Animal traceability information management method and system

Abstract

The invention relates to the technical field of animal traceability information management, and provides an animal traceability information management method and system, and the method comprises the steps: obtaining physical environment information from a data collection terminal; associating and storing the physical environment information and the service data to obtain an associated data unit; uploading the associated data unit to a central processing system; after the central processing system performs data analysis on the physical environment information in the associated data unit, an analysis result is returned; and according to the returned analysis result, carrying out credibility marking on the associated business data, and generating early warning information for guiding verification, correction or marking operation recorded in the animal traceability database so as to optimize animal traceability information management. The method has the advantages that the accuracy and reliability of animal traceability information are improved, error data accumulation and diffusion are avoided, and therefore the commercial trust and product safety are guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of animal traceability information management, and in particular to an animal traceability information management method and system. BACKGROUND

[0002] The animal traceability information management system aims to accurately record and track the key event information of the life cycle of an animal individual. In the prior art, an identity recognition device is usually worn by an animal, and a handheld information collection terminal is used to enter data, so as to associate the physiological, behavioral or environmental data of the animal with its unique identity.

[0003] However, in actual application, due to the performance fluctuation of the internal physical components of the data collection terminal and the complexity of the specific working environment, accidental cross-body association errors may occur, that is, the physiological or behavioral data of a certain individual are incorrectly associated with the identity recognition code of another different individual. In the existing information management system, the identification and processing of such error data mainly depend on the subsequent review of the data logic. However, when such error data is still within a reasonable range in terms of value, it is often misjudged as a routine operation error or normal physiological fluctuation, so that its deep physical root cause cannot be revealed and corrected. This condition of not being able to effectively identify and correct the error root cause causes the error data in the system to continue to accumulate and spread, eventually seriously damaging the correctness and reliability of the underlying information association relationship of the entire traceability database, causing potential risks to business trust and product safety. Therefore, how to establish a mechanism in the management system that can identify and distinguish such cross-body association errors caused by specific physical reasons, which are latent and spread, from ordinary human operation errors or isolated equipment failures, so as to prevent the error root cause from being covered up and achieve early warning and positioning of data pollution, is a technical problem to be solved at present.

[0004] The prior art needs to be improved in view of the above problems. SUMMARY

[0005] In order to solve the problems of the prior art, the present application provides an animal traceability information management method and system, which can effectively identify and warn the hidden "cross-body association error" caused by the physical performance fluctuation of the data collection terminal or the complex working environment, improve the accuracy and reliability of the animal traceability information from the source, avoid the accumulation and spread of error data, and thus ensure business trust and product safety.

[0006] The present application provides an animal traceability information management method, and the technical points are as follows: An animal traceability information management method, comprising: Obtain physical environment information corresponding to the data acquisition operation from the data acquisition terminal; the physical environment information includes communication quality parameters, environmental density parameters, and the power supply stability parameters of the data acquisition terminal itself; The physical environment information is associated with and stored with business data to obtain associated data units; Upload the associated data units to the central processing system; After the central processing system analyzes the physical environment information in the associated data unit, it returns the analysis results. The data analysis includes: determining whether the data acquisition terminal has performance abnormalities or whether there is interference in the working environment based on communication quality parameters and environmental density parameters; and determining whether the power supply stability of the data acquisition terminal meets the standards based on the correlation between its own power supply stability parameters and communication quality parameters. Based on the returned analysis results, the credibility of the associated business data is marked and early warning information is generated to guide the verification, correction or marking operations of records in the animal traceability database, so as to optimize the management of animal traceability information.

[0007] The above solution enables early identification and warning of potential data errors caused by equipment performance fluctuations or complex operating environments, thereby improving the accuracy and reliability of animal traceability information and solving the problem of difficulty in identifying and correcting hidden "cross-body association errors" in existing technologies.

[0008] To further address this issue, this application also proposes steps for determining whether the power supply stability of a data acquisition terminal meets the standards based on the correlation between its own power supply stability parameters and communication quality parameters. These steps include: Acquire real-time power supply voltage fluctuation data from the data acquisition terminal; A time-series correlation analysis was performed between real-time power supply voltage fluctuation data and communication quality parameter fluctuation data to obtain the time-series correlation analysis results. According to the time-series correlation analysis results, if the real-time power supply voltage fluctuation data and the communication quality parameter fluctuation data show a preset synchronous fluctuation pattern, and the fluctuation amplitude of the real-time power supply voltage fluctuation data exceeds the preset threshold, then it is determined that the power supply stability of the data acquisition terminal is not up to standard.

[0009] The above scheme enables a more accurate assessment of whether the power supply stability of the data acquisition terminal meets the standards by analyzing the correlation between power supply voltage and communication quality, thereby identifying abnormal equipment performance caused by unstable power supply.

[0010] To improve the solution, this application also proposes steps for determining whether the data acquisition terminal is experiencing performance abnormalities or whether there is interference in the operating environment, based on communication quality parameters and environmental density parameters. Obtain the currently active device operating mode identifier of the data acquisition terminal; Select the corresponding judgment logic based on the device operating mode identifier; If the device operating mode identifier indicates that the data acquisition terminal is in a low-frequency detection and high-frequency reading mode, then based on the communication quality parameter and the environmental density parameter, when the communication quality parameter indicates that the signal strength exceeds the threshold and the environmental density parameter indicates that there is a surrounding identification code, it is determined that the data acquisition terminal has a risk of individual association error. If the device operating mode identifier indicates that the data acquisition terminal is in standard high-frequency scanning mode, then based on the communication quality parameters and environmental density parameters, it is determined whether the data acquisition terminal is experiencing performance abnormalities or whether there is interference in the operating environment.

[0011] The above solution allows for the selection of different judgment logics based on the equipment's operating mode, enabling more precise identification of individual correlation error risks that may occur under specific operating modes and improving the targeting of error identification.

[0012] To further address this issue, this application also proposes steps for determining the risk of individual association errors in data acquisition terminals, including: In the low-frequency detection and high-frequency reading mode, when the communication quality parameter indicates that the signal strength exceeds the threshold and the environmental density parameter indicates that the known target animal has a surrounding identification code, the data acquisition terminal acquires all identification codes and corresponding signal strengths detected during the low-frequency detection process. The signal strength of the known target animal's identification code is compared with the signal strength of the highest-signal-strength identification code among other detected identification codes to obtain the signal strength comparison result. Based on the signal strength comparison results, if the difference between the signal strength of the known target animal's identification code and the signal strength of the highest-signal-strength identification code among the other detected identification codes is less than a preset threshold, then it is determined that the data acquisition terminal has a risk of individual association error.

[0013] The above scheme further refines the method for determining the risk of individual association errors under low-frequency detection and high-frequency reading modes. By comparing the differences in signal strength, it is possible to more accurately identify the occurrence of "cross-entity association errors".

[0014] To improve the solution, this application also proposes a step involving marking the credibility of related business data based on the returned analysis results and generating early warning information, including: Based on the returned analysis results, the risk levels corresponding to the communication quality parameters and environmental density parameters are determined, and the risk levels are mapped to the corresponding credibility tags of the business data. If the analysis results indicate that the data acquisition terminal has a degraded recognition performance or the power supply stability does not meet the preset standard, then an early warning message containing the terminal number, the time of occurrence, and the type of abnormality will be generated. The credibility markers and corresponding business data are stored in the animal traceability database, and the early warning information is used to trigger the verification process of historical business data, record correction or anomaly marking operations, so as to optimize the management of animal traceability information.

[0015] The above approach enables the credibility labeling of business data and the generation of early warning information based on the analysis results, effectively guiding subsequent data verification, correction, or labeling operations, thereby optimizing the management of animal traceability information and intervening in erroneous data at an early stage.

[0016] To further address the problem, this application also proposes that the steps for obtaining environmental density parameters include: Obtain the number of identity verification codes detected by the data acquisition terminal; Obtain distance information between the data acquisition terminal and surrounding physical objects; Based on the number of identification codes and distance information, the physical density of the data collection environment is assessed as a parameter of environmental density.

[0017] The above scheme provides a specific method for obtaining environmental density parameters, making the assessment of the complexity of the working environment more quantitative and accurate.

[0018] To improve the solution, this application also proposes that the steps for obtaining communication quality parameters include: Acquire a preset number of data packets sent from the data acquisition terminal; Based on a preset number of data packets, a confirmation message is returned to the data acquisition terminal; The data acquisition terminal obtains statistics on the number of successfully transmitted data packets based on the confirmation information. Calculate the data transmission rate based on the total number of data packets compared to the preset number of data packets; Data transmission rate is used as a communication quality parameter.

[0019] The above scheme provides a specific method for obtaining communication quality parameters, making the evaluation of the communication performance of data acquisition terminals more objective and reliable.

[0020] To further address the issue, this application also proposes that the steps for obtaining the self-power supply stability parameters of the data acquisition terminal include: Acquire real-time output voltage data and / or real-time output current data from the data acquisition terminal; Calculate the fluctuation range of the real-time output voltage data and / or real-time output current data based on the real-time output voltage data and / or real-time output current data; The fluctuation range is used as a parameter for the power supply stability of the data acquisition terminal.

[0021] The above scheme provides a specific method for obtaining the power supply stability parameters of the data acquisition terminal itself, making the monitoring of the power supply status of the equipment more comprehensive and effective.

[0022] To improve the solution, this application also proposes that, based on the returned analysis results, the related business data be marked with credibility and early warning information be generated to guide the verification, correction or marking operations of records in the animal traceability database in order to optimize the management of animal traceability information. After this step, it also includes: identifying and displaying the potential failure modes of the data acquisition terminal or the risk evolution trend of the operating environment. The steps to identify and demonstrate potential failure modes of data acquisition terminals or the risk evolution trends of the operating environment include: Preset physical environment parameter combination characteristics or time series patterns corresponding to known failure modes or risk evolution trends; Perform pattern matching on the physical environment information in the associated data unit to obtain the pattern matching result; Based on the pattern matching results, identify potential failure modes of the data acquisition terminal or the risk evolution trend of the operating environment.

[0023] The above solution further provides the function of identifying and displaying the potential failure modes of data acquisition terminals or the evolution trend of operating environment risks, which helps to solve problems at the root and achieve deeper early warning and management optimization.

[0024] An animal traceability information management system, used to manage animal traceability information, includes: The environmental information acquisition module is used to acquire physical environment information corresponding to the data acquisition operation from the data acquisition terminal; the physical environment information includes communication quality parameters, environmental density parameters, and the power supply stability parameters of the data acquisition terminal itself. The associated storage execution module is used to associate and store physical environment information with business data to obtain associated data units; The data unit upload module is used to upload associated data units to the central processing system; The analysis result receiving module is used to wait for the central processing system to return the analysis results after analyzing the physical environment information in the associated data unit. The data analysis includes: judging whether the data acquisition terminal has abnormal performance or whether there is interference in the working environment based on communication quality parameters and environmental density parameters; judging whether the power supply stability of the data acquisition terminal meets the standard based on the correlation between its own power supply stability parameters and communication quality parameters. The early warning information generation module is used to mark the credibility of related business data based on the returned analysis results and generate early warning information to guide the verification, correction or marking operations of records in the animal traceability database, so as to optimize the management of animal traceability information.

[0025] The above scheme provides a system for implementing the aforementioned animal traceability information management method. Through modular design, the implementation of the method becomes more convenient and efficient.

[0026] In summary, this application provides an animal traceability information management method and system. By acquiring and analyzing physical environment information, it can effectively identify and warn of hidden "cross-body association errors" caused by fluctuations in the physical performance of data acquisition terminals or complex operating environments. This improves the accuracy and reliability of animal traceability information from the source, avoids the accumulation and spread of erroneous data, and thus protects business trust and product safety. Attached Figure Description

[0027] Figure 1 This is a flowchart of an animal traceability information management method according to one embodiment of the present invention; Figure 2 This is one of the flowcharts of an animal traceability information management method according to another embodiment of the present invention; Figure 3 This is a second flowchart of an animal traceability information management method according to another embodiment of the present invention; Figure 4 This is the third flowchart of an animal traceability information management method according to another embodiment of the present invention; Figure 5 This is the fourth flowchart of an animal traceability information management method according to another embodiment of the present invention; Figure 6 This is a system block diagram of an animal traceability information management system according to another embodiment of the present invention; Explanation of reference numerals in the attached figures: 1. Animal traceability information management system; 11. Environmental information acquisition module; 12. Association storage and execution module; 13. Data unit upload module; 14. Analysis result receiving module; 15. Early warning information generation module. Detailed Implementation

[0028] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0029] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0030] Traditional animal traceability information management methods suffer from "cross-entity association errors" during data collection, caused by performance fluctuations of internal physical components in data collection terminals and specific operating environments. These errors are latent and pervasive. Because the erroneous data may be within a reasonable numerical range, existing methods rely on post-hoc data logic review, easily misclassifying them as routine operational errors or isolated equipment malfunctions, thus masking the physical root cause of the error. This masking leads to the persistence of the error source and the random generation of more different types of cross-entity association errors within the system, ultimately undermining the correctness of the underlying information relationships in the entire traceability database and affecting data reliability.

[0031] In response, this application proposes a method for managing animal traceability information, combining... Figure 1 As shown, it includes: S1, Obtain physical environment information corresponding to the data acquisition operation from the data acquisition terminal; the physical environment information includes communication quality parameters, environmental density parameters, and the power supply stability parameters of the data acquisition terminal itself; S2, associates and stores physical environment information with business data to obtain associated data units; S3, uploads the associated data unit to the central processing system; S4, wait for the central processing system to return the analysis results after performing data analysis on the physical environment information in the associated data unit; the data analysis includes: judging whether the data acquisition terminal has abnormal performance or whether there is interference in the working environment based on communication quality parameters and environmental density parameters; judging whether the power supply stability of the data acquisition terminal meets the standard based on the correlation between its own power supply stability parameters and communication quality parameters; S5, based on the returned analysis results, marks the credibility of the associated business data and generates early warning information to guide the verification, correction or marking operations of records in the animal traceability database, so as to optimize the management of animal traceability information.

[0032] The physical environment information refers to the quantitative indicators of the environment and status of the data acquisition terminal during the data acquisition operation. This includes communication quality parameters, environmental density parameters, and the terminal's own power supply stability parameters. Its primary purpose is to obtain environmental and equipment status data directly related to the accuracy of data acquisition. Communication quality parameters refer to the performance indicators of the data transmission link between the data acquisition terminal and the receiving end. These can be measured using data transmission rate, signal strength, and packet loss rate, reflecting the reliability of data transmission and thus identifying potential communication anomalies in the data acquisition terminal. Environmental density parameters refer to the distribution density of target objects or interference sources within the data acquisition terminal's operating area. This can be assessed using the number of detected identification codes, distance information from surrounding physical objects, or environmental noise levels, identifying the density of the operating environment that may lead to "cross-body association errors." The data acquisition terminal's own power supply stability parameters refer to the stability of its internal power output, characterized by real-time output voltage fluctuations, real-time output current fluctuations, or battery internal resistance change rates, revealing the risk of reduced equipment performance due to power supply instability. Business data refers to various types of information related to the life cycle of individual animals, including physiological, behavioral, environmental, and management event records. It forms the foundation of the animal traceability database. Associated data units are data structures formed by binding physical environment information with business data. This ensures that each piece of business data is accompanied by its physical environment at the time of collection, providing a comprehensive background for subsequent data analysis. The central processing system (CPU) is the computing entity responsible for receiving, storing, and analyzing associated data units. It can be implemented using server clusters, cloud computing platforms, or high-performance computing devices. Its primary function is to centrally process large amounts of data and execute analytical logic. Data analysis refers to the CPU's judgment and calculation process of the physical environment information in the associated data units. This primarily aims to identify performance anomalies in data acquisition terminals, interference from the operating environment, or power supply instability issues. Trustworthiness markers quantify or classify the reliability of business data. These can be represented using numerical scores, rating scales, or Boolean symbols. Their main purpose is to differentiate data levels of reliability and guide subsequent data processing. Warning information refers to the notification issued by the system when it identifies risks or anomalies. It can be presented in the form of text messages, alarm logs or visual cues. Its main purpose is to remind managers to pay attention to and deal with erroneous data.

[0033] In one embodiment, this application is implemented as follows. When staff use a handheld information collection terminal to collect data from animals, the terminal simultaneously acquires its communication quality parameters, such as calculating the data transmission rate by sending a set number of data packets and counting the number of successfully transmitted data packets; acquires environmental density parameters, such as assessing environmental density by detecting the number of surrounding identification codes and distance information from the target animal; and acquires its own power supply stability parameters, such as monitoring the fluctuation range of the real-time output voltage. This physical environment information is associated with and stored with the collected business data, such as the animal's weight and vaccination records, forming associated data units. Subsequently, these associated data units are uploaded to the central processing system. After receiving the data, the central processing system analyzes the physical environment information. For example, if the communication quality parameter indicates that the signal strength is lower than a set threshold, or if the environmental density parameter indicates that the number of identification codes is large and the signal strength is close, it is determined that there may be performance abnormalities or environmental interference. At the same time, if the power supply stability parameter indicates that the voltage fluctuation range exceeds a set range and is correlated with the fluctuation of the communication quality parameter, it is determined that the power supply stability does not meet the standard. The central processing system returns these analysis results to the data management module. Based on the analysis results, the data management module assigns credibility labels to the relevant business data. For example, if performance anomalies or unstable power supply are detected, the business data is marked as "low credibility" or "pending verification." Simultaneously, the system generates an early warning message, including the terminal number, occurrence time, and anomaly type, and pushes this warning message to administrators to guide the verification, correction, or anomaly marking of related records in the animal traceability database, thereby optimizing animal traceability information management.

[0034] Optional, combined Figure 2 As shown, the step S4, which determines whether the power supply stability of the data acquisition terminal meets the standard based on the correlation between its own power supply stability parameters and communication quality parameters, includes: A1, acquire real-time power supply voltage fluctuation data of the data acquisition terminal; A2, perform time-series correlation analysis on the real-time power supply voltage fluctuation data and the communication quality parameter fluctuation data to obtain the time-series correlation analysis results; A3. According to the time-series correlation analysis results, if the real-time power supply voltage fluctuation data and the communication quality parameter fluctuation data show a preset synchronous fluctuation pattern, and the fluctuation amplitude of the real-time power supply voltage fluctuation data exceeds the preset threshold, then it is determined that the power supply stability of the data acquisition terminal is not up to standard.

[0035] Among them, time-series correlation analysis refers to the analysis of two or more time series data to determine whether there is a statistical correlation or dependency between them. Specifically, it can be achieved by methods such as cross-correlation analysis, Granger causality test, or dynamic time warping. Its purpose is to reveal the intrinsic relationship between power supply voltage fluctuations and communication quality fluctuations in the time dimension. Preset synchronous fluctuation mode refers to the specific trend or correlation between power supply voltage fluctuation data and communication quality parameter fluctuation data in time, which is determined in advance based on experience or experimental data during the system design or debugging phase. For example, when the power supply voltage drops, the communication quality parameters also drop accordingly. Its purpose is to identify the direct impact of power supply anomalies on communication performance. Preset threshold refers to a numerical limit set in advance before the system is put into operation, based on equipment performance, application scenario, or industry standards. When the fluctuation amplitude of real-time power supply voltage fluctuation data exceeds this value, it indicates that the power supply stability has reached a level that may affect the normal operation of the equipment. Its purpose is to quantify the severity of power supply fluctuations and avoid misjudgment.

[0036] In some preferred embodiments, specifically, to determine whether the power supply stability of the data acquisition terminal meets the standard, a high-precision voltage sensor can be integrated inside the data acquisition terminal. For example, an analog-to-digital converter connected to the output of the power management unit can continuously acquire and record the real-time power supply voltage data of the terminal at a frequency of 100 times per second, forming a real-time power supply voltage fluctuation data stream. Simultaneously, the communication module of the data acquisition terminal can continuously monitor and record its communication quality parameters, such as by statistically analyzing the successful transmission rate or signal strength of data packets within a specific time window, and using their changing trends as fluctuation data of the communication quality parameters. Subsequently, a built-in processing unit, such as a microcontroller or embedded processor, can execute a time-series correlation analysis algorithm, such as calculating the sliding window cross-correlation coefficient between the real-time power supply voltage fluctuation data and the communication quality parameter fluctuation data, to obtain the time-series correlation analysis results. For example, when the voltage drops significantly within a certain time period, if the communication quality parameters also decrease synchronously, the cross-correlation coefficient will show a strong positive correlation. Finally, the processing unit makes a judgment based on the time-series correlation analysis results. If the real-time power supply voltage fluctuation data and the communication quality parameter fluctuation data show a preset synchronous fluctuation pattern, for example, voltage drop and communication quality degradation occur simultaneously, and the fluctuation amplitude of the real-time power supply voltage fluctuation data, such as the difference between voltage peak and valley values, exceeds a preset threshold, such as 0.5 volts, then the system can determine that the power supply stability of the data acquisition terminal is substandard. This judgment result can immediately trigger an internal flag, which is uploaded to the central processing system along with the business data for subsequent credibility marking and early warning processing.

[0037] Optional, combined Figure 3As shown, step S4, which involves determining whether the data acquisition terminal is experiencing performance abnormalities or whether there is interference in the operating environment based on communication quality parameters and environmental density parameters, includes: B1, obtain the currently active device working mode identifier of the data acquisition terminal; B2, select the corresponding judgment logic based on the device operating mode identifier; B3. If the device working mode identifier indicates that the data acquisition terminal is in low-frequency detection and high-frequency reading mode, then based on the communication quality parameter and the environmental density parameter, when the communication quality parameter indicates that the signal strength exceeds the threshold and the environmental density parameter indicates that there is a surrounding identification code, it is determined that the data acquisition terminal has a risk of individual association error. B4. If the device operating mode identifier indicates that the data acquisition terminal is in the standard high-frequency scanning mode, then based on the communication quality parameters and the environmental density parameters, determine whether the data acquisition terminal has abnormal performance or whether there is interference in the operating environment.

[0038] The device operating mode identifier indicates the current operating state or configuration of the data acquisition terminal. For example, it can be an enumeration value, a string, or a binary flag, used to distinguish whether the terminal is optimized for a specific scenario or performing general operations. Its purpose is to provide a basis for subsequent judgment logic, ensuring the targeting and accuracy of the judgment. Low-frequency detection and high-frequency reading mode refers to the data acquisition terminal using low-frequency signals for initial detection or awakening of the target animal during identification, and then switching to high-frequency signals for detailed identification information reading. This mode is typically used in scenarios requiring precise identification of specific targets, where other interference sources may exist. Its purpose is to optimize identification efficiency and accuracy in specific scenarios, but it also introduces specific risks. Standard high-frequency scanning mode refers to the data acquisition terminal primarily using high-frequency signals to scan and read identification codes, typically used for large-scale, rapid identification of multiple targets or in relatively simple environments. Its purpose is to achieve efficient data acquisition and to provide a general assessment of terminal performance and environmental interference. The communication quality parameter indicating signal strength exceeding the threshold means that, in low-frequency detection and high-frequency reading mode, the signal strength received by the data acquisition terminal exceeds the preset upper limit of the normal range. This could indicate signal interference, abnormal reader sensitivity, or an unusually high signal strength for a non-target identification code, increasing the risk of erroneous association. Its purpose is to identify abnormal signal conditions that may lead to incorrect individual associations. The environmental density parameter indicating the presence of surrounding identification codes means that, in a low-frequency detection, high-frequency reading mode, the environmental density parameter indicates the presence of other identification codes near the target identification code. This typically means that multiple animal individuals or identification devices are within the effective reading range of the data acquisition terminal, increasing the probability of misreading or cross-referencing. Its purpose is to identify dense environmental conditions that may lead to incorrect individual associations.

[0039] In some preferred embodiments, this application is implemented as follows. The control module inside the data acquisition terminal can maintain a device operating mode identifier. For example, this identifier can be an enumerated variable, and its value can be "low-frequency detection high-frequency reading mode" or "standard high-frequency scanning mode". When the data acquisition terminal starts or switches tasks, the control module updates this identifier. Specifically, after the central processing system receives the associated data unit, its data analysis module first obtains the currently active device operating mode identifier of the data acquisition terminal. For example, if the obtained identifier is "low-frequency detection high-frequency reading mode", the system will enable specific judgment logic for this mode. Under this specific logic, the system checks the communication quality parameters in the associated data unit, for example, determining whether the indicated signal strength exceeds a preset threshold, which may be set to an abnormally high value that should not be reached in normal low-frequency detection high-frequency reading operations. Simultaneously, the system also checks the environmental density parameters, for example, determining whether it indicates the presence of one or more surrounding identification codes during data acquisition. If both conditions are met simultaneously, i.e., the signal strength is abnormally high and surrounding identification codes are present, the system will determine that the current data acquisition terminal has a risk of individual association error. As another specific implementation, if the acquired device operating mode identifier is "standard high-frequency scanning mode," the system will select and execute a general judgment logic for that mode. Under this general logic, the system will comprehensively analyze communication quality parameters (e.g., data transmission rate, signal stability) and environmental density parameters (e.g., number of identification codes, environmental noise level) to determine whether the data acquisition terminal is experiencing widespread performance anomalies, such as decreased reading efficiency, or whether there is widespread interference in the operating environment, such as electromagnetic interference. In this way, the system can apply the most appropriate judgment rules based on the actual working scenario of the terminal, thereby improving the accuracy and effectiveness of the judgment.

[0040] Optionally, the steps to determine the risk of individual association errors in the data acquisition terminal include: In the low-frequency detection and high-frequency reading mode, when the communication quality parameter indicates that the signal strength exceeds the threshold and the environmental density parameter indicates that the known target animal has a surrounding identification code, the data acquisition terminal acquires all identification codes and corresponding signal strengths detected during the low-frequency detection process. The signal strength of the known target animal's identification code is compared with the signal strength of the highest-signal-strength identification code among other detected identification codes to obtain the signal strength comparison result. Based on the signal strength comparison results, if the difference between the signal strength of the known target animal's identification code and the signal strength of the highest-signal-strength identification code among the other detected identification codes is less than a preset threshold, then it is determined that the data acquisition terminal has a risk of individual association error.

[0041] The preset threshold is a critical value used to measure the difference between the signal strength of the known target animal's identification code and the signal strength of the identification code with the highest signal strength among other detected identification codes. It can be set according to the actual application scenario, the performance characteristics of the data acquisition terminal, and the tolerance for individual association error risk. Its purpose is to ensure the accuracy and sensitivity of the judgment and avoid misjudgment or omission.

[0042] In some preferred embodiments, this application is implemented as follows: Assume that in a beef cattle farm, workers use a data acquisition terminal to collect weight data from a cow numbered A37. At this time, the data acquisition terminal is in a low-frequency detection, high-frequency reading mode. Because the cattle herd is densely packed in the passage, the environmental density parameter indicates the presence of surrounding identification codes, and the communication quality parameter indicates that the signal strength exceeds the threshold, indicating a good signal environment. In this case, during low-frequency detection, the data acquisition terminal not only detects the identification code of the target cow A37, but also detects the identification code of another adjacent cow, A38, and obtains the signal strength corresponding to each of these two identification codes. For example, the signal strength of A37's identification code is -60dBm, while the signal strength of A38's identification code is -62dBm. The system then compares the signal strength of the known target animal A37's identification code (-60dBm) with the signal strength of the highest-signal-strength identification code among the other detected identification codes (i.e., A38's identification code, -62dBm), and obtains the signal strength comparison result. If the preset threshold is set to 5dBm, then the difference between -60dBm and -62dBm is 2dBm, which is less than the preset threshold of 5dBm. Based on this signal strength comparison result, the system determines that the data acquisition terminal has a risk of individual association error. This judgment mechanism can effectively identify the potential risk of data association errors in dense environments due to similar signal strengths, thereby issuing timely warnings and preventing the incorrect association of A37's weight data with A38's identification code.

[0043] Optional, combined Figure 4 As shown, the step S5, which involves marking the credibility of the associated business data based on the returned analysis results and generating early warning information, includes: S51, based on the returned analysis results, determine the risk level corresponding to the communication quality parameters and the environmental density parameters, and map the risk level to the corresponding credibility label of the business data; S52, if the analysis results indicate that the data acquisition terminal has a decreased recognition performance or the power supply stability does not meet the preset standard, then an early warning message containing the terminal number, the time of occurrence and the type of abnormality will be generated. S53 stores the credibility marker and corresponding business data into the animal traceability database, and uses the early warning information to trigger the verification process of historical business data, perform record correction or anomaly marking operations, so as to optimize the management of animal traceability information.

[0044] Risk level refers to the level of quantitative assessment of possible anomalies or potential problems during data acquisition. Based on preset rules or models, the numerical ranges of communication quality parameters and environmental density parameters can be divided into different risk intervals, such as low risk, medium risk, and high risk. The purpose is to provide a refined basis for the credibility assessment of business data. Credibility markers are identifiers attached to business data to indicate its reliability. These can take the form of numerical scores, level classifications, or Boolean values, and their purpose is to provide data quality references for subsequent data use and decision-making. Degraded recognition performance refers to key performance indicators such as recognition accuracy, recognition distance, or recognition speed of the data acquisition terminal when recognizing the target object's identification code falling below normal operating levels or preset thresholds. This indicates potential problems with the terminal's hardware or software. Power supply stability failure to meet preset standards refers to fluctuations in the power supply voltage or current of the data acquisition terminal exceeding the allowable range, or power outages, causing the terminal to malfunction or become unstable. This indicates a problem with the terminal's power system. The system's functions include: **Early Warning Information:** Early warning information refers to notifications automatically generated and sent by the system when an anomaly is detected. These notifications may include key information such as the terminal number, occurrence time, and anomaly type. Their purpose is to promptly inform management personnel or relevant systems of existing problems so that intervention measures can be taken. **Historical Business Data Verification Process:** This refers to the systematic inspection and verification of historical business data stored in the database. This may include data consistency checks, logical rationality verification, or comparison with external reference data. Its purpose is to identify and correct errors or inconsistencies in historical data. **Record Correction:** This refers to the modification or updating of erroneous or inaccurate business data in the database. Its purpose is to ensure data accuracy and consistency. **Anomaly Marking:** This refers to the special identification of problematic business data in the database without directly modifying the original data. This could involve adding an "anomaly" tag or status code. Its purpose is to distinguish problematic data, prevent misuse, and provide clues for subsequent review or processing. **Animal Traceability Information Management Optimization:** This involves improving all aspects of data collection, processing, storage, and utilization to enhance the data quality, reliability, and management efficiency of the entire animal traceability system. Its purpose is to ensure the accuracy of traceability information and enhance business trust and product safety.

[0045] In some embodiments, this application is implemented as follows. Assume that in a farm, a data acquisition terminal periodically uploads business data such as animal weight and vaccination records, along with communication quality parameters (e.g., signal strength, data transmission rate) and environmental density parameters (e.g., the number of surrounding animals, the distance between the terminal and the animals). The central processing system analyzes this physical environment information and returns the analysis results.

[0046] Specifically, the system can determine the risk level corresponding to communication quality parameters and environmental density parameters based on the returned analysis results. For example, if the communication quality parameter indicates a signal strength below -80dBm and the environmental density parameter indicates the presence of more than 5 identification codes in the vicinity, the system can classify it as "medium risk." If the signal strength is below -95dBm and there are more than 10 identification codes in the vicinity, it is classified as "high risk." These risk levels are then mapped to credibility markers for business data. For example, "low risk" is mapped to "high credibility," "medium risk" to "medium credibility," and "high risk" to "low credibility."

[0047] If the analysis results indicate that a data acquisition terminal has experienced a decline in recognition performance (e.g., historical data analysis shows that its recognition success rate is consistently below 90%), or that its power supply stability does not meet the preset standard (e.g., real-time power supply voltage fluctuation exceeds 0.5V), the system will immediately generate an early warning message. This early warning message can be a structured data packet containing the terminal number (e.g., "RFID_Reader_007"), the time of occurrence (e.g., "2023-10-26 14:35:22"), and the type of anomaly (e.g., "decreased recognition performance" or "unstable power supply").

[0048] Subsequently, business data with credibility markers, such as "Cattle A37, weight 450kg, credibility: medium," is stored in the animal traceability database. Simultaneously, generated alerts, such as an alert about unstable power supply to "RFID_Reader_007," can trigger a verification process for all historical business data collected by that terminal in the past 24 hours. This verification process automatically compares this historical data with other related data (e.g., data collected by other terminals within the same time period, or the animal's normal growth curve) to identify logical conflicts or anomalies. If the verification finds a problem, the system can automatically correct the record (e.g., correcting incorrectly associated weight data from A38 back to A37) or mark the relevant record as abnormal (e.g., adding a "data abnormal, pending verification" label to the weight record in A38), thereby ensuring the accuracy and integrity of the animal traceability database.

[0049] Optionally, the steps for obtaining the environmental density parameter include: Obtain the number of identity verification codes detected by the data acquisition terminal; Obtain distance information between the data acquisition terminal and surrounding physical objects; Based on the number of identification codes and distance information, the physical density of the data collection environment is assessed as a parameter of environmental density.

[0050] Physical density refers to the degree of compactness or concentration of physical objects within a specific spatial range. It can be expressed as the number of objects per unit volume, the reciprocal of the average distance between objects, or a relative density value calculated based on a signal strength attenuation model. Environmental density parameter refers to a numerical indicator used to quantify the congestion level of the physical environment in which the data acquisition terminal is located. It can be expressed as a normalized value, a classification system, or a composite index calculated based on a specific algorithm.

[0051] In some preferred embodiments, the data acquisition terminal can be a handheld RFID reader that can detect multiple RFID ear tags when scanning animal identification codes. The number of identification codes detected by the data acquisition terminal can be obtained by counting the total number of unique identification codes successfully identified and received by the reader within a single scanning cycle. For example, when a worker scans in a herd of cattle, the reader may detect five identification codes simultaneously. Simultaneously, the distance information between the data acquisition terminal and surrounding physical objects can be obtained, specifically estimated using the signal strength (RSSI) of each identification code received by the reader. Signal strength and distance are generally inversely proportional; a pre-established signal strength-distance model can convert the received signal strength into an approximate distance value. For example, for five detected identification codes, the distance between each code and the reader can be obtained separately; for instance, the first identification code is 0.3 meters away, the second 0.35 meters, the third 0.8 meters, the fourth 1.2 meters, and the fifth 1.5 meters. Subsequently, based on the number of identification codes and distance information, the physical density of the data collection environment is assessed as a parameter of environmental density. Specifically, this can be calculated using a weighted average distance or a density function. For example, the average distance of all detected identification codes can be calculated, or the number of identification codes within a preset effective recognition range (e.g., 0.5 meters) can be counted. If the average distance is small and the number of codes within the effective recognition range is large, it indicates a high physical density of the environment. This assessment result, such as a normalized value between 0 and 1, or a "low," "medium," or "high" level, can be used as a parameter of environmental density for subsequent analysis of physical environment information.

[0052] Optionally, the steps for obtaining communication quality parameters include: Acquire a preset number of data packets sent from the data acquisition terminal; Based on a preset number of data packets, a confirmation message is returned to the data acquisition terminal; The data acquisition terminal obtains statistics on the number of successfully transmitted data packets based on the confirmation information. Calculate the data transmission rate based on the total number of data packets compared to the preset number of data packets; Data transmission rate is used as a communication quality parameter.

[0053] The preset number of data packets refers to a fixed set of data units predetermined and sent during communication quality probing, intended to provide a clear benchmark for subsequent data transmission rate calculations. Acknowledgment information refers to the feedback information returned by the receiver to the sender after receiving a data packet, indicating the data packet reception status, allowing the sender to count the number of successfully transmitted data packets. Data packet count data refers to the total number of data packets successfully sent and acknowledged by the receiver, counted by the data acquisition terminal based on received acknowledgment information, directly reflecting the effective transmission capacity of the communication link. Data transmission rate refers to the ratio of successfully transmitted data to the total amount of data sent within a specific time period, usually expressed as a percentage, used to quantitatively evaluate the efficiency and reliability of the communication link.

[0054] In some preferred embodiments, the acquisition of communication quality parameters can be specifically implemented as follows: First, the central processing system can send an instruction to the data acquisition terminal, requesting it to continuously send 100 data packets within a specific time period, such as 10 seconds. Upon receiving the instruction, the data acquisition terminal immediately begins sending these preset number of data packets. When the central processing system receives these data packets, it immediately returns an acknowledgment message to the data acquisition terminal. This acknowledgment message can be a simple ACK signal or an acknowledgment frame containing the sequence number of the received data packets. After sending all data packets, the data acquisition terminal counts the number of successfully transmitted data packets based on the received acknowledgment messages. For example, if it receives 95 acknowledgment signals, the number of successfully transmitted data packets is 95. Subsequently, the data acquisition terminal sends this number of successfully transmitted data packets back to the central processing system. Upon receiving this data, the central processing system calculates the data transmission rate based on the preset total number of data packets (e.g., 100) and the number of successfully transmitted data packets returned by the data acquisition terminal (e.g., 95), i.e., 95 / 100 = 0.95. Ultimately, the central processing system uses this calculated value of 0.95 as a communication quality parameter for subsequent physical environment information analysis and anomaly detection. This proactive detection and feedback mechanism ensures that the communication quality parameter is obtained based on actual data transmission performance, rather than a simple signal strength estimate.

[0055] Optionally, the steps for obtaining the power supply stability parameters of the data acquisition terminal include: Acquire real-time output voltage data and / or real-time output current data from the data acquisition terminal; Calculate the fluctuation range of the real-time output voltage data and / or real-time output current data based on the real-time output voltage data and / or real-time output current data; The fluctuation range is used as a parameter for the power supply stability of the data acquisition terminal.

[0056] Among them, fluctuation amplitude refers to the range or dispersion of real-time output voltage data and / or real-time output current data over a period of time. It can be achieved using statistical methods, such as calculating standard deviation, variance, mean absolute deviation, or the difference between the maximum and minimum values. Its purpose is to quantify the dynamic stability of the power supply. The power supply stability parameter of the data acquisition terminal refers to a quantitative indicator that reflects the stability of the internal power supply system of the data acquisition terminal, calculated through fluctuation amplitude. Its purpose is to provide an accurate basis for evaluating the power supply status of the data acquisition.

[0057] In some preferred embodiments, the acquisition of the power supply stability parameters of the data acquisition terminal can be specifically implemented as follows: The data acquisition terminal can integrate high-precision voltage sensors and / or current sensors, which can continuously collect real-time output voltage data and / or real-time output current data from the terminal's internal power management module at a preset sampling frequency, such as 100 times per second. These real-time data streams can be sent to the terminal's built-in microcontroller or dedicated signal processing unit. The processing unit can be configured with a sliding time window, such as 5 seconds, for all voltage or current data points collected within this window. Then, the processing unit can perform statistical calculations on the data points within the window, such as calculating their standard deviation. The calculated standard deviation value represents the fluctuation amplitude of the power supply voltage or current within the 5-second time window. This fluctuation amplitude value can then be periodically updated and stored as the power supply stability parameter of the data acquisition terminal. For example, if the calculated standard deviation exceeds a preset threshold, it indicates that there is a significant fluctuation in the power supply, and this fluctuation amplitude parameter can be further used to evaluate the performance status of the data acquisition terminal.

[0058] Optionally, based on the returned analysis results, the associated business data is marked with credibility and early warning information is generated to guide the verification, correction, or marking operations of records in the animal traceability database. Following this step of optimizing animal traceability information management, the system also includes: identifying and displaying potential failure modes of the data acquisition terminal or the risk evolution trend of the operating environment; combined with... Figure 5 As shown, the steps for identifying and demonstrating the potential failure modes or risk evolution trends of the operating environment of a data acquisition terminal include: C1, a preset combination of physical environment parameters or time-series patterns corresponding to known failure modes or risk evolution trends; C2 performs pattern matching on the physical environment information in the associated data unit to obtain the pattern matching result; C3, based on the pattern matching results, identifies potential failure modes of the data acquisition terminal or the risk evolution trend of the operating environment.

[0059] Among them, physical environment parameter combination characteristics or time series patterns refer to a predefined set or series of physical environment data patterns. These patterns are related to the specific fault states or risk evolution trends of the operating environment of the data acquisition terminal. They can be constructed using rule-based definitions, statistical models, or machine learning models, with the aim of providing a benchmark for subsequent pattern matching. Pattern matching refers to the process of comparing the actually collected physical environment information with the pre-defined physical environment parameter combination characteristics or time series patterns. It can be implemented using rule-based matching algorithms, statistical methods, or deep learning algorithms, with the aim of discovering whether there are features in the actual data that match known fault or risk patterns.

[0060] In some preferred embodiments, this application is implemented as follows: To identify potential failure modes of the data acquisition terminal, multiple combinations of physical environment parameters can be preset. For example, to address the problem of shortened identification distance caused by battery performance degradation, a combination of physical environment parameters can be preset where "the power supply voltage fluctuation exceeds a preset threshold and the communication quality parameter indicates signal strength is below the normal range." Simultaneously, to address the risk evolution trend of the operating environment, a time-series pattern can be preset where "the environmental density parameter continuously increases and the communication quality parameter continuously decreases within a specific time period." When the central processing system receives the associated data unit, it extracts the physical environment information, including communication quality parameters, environmental density parameters, and the data acquisition terminal's own power supply stability parameters. The system performs pattern matching between this real-time acquired physical environment information and the preset failure mode or risk evolution trend corresponding to the features. For example, if the system detects that the real-time power supply voltage fluctuation of a data acquisition terminal continuously exceeds a preset threshold, and its communication quality parameters (e.g., data transmission rate) simultaneously show a significant decrease, then the pattern matching result will indicate the presence of features related to "battery performance degradation." Based on this pattern matching result, the system can identify a potential battery failure mode in the data acquisition terminal. Simultaneously, if the system detects a significant increase in environmental density parameters (e.g., the number of detected identification codes) and a corresponding decrease in communication quality parameters (e.g., signal strength) in a certain area during specific time periods each day (e.g., the afternoon peak weighing period), the system can identify a risk evolution trend of periodic signal interference in that working environment. The identified potential failure modes or risk evolution trends can be displayed to management personnel, for example, through a visual interface, reports, or real-time notifications, enabling them to take timely preventative maintenance, adjust work processes, or optimize equipment configurations. This intervention prevents problems from causing data errors, ensuring the accuracy and reliability of animal traceability information.

[0061] Through the above technical solution, this application can proactively identify potential failure modes of data acquisition terminals or risk evolution trends of the operating environment. By pre-setting physical environment parameter combinations or time-series patterns corresponding to known failure modes or risk evolution trends, and performing pattern matching on the physical environment information in associated data units, the underlying physical causes or environmental risks that may lead to data errors can be promptly discovered and identified. This proactive identification capability allows managers to provide early warnings and prevent problems from occurring, avoiding the concealment of the root causes of errors, and achieving early warning and location of data contamination, thereby significantly improving the accuracy and reliability of information associations in the animal traceability database.

[0062] An animal traceability information management system is used to manage animal traceability information, combined with... Figure 6 As shown, the animal traceability information management system 1 includes: The environmental information acquisition module 11 is used to acquire physical environment information corresponding to the data acquisition operation from the data acquisition terminal; the physical environment information includes communication quality parameters, environmental density parameters, and the power supply stability parameters of the data acquisition terminal itself. The associated storage execution module 12 is used to associate and store physical environment information with business data to obtain associated data units; The data unit upload module 13 is used to upload the associated data units to the central processing system; The analysis result receiving module 14 is used to wait for the central processing system to return the analysis results after performing data analysis on the physical environment information in the associated data unit. The data analysis includes: judging whether the data acquisition terminal has abnormal performance or whether there is interference in the working environment based on communication quality parameters and environmental density parameters; judging whether the power supply stability of the data acquisition terminal meets the standard based on the correlation between its own power supply stability parameters and communication quality parameters. The early warning information generation module 15 is used to mark the credibility of related business data based on the returned analysis results and generate early warning information to guide the verification, correction or marking operations of records in the animal traceability database, so as to optimize the management of animal traceability information.

[0063] The environmental information acquisition module is responsible for acquiring physical environmental information related to data acquisition operations from the data acquisition terminal. It can be implemented using a sensor array, communication interface, or dedicated data acquisition circuit integrated within the data acquisition terminal. Its purpose is to provide basic data for subsequent risk assessment and data credibility analysis. The associated storage execution module is responsible for binding and persistently storing the acquired physical environmental information with the corresponding business data. It can be implemented using a database management system, file storage system, or distributed storage mechanism. Its purpose is to ensure the data correlation between physical environmental information and business data, laying the foundation for subsequent unified analysis. The data unit upload module is responsible for transmitting the associated and stored data units to the central processing system. It can be implemented using a wired network interface, The system utilizes wireless communication technology or dedicated data transmission protocols to aggregate locally collected data into a central system for centralized processing and analysis. The analysis result receiving module receives the analysis results returned by the central processing system after analyzing physical environment information. This can be implemented using message queues, API interfaces, or data synchronization mechanisms. Its purpose is to promptly obtain the central system's analysis and judgment, providing a basis for local early warning and labeling operations. The early warning information generation module, based on the received analysis results, assigns credibility labels to related business data and generates early warning information. This can be implemented using rule engines, decision tree algorithms, or machine learning models. Its purpose is to identify and alert to potential risks early and guide the verification, correction, or labeling of records in the animal traceability database.

[0064] In some preferred embodiments, this application is implemented as follows. An animal traceability information management system can be deployed in a large-scale farm to track the growth and health status of cattle. The environmental information acquisition module in this system can be integrated into a handheld data acquisition terminal. For example, it can monitor signal strength as a communication quality parameter through a built-in Wi-Fi module, assess the density of surrounding cattle as an environmental density parameter through an ultrasonic or infrared sensor array combined with image recognition technology, and monitor the output voltage and current fluctuations of the battery in real time as a power supply stability parameter through a power management unit. When staff use the handheld terminal to scan cattle ear tags and enter business data such as weight and vaccination, this physical environmental information is simultaneously collected by the environmental information acquisition module. Subsequently, the associated storage execution module can bind this collected physical environmental information with the corresponding business data in the local memory of the handheld terminal to form an associated data unit containing timestamps, cattle identification codes, business data, and various physical environmental parameters. For example, a linked data unit might contain information such as "Cattle A37, weight 450kg, vaccination, time: 2023-10-26 10:30:00, communication signal strength: -70dBm, environmental density: high, battery voltage fluctuation: 0.5V". The data unit upload module uploads these linked data units in real-time or periodically to the central processing system located on the central server of the farm via the terminal's wireless network connection. Upon receiving this data, the analysis module in the central processing system immediately analyzes the physical environment information. For example, based on the communication signal strength and environmental density parameters, it determines whether the current data acquisition operation is being performed in an area with weak signal or excessive cattle density, thereby assessing the risk of identification errors or data interference. Simultaneously, it analyzes the correlation between battery voltage fluctuations and communication signal strength fluctuations. For instance, if voltage fluctuations exceed a preset threshold and occur synchronously with a decrease in signal strength, it determines that there is a problem with the terminal's power supply stability. After the analysis is completed in the central processing system, the analysis result receiving module receives analysis results such as "unstable terminal power supply" and "excessively high environmental density, posing a potential risk of association errors". Finally, the early warning information generation module assigns credibility ratings to the corresponding business data based on these analysis results. For example, if a piece of weight data was collected under conditions of "excessively high environmental density," the module might mark it as "Confidence: Medium, with potential risks." Simultaneously, the system generates an early warning message, such as "Terminal ID: XYZ001, Anomaly Type: Power Supply Stability Anomaly, Occurrence Time: 2023-10-26 10:30:00," and pushes this warning message to administrators or automatically triggers a verification process for relevant records in the animal traceability database. For example, it might prompt manual review of all data collected by this terminal within that time period, or rescan and correct the relevant cattle, thereby optimizing animal traceability information management.

[0065] Through the above technical solution, the animal traceability information management system of this application can proactively acquire and analyze the physical environment information of the data acquisition terminal, including communication quality, environmental density, and its own power supply stability. This enables the system to determine in real time whether the data acquisition terminal has performance abnormalities or whether there is interference in the working environment, and to identify whether the terminal's power supply stability meets the standards. Therefore, the system can predict potential failure modes of the data acquisition terminal and the risk evolution trend of the working environment in advance, thereby transforming the traditional post-event data logic review into pre-event and in-event risk warning. This effectively avoids the accumulation and spread of erroneous data in the traceability database, improves the accuracy and reliability of traceability information, and makes the response measures for traceability information management timely and effective, reducing the business risks caused by data errors.

[0066] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for managing animal traceability information, characterized in that, include: Obtain physical environment information from the data acquisition terminal corresponding to the data acquisition operation; The physical environment information includes communication quality parameters, environmental density parameters, and the power supply stability parameters of the data acquisition terminal. The physical environment information is associated with and stored with business data to obtain associated data units; The associated data unit is uploaded to the central processing system; After the central processing system performs data analysis on the physical environment information in the associated data unit, it returns the analysis results. The data analysis includes: determining whether the data acquisition terminal has performance abnormalities or whether there is interference in the working environment based on the communication quality parameters and the environmental density parameters; and determining whether the power supply stability of the data acquisition terminal meets the standards based on the correlation between the self-power supply stability parameters and the communication quality parameters. Based on the returned analysis results, the credibility of the associated business data is marked and early warning information is generated to guide the verification, correction or marking operations of records in the animal traceability database, so as to optimize the management of animal traceability information.

2. The animal traceability information management method according to claim 1, characterized in that, The step of determining whether the power supply stability of the data acquisition terminal meets the standard based on the correlation between the self-power supply stability parameter and the communication quality parameter includes: Acquire real-time power supply voltage fluctuation data from the data acquisition terminal; The real-time power supply voltage fluctuation data and the communication quality parameter fluctuation data are subjected to time-series correlation analysis to obtain the time-series correlation analysis results. According to the time-series correlation analysis results, if the real-time power supply voltage fluctuation data and the communication quality parameter fluctuation data show a preset synchronous fluctuation pattern, and the fluctuation amplitude of the real-time power supply voltage fluctuation data exceeds a preset threshold, then it is determined that the power supply stability of the data acquisition terminal is not up to standard.

3. The animal traceability information management method according to claim 1, characterized in that, The step of determining whether the data acquisition terminal is experiencing performance abnormalities or whether there is interference in the operating environment based on the communication quality parameters and the environmental density parameters includes: Obtain the currently active device operating mode identifier of the data acquisition terminal; Select the corresponding judgment logic based on the device operating mode identifier; If the device working mode identifier indicates that the data acquisition terminal is in a low-frequency detection and high-frequency reading mode, then according to the communication quality parameter and the environmental density parameter, when the communication quality parameter indicates that the signal strength exceeds the threshold and the environmental density parameter indicates that there is a surrounding identification code, it is determined that the data acquisition terminal has a risk of individual association error. If the device operating mode identifier indicates that the data acquisition terminal is in standard high-frequency scanning mode, then based on the communication quality parameters and the environmental density parameters, it is determined whether the data acquisition terminal is experiencing performance abnormalities or whether there is interference in the operating environment.

4. The animal traceability information management method according to claim 3, characterized in that, The steps for determining whether the data acquisition terminal has a risk of individual association errors include: In the low-frequency detection and high-frequency reading mode, when the communication quality parameter indicates that the signal strength exceeds the threshold and the environmental density parameter indicates that a known target animal has a surrounding identification code, all identification codes and corresponding signal strengths detected by the data acquisition terminal during the low-frequency detection process are acquired. The signal strength of the known target animal's identification code is compared with the signal strength of the highest-signal-strength identification code among other detected identification codes to obtain the signal strength comparison result. Based on the signal strength comparison results, if the difference between the signal strength of the known target animal's identification code and the signal strength of the highest-signal-strength identification code among the other detected identification codes is less than a preset threshold, then it is determined that the data acquisition terminal has a risk of individual association error.

5. The method for managing animal traceability information according to claim 1, characterized in that, The step of assigning credibility tags to the associated business data based on the returned analysis results and generating early warning information includes: Based on the returned analysis results, the risk levels corresponding to the communication quality parameters and environmental density parameters are determined, and the risk levels are mapped to the corresponding credibility tags of the business data. If the analysis results indicate that the data acquisition terminal has a decreased recognition performance or the power supply stability does not meet the preset standard, then an early warning message containing the terminal number, the time of occurrence, and the type of abnormality will be generated. The credibility marker and corresponding business data are stored in the animal traceability database, and the early warning information is used to trigger the verification process of historical business data, perform record correction or anomaly marking operations, so as to optimize the management of animal traceability information.

6. The method for managing animal traceability information according to claim 1, characterized in that, The steps for obtaining the environmental density parameter include: Obtain the number of identity verification codes detected by the data acquisition terminal; Obtain distance information between the data acquisition terminal and surrounding physical objects; Based on the number of identification codes and distance information, the physical density of the data collection environment is assessed as a parameter of environmental density.

7. The method for managing animal traceability information according to claim 1, characterized in that, The steps for obtaining the communication quality parameters include: Acquire a preset number of data packets sent from the data acquisition terminal; Based on a preset number of data packets, a confirmation message is returned to the data acquisition terminal; The data acquisition terminal obtains statistics reflecting the number of successfully transmitted data packets based on the confirmation information. Calculate the data transmission rate based on the total number of data packets compared to the preset number of data packets; Data transmission rate is used as a communication quality parameter.

8. The method for managing animal traceability information according to claim 1, characterized in that, The steps for obtaining the self-power supply stability parameters of the data acquisition terminal include: Acquire real-time output voltage data and / or real-time output current data from the data acquisition terminal; Calculate the fluctuation range of the real-time output voltage data and / or real-time output current data based on the real-time output voltage data and / or real-time output current data; The fluctuation amplitude is used as a parameter for the power supply stability of the data acquisition terminal.

9. The method for managing animal traceability information according to claim 1, characterized in that, The step of marking the credibility of the associated business data based on the returned analysis results and generating early warning information to guide the verification, correction or marking operations of records in the animal traceability database in order to optimize the management of animal traceability information also includes: identifying and displaying the potential failure modes of the data acquisition terminal or the risk evolution trend of the operating environment. The steps for identifying and displaying potential failure modes of the data acquisition terminal or the risk evolution trend of the operating environment include: Preset physical environment parameter combination characteristics or time series patterns corresponding to known failure modes or risk evolution trends; Perform pattern matching on the physical environment information in the associated data unit to obtain the pattern matching result; Based on the pattern matching results, identify potential failure modes of the data acquisition terminal or risk evolution trends of the operating environment.

10. An animal traceability information management system, used to manage animal traceability information, characterized in that, include: The environmental information acquisition module is used to acquire physical environmental information corresponding to the data acquisition operation from the data acquisition terminal; The physical environment information includes communication quality parameters, environmental density parameters, and the power supply stability parameters of the data acquisition terminal. The associated storage execution module is used to associate and store the physical environment information with business data to obtain associated data units; The data unit upload module is used to upload the associated data unit to the central processing system; The analysis result receiving module is used to wait for the central processing system to return the analysis results after performing data analysis on the physical environment information in the associated data unit; the data analysis includes: determining whether the data acquisition terminal has performance abnormalities or whether there is interference in the working environment based on the communication quality parameters and the environmental density parameters; and determining whether the power supply stability of the data acquisition terminal meets the standard based on the correlation between the self-power supply stability parameters and the communication quality parameters. The early warning information generation module is used to mark the credibility of related business data based on the returned analysis results and generate early warning information to guide the verification, correction or marking operations of records in the animal traceability database, so as to optimize the management of animal traceability information.

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