a dynamic stability analysis based on trajectory divergence are used to identify potential abnormal behavior patterns in pig behavior and generate abnormal pattern monitoring results; Based on the abnormal pattern monitoring results, a dynamic resource allocation algorithm is used to adjust the allocation of computing resources and network bandwidth, optimize resource allocation for the detected abnormal data, optimize processing and transmission efficiency, and generate resource adjustment strategy results; Based on the resource regulation strategy results, a behavioral early warning algorithm is used to generate early warning signals using the identified abnormal behavior patterns, and targeted intervention measures are provided to promote timely response of aquaculture management and generate behavioral early warning signal results; Based on the behavioral warning signal results, a response optimization algorithm is used to analyze the severity and cause of the warning signal, formulate management response and intervention strategies, and generate an optimized response strategy.

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