A performance testing method and system for RFID tags

Abstract

The present invention discloses a performance testing method and system for RFID tags, relating to the technical field of RFID tag performance testing. The method comprises the following components: S1, a data collection step, S2, a time series analysis step, S3, a prediction and early warning step, and S4, an adaptive optimization step. The present invention adopts a performance testing method for RFID tags proposed by the present invention, and can collect various performance data of RFID tags during the test process in real time, and store them in chronological order to form time series data. Subsequently, an improved adaptive weighted dynamic time series algorithm is used to analyze and process the time series data, and the laws and trends of tag performance changes over time are explored, thereby establishing a more accurate performance prediction model. In addition, the method also adopts a denoising method based on wavelet transform, which effectively improves data quality and further enhances the reliability of the prediction model.

Description

Technical Field

[0001] The present invention relates to the technical field of RFID tag performance testing, and in particular to a performance testing method and system for RFID tags. Background Art

[0002] With the rapid development of Internet of Things (IoT), RFID technology has been widely used in warehouse management, logistics tracking, and intelligent manufacturing. As a key component of the IoT, the stability and reliability of RFID tags are crucial to the operating efficiency of the entire system.

[0003] There are shortcomings in traditional RFID tag performance testing technology. On the one hand, the performance prediction models established by traditional methods often cannot accurately capture the patterns and trends of RFID tag performance changes over time, which leads to inaccurate prediction results and makes it difficult to provide users with reliable maintenance recommendations and replacement reminders. On the other hand, traditional test systems often lack adaptive optimization capabilities and are unable to automatically adjust test strategies based on system operating status and prediction results. This makes it difficult for the test system to maintain efficient and stable operation when facing complex and changing application environments.

[0004] In summary, the traditional RFID tag performance testing technology has obvious shortcomings. Therefore, it is particularly important to develop a RFID tag performance testing method and system. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a performance testing method and system for RFID tags. It can achieve comprehensive and accurate testing of RFID tag performance through multi-source data collection, an improved time series analysis algorithm and an adaptive optimization mechanism, and timely predict its future performance change trend, providing a strong guarantee for the stable operation of the system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a performance testing method for RFID tags, the specific steps of the method are as follows:

[0007] S1. Data collection step: used to collect various performance data of RFID tags in real time during the test process and store them in chronological order to form time series data;

[0008] S2, time series analysis step: using a time series analysis algorithm to analyze and process the time series data, to mine the patterns and trends of label performance changes over time, and to establish a performance prediction model;

[0009] S3, prediction and warning step: predicting the performance of the RFID tag over a period of time in the future based on the performance prediction model. When the prediction result shows that the tag performance will reach a threshold of failure or severe degradation, a warning message is issued in a timely manner, and tag maintenance suggestions and replacement reminders are provided according to a preset maintenance strategy;

[0010] S4, Adaptive optimization step: Automatically optimize the entire test system based on the system operating status and prediction results.

[0011] Furthermore, the data acquisition module adopts a multi-source data acquisition method and also uses environmental sensors to collect temperature, humidity, and electromagnetic interference intensity environmental data around the tag. For the acquisition of signal strength, a radio frequency signal receiver is used, and its accuracy can reach ±0.1dBm. In the statistics of the reading success rate, each data reading operation is used as a counting unit, and the number of successful readings within 100 consecutive reading operations is counted to calculate the reading success rate. For environmental data, the temperature sensor uses a digital temperature sensor with an accuracy of ±0.5°C, the humidity sensor has an accuracy of ±3%RH, and the electromagnetic interference intensity sensor can detect magnetic field strength in the range of 0-100μT. Through the fusion acquisition of multi-source data, a more comprehensive data basis is provided for subsequent time series analysis, thereby improving the accuracy of the analysis of the relationship between tag performance and the environment.

[0012] Furthermore, the time series analysis algorithm used by the time series analysis module is an improved adaptive weighted dynamic time series model, and the algorithm formula is:

[0013]

[0014] in for The predicted value of the moment label performance, is the number of data points in the historical data window, yes Moment The weight of each data point is determined as follows:

[0015]

[0016] in is the attenuation factor, which is determined through multiple tests and optimizations of historical data, and has a value range of 0.01-0.1. For the The acquisition time of each data point, It is a historical data point Elapsed time delay The feature extraction function after According to the periodicity and correlation analysis of tag performance data, different performance indicators are determined. The value is different, the signal strength The value range is 1-5 time intervals, and the reading success rate The value range is 2-8 time intervals. The algorithm dynamically adjusts the weights to highlight the impact of recent data on the prediction results, while considering the time delay characteristics of different performance indicators to improve the accuracy and adaptability of the prediction model.

[0017] Furthermore, in the time series analysis module, when preprocessing the collected time series data, a denoising method based on wavelet transform is adopted. First, the original time series data is decomposed by wavelet to obtain wavelet coefficients of different frequency bands. For the high-frequency coefficients, an adaptive threshold is set. The threshold is dynamically determined according to the standard deviation and noise level of the data. The formula is:

[0018]

[0019] in is the threshold, is the adjustment coefficient, with a value range of 1.5-3, determined through experiments based on the noise characteristics of the data. is the standard deviation of the data, For the data length, high-frequency coefficients less than the threshold are set to zero, and then wavelet reconstruction is performed to obtain the denoised time series data. After this preprocessing step, the noise interference in the data is removed, the data quality is improved, which is conducive to the subsequent time series analysis algorithm to more accurately mine data features and rules, and improve the reliability of the performance prediction model.

[0020] Furthermore, in the prediction and warning module, the threshold determination method for performance failure or severe degradation is as follows: a large amount of performance data of different types of RFID tags before failure in actual applications is collected, and a failure sample library is constructed. For each performance indicator, a cluster analysis method is used to classify the sample data into different categories, and the boundary values ​​of each category are determined. Then, based on expert experience and actual application needs, the boundary values ​​are adjusted and optimized to obtain the final performance failure or severe degradation threshold. Through this scientific threshold determination method, the accuracy and reliability of the warning are improved, and users can be provided with more timely and effective maintenance suggestions and replacement reminders.

[0021] Furthermore, in the adaptive optimization module, when it is detected that the computing load of the time series analysis module is too high, a task scheduling and resource allocation optimization strategy is adopted. First, the computing resources in the system are monitored in real time to obtain the current resource usage. Then, according to the priority and resource requirements of the time series analysis task, a task scheduling algorithm based on a priority queue is adopted. For high-priority tasks and urgent label performance prediction tasks, more computing resources are allocated first. At the same time, memory resources are dynamically managed. According to the memory usage and historical data of the task, the future memory requirements of the task are predicted, and memory allocation and recovery operations are performed in advance to avoid task interruption or performance degradation due to insufficient memory. Through this task scheduling and resource allocation optimization strategy, it is ensured that the time series analysis module can still run efficiently under high load conditions, thereby ensuring the performance and stability of the entire system.

[0022] Furthermore, in the adaptive optimization module, if the prediction and warning module frequently issues warnings, a data feature enhancement method is also used. Specifically, feature engineering is performed on the collected tag performance data. By calculating the first-order difference, second-order difference, and moving average derivative features of the data, the feature dimension of the data is increased. For the signal strength data, its first-order difference within five consecutive time intervals is calculated. The formula is:

[0023]

[0024] in for The first-order difference of the signal strength at time , for The signal strength at the moment is calculated, and the moving average is calculated at the same time. The moving average formula for 5 time intervals is:

[0025]

[0026] Inputting these derived features into the time series analysis module together with the original data enriches the data information, helps the model more comprehensively capture the characteristics and patterns of changes in label performance, further optimizes the prediction model, improves prediction accuracy, and reduces the occurrence of false alarms.

[0027] Furthermore, the system also includes a data storage and management module, which adopts a distributed database storage architecture to distribute the collected time series data, pre-processed data, prediction model parameters and system operation log data on multiple storage nodes. Through data redundancy and load balancing technology, the security of data and the efficient operation of the storage system are ensured. In terms of data management, a data indexing mechanism is established to establish multi-level indexes based on tag ID, timestamp, and performance indicator information to facilitate rapid query and retrieval of data. At the same time, the database is regularly cleaned and optimized, expired useless data is deleted, and storage space is released to ensure the efficient and stable operation of the data storage and management module, providing data support for the reliable operation of the entire system.

[0028] Furthermore, the system also has remote monitoring and management functions. Through the network communication module, the system's operating status data is transmitted to the remote monitoring center in real time. The managers of the remote monitoring center can view the system's operating status anytime and anywhere through a browser or dedicated client software. When the system has an abnormality, the remote monitoring center will be notified immediately and can adjust the system parameters, diagnose faults and repair operations through remote operations. When it is found that the prediction error of the time series analysis module suddenly increases, the manager can remotely adjust the algorithm parameters and retrain the model to ensure that the system can continuously and stably provide users with accurate label life prediction and maintenance recommendation services.

[0029] On the other hand, a performance testing system for RFID tags is characterized in that the system includes a data acquisition module, a time series analysis module, a prediction and early warning module, and an adaptive optimization module:

[0030] The data acquisition module is set in the RFID tag performance test system. The module collects various performance data of the tag in real time during the test process, including but not limited to signal strength, read success rate, and data transmission rate. The collected data is stored in chronological order to form time series data;

[0031] The time series analysis module uses a time series analysis algorithm to analyze and process the collected time series data. By learning and training historical data, it mines the patterns and trends of label performance changes over time and establishes a performance prediction model.

[0032] The prediction and warning module predicts the performance of RFID tags over a period of time based on the established performance prediction model. When the prediction results show that the tag performance will reach the threshold of failure or severe degradation, the prediction and warning module promptly issues a warning message to the user. At the same time, based on the prediction results and the preset maintenance strategy, the module provides the user with detailed tag maintenance suggestions and accurate replacement reminder time.

[0033] The adaptive optimization module is used to automatically optimize the entire test system based on the system operating status and prediction results. It can monitor the computing load of the time series analysis module and the rationality of the sampling frequency of the data acquisition module in real time. When it is found that the efficiency of the analysis algorithm is reduced, the adaptive optimization module automatically adjusts the algorithm parameters. At the same time, if the prediction and warning module frequently issues warnings, the module will intelligently increase the data collection frequency according to preset rules to obtain more accurate label performance data, further optimize the prediction model, and ensure that the system is always in an efficient operation state.

[0034] Compared with the existing technology, this RFID tag performance testing method and system has the following beneficial effects:

[0035] 1. The present invention collects various performance data of RFID tags in real time during the test process and stores them in chronological order to form time series data. Subsequently, an improved adaptive weighted dynamic time series algorithm is used to analyze and process the time series data, exploring the patterns and trends of tag performance changes over time, thereby establishing a more accurate performance prediction model. In addition, the method also uses a denoising method based on wavelet transform to effectively improve data quality and further enhance the reliability of the prediction model. Therefore, the present invention can more accurately predict the future performance of RFID tags. When the prediction results show that the tag performance is about to reach the threshold of failure or severe attenuation, an early warning message is issued in a timely manner, providing users with more reliable tag maintenance suggestions and replacement reminders.

[0036] 2. The present invention introduces an adaptive optimization module, which can automatically optimize the entire test system based on the system operating status and prediction results. For example, when it is monitored that the computing load of the time series analysis module is too high, the adaptive optimization module will automatically adopt task scheduling and resource allocation optimization strategies to ensure that high-priority tasks can obtain more computing resources, thereby improving the system's operating efficiency. In addition, if the prediction and early warning module frequently issues early warnings, the adaptive optimization module will intelligently increase the data collection frequency to obtain more accurate tag performance data and further optimize the prediction model. Through these optimization measures, the present invention can ensure that the RFID tag performance test system is always in an efficient and stable operating state, providing users with better quality testing services.

[0037] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0039] Figure 1 A flow chart of a performance test method for an RFID tag;

[0040] Figure 2 This is a flow chart of an RFID tag performance test system. DETAILED DESCRIPTION

[0041] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0042] Example 1

[0043] This embodiment describes that fast and accurate management of goods is crucial in large-scale logistics and warehousing centers. A large number of RFID tags are deployed here for cargo tracking. Thousands of goods enter and leave the warehouse every day, and complex environmental factors constantly test the tag performance.

[0044] The data acquisition module uses a multi-source data acquisition method. In terms of signal strength acquisition, a high-precision radio frequency signal receiver with a sensitivity of up to -100dBm is used to ensure that weak signal changes can be accurately captured. The reading success rate statistics use each data reading operation as the counting unit. At the same time, a digital temperature sensor and an electromagnetic interference intensity sensor that can detect magnetic field strength are used to collect environmental data. Every 1 minute, the system automatically collects data and records the signal strength, reading success rate and corresponding environmental data of the tag at each moment. For example, at a certain moment, the RFID tag signal strength of cargo A is recorded to be -80dBm, the reading success rate is 95%, the warehouse temperature is 25℃, the humidity is 50%, and the electromagnetic interference intensity is 50μT. These data are stored in chronological order to form time series data, which provides a basis for subsequent analysis.

[0045] The time series analysis module uses an improved adaptive weighted dynamic time series model. Assuming that there are 10 data points (n=10) in the historical data window, the attenuation factor , taking signal strength as an example, the formula for calculating the tag performance prediction value at time t is:

[0046]

[0047] in , the collected time series data is preprocessed using a denoising method based on wavelet transform. Assuming that the data length N = 100 and the adjustment coefficient k = 1.5, the denoising threshold is calculated: This method removes noise interference, explores the patterns of tag performance changes over time, and establishes a performance prediction model. In actual operation, as time goes by, the model continuously learns new data and continuously adjusts its predictions of future tag performance, such as predicting the signal strength change trend of a tag in the next few hours.

[0048] By collecting the performance data of a large number of similar RFID tags before they fail in logistics and warehousing environments, a failure sample library is constructed, and cluster analysis is used to determine the threshold for performance failure or severe attenuation. When the prediction model shows that the signal strength of a tag will be lower than the threshold within the next 5 hours, the prediction and warning module issues a warning message to remind staff to check the goods corresponding to the tag in time and, based on preset maintenance strategies, such as recommending replacing the label within 24 hours, to ensure the accuracy of cargo tracking. The warning information is not only displayed on the local terminal, but also sent to the relevant person in charge via SMS and email to facilitate their timely processing. After receiving the warning, the staff will arrange a reasonable time for label replacement based on the importance of the goods and the current inventory situation to avoid loss or misdelivery of goods due to label failure.

[0049] If the computational load of the time series analysis module is too high, the adaptive optimization module monitors the usage of computing resources in real time and uses a task scheduling algorithm based on a priority queue to prioritize more computing resources for urgent tag performance prediction tasks. It also dynamically manages memory resources. If the prediction and warning module frequently issues warnings, it enhances the features of the collected tag performance data and calculates the first-order difference of the signal strength:

[0050]

[0051] and a moving average over 5 time intervals:

[0052]

[0053] These derived features are input into the time series analysis module along with the original data to optimize the prediction model. For example, when a label in a certain area frequently issues warnings, the system automatically increases the frequency of data collection in that area from once every minute to once every 30 seconds. This allows for more data to be collected to optimize the model and improve prediction accuracy. At the same time, the server's CPU and memory resources are rationally allocated based on the priority of different tasks to ensure the smooth progress of important tasks.

[0054] Example 2

[0055] This embodiment describes that in a smart retail store, efficient merchandise management and anti-theft monitoring are key. A large number of RFID tags are deployed in the store for inventory management and anti-theft. A large number of customers come and go every day, and the store is full of electronic devices, resulting in a complex electromagnetic environment.

[0056] The data acquisition module continuously collects RFID tag signal strength, read success rate, data transmission rate performance data, as well as environmental data such as temperature, humidity, and electromagnetic interference intensity in the store, and stores it as time series data with a collection cycle of every 30 seconds. For example, data collection points are set up in different shelf areas of the store to record various data of product tags in different locations. At a certain moment, the RFID tag signal strength of product B on shelf A is recorded to be -75dBm, the read success rate is 98%, the data transmission rate is 10kbps, the store temperature is 22°C, the humidity is 45%, and the electromagnetic interference intensity is 40μT. This data is collected and stored in real time, providing a rich data source for subsequent analysis.

[0057] The time series analysis module uses an improved adaptive weighted dynamic time series model for analysis. Assuming the number of data points in the historical data window n = 8 and the decay factor λ = 0.3, the label performance prediction value is calculated:

[0058]

[0059] in , the original time series data is processed using a denoising method based on wavelet transform, assuming that the data standard deviation , data length N=80, adjustment coefficient k=1.2, calculate the denoising threshold:

[0060]

[0061] After denoising, a performance prediction model is established. In actual operation, the model will automatically adjust parameters based on the data characteristics of different time periods, such as peak and off-peak periods, to improve prediction accuracy. For example, during peak hours with high customer traffic, the model will place more emphasis on the weight of recent data to quickly respond to changes in label performance.

[0062] Performance data of different types of RFID tags before expiration in retail scenarios is collected to build a failure sample library, and cluster analysis is used to determine the performance threshold. If the prediction model shows that the reading success rate of a tag will be lower than the threshold within the next three hours, the prediction and warning module will issue an early warning to the staff, suggesting that the tag be checked or replaced after the business day to avoid affecting product management. The early warning information will be highlighted in the store's management system, and at the same time remind the staff to pay special attention to the product during inventory counting. According to the early warning prompts, the staff will check the relevant tags after the business day, and replace the tags with weak signals or frequent reading failures in a timely manner to ensure the accuracy of product management.

[0063] When it is detected that the computing load of the time series analysis module is too high, the adaptive optimization module monitors and schedules the computing resources in real time, giving priority to performance prediction task resources in important areas (such as product tags near the checkout counter). If the prediction and warning module frequently issues warnings, the tag performance data is feature enhanced, the first-order difference and moving average of the signal strength are calculated, the prediction model is optimized, and the prediction accuracy is improved. For example, when it is found that the tags near the checkout counter frequently issue warnings, the system automatically increases the data collection frequency in this area from once every 30 seconds to once every 15 seconds. At the same time, more in-depth feature engineering processing is performed on these data, such as calculating the second-order difference of the signal strength, to further enrich the data features and improve the prediction ability of the model. In addition, according to the priority of different tasks, the server's computing resources are reasonably allocated to ensure that the performance prediction task of the product tags near the checkout counter can be completed quickly and accurately, so as to avoid affecting the customer's checkout speed.

[0064] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any brief modifications, changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A performance testing method for an RFID tag, characterized in that: The specific steps of this method are: S1, data collection step: used to collect various performance data of RFID tags in real time during the test process, and store them in chronological order to form time series data; S2. Time series analysis step: Analyze and process the time series data using a time series analysis algorithm. The time series analysis algorithm is an improved adaptive weighted dynamic time series model. The algorithm formula is: in for The predicted value of the moment label performance, is the number of data points in the historical data window, yes Moment The weight of each data point is determined as follows: in is the attenuation factor, For the The acquisition time of each data point, It is a historical data point Elapsed time delay The algorithm dynamically adjusts the weights to highlight the impact of recent data on the prediction results. It also considers the time delay characteristics of different performance indicators, explores the patterns and trends of label performance changes over time, and establishes a performance prediction model. S3. Prediction and warning step: Based on the performance prediction model, the performance of the RFID tag in the future is predicted. When the prediction result shows that the tag performance will reach the threshold of failure or severe attenuation, a warning message is issued in a timely manner, and tag maintenance suggestions and replacement reminder time are provided according to the preset maintenance strategy. If the prediction and warning module frequently issues warnings, a data feature enhancement method is also adopted. Specifically, feature engineering processing is performed on the collected tag performance data. By calculating the first-order difference, second-order difference, and moving average derivative features of the data, the feature dimension of the data is increased. For the signal strength data, its first-order difference in 5 consecutive time intervals is calculated. The formula is: in for The first-order difference of the signal strength at time , for The signal strength at the moment is calculated, and the moving average is calculated at the same time. The moving average formula for 5 time intervals is: Inputting these derived features into the time series analysis module together with the original data enriches the data information, helps the model more comprehensively capture the characteristics and patterns of label performance changes, and further optimizes the prediction model; S4, Adaptive optimization step: Automatically optimize the entire test system based on the system operating status and prediction results.

2. The performance testing method of an RFID tag according to claim 1, characterized in that: The data collection step adopts a multi-source data collection method, and also uses environmental sensors to collect temperature, humidity, and electromagnetic interference intensity environmental data around the tag. For signal strength collection, a radio frequency signal receiver is used. In the statistics of the reading success rate, each data reading operation is used as a counting unit. For environmental data, the temperature sensor uses a digital temperature sensor, and the electromagnetic interference intensity sensor can detect the magnetic field strength.

3. The performance testing method of an RFID tag according to claim 1, characterized in that: In the time series analysis step, when preprocessing the collected time series data, a denoising method based on wavelet transform is adopted. First, the original time series data is decomposed by wavelet to obtain wavelet coefficients of different frequency bands. For the high-frequency coefficients, an adaptive threshold is set. The threshold is dynamically determined according to the standard deviation and noise level of the data. The formula is: in is the threshold, is the adjustment coefficient, is the standard deviation of the data, The high-frequency coefficients smaller than the threshold are set to zero, and then wavelet reconstruction is performed to obtain the denoised time series data. After this preprocessing step, the noise interference in the data is removed.

4. The performance testing method of an RFID tag according to claim 1, characterized in that: In the prediction and early warning steps, the method for determining the threshold for performance failure or severe degradation is as follows: a large amount of performance data of different types of RFID tags before failure in actual applications is collected to build a failure sample library. For each performance indicator, a cluster analysis method is used to classify the sample data into different categories, and the boundary values ​​of each category are determined. The boundary values ​​are adjusted and optimized to obtain the final performance failure or severe degradation threshold.

5. The performance testing method of an RFID tag according to claim 1, characterized in that: In the adaptive optimization step, when it is detected that the computing load of the time series analysis module is too high, a task scheduling and resource allocation optimization strategy is adopted. First, the computing resources in the system are monitored in real time to obtain the current resource usage. Then, according to the priority and resource requirements of the time series analysis task, a task scheduling algorithm based on a priority queue is adopted. For high-priority tasks and urgent label performance prediction tasks, more computing resources are allocated preferentially. At the same time, memory resources are dynamically managed. According to the memory usage and historical data of the task, the future memory requirements of the task are predicted, and memory allocation and recovery operations are performed in advance.

6. The performance testing method of an RFID tag according to claim 1, characterized in that: The system also includes a data storage and management module, which adopts a distributed database storage architecture to distribute and store the collected time series data, pre-processed data, prediction model parameters and system operation log data on multiple storage nodes. In terms of data management, a data indexing mechanism is established to establish a multi-level index based on tag ID, timestamp and performance indicator information. At the same time, the database is regularly cleaned and optimized to delete expired and useless data and release storage space.

7. The performance testing method of an RFID tag according to claim 1, characterized in that: The system also has remote monitoring and management functions. Through the network communication module, the system's operating status data is transmitted to the remote monitoring center in real time. The administrator of the remote monitoring center can view the system's operating status anytime and anywhere through a browser or dedicated client software. When the system has an abnormality, the remote monitoring center will be notified immediately and perform parameter adjustment, fault diagnosis and repair operations on the system through remote operation. When it is found that the prediction error of the time series analysis module suddenly increases, the administrator remotely adjusts the algorithm parameters and retrains the model.

8. A performance testing system for RFID tags, applicable to the performance testing method for RFID tags according to any one of claims 1 to 7, characterized in that: The system includes data acquisition module, time series analysis module, prediction and warning module and adaptive optimization module: The data acquisition module is set in the RFID tag performance test system. The module collects various performance data of the tag in real time during the test process, including but not limited to signal strength, read success rate, and data transmission rate. The collected data is stored in chronological order to form time series data; The time series analysis module uses a time series analysis algorithm to analyze and process the collected time series data. By learning and training historical data, it mines the patterns and trends of label performance changes over time and establishes a performance prediction model. The prediction and warning module predicts the performance of RFID tags over a period of time based on the established performance prediction model. When the prediction results show that the tag performance will reach the threshold of failure or severe degradation, the prediction and warning module promptly issues a warning message to the user. At the same time, based on the prediction results and the preset maintenance strategy, the module provides the user with detailed tag maintenance suggestions and accurate replacement reminder time. The adaptive optimization module is used to automatically optimize the entire test system based on the system operating status and prediction results. It can monitor the computing load of the time series analysis module and the rationality of the sampling frequency of the data acquisition module in real time. When it is found that the efficiency of the analysis algorithm is reduced, the adaptive optimization module automatically adjusts the algorithm parameters. At the same time, if the prediction and warning module frequently issues warnings, the module will intelligently increase the data collection frequency according to preset rules.

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