RFID temperature measurement frequency point adaptive method and system fused with environment learning

Abstract

The invention relates to the technical field of radio frequency identification, and particularly discloses an RFID temperature measurement frequency point self-adaption method and system fused with environmental learning. According to the method, environmental physical and state parameters of a target area are obtained through a sensor and an ultra-wideband radar, a data foundation is laid for frequency point selection, a frequency point scanning sequence is generated according to a preset frequency band and step length, and a reader-writer transmits a reading instruction in a standardized manner; then collecting the received signal strength, bit error rate and reading success rate of each frequency point to form an initial communication quality parameter, quantifying an obstacle influence index of a metal obstacle on each frequency point in combination with ultra-wideband radar data, a backward projection algorithm and the like, and calculating an environmental influence characteristic value of each frequency point according to environmental temperature and humidity and the like; the method comprises the following steps of: obtaining a comprehensive score of each frequency point through normalization and weighting calculation, selecting an optimal frequency point, finally switching to the frequency point to establish a communication link, dynamically monitoring the link quality, and resetting and reselecting when the environment changes, thereby realizing frequency point self-adaption, and solving the problem of unstable fixed frequency point communication.

Description

Technical Field

[0001] This invention relates to the field of radio frequency identification (RFID) technology, and in particular to an adaptive method and system for RFID temperature measurement frequency points that integrates environmental learning. Background Technology

[0002] Against the backdrop of the rapid development of Internet of Things (IoT) technology, Radio Frequency Identification (RFID) technology has been widely applied in logistics, warehousing, and industrial monitoring due to its advantages such as non-contact and rapid identification. RFID temperature measurement tags, which combine RFID technology with temperature sensing, can achieve the identification of target objects and real-time monitoring of their temperature status, and have significant application value, especially in scenarios such as power equipment inspection and cold chain logistics. However, in practical applications, the performance of RFID temperature measurement systems is significantly affected by complex environmental factors. First, the resonant frequency of RFID tags drifts with changes in ambient temperature, humidity, and the dielectric properties of the attached material. Second, the relative position between the reader antenna and the tag, as well as interference from reflections from surrounding metal objects, can lead to multipath fading of the signal. This makes it difficult for existing fixed-frequency operation methods to guarantee stable power transmission. Therefore, an adaptive RFID temperature measurement frequency method incorporating environmental learning is needed to address these issues. Summary of the Invention

[0003] The purpose of this invention is to provide an adaptive RFID temperature measurement frequency method that integrates environmental learning, comprising: Acquire environmental data of the RFID temperature measurement tag in the target monitoring area at the current moment, wherein the environmental data includes environmental state parameters and environmental physical parameters; Based on the preset initial frequency band range and scanning step size, a frequency point scanning sequence is generated, and the reader is controlled to transmit reading commands on each frequency point in the frequency point scanning sequence in sequence. The reader is used to connect the RFID temperature measurement tag and the back-end system in the RFID system. The initial communication quality parameters returned by the RFID temperature measurement tag at each frequency point in response to the read command are collected, wherein the initial communication quality parameters include the received signal strength indication value, the bit error rate, and the read success rate; The distribution area of ​​metal obstacles within the target monitoring area is obtained based on the environmental state parameters, and multiple obstacle influence indices are obtained based on the distribution area and the frequency scanning sequence. Multiple environmental impact characteristic values ​​are obtained based on the environmental physical parameters and the frequency point scanning sequence; The comprehensive score of each frequency point is obtained based on the initial communication quality parameters, multiple obstacle impact indices, and multiple environmental impact characteristic values, and the frequency point with the highest comprehensive score is determined as the current optimal working frequency point. The reader is controlled to switch to the current optimal operating frequency, and a communication link with the RFID temperature measurement tag is established based on the current optimal operating frequency to adaptively read the temperature data collected by the RFID temperature measurement tag.

[0004] Preferably, the step of collecting the initial communication quality parameters returned by the RFID temperature measurement tag at each frequency point in response to the read command includes: For each frequency point in the frequency scanning sequence, the reader is controlled to continuously transmit K reading commands at that frequency point, where K is a preset integer greater than 1, and it is recorded whether the response signal returned by the RFID temperature measurement tag is successfully received after each transmission. The number of successful responses and the total number of transmissions on the frequency point are obtained, and the reading success rate is obtained based on the number of successful responses and the total number of transmissions. For each successfully received response signal, the received signal strength indication value carried by its physical layer is extracted to obtain multiple RSSI values, and the average received signal strength indication value is calculated based on the multiple RSSI values. Cyclic redundancy check is performed on each successfully received response signal to obtain the number of data packets that failed the check, and the bit error rate is obtained by comparing the number of data packets with the number of successful responses. The initial communication quality parameters are obtained based on the read success rate, the average received signal strength indicator value, and the bit error rate.

[0005] Preferably, the step of obtaining the distribution location area of ​​metal obstacles within the target monitoring area based on the environmental state parameters, and obtaining multiple obstacle influence indices based on the distribution location area and the frequency point scanning sequence, includes: The environmental state parameters are obtained, including the echo signal dataset obtained by the reader through ultra-wideband radar detection, wherein the echo signal dataset contains the flight time and echo intensity of multiple sampling points; Using the flight time and echo intensity, a three-dimensional spatial reflection intensity distribution map of the target monitoring area is constructed using a back projection algorithm; Threshold segmentation and connected component analysis are performed on the three-dimensional spatial reflection intensity distribution map to extract the contour information of at least one metal obstacle. Each metal obstacle corresponds to a connected component, and the connected component is used as the distribution location region. For each frequency point in the frequency scanning sequence, calculate the direct path of the electromagnetic wave from the reader antenna to the RFID temperature measurement tag at that frequency point, and determine in turn whether the direct path intersects with multiple distribution location areas. If the direct path intersects with the distribution area, it is determined that it is blocked by a metal obstacle, and multiple reflection coefficients and multiple blocking area ratios of multiple metal obstacles are obtained. Multiple blocking loss factors are calculated based on the multiple reflection coefficients and multiple blocking area ratios. If the direct path does not intersect with the distribution area, it is determined that it is not blocked by metal obstacles, and the multipath reflection interference of multiple metal obstacles on the direct path is obtained, and multiple interference factors are calculated based on the multipath reflection interference. Multiple obstacle influence indices for a frequency point are obtained based on multiple occlusion loss factors and multiple interference factors.

[0006] Preferably, the step of obtaining multiple environmental impact feature values ​​based on the environmental physical parameters and the frequency point scanning sequence includes: The ambient temperature and humidity are obtained, and the relative permittivity and conductivity of the current air are obtained based on the ambient temperature and humidity. The distance between the reader and the RFID temperature measurement tag is obtained, and the propagation attenuation factor of the electromagnetic wave in the air is obtained based on the distance, the relative permittivity and the conductivity. The input impedance of the RFID temperature measurement tag due to thermal expansion is obtained based on the ambient temperature, and the impedance mismatch factor is calculated based on the difference between the input impedance and the preset reader characteristic impedance. The environmental impact characteristic value corresponding to each frequency point is obtained based on the propagation attenuation factor, the impedance mismatch factor, and the frequency point scanning sequence.

[0007] Preferably, the step of obtaining the comprehensive score for each frequency point based on the initial communication quality parameters, multiple obstacle impact indices, and multiple environmental impact characteristic values ​​includes: The initial communication quality parameters are normalized to obtain normalized initial communication quality values; The multiple obstacle impact indices are sequentially normalized to obtain multiple obstacle impact normalized values; The environmental impact feature values ​​are normalized sequentially to obtain multiple normalized environmental impact feature values; The comprehensive score for each frequency point is calculated based on the normalized value of the initial communication quality, the normalized values ​​of multiple obstacle effects, and the normalized values ​​of multiple environmental impact characteristics.

[0008] Preferably, the step of establishing a communication link with the RFID temperature measurement tag based on the current optimal operating frequency to adaptively read the temperature data collected by the RFID temperature measurement tag includes: While continuously reading temperature data at the current optimal operating frequency, the real-time received signal strength indicator and real-time bit error rate are periodically collected. Obtain the historical average received signal strength indication value of the current optimal operating frequency, and calculate the link quality score based on the real-time received signal strength indication value, the real-time bit error rate, and the historical average received signal strength indication value; Determine whether the link quality score is lower than a preset threshold and the duration exceeds a set window; If the link quality score is lower than the preset threshold and the duration exceeds the set window, the communication is immediately interrupted and the entire process is reset. The process returns to the step of obtaining the environmental data of the RFID temperature measurement tag in the target monitoring area at the current moment, re-sensing the environment, and scanning the frequency point. If the link quality score is not lower than the preset threshold and the duration exceeds the set window, the current working frequency is maintained, and the temperature data, environmental feature data, working frequency and link quality score read this time are associated and stored in the database to update the mapping relationship between environmental features and the optimal frequency, so as to adaptively read the temperature data collected by the RFID temperature measurement tag.

[0009] This application also provides an RFID temperature measurement frequency adaptive system that integrates environmental learning, including: The data acquisition module is used to acquire the environmental data of the RFID temperature measurement tag in the target monitoring area at the current moment, wherein the environmental data includes environmental state parameters and environmental physical parameters; The scanning module is used to generate a frequency point scanning sequence according to the preset initial frequency band range and scanning step size, and control the reader to transmit reading commands on each frequency point in the frequency point scanning sequence in sequence. The reader is used to connect the RFID temperature measurement tag and the back-end system in the RFID system. The communication quality acquisition module is used to collect the initial communication quality parameters returned by the RFID temperature measurement tag at each frequency point in response to the reading command, wherein the initial communication quality parameters include the received signal strength indication value, the bit error rate, and the reading success rate; The obstacle acquisition module is used to acquire the distribution location area of ​​metal obstacles within the target monitoring area based on the environmental state parameters, and to acquire multiple obstacle influence indices based on the distribution location area and the frequency point scanning sequence. An environmental parameter module is used to obtain multiple environmental impact characteristic values ​​based on the environmental physical parameters and the frequency point scanning sequence. The calculation module is used to obtain the comprehensive score of each frequency point based on the initial communication quality parameters, multiple obstacle impact indices and multiple environmental impact characteristic values, and to determine the frequency point with the highest comprehensive score as the current optimal working frequency point; The temperature data acquisition module is used to control the reader to switch to the current optimal operating frequency and establish a communication link with the RFID temperature measurement tag based on the current optimal operating frequency, so as to adaptively read the temperature data collected by the RFID temperature measurement tag.

[0010] Preferably, the scanning module includes: The signal receiving unit is used to control the reader to continuously transmit K reading commands at each frequency point in the frequency scanning sequence, where K is a preset integer greater than 1, and to record whether the response signal returned by the RFID temperature measurement tag is successfully received after each transmission. The acquisition unit is used to acquire the number of successful responses and the total number of transmissions on the frequency point, and to acquire the reading success rate based on the number of successful responses and the total number of transmissions; The signal strength acquisition unit is used to extract the received signal strength indication value carried by the physical layer for each successfully received response signal, obtain multiple RSSI values, and calculate the average received signal strength indication value based on the multiple RSSI values. The bit error rate acquisition unit is used to perform cyclic redundancy check on each successfully received acknowledgment signal, obtain the number of data packets that failed the check, and compare the number of data packets with the number of successful acknowledgments to obtain the bit error rate. The quality parameter acquisition unit is used to acquire initial communication quality parameters based on the read success rate, the average received signal strength indication value, and the bit error rate.

[0011] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0012] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0013] The beneficial effects of this application are as follows: This invention first acquires the environmental physical and state parameters of the target area through sensors and ultra-wideband radar, laying the data foundation for frequency point selection. Then, it generates a frequency point scanning sequence according to a preset frequency band and step size, allowing the reader to transmit reading commands in a standardized manner. Subsequently, it collects the received signal strength, bit error rate, and reading success rate of each frequency point to form initial communication quality parameters. It combines ultra-wideband radar data and back projection algorithms to quantify the obstacle influence index of metal obstacles on each frequency point. Based on environmental temperature and humidity, it calculates the environmental impact characteristic value of each frequency point. After normalization and weighted calculation, it obtains the comprehensive score of each frequency point, selects the optimal frequency point, and finally switches to this frequency point to establish a communication link. It dynamically monitors the link quality and resets and reselects when the environment changes, realizing frequency point self-adaptation and solving the problem of unstable communication at fixed frequency points. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of a method flow according to an embodiment of this application.

[0015] Figure 2 This is a schematic diagram of the system structure according to an embodiment of this application.

[0016] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0018] like Figure 1 As shown, this application provides an adaptive RFID temperature measurement frequency method that integrates environmental learning, including: S1. Obtain the environmental data of the RFID temperature measurement tag in the target monitoring area at the current moment, wherein the environmental data includes environmental state parameters and environmental physical parameters; S2. Based on the preset initial frequency band range and scanning step size, generate a frequency point scanning sequence, and control the reader to transmit reading commands sequentially on each frequency point in the frequency point scanning sequence. The reader is used to connect the RFID temperature measurement tag and the back-end system in the RFID system. S3. Collect the initial communication quality parameters returned by the RFID temperature measurement tag at each frequency point in response to the reading command, wherein the initial communication quality parameters include the received signal strength indication value, bit error rate, and reading success rate; S4. Obtain the distribution location area of ​​metal obstacles within the target monitoring area based on the environmental state parameters, and obtain multiple obstacle influence indices based on the distribution location area and the frequency point scanning sequence; S5. Obtain multiple environmental impact characteristic values ​​based on the environmental physical parameters and the frequency point scanning sequence; S6. Obtain the comprehensive score of each frequency point based on the initial communication quality parameters, multiple obstacle impact indices and multiple environmental impact characteristic values, and determine the frequency point with the highest comprehensive score as the current optimal working frequency point; S7. Control the reader to switch to the current optimal operating frequency, and establish a communication link with the RFID temperature measurement tag based on the current optimal operating frequency, so as to adaptively read the temperature data collected by the RFID temperature measurement tag.

[0019] As described in steps S1-S7 above, step S1 of this invention involves acquiring the environmental data of the RFID temperature measurement tag in the target monitoring area at the current moment. This environmental data includes environmental state parameters and environmental physical parameters. This step is fundamental to the entire adaptive method and a prerequisite for environmental learning. From a physical perspective, only by accurately acquiring the environmental characteristic data at the current moment can we provide a realistic analytical basis for subsequent frequency point selection, avoiding a disconnect between frequency point selection and the actual environment. In this step, environmental physical parameters can be directly collected through hardware devices such as temperature and humidity sensors and ranging sensors to obtain the environmental temperature, humidity, and actual distance between the reader and the RFID temperature measurement tag in the target monitoring area. The environmental state parameters are acquired by the reader through ultra-wideband radar detection, specifically an echo signal dataset containing the flight time and echo intensity of multiple sampling points. The detection characteristics of ultra-wideband radar can accurately capture the reflection characteristics of objects within the target monitoring area, providing raw data for subsequent identification of metal obstacles. For example, in the application scenario of power equipment inspection, there are metal objects such as distribution cabinets and metal brackets in the monitoring area. At the same time, the ambient temperature will change with the operation of the equipment. By collecting the temperature and humidity of the area, the distance between the reader and the tag, and the echo signal of the ultra-wideband radar through step S1, the basic characteristics of the current environment can be accurately grasped. This provides data support for subsequent analysis of the impact of metal obstacles on different frequency points and the impact of ambient temperature and humidity on electromagnetic wave propagation. The technical effect of this step is to make the entire frequency adaptive method based on actual environmental data, avoid the selection of frequency points without basis, and lay a data foundation for the subsequent steps.

[0020] S2 generates a frequency point scanning sequence based on a preset initial frequency band range and scanning step size. The reader then sequentially transmits reading commands at each frequency point in the scanning sequence. The reader, as the core device connecting the RFID temperature measurement tag and the backend system, is the carrier for frequency point transmission and signal reception. The physical significance of this step is to construct a multi-frequency point scanning system, providing a testing basis for subsequent acquisition of communication quality data at different frequencies and analysis of the performance of different frequencies in the current environment. In this step, the initial frequency band range can be preset according to the operating frequency band of the RFID temperature measurement system, such as the approximately 900MHz band of UHF RFID. The scanning step size is set according to the accuracy requirements of the actual application; a smaller step size results in higher frequency point scanning accuracy. The generated frequency point scanning sequence is a continuous set of equally spaced frequency points. The reader then transmits standardized reading commands at each frequency point in the order of this sequence, ensuring consistent testing conditions for each frequency point. For example, in the temperature measurement scenario of cold chain logistics, the preset initial frequency band is 860-960MHz, and the scanning step size is 1MHz. At this time, a scanning sequence of 101 frequency points will be generated. The reader will transmit reading commands on the frequency points of 860MHz, 861MHz...960MHz in sequence. The technical effect of this step is to realize the standardization and orderly scanning of multiple frequency points within the preset frequency band, ensuring the comparability of the initial communication quality parameters and influencing factor analysis of each frequency point in the subsequent analysis, and avoiding analysis errors caused by inconsistent test conditions.

[0021] S3 collects the initial communication quality parameters returned by the RFID temperature measurement tag at each frequency point in response to the read command. The initial communication quality parameters include the received signal strength indication value, bit error rate, and read success rate. The physical meaning of this step is to quantify the basic communication performance of each frequency point in the current environment. It is one of the core basic data for subsequent comprehensive frequency point scoring. The initial communication quality parameters of different frequency points directly reflect the ability of that frequency point to achieve effective communication in the current environment. The technical implementation of this step involves controlling the reader to continuously transmit K read commands (K being a preset integer greater than 1) at each frequency point in the frequency scanning sequence, and recording whether a response signal from the RFID temperature measurement tag is successfully received after each transmission. The read success rate is then calculated based on the number of successful responses and the total number of transmissions. For each successfully received response signal, the Received Signal Strength Indicator (RSSI) value carried by the physical layer is extracted, resulting in multiple RSSI values, which are then averaged. Simultaneously, a cyclic redundancy check (CRC) is performed on each successfully received response signal. The bit error rate (BER) is calculated based on the ratio of the number of failed verification packets to the number of successful responses. Ultimately, the read success rate, the average RSSI value, and the BER together constitute the initial communication quality parameters for that frequency point. For example, if K is preset to 10, and there are 8 successful responses at a certain frequency point, the read success rate is 80%. The average RSSI value is obtained by extracting the RSSI values ​​from the 8 successful response signals and calculating their average. If 2 of the response signals fail verification, the BER is 25%. These three parameters collectively reflect the basic communication performance of that frequency point. The technical effect of this step is to achieve multi-dimensional quantification of the communication performance of each frequency point. Compared with single signal strength detection, it combines read success rate and bit error rate to more comprehensively and accurately reflect the actual communication capability of the frequency point, providing real and effective basic data for subsequent comprehensive evaluation of the frequency point.

[0022] S4 involves obtaining the distribution area of ​​metal obstacles within the target monitoring area based on environmental state parameters, and acquiring multiple obstacle influence indices based on the distribution area and frequency scanning sequence. The physical significance of this step is to quantify the degree of influence of metal obstacles within the target monitoring area on the propagation of electromagnetic waves at different frequencies. This is because metal obstacles are an important factor causing RFID communication signal fading, and the degree of influence of metal obstacles on electromagnetic waves at different frequencies varies. This step is an important link in realizing environmental learning, as it correlates the environmental characteristics of metal obstacles with frequency performance. The technical implementation of this step involves first acquiring the echo signal dataset detected by ultra-wideband radar. The flight time and echo intensity are then used to construct a three-dimensional spatial reflection intensity distribution map of the target monitoring area using a back projection algorithm. This algorithm can reconstruct the spatial reflection characteristics of the detection area based on the echo flight time and intensity. Subsequently, threshold segmentation and connected component analysis are performed on this distribution map to extract the contour information of metal obstacles. The connected component corresponding to each metal obstacle is taken as the distribution location region. For each frequency point in the frequency scanning sequence, the direct path of the electromagnetic wave from the reader antenna to the RFID temperature measurement tag at that frequency point is calculated. It is determined whether this direct path intersects with the distribution location region of the metal obstacle. Intersection here means that the direct path and the distribution location region of the metal obstacle have an overlap. If they overlap, it is determined that the object is blocked by a metal obstacle. The blocking loss factor is calculated based on the reflection coefficient of the metal obstacle and the proportion of the blocked area. If they do not overlap, it is determined that the object is not blocked. The interference factor is calculated based on the multipath reflection interference of the metal obstacle on the direct path. Finally, the blocking loss factor and the interference factor together constitute the obstacle influence index for that frequency point. For example, in an industrial equipment temperature measurement scenario, if a metal pipe is present as an obstacle within the monitoring area, a 3D spatial reflection intensity distribution map constructed using a back projection algorithm can clearly show the location and geometric center of the pipe. After calculating the direct path of the electromagnetic wave at a certain frequency, it is found that it coincides with the geometric center of the pipe. At this point, based on the metal reflection coefficient of the pipe and the proportion of the area obstructing the direct path, an obstruction loss factor is calculated. This factor directly reflects the degree of signal loss at that frequency due to obstruction by the metal pipe. The technical effect of this step is to achieve quantitative and frequency-specific analysis of the impact of metal obstacles, breaking through the limitation of existing technologies that only qualitatively consider the impact of obstacles. By combining the distribution characteristics of metal obstacles with the propagation characteristics of electromagnetic waves at different frequencies, subsequent frequency selection can fully avoid communication interference caused by metal obstacles.

[0023] S5 involves obtaining multiple environmental impact characteristic values ​​based on environmental physical parameters and frequency scanning sequences. The physical significance of this step is to quantify the impact of environmental physical parameters (temperature, humidity) on the propagation of electromagnetic waves at different frequencies and the hardware characteristics of RFID temperature measurement tags. Changes in environmental physical parameters directly alter the propagation environment of electromagnetic waves and the impedance characteristics of the tags, thereby affecting the communication performance at different frequencies. This step, in conjunction with S4, completes the quantitative analysis of two types of parameters: environmental state and environmental physical parameters, achieving comprehensive learning of environmental characteristics. The technical implementation of this step involves first calculating the relative permittivity and conductivity of the air based on the collected ambient temperature and humidity. The permittivity and conductivity of air are key physical parameters determining the attenuation of electromagnetic wave propagation. Then, based on the distance between the reader and the RFID temperature tag, and combined with the relative permittivity and conductivity, the propagation attenuation factor of the electromagnetic wave in the air is calculated. This factor reflects the impact of the environment on the loss of electromagnetic wave propagation. Simultaneously, the input impedance of the RFID temperature tag due to thermal expansion is obtained based on the ambient temperature. Based on the difference between this input impedance and the preset characteristic impedance of the reader, an impedance mismatch factor is calculated. This factor reflects the communication impact caused by the impedance mismatch between the tag and the reader due to ambient temperature. Finally, the propagation attenuation factor and impedance mismatch factor are combined with the frequency scanning sequence to obtain the environmental impact characteristic value corresponding to each frequency point. For example, in high-temperature industrial temperature measurement environments, increased ambient temperature leads to changes in air conductivity, increasing the electromagnetic wave propagation attenuation factor. Simultaneously, the RFID temperature measurement tag's hardware undergoes input impedance changes due to thermal expansion, creating a difference between its input impedance and the reader's characteristic impedance, thus increasing the impedance mismatch factor. These two factors together constitute the environmental impact characteristic values ​​at different frequencies in this environment. The technical effect of this step is to achieve quantitative and frequency-specific analysis of the impact of environmental physical parameters, linking changes in ambient temperature and humidity with the communication performance of different frequencies. This solves the problem of neglecting the differential impact of environmental physical parameters on different frequencies in existing technologies, allowing frequency selection to adapt to changes in environmental physical parameters.

[0024] S6 involves obtaining a comprehensive score for each frequency point based on initial communication quality parameters, multiple obstacle impact indices, and multiple environmental impact characteristic values. The frequency point with the highest comprehensive score is then determined as the current optimal operating frequency point. The physical significance of this step is to evaluate and rank the comprehensive performance of each frequency point from multiple dimensions. Combining three dimensions—basic communication performance, the impact of metal obstacles, and the impact of environmental physical parameters—it achieves optimal frequency point selection and is the core decision-making step of the entire adaptive method. Technically, this step involves first normalizing the initial communication quality parameters, multiple obstacle impact indices, and multiple environmental impact characteristic values ​​to obtain normalized values ​​for initial communication quality, obstacle impact, and environmental impact characteristics. Normalization eliminates the dimensional differences between different parameters, ensuring comparability of parameters across dimensions in the comprehensive score. Subsequently, the three types of normalized values ​​are weighted to obtain a comprehensive score for each frequency point. The weighting coefficients can be preset according to the influence weights of each factor in the actual application scenario. Finally, the frequency point with the highest comprehensive score is selected as the current optimal operating frequency point. For example, in a certain monitoring environment, the initial communication quality normalization value of a certain frequency point is 0.9, the obstacle influence normalization value is 0.85, and the environmental influence characteristic normalization value is 0.88. After weighted calculation, the comprehensive score is 0.88, which is the highest among all frequency points. At this time, this frequency point is determined to be the current optimal operating frequency point. The technical effect of this step is to achieve a scientific and comprehensive evaluation of the overall performance of the frequency point. Compared with the method of selecting a frequency point solely based on communication quality, it combines the influence factors of obstacles and environmental physics, making the selected optimal operating frequency point more in line with the actual environmental characteristics, effectively avoiding various interferences caused by the environment, and ensuring the stability of communication.

[0025] S7 controls the reader to switch to the current optimal operating frequency and establishes a communication link with the RFID temperature measurement tag based on the current optimal operating frequency to adaptively read the temperature data collected by the RFID temperature measurement tag. The physical significance of this step is to implement the frequency selection decision result, realize the practical application of the optimal frequency, and through dynamic monitoring of the communication link and continuous updating of environmental learning, the frequency selection can adapt to the dynamic changes of the environment. It is the implementation and closed-loop link of the entire adaptive method. The technical implementation of this step involves first controlling the reader's radio frequency module to switch to the determined optimal operating frequency, establishing a stable communication link with the RFID temperature measurement tag, and enabling the reading of temperature data. During the continuous reading of temperature data, the real-time received signal strength indicator and real-time bit error rate at this frequency are periodically collected, and the historical average received signal strength indicator value at this frequency is retrieved. The link quality score is calculated in combination with the real-time parameters. Then, it is determined whether the link quality score is lower than a preset threshold and the duration exceeds a set window. If this condition is met, it indicates that the current environment has changed significantly and the optimal operating frequency is no longer suitable. At this time, communication is immediately interrupted and a full process reset is triggered, returning to step S1 to re-sensitize the environment and scan for frequencies. If the condition is not met, the current operating frequency is maintained, and the temperature data, environmental feature data, operating frequency, and link quality score read this time are associated and stored in the database to update the mapping relationship between environmental features and the optimal frequency, realizing continuous iteration of environmental learning. For example, in cold chain logistics transportation, the temperature and humidity of the monitored environment change with the transportation route. When the link quality score continuously falls below a preset threshold, the system automatically triggers a reset, re-perceiving the environment and scanning frequencies to select a new optimal operating frequency. If the environment does not change significantly, the environmental and frequency data are stored in the database, allowing subsequent frequency selections to draw on historical data and improve selection efficiency. This step achieves the practical application and dynamic adjustment of the optimal frequency, constructing a closed-loop system of "perception-selection-application-monitoring-update." It not only enables adaptive frequency selection in a single instance but also adapts to dynamic environmental changes through continuous environmental learning and link monitoring. Furthermore, the storage of historical data and the updating of mapping relationships improve the efficiency and accuracy of subsequent frequency selections. In summary, this invention achieves accurate perception of complex dynamic environments and real-time frequency adaptation through a full-process design that goes from environmental data acquisition to multi-dimensional impact quantification, frequency point optimization, and dynamic monitoring. It solves the problem of communication instability caused by fixed frequency points being unable to cope with changes in environmental temperature and humidity and interference from metal obstacles.

[0026] In one embodiment, step S3, which involves acquiring the initial communication quality parameters returned by the RFID temperature measurement tag at each frequency point in response to the read command, includes: S31. For each frequency point in the frequency point scanning sequence, control the reader to continuously transmit K reading commands at that frequency point, where K is a preset integer greater than 1, and record whether the response signal returned by the RFID temperature measurement tag is successfully received after each transmission. S32. Obtain the number of successful responses and the total number of transmissions on the frequency point where the response signal is successfully received, and obtain the reading success rate based on the number of successful responses and the total number of transmissions; S33. For each successfully received response signal, extract the received signal strength indication value carried by its physical layer to obtain multiple RSSI values, and calculate the average received signal strength indication value based on the multiple RSSI values. S34. Perform cyclic redundancy check on each successfully received response signal, obtain the number of data packets that failed the check, and compare the number of data packets with the number of successful responses to obtain the bit error rate. S35. Obtain the initial communication quality parameters based on the read success rate, the average received signal strength indication value, and the bit error rate.

[0027] As described in steps S31-S35 above, this invention controls the reader to continuously transmit K read commands at each frequency point in the frequency scanning sequence, where K is a preset integer greater than 1. It records whether a response signal from the RFID temperature measurement tag is successfully received after each transmission. This step is the fundamental test step for acquiring the initial communication quality parameters. Its physical significance lies in avoiding the randomness caused by instantaneous environmental interference from electromagnetic wave propagation through repeated command transmission and response recording, ensuring that subsequent parameter calculations are based on sufficient sample data and guaranteeing the statistical significance of the parameters. In this step, the value of K can be preset according to the test accuracy and efficiency requirements of the actual application scenario, generally selected as an integer between 10 and 50. The reader will maintain the current frequency point unchanged and continuously transmit standardized read commands at fixed time intervals. Simultaneously, its own signal receiving module monitors and records in real time whether a response signal from the RFID temperature measurement tag is received within a preset response time after each transmission, forming a response record dataset containing K test results, which only records the success and failure status of the response. For example, in an RFID temperature measurement scenario during power equipment inspection, K is preset to 20. For the 915MHz frequency point, the reader continuously transmits 20 read commands, while recording whether a response signal from the tag is received each time. Finally, 20 success / failure response records are obtained. The technical effect of this step is to provide basic sample data for subsequent calculations of read success rate, average received signal strength indication value, and bit error rate. By conducting multiple tests, the impact of instantaneous environmental interference on the test results is effectively reduced, making subsequent parameter quantification more statistically objective. At the same time, the standardized command transmission and response recording method ensures the consistency of test conditions between different frequency points, laying the foundation for subsequent comparison of communication quality between frequency points.

[0028] S32 involves obtaining the number of successful responses and the total number of transmissions on the frequency point, and then calculating the read success rate based on these metrics. This step performs preliminary statistical quantification on the response record dataset from S31. Physically, the read success rate quantifies the probability of establishing a communication connection between the reader and the RFID temperature tag on that frequency point. A higher read success rate indicates stronger stability of the communication connection on that frequency point, making it one of the core indicators for evaluating frequency point communication performance. Technically, this step involves counting the number of successful responses from the response record dataset obtained in S31. The total number of transmissions is the preset K value. Then, the read success rate is calculated using basic division. The formula for the read success rate is: Read Success Rate = Number of Successful Responses / Total Number of Transmissions × 100%. The result is presented as a percentage, directly reflecting the success probability of command transmission and response on that frequency point. For example, in the 20 tests conducted on the 915MHz frequency point mentioned above, 18 responses were successful, with a total of 20 transmissions. At this point, the read success rate for this frequency point is 18 / 20×100%=90%. This value directly reflects the probability of establishing a communication connection at this frequency point. The technical effect of this step is to quantify the communication connection success rate of the frequency point, transforming the qualitative response success / failure records into quantitative evaluation indicators. This makes the communication connection stability of different frequency points comparable. At the same time, as an important component of the initial communication quality parameters, this indicator can reflect the communication connection capability of the frequency point in the face of environmental interference, providing an intuitive reference for the comprehensive evaluation of the frequency point in the future.

[0029] S33 involves extracting the received signal strength indicator value carried by the physical layer for each successfully received response signal, obtaining multiple RSSI values, and calculating the average received signal strength indicator value based on the multiple RSSI values. This step extracts and statistically analyzes the core physical parameters of the successfully received signal. Its physical meaning is to quantify the energy intensity of the tag response signal received by the reader at this frequency point through the received signal strength indicator value. The higher the signal strength, the less energy loss of the electromagnetic wave during propagation and the better the signal transmission quality of the communication link. By calculating the average value, the instantaneous fluctuation of the signal strength of a single signal can be eliminated, and the average signal transmission strength at this frequency point can be reflected more objectively. The technical implementation of this step is that the reader extracts the received signal strength indication value at a preset position from the physical layer data frame of each successfully received response signal through its own radio frequency signal processing module, and obtains an RSSI value dataset consistent with the number of successful responses. Then, the average received signal strength indication value is obtained by calculating the arithmetic mean. The calculation formula is: average received signal strength indication value = sum of RSSI values ​​of all successful responses / number of successful responses, where RSSI value is in dBm and is a standard physical quantity that characterizes signal strength in radio frequency communication. For example, in the 18 successful responses at the 915MHz frequency point mentioned above, 18 RSSI values ​​were extracted, with values ​​ranging from -55dBm to -62dBm. Summing these 18 values ​​and dividing by 18 yields the average received signal strength indication value for that frequency point. The technical effect of this step is to achieve accurate quantification of the signal transmission strength of the frequency point. Extracting standard RSSI values ​​from the physical layer and averaging them ensures the standardization and professionalism of the parameters while avoiding the influence of fluctuations in single signal strength, making the evaluation of signal transmission strength more objective. At the same time, this indicator, together with the read success rate, reflects the communication transmission performance of the frequency point from two dimensions: "connection probability" and "signal strength," making the evaluation of communication quality more comprehensive.

[0030] S34 performs cyclic redundancy check on each successfully received response signal, obtains the number of data packets that failed the check, and calculates the bit error rate by comparing the number of data packets with the number of successful responses. This step detects and quantifies the transmission accuracy of the successful response signal. Its physical meaning is to quantify the probability of data errors occurring during the transmission of the response signal at this frequency point through the bit error rate. The lower the bit error rate, the higher the accuracy of the signal transmission and the smaller the possibility of errors occurring during the transmission of temperature data. It is another core indicator for evaluating the communication performance of a frequency point, making up for the shortcomings of read success rate and signal strength, which only reflect "connectivity" and "strength" and do not reflect "data accuracy". The technical implementation of this step involves the reader using a standard cyclic redundancy check algorithm (such as CRC16 or CRC32) to verify the integrity and accuracy of each successfully received acknowledgment signal data packet. During the verification process, the checksum of the data packet is compared with a preset calculated value to determine whether there are any transmission errors in the data packet. Subsequently, the number of data packets that failed verification is counted, and the bit error rate is calculated by division. The formula for calculating the bit error rate is: Bit error rate = Number of data packets that failed verification / Number of successful acknowledgments × 100%, where the number of successful acknowledgments is consistent with the value counted in S32, because only successfully received acknowledgment signals are verified. For example, in the 18 successful responses at the 915MHz frequency point mentioned above, one data packet failed the CRC16 check. At this time, the bit error rate of this frequency point is 1 / 18×100%≈5.56%. This value directly reflects the data accuracy of signal transmission at this frequency point. The technical effect of this step is to achieve the standardization and quantification of the accuracy of communication data at the frequency point. The use of the industry-standard cyclic redundancy check algorithm ensures the accuracy and professionalism of the check results, making the bit error rate an important indicator for evaluating the communication performance of the frequency point. At the same time, the addition of this indicator extends the evaluation of communication quality from "connection probability" and "signal strength" to "data accuracy", forming a three-dimensional evaluation system, making the evaluation of the communication performance of each frequency point more comprehensive and accurate.

[0031] S35 involves obtaining initial communication quality parameters based on the read success rate, average received signal strength indicator (RSSSI), and bit error rate (BER). This step integrates the three core indicators quantified in S32 to S34. Physically, it integrates three independent indicators evaluating frequency point communication performance from different dimensions into a standardized initial communication quality parameter. This parameter serves as the core foundational data for subsequent comprehensive frequency point evaluation, providing a holistic representation of the initial communication performance of each frequency point and offering a unified reference for the initial communication quality of different frequencies. Technically, this step integrates and stores the read success rate calculated in S32, the average RSSSI calculated in S33, and the BER calculated in S34, forming a dataset of initial communication quality parameters for that frequency point. This dataset retains the original quantified values ​​of the three indicators without additional fusion calculations, ensuring that subsequent comprehensive evaluations can weight each indicator according to the needs of the actual application scenario. The read success rate reflects communication connection stability, the average RSSSI reflects signal transmission strength, and the BER reflects data transmission accuracy; all three are complementary and indispensable. For example, the initial communication quality parameters for the 915MHz frequency point mentioned above are {read success rate 90%, average received signal strength indication value -58.2dBm, bit error rate 5.56%}. This dataset fully characterizes the initial communication performance at this frequency point. The technical effect of this step is to achieve the standardized integration of the three core communication performance indicators, forming a parameter system that can comprehensively reflect the initial communication quality of the frequency point. This provides unified and comprehensive basic data for subsequent comprehensive scoring of the frequency point by combining the obstacle impact index and environmental impact characteristic value. At the same time, the integration method of retaining the original values ​​makes the subsequent comprehensive evaluation more flexible and can adapt to the different weight requirements of communication performance indicators for different application scenarios.

[0032] In summary, compared to existing RFID communication quality testing methods that rely on a single dimension and a single test, this invention constructs a communication initial quality parameter acquisition process that involves "multiple tests - multi-dimensional extraction - standardized calculation - indicator integration." By pre-setting K consecutive command transmissions, it effectively avoids the randomness of testing caused by instantaneous environmental interference, ensuring the statistical objectivity of the test results. By extracting three core indicators—read success rate, average received signal strength indication value, and bit error rate—it comprehensively quantifies the initial communication performance of a frequency point from three dimensions: communication connection stability, signal transmission strength, and data transmission accuracy, thus overcoming the shortcomings of existing technologies that rely on a single evaluation dimension. By adopting standardized parameter extraction, calculation, and integration methods, it ensures the consistency of test conditions and the comparability of parameters between different frequency points, making the initial communication quality evaluation of each frequency point more objective and accurate. The steps are progressive and closely related. S31 provides basic sample data for all subsequent parameter calculations. S32, S33, and S34 standardize and quantify the sample data from three different dimensions. S35 integrates the three quantified indicators into a unified initial communication quality parameter. This provides comprehensive, accurate, and objective basic communication performance data for subsequent frequency point comprehensive evaluation and optimal operating frequency point determination. It solves the problems of inaccurate, incomplete, and incomparable communication quality detection in existing technologies. At the same time, this data acquisition process, in conjunction with subsequent obstacle impact index and environmental impact characteristic value analysis, allows the determination of the optimal operating frequency point to take into account both basic communication performance and environmental impact factors. This further improves the scientificity and adaptability of RFID temperature measurement frequency point selection and effectively ensures the stability and accuracy of temperature data reading by the RFID temperature measurement system in complex environments.

[0033] In one embodiment, step S4, which involves obtaining the distribution area of ​​metal obstacles within the target monitoring area based on the environmental state parameters and obtaining multiple obstacle influence indices based on the distribution area and the frequency scan sequence, includes: S41. Obtain the environmental state parameters, which include an echo signal dataset obtained by the reader through ultra-wideband radar detection, wherein the echo signal dataset contains the flight time and echo intensity of multiple sampling points. S42. Using the flight time and echo intensity, construct a three-dimensional spatial reflection intensity distribution map of the target monitoring area using a back projection algorithm; S43. Perform threshold segmentation and connected component analysis on the three-dimensional spatial reflection intensity distribution map to extract the contour information of at least one metal obstacle. Each metal obstacle corresponds to a connected component, and the connected component is used as the distribution location area. S44. For each frequency point in the frequency point scanning sequence, calculate the direct path of the electromagnetic wave from the reader antenna to the RFID temperature measurement tag at that frequency point, and determine in turn whether the direct path intersects with multiple distribution location areas. If the direct path intersects with the distribution area, it is determined that it is blocked by a metal obstacle, and multiple reflection coefficients and multiple blocking area ratios of multiple metal obstacles are obtained. Multiple blocking loss factors are calculated based on the multiple reflection coefficients and multiple blocking area ratios. If the direct path does not intersect with the distribution area, it is determined that it is not blocked by metal obstacles, and the multipath reflection interference of multiple metal obstacles on the direct path is obtained, and multiple interference factors are calculated based on the multipath reflection interference. S45. Obtain multiple obstacle influence indices for a frequency point based on multiple occlusion loss factors and multiple interference factors.

[0034] As described in steps S41-S45 above, step S41 of this invention involves acquiring environmental state parameters. These parameters include an echo signal dataset obtained by the reader / writer through ultra-wideband radar detection, which contains the flight time and echo intensity of multiple sampling points. This step is the fundamental data acquisition stage for the entire metal obstacle recognition and obstacle impact index calculation. Its physical significance lies in leveraging the high-resolution detection characteristics of ultra-wideband radar to capture the reflection signal characteristics of objects within the target monitoring area, providing raw physical signal data for subsequent reconstruction of the spatial distribution of metal obstacles. Ultra-wideband radar has the characteristics of wide bandwidth and short pulses, enabling it to accurately identify the reflection characteristics of different objects in space, especially showing good recognition performance for highly reflective objects such as metals. In this step, the ultra-wideband radar module integrated in the reader / writer transmits ultra-wideband detection pulses to the target monitoring area and simultaneously receives echo signals formed by reflections from various objects within the area. After sampling the echo signals, an echo signal dataset containing multiple sampling points is obtained. Each sampling point corresponds to a set of flight time and echo intensity data. Flight time reflects the spatial distance between the radar and the reflecting object, while echo intensity reflects the object's reflection characteristics. The echo intensity of metallic objects is much higher than that of non-metallic objects, a characteristic that provides crucial information for subsequent identification of metallic obstacles. For example, in a power equipment inspection scenario, the reader's ultra-wideband radar emits detection pulses to the monitoring area around the distribution cabinet, receiving echo signals from metallic objects such as distribution cabinets and metal supports, as well as non-metallic objects such as walls and cables. This ultimately yields a dataset containing flight time and echo intensity data from thousands of sampling points. The technical effect of this step is to acquire raw data that reflects the spatial position and reflection characteristics of objects within the target monitoring area. Leveraging the high-resolution detection characteristics of ultra-wideband radar, the spatial accuracy of the data and the effectiveness of object identification are ensured, laying a solid physical data foundation for the subsequent three-dimensional spatial positioning of metallic obstacles. Simultaneously, binding environmental state parameters with ultra-wideband radar detection data makes the perception of environmental features more targeted and accurate.

[0035] S42 involves constructing a 3D spatial reflection intensity distribution map of the target monitoring area using a back projection algorithm, combining flight time and echo intensity. This step involves spatial analysis and visualization of the echo signal dataset. Physically, it uses the back projection algorithm to restore one-dimensional sampling point data to a 3D spatial reflection intensity distribution characteristic, presenting the reflection characteristics of different locations within the target monitoring area in the form of a spatial distribution map. This allows for intuitive identification of spatial regions containing highly reflective metallic obstacles. The technical implementation of this step involves first establishing a 3D spatial coordinate system for the target monitoring area based on the detection parameters of the ultra-wideband radar. The flight time of each sampling point is converted into 3D spatial coordinates, corresponding to the spatial location detected by the radar. The echo intensity of each sampling point is then used as the reflection intensity value for that spatial coordinate. Subsequently, the back projection algorithm is used to interpolate and reconstruct the spatial coordinates and reflection intensity values ​​of all sampling points, filling all grid points in the 3D spatial coordinate system, ultimately generating a 3D spatial reflection intensity distribution map of the target monitoring area. Each spatial location in this distribution map has a corresponding reflection intensity value, and areas corresponding to metallic obstacles will exhibit significantly high reflection intensity characteristics. For example, the flight time of the sampling points obtained in S41 is converted into coordinate values ​​of the three-dimensional space surrounding the distribution cabinet. The echo intensity is used as the reflection intensity of each coordinate. After reconstruction by the back projection algorithm, the positions of the distribution cabinet and metal support in the resulting three-dimensional spatial reflection intensity distribution map will show reflection intensity values ​​much higher than those of the surrounding area, clearly distinguishing the spatial positions of metal and non-metal objects. The technical effect of this step is to realize the transformation from raw echo signal data to three-dimensional spatial reflection characteristics. The spatial reconstruction characteristics of the back projection algorithm ensure the accuracy of spatial positioning of metal obstacles, allowing the distribution position of metal obstacles to be presented in the form of quantified spatial data. This breaks through the fuzzy judgment of obstacle distribution in existing technologies and provides a quantitative spatial analysis basis for subsequent accurate extraction of metal obstacle contour information.

[0036] S43 involves threshold segmentation and connected component analysis of the 3D spatial reflection intensity distribution map to extract the contour information of at least one metallic obstacle. Each metallic obstacle corresponds to a connected component, which is then used as the distribution location region. This step involves feature extraction and quantification of the 3D spatial reflection intensity distribution map. Physically, it involves using threshold segmentation to filter out regions of metallic obstacles with high reflection intensity, then using connected component analysis to achieve independent identification of individual metallic obstacles, and finally extracting the distribution location features of metallic obstacles. This provides accurate spatial parameters for subsequent analysis of the relationship between the electromagnetic wave's direct path and the position of metallic obstacles. Technically, this step involves first setting a reflection intensity threshold based on the difference in echo intensity between metallic and non-metallic objects, then performing threshold segmentation on the 3D spatial reflection intensity distribution map. Regions with reflection intensity higher than the threshold are identified as suspected metallic obstacle regions, while regions with reflection intensity lower than the threshold are discarded. Subsequently, 3D connected component analysis is performed on the threshold-segmented suspected metallic obstacle regions, identifying spatially connected high-reflection-intensity regions as independent metallic obstacles. Each independent connected component represents the distribution location region of a metallic obstacle. For example, with a preset reflection intensity threshold of -20dB, after threshold segmentation of the three-dimensional spatial reflection intensity distribution map around the distribution cabinet, high reflection intensity areas corresponding to the distribution cabinet and metal support are obtained. Then, through connected component analysis, two independent high reflection intensity areas are identified as two metal obstacles, and their corresponding connected components represent their distribution locations. The technical effect of this step is to achieve accurate identification and spatial parameter quantification of metal obstacles within the target monitoring area. Threshold segmentation ensures effective differentiation between metal obstacles and non-metallic backgrounds, and connected component analysis enables independent identification of multiple metal obstacles, presenting their distribution locations in a clear spatial region form. This provides a clear and accurate spatial reference for subsequent determination of the relationship between the electromagnetic wave direct path and the obstacle's position.

[0037] S44 involves calculating the direct path of the electromagnetic wave from the reader antenna to the RFID temperature tag at each frequency point in the frequency scanning sequence, and sequentially determining whether the direct path intersects with multiple distribution areas. If the direct path intersects with a distribution area, it is determined that it is blocked by a metal obstacle, and multiple reflection coefficients and multiple blocking area ratios of the metal obstacles are obtained. Multiple blocking loss factors are calculated based on the multiple reflection coefficients and multiple blocking area ratios. If the direct path does not intersect with a distribution area, it is determined that it is not blocked by a metal obstacle, and multiple multipath reflection interferences of the metal obstacles on the direct path are obtained. Multiple interference factors are calculated based on the multipath reflection interference. This step is the core analysis link in the obstacle influence index calculation. Its physical meaning is to combine the spatial location of the metal obstacle with the electromagnetic wave propagation characteristics at different frequencies to analyze the type of influence of the electromagnetic wave direct path on the metal obstacle at each frequency point, and then quantify the degree of influence through factor calculation. Electromagnetic wave propagation characteristics differ at different frequencies, and their loss due to metal obstruction and interference from multipath reflection also vary significantly. Therefore, it is necessary to calculate frequency-specific factors. The technical implementation of this step involves first calculating the linear equation of the direct path of the electromagnetic wave from the reader antenna to the tag based on the three-dimensional spatial coordinates of the reader antenna and the RFID temperature measurement tag. For each frequency in the frequency scanning sequence, this linear equation is used as a reference for the direct path of the electromagnetic wave. Then, it is determined whether the direct path intersects with the distribution area of ​​each metal obstacle. If they overlap, it is determined that the direct path of the electromagnetic wave at that frequency is blocked by the metal obstacle. At this time, the material reflection coefficient of the metal obstacle (the reflection coefficient of the metal material is a known fixed value, such as the reflection coefficient of copper is about 0.98) and the proportion of the area blocked by the obstacle on the direct path are obtained. The blocking loss factor of a single metal obstacle is calculated by the formula "blocking loss factor = reflection coefficient × blocking area proportion". If there are multiple metal obstacles blocking, they are calculated separately. If the direct path does not intersect with the distribution area, it is determined that it is not blocked. At this time, the multipath reflection interference of the metal obstacle on the direct path is analyzed, and parameters such as the path difference and phase difference between the reflected wave and the direct wave are calculated. Combined with the electromagnetic wave wavelength at the frequency, the interference factor caused by multipath reflection is calculated by electromagnetic wave interference theory. If there are multiple metal obstacles, their respective interference factors are calculated separately. For example, at a frequency of 915MHz, the calculated direct path of the electromagnetic wave passes through the distribution area of ​​the distribution cabinet, and is determined to be blocked. At this time, the reflection coefficient of the metal material of the distribution cabinet is 0.98 and the blocking area ratio is 0.8. The blocking loss factor is calculated to be 0.98×0.8=0.784. However, at a frequency of 860MHz, the direct path does not pass through the distribution area of ​​any metal obstacles, and is determined to be unblocked. The multipath reflection interference of the metal bracket on the direct path is calculated, and the interference factor is 0.12.The technical effect of this step is to achieve accurate judgment of the type of impact of metal obstacles at different frequency points and quantitative calculation of the degree of impact. It accurately correlates the spatial location of metal obstacles with the electromagnetic wave propagation characteristics of each frequency point, breaking through the holistic and undifferentiated analysis method of the impact of metal obstacles in the existing technology. It realizes the quantification of the impact of frequency points, and provides accurate factor data for the subsequent acquisition of obstacle impact index, so that the degree of metal interference at each frequency point can be presented in quantitative value.

[0038] S45 involves obtaining multiple obstacle impact indices for a frequency point based on multiple shading loss factors and multiple interference factors. This step integrates the shading loss factors and interference factors. Physically, it integrates the shading loss factors or interference factors caused by a single metal obstacle into an overall obstacle impact index for that frequency point. This achieves a quantitative characterization of the comprehensive impact of all metal obstacles on a single frequency point, allowing the obstacle impact index to directly reflect the degree of electromagnetic wave propagation interference at that frequency point in the current metal obstacle distribution environment, providing a unified quantitative indicator of metal interference for subsequent comprehensive frequency point assessment. Technically, for each frequency point, if multiple shading loss factors exist, all shading loss factors are summed to obtain the total shading loss factor for that frequency point, which serves as the obstacle impact index. If multiple interference factors exist, all interference factors are summed to obtain the total interference factor for that frequency point, which serves as the obstacle impact index. If some metal obstacles cause shading loss and others cause multipath interference, the total shading loss factor and the total interference factor are summed to obtain the comprehensive obstacle impact index for that frequency point. The higher the obstacle impact index, the greater the influence of metallic obstacles on that frequency point, and the more severe the interference with electromagnetic wave propagation. For example, if two metallic obstacles block a frequency point with blocking loss factors of 0.784 and 0.21 respectively, the sum of these factors yields a total blocking loss factor of 0.994, which is the obstacle impact index for that frequency point. Similarly, if three metallic obstacles cause multipath interference at another frequency point with interference factors of 0.12, 0.08, and 0.05 respectively, the sum of these factors yields a total interference factor of 0.25, which is also the obstacle impact index for that frequency point. This step achieves a quantitative integration of the overall impact of metallic obstacles on a single frequency point, consolidating the independent impact factors of multiple metallic obstacles into a unified obstacle impact index. This makes the degree of metallic interference at different frequencies comparable, and the quantitative form of this index can be directly incorporated into subsequent frequency point comprehensive score calculations. This achieves precise alignment between the impact of metallic obstacles and the comprehensive frequency point assessment, providing a clear quantitative basis for selecting frequencies with strong resistance to metallic interference.

[0039] In summary, compared with the qualitative judgment and overall assessment of the impact of metal obstacles in the prior art, the present invention constructs a complete technical process from "precise detection by ultra-wideband radar - three-dimensional spatial feature reconstruction - precise identification of metal obstacles - frequency point difference impact analysis - quantitative integration of interference degree", realizing the three-dimensional spatial precise positioning of the distribution of metal obstacles in the target monitoring area and the differential and quantitative analysis of the impact degree of obstacles at different frequency points. S41 leverages the high resolution of ultra-wideband radar to acquire accurate raw echo signal data, laying the data foundation for subsequent analysis; S42 uses a back projection algorithm to transform raw data into three-dimensional spatial reflection characteristics, allowing the distribution of metal obstacles to be presented as quantified spatial data; S43 uses threshold segmentation and connected component analysis to achieve accurate identification and spatial parameter quantification of metal obstacles, clarifying their distribution location; S44 combines electromagnetic wave direct path judgment and factor calculation to achieve differentiated quantification of the type and degree of influence of metal obstacles at different frequencies, allowing for precise correlation between frequency performance and metal obstacle distribution; S45 uses factor integration to obtain a unified obstacle influence index, making the degree of metal interference at different frequencies comparable. The entire process deeply correlates the characteristics of metal obstacles in environmental perception with the performance of each frequency point in the frequency scanning sequence. This overcomes the problems of inaccurate metal obstacle impact analysis and disconnection from frequency point selection in existing technologies. The resulting obstacle impact index can accurately reflect the degree of interference of each frequency point by metal obstacles, providing accurate quantitative data for subsequent frequency point comprehensive score calculation. This allows the selection of the optimal working frequency point to accurately avoid interference from metal obstacles, effectively solving the problems of signal multipath fading and communication instability caused by metal obstacle reflection interference in the technical background. It improves the frequency point adaptability and communication stability of the RFID temperature measurement system in complex environments with metal obstacles, enabling the system to maintain stable temperature data reading capabilities in application scenarios with many metal obstacles, such as power equipment inspection and industrial monitoring.

[0040] In one embodiment, step S5, which involves obtaining multiple environmental impact characteristic values ​​based on the environmental physical parameters and the frequency point scanning sequence, includes: S51. Obtain the ambient temperature and humidity, and obtain the relative permittivity and conductivity of the current air based on the ambient temperature and humidity. S52. Obtain the distance between the reader and the RFID temperature measurement tag, and obtain the propagation attenuation factor of electromagnetic waves in air based on the distance, the relative permittivity and the conductivity. S53. Obtain the input impedance of the RFID temperature measurement tag caused by thermal expansion based on the ambient temperature, and calculate the impedance mismatch factor based on the difference between the input impedance and the preset reader characteristic impedance. S54. Obtain the environmental impact characteristic value corresponding to each frequency point based on the propagation attenuation factor, the impedance mismatch factor, and the frequency point scanning sequence.

[0041] As described in steps S51-S54 above, step S51 of this invention involves acquiring the ambient temperature and humidity, and then obtaining the relative permittivity and conductivity of the current air based on these parameters. This step is the fundamental parameter analysis step for the entire calculation of environmental impact characteristic values. Its physical significance is to transform the intuitive ambient temperature and humidity parameters into core physical quantities characterizing the electromagnetic properties of the air medium. The relative permittivity and conductivity of the air are key parameters that determine the propagation and attenuation characteristics of electromagnetic waves in the air. Different combinations of temperature and humidity correspond to different electromagnetic properties of the medium, directly affecting the propagation state of electromagnetic waves. In this step, the ambient temperature and humidity are collected in real time by temperature and humidity sensors deployed in the target monitoring area, and are continuous physical quantity values. Subsequently, according to the classic electromagnetic medium characteristic calculation formula, the collected temperature and humidity values ​​are substituted into the calculation to obtain the relative permittivity and conductivity of the current air. The relative permittivity reflects the polarization effect of the air on the electromagnetic wave electric field, and the conductivity reflects the conduction loss of the air on the electromagnetic wave. Both calculations are based on linear or nonlinear fitting formulas for temperature and humidity, which are mature algorithms in the field of radio frequency communication. The parameters can be calibrated according to the actual temperature and humidity range of the application. For example, in low-temperature monitoring scenarios in cold chain logistics, a temperature and humidity sensor collects data showing an ambient temperature of 2℃ and humidity of 60%. Substituting this data into a formula, the relative permittivity of the air is calculated to be approximately 1.0006 and the conductivity to be approximately 1.2 × 10^-8 S / m. In high-temperature industrial scenarios, with a temperature of 60℃ and humidity of 80%, the calculated relative permittivity is approximately 1.0012 and the conductivity to be approximately 3.5 × 10^-8 S / m. The technical effect of this step is to realize the transformation from basic environmental physical parameters to core electromagnetic medium characteristics, allowing changes in ambient temperature and humidity to be presented as quantifiable electromagnetic physical quantities. This provides an accurate basis for calculating the electromagnetic wave propagation attenuation factor. At the same time, the real-time acquisition method ensures the synchronization of medium characteristic parameters with environmental conditions, allowing subsequent impact analysis to be consistent with the current actual environment.

[0042] S52 involves obtaining the distance between the reader and the RFID temperature measurement tag, and obtaining the propagation attenuation factor of the electromagnetic wave in the air based on the distance, the relative permittivity, and the conductivity. This step is a quantitative calculation of the environmental impact of the electromagnetic wave propagation layer. Its physical meaning is to combine the propagation distance of the electromagnetic wave with the electromagnetic properties of the air medium to quantitatively calculate the degree of energy attenuation caused by the air medium during the process of the electromagnetic wave from the reader to the RFID temperature measurement tag at different frequencies. The larger the value of the propagation attenuation factor, the more serious the energy loss of the electromagnetic wave during the propagation process, and the more obvious the attenuation of the communication signal strength. Moreover, this factor is related to the frequency, and the propagation attenuation characteristics of electromagnetic waves at different frequencies in the same medium are different. In this step, the distance between the reader and the RFID temperature measurement tag is obtained in real time by the ranging sensor or the positioning function of the RFID system, providing an accurate spatial distance value. Then, based on the propagation attenuation formula of electromagnetic waves in lossy media, the distance, relative permittivity, conductivity, and frequency parameters are substituted into the calculation to obtain the propagation attenuation factor corresponding to each frequency point. The propagation attenuation formula follows the electromagnetic wave propagation theory in radio frequency communication, taking into account the polarization loss, conduction loss, and attenuation effect of the propagation distance of the medium. The frequency point is the independent variable in the formula, ensuring differentiated calculation for different frequency points. For example, in a power equipment inspection scenario, the distance between the reader and the tag is 5 meters. Combining the relative permittivity and conductivity of air calculated in S51, the propagation attenuation factor at 915MHz is calculated to be 0.15, and the propagation attenuation factor at 860MHz is 0.12. This indicates that the energy loss of electromagnetic waves propagating at 915MHz is greater in this environment. The technical effect of this step is to realize the frequency-specific quantification of electromagnetic wave propagation attenuation, accurately correlate environmental physical parameters, propagation distance and frequency performance, and break through the overall estimation method of propagation attenuation in the existing technology. It allows the degree of propagation attenuation at each frequency to be presented in a quantitative value, providing a core factor reflecting the environmental impact at the electromagnetic wave propagation level for the subsequent generation of environmental impact characteristic values.

[0043] S53 obtains the input impedance of the RFID temperature measuring tag caused by thermal expansion based on the ambient temperature, and calculates the impedance mismatch factor based on the difference between the input impedance and the preset reader characteristic impedance. This step is a quantitative calculation of the environmental impact on the hardware characteristics of the RFID temperature measuring tag. Its physical meaning is to quantify the communication impact caused by the change in tag input impedance due to changes in ambient temperature and the impedance mismatch of the reader. Impedance mismatch will reduce the energy transmission efficiency between the reader and the tag, and some energy will be reflected, making it impossible to effectively activate the tag and realize signal communication. Moreover, the degree of thermal expansion of the tag is related to temperature, and the change in input impedance is also related to temperature, which is ultimately reflected as the difference in impedance mismatch factor under different ambient temperatures. In this step, the thermal expansion coefficient of the RFID temperature measurement tag is first obtained based on the hardware material characteristics (such as the metal material of the antenna and the dielectric material of the substrate). The size change of the tag antenna and chip is calculated in combination with the change in ambient temperature. Then, the actual input impedance of the tag is obtained from the size change according to the impedance calculation formula of the radio frequency circuit. The characteristic impedance of the reader is a preset fixed value, generally 50Ω, which is the standard characteristic impedance of radio frequency communication. Subsequently, the reflection coefficient is calculated according to the reflection coefficient formula of impedance mismatch. Then, the impedance mismatch factor is obtained from the reflection coefficient. The larger the value of the impedance mismatch factor, the more serious the impedance mismatch and the lower the energy transmission efficiency. For example, the antenna of an RFID temperature measurement tag is made of copper, with a coefficient of thermal expansion of 1.7 × 10^-5 / ℃. When the ambient temperature rises from 25℃ to 55℃, the calculated tag input impedance changes from 50Ω to 58Ω, which differs from the reader's characteristic impedance of 50Ω. The calculated impedance mismatch factor is 0.07. When the temperature drops to 0℃, the input impedance becomes 45Ω, and the impedance mismatch factor is 0.05. The technical effect of this step is to accurately quantify the impact of impedance mismatch caused by ambient temperature, filling the gap in existing technologies that ignore the changes in tag hardware characteristics with ambient temperature. It links environmental physical parameters with the energy transmission characteristics of the RFID system, providing core factors reflecting the environmental impact at the hardware level for the subsequent generation of environmental impact characteristic values, making the analysis of environmental impact more comprehensive.

[0044] S54 involves obtaining the environmental impact characteristic value corresponding to each frequency point based on the propagation attenuation factor, the impedance mismatch factor, and the frequency point scanning sequence. This step integrates the propagation attenuation factor and the impedance mismatch factor with frequency point matching. Its physical meaning is to integrate two core environmental impact factors reflecting electromagnetic wave propagation and hardware characteristics into the environmental impact characteristic value corresponding to each frequency point. This achieves a quantitative characterization of the comprehensive influence of environmental physical parameters on a single frequency point, allowing the environmental impact characteristic value to directly reflect the communication adaptability of the frequency point under the current environmental physical state, providing a unified quantitative index of environmental physical impact for subsequent comprehensive evaluation of frequency points. Technically, this step integrates the propagation attenuation factor calculated in S52 with the impedance mismatch factor calculated in S53. These two factors together constitute the environmental impact characteristic value of the frequency point. The integration method can be to retain the original quantitative values ​​of the two factors to form a characteristic value dataset, or to perform a weighted summation of the two factors according to the needs of the actual application scenario to obtain a single characteristic value. The larger the overall value of the environmental impact characteristic value, the higher the degree of influence of environmental physical parameters on the frequency point, and the more significant the impact on communication performance. For example, if the propagation attenuation factor of a certain frequency is 0.15 and the impedance mismatch factor is 0.07, these two factors together constitute the environmental impact characteristic value of that frequency. However, if the propagation attenuation factor of another frequency is 0.12 and the impedance mismatch factor is 0.05, then its overall environmental impact characteristic value is lower than the former, indicating that this frequency is less affected by environmental physical parameters. The technical effect of this step is to achieve a quantitative integration of the comprehensive impact of environmental physical parameters on a single frequency. It accurately matches two different levels of environmental impact factors with the frequency scanning sequence, so that each frequency has a corresponding quantitative characteristic value that can reflect the environmental physical impact. At the same time, the quantitative form of this characteristic value can be directly integrated into the subsequent frequency comprehensive score calculation, realizing the precise connection between the impact of environmental physical parameters and the comprehensive evaluation of frequency, and providing a clear quantitative basis for the subsequent selection of frequencies suitable for the environmental physical state.

[0045] In summary, compared with the qualitative judgment and overall assessment methods of the impact of environmental physical parameters in the prior art, the present invention constructs a complete technical process from "basic physical parameter acquisition - medium electromagnetic property analysis - multi-level influencing factor quantification - frequency point feature value generation", realizing comprehensive, quantitative and frequency point differential analysis of the impact of environmental physical parameters, and effectively solving the problems of RFID tag resonant frequency drift and electromagnetic wave propagation attenuation caused by changes in environmental temperature and humidity in the technical background. S51 converts temperature and humidity parameters into the relative permittivity and conductivity of air, laying the electromagnetic medium foundation for subsequent propagation attenuation calculations, and ensuring the synchronization of parameters with environmental conditions through real-time acquisition; S52 calculates the propagation attenuation factor for each frequency point by combining propagation distance and medium characteristics, realizing the frequency-point differentiation quantification of environmental impact at the electromagnetic wave propagation level, making the propagation attenuation analysis more accurate; S53 calculates the input impedance change and impedance mismatch factor of the tag based on the ambient temperature, filling the gap in the existing technology that ignores the change of hardware characteristics with temperature, and realizing the accurate quantification of the environmental impact at the hardware characteristic level; S54 integrates the two core influencing factors with the frequency point scanning sequence into environmental impact feature values, realizing the comprehensive characterization of the environmental physical impact of a single frequency point, making the environmental adaptability of different frequencies comparable. The entire process deeply integrates the real-time status of environmental physical parameters with the communication performance of frequency points, overcoming the problems of inaccurate and incomplete environmental physical impact analysis and disconnection from frequency point selection in existing technologies. The obtained environmental impact characteristic values ​​can accurately reflect the degree of influence of environmental temperature and humidity on each frequency point, providing accurate quantitative data for subsequent frequency point comprehensive score calculation. This allows the selection of the optimal working frequency point to accurately adapt to the real-time environmental physical state of the target monitoring area, effectively improving the frequency point adaptability and communication stability of the RFID temperature measurement system in complex environments with dynamic temperature and humidity changes. In application scenarios with large temperature and humidity fluctuations, such as cold chain logistics, industrial high-temperature monitoring, and outdoor power equipment inspection, the system can also effectively avoid communication problems caused by changes in environmental physical parameters, ensuring the stability and accuracy of temperature data reading.

[0046] In one embodiment, step S6, which involves obtaining a comprehensive score for each frequency point based on the initial communication quality parameters, multiple obstacle impact indices, and multiple environmental impact characteristic values, includes: S61. Normalize the initial communication quality parameters to obtain the normalized initial communication quality value; S62. Normalize the multiple obstacle influence indices in sequence to obtain multiple obstacle influence normalized values; S63. Normalize the multiple environmental impact feature values ​​in sequence to obtain multiple environmental impact feature normalized values; S64. Calculate the comprehensive score for each frequency point based on the normalized value of the initial communication quality, the normalized values ​​of multiple obstacle effects, and the normalized values ​​of multiple environmental impact characteristics.

[0047] As described in steps S61-S64 above, step S61 of this invention is to normalize the initial communication quality parameters to obtain normalized initial communication quality values. This step is to standardize the parameters that reflect the basic communication performance of the frequency point. Its physical meaning is to transform communication performance parameters with different dimensions and numerical ranges, such as read success rate, average received signal strength indication value, and bit error rate, into normalized values ​​within a unified numerical range, eliminate the scale differences between parameters, and allow each communication performance index to be fairly fused and calculated in the same dimension, while retaining the actual impact of each parameter on the frequency point communication performance. The technical implementation of this step involves first determining the normalization mapping rule for each sub-parameter in the initial communication quality parameters. For positive indicators such as read success rate and average received signal strength indication, which indicate better performance with larger values, a linear normalization formula is used to map them to the [0,1] interval, with the formula: Normalized value = (Original value - Minimum value) / (Maximum value - Minimum value). For negative indicators such as bit error rate, which indicate worse performance with larger values, a reverse linear normalization formula is used to map them to the [0,1] interval, with the formula: Normalized value = (Maximum value - Original value) / (Maximum value - Minimum value), where the minimum and maximum values ​​are the extreme values ​​of the corresponding sub-parameters for all frequency points in the frequency point scanning sequence. Then, the normalized values ​​of read success rate, average received signal strength indication, and bit error rate are weighted and summed according to preset weights to obtain the normalized value of the initial communication quality for a single frequency point. The weights can be set according to the actual application scenario's requirements for communication performance. For example, in cold chain logistics scenarios with high data accuracy requirements, the weight of bit error rate can be increased. For example, in a certain monitoring environment, the extreme values ​​of the read success rate within a frequency scan sequence are 60% and 98%. The read success rate for a certain frequency is 90%, and its normalized value is (90-60) / (98-60)≈0.789; the extreme values ​​of the average received signal strength indication are -70dBm and -50dBm, and the average value for this frequency is -55dBm, with a normalized value of (-55+70) / (-50+70)=0.75; the extreme values ​​of the bit error rate are 1% and 30%, and the bit error rate for this frequency is 5%, with a normalized value of (30-5) / ( If the weights of the three parameters are all 1 / 3, the normalized value of the initial communication quality at this frequency point is (0.789+0.75+0.862) / 3≈0.8. The technical effect of this step is to achieve the standardization and normalization of the initial communication quality parameters, eliminate the differences in the dimensions and numerical scales between different sub-parameters, and allow each communication performance index to be calculated fairly. At the same time, through the positive and negative differential normalization rules, the actual influence trend of each parameter on communication performance is preserved, laying a standardized foundation for the subsequent multi-dimensional parameter fusion.

[0048] S62 involves sequentially normalizing multiple obstacle impact indices to obtain multiple normalized obstacle impact values. This step standardizes the parameters reflecting the degree of influence of metal obstacles on frequency points. Its physical meaning is to transform the obstacle impact indices of different frequency points into normalized values ​​within a unified numerical range, eliminating the numerical scale differences of the obstacle impact indices between different frequency points. At the same time, since a larger obstacle impact index value indicates a more severe metal interference at a frequency point, it is necessary to normalize it to make it inversely correlated with communication performance, ensuring that frequency points with lower interference levels receive higher score weights in the comprehensive score calculation. The technical implementation of this step is to first determine the maximum and minimum values ​​of the obstacle influence index for all frequency points in the frequency scanning sequence. Since the obstacle influence index is a negative indicator, the same inverse linear normalization formula as the bit error rate is used to map the obstacle influence index of each frequency point to the [0,1] interval. The formula is: Obstacle influence normalization value = (maximum value of obstacle influence index - obstacle influence index of this frequency point) / (maximum value of obstacle influence index - minimum value of obstacle influence index). If there are multiple metal obstacles corresponding to multiple obstacle influence indices in the target monitoring area, the multiple obstacle influence indices of a single frequency point need to be summed to obtain the total obstacle influence index, and then the total obstacle influence index is normalized as described above.

[0049] For example, in a certain monitoring environment, the extreme values ​​of the total obstacle impact index for all frequency points are 0.1 and 0.95, and the total obstacle impact index for a certain frequency point is 0.4. Its normalized obstacle impact value is: (0.95-0.4) / (0.95-0.1)=0.647. The larger this value is, the lower the degree of interference from metal obstacles at this frequency point. The technical effect of this step is to achieve the standardization and normalization of the obstacle impact index, eliminate the numerical scale difference between different frequency points, and at the same time, make the normalized value positively correlated with the anti-interference ability of the frequency point through the reverse normalization rule, so that it can be directly fused with the normalized value of the initial communication quality, ensuring the fairness and rationality of the metal obstacle impact dimension in the comprehensive evaluation.

[0050] S63 involves sequentially normalizing multiple environmental impact feature values ​​to obtain multiple normalized environmental impact feature values. This step standardizes the parameters reflecting the degree of influence of environmental physical parameters on frequency points. Its physical meaning is to transform the environmental impact feature values ​​composed of propagation attenuation factor and impedance mismatch factor into normalized values ​​within a unified numerical range, eliminating the numerical scale differences of environmental impact feature values ​​between different frequency points. At the same time, since the larger the value of the environmental impact feature value, the more severe the influence of environmental physical parameters on the frequency point, it is necessary to normalize it to make it inversely correlated with communication performance, ensuring that in the comprehensive score calculation, the frequency point with stronger environmental adaptability receives a higher score weight. The technical implementation of this step is as follows: First, the propagation attenuation factor and impedance mismatch factor of a single frequency point are summed according to preset weights to obtain the total environmental impact characteristic value of that frequency point. The weights can be set according to the environmental characteristics of the actual application scenario. For example, in outdoor scenarios with large temperature and humidity fluctuations, the weight of the impedance mismatch factor can be increased. Then, the maximum and minimum values ​​of the total environmental impact characteristic value of all frequency points in the frequency point scanning sequence are determined. Since the total environmental impact characteristic value is a negative indicator, it is mapped to the [0,1] interval using an inverse linear normalization formula. The formula is: Normalized value of environmental impact characteristic = (maximum value of total environmental impact characteristic value - total environmental impact characteristic value of that frequency point) / (maximum value of total environmental impact characteristic value - minimum value of total environmental impact characteristic value). For example, in a certain monitoring environment, the weights of the propagation attenuation factor and the impedance mismatch factor are both 0.5. At a certain frequency, the propagation attenuation factor is 0.18 and the impedance mismatch factor is 0.09. Its total environmental impact characteristic value is 0.18 × 0.5 + 0.09 × 0.5 = 0.135. The extreme values ​​of the total environmental impact characteristic value for all frequency points are 0.05 and 0.25. The normalized value of the environmental impact characteristic value at this frequency is: (0.25-0.135) / (0.25-0.05)=0.115 / 0.2=0.575. The larger this value is, the stronger the adaptability of the frequency point to the current environmental physical parameters. The technical effect of this step is to achieve the standardization and normalization of the environmental impact characteristic value. First, the two types of environmental impact, propagation attenuation and impedance mismatch, are integrated by weighted summation. Then, the numerical scale difference between different frequency points is eliminated by reverse normalization. This allows the parameters of the environmental adaptability dimension to be directly integrated and calculated with the parameters of the communication performance and anti-interference capability dimensions, ensuring the fairness and comprehensiveness of the environmental physical impact dimension in the comprehensive evaluation.

[0051] S64 calculates the comprehensive score for each frequency point based on the normalized value of the initial communication quality, the normalized values ​​of multiple obstacles, and the normalized values ​​of multiple environmental impact characteristics. This step is a final fusion calculation of the normalized values ​​of the three dimensions. Its physical meaning is to systematically weight and fuse the three types of normalized values ​​that reflect the basic communication performance of the frequency point, the ability to resist interference from metal obstacles, and the adaptability to environmental physical parameters, to obtain a quantitative score that can comprehensively reflect the comprehensive performance of the frequency point, realize the ranking of the comprehensive performance of all frequency points, and provide a direct and quantitative basis for determining the optimal operating frequency point. The technical implementation of this step involves first setting reasonable weighting coefficients for the normalized values ​​of initial communication quality, obstacle impact, and environmental impact characteristics, based on the needs of the actual application scenario. The sum of the weighting coefficients is 1, and the magnitude of the coefficients is determined according to the importance of each factor in the scenario. For example, in the scenario of power equipment inspection with dense metal obstacles, the weighting coefficient of the obstacle impact normalized value is increased; in the scenario of cold chain logistics with large temperature and humidity fluctuations, the weighting coefficient of the environmental impact characteristic normalized value is increased; and in the scenario of industrial monitoring with high requirements for communication stability, the weighting coefficient of the initial communication quality normalized value is increased. Then, a linear weighted summation formula is used to calculate the comprehensive score of each frequency point. The formula is: Comprehensive score = Normalized value of initial communication quality × weight 1 + Normalized value of obstacle impact × weight 2 + Normalized value of environmental impact characteristics × weight 3. Finally, the comprehensive scores of all frequency points are sorted in descending order, and the frequency point with the highest score is the current optimal working frequency point. For example, in a power equipment inspection scenario, weight 1 is set to 0.3, weight 2 to 0.4, and weight 3 to 0.3. The normalized value of the initial communication quality of a certain frequency point is 0.684, the normalized value of obstacle impact is 0.588, and the normalized value of environmental impact characteristics is 0.575. Its comprehensive score is 0.8×0.3 +0.647×0.4 + 0.575×0.3 = 0.24 + 0.2588 + 0.1725 = 0.6713. If this score is the highest among all frequency points, then this frequency point is determined to be the current optimal operating frequency point. The technical effect of this step is to achieve the systematic integration of multi-dimensional normalized parameters. Through the setting of scenario-based weighting coefficients, the comprehensive score can be made to fit the performance requirements of the actual application scenario. At the same time, the quantitative scoring form makes the comprehensive performance of the frequency point clearly comparable, providing an objective and accurate quantitative basis for determining the optimal operating frequency point, making the frequency point selection decision-making process more scientific and targeted.

[0052] In summary, compared with the existing RFID frequency point evaluation methods that rely on single-parameter evaluation and direct fusion of heterogeneous parameters, this invention constructs a complete frequency point comprehensive evaluation system that integrates "dimension-level normalization, heterogeneous parameter standardization, scenario-based weighted fusion, and quantitative comprehensive scoring." This effectively solves the problem of one-sided and unscientific frequency point selection due to the influence of complex environments in the technical background. Specifically, S61 addresses the multi-sub-parameter and multi-dimensional characteristics of initial communication quality parameters by employing forward and reverse differentiated normalization rules to standardize basic communication performance parameters while preserving the actual impact trends of each sub-parameter. S62 performs reverse normalization on the obstacle impact index, transforming the degree of metallic interference into a standardized indicator that can be integrated with communication performance, ensuring the fairness of the anti-interference capability dimension assessment. S63 first integrates the environmental impacts of propagation attenuation and impedance mismatch, and then performs reverse normalization to standardize the environmental adaptability dimension, allowing the impact of environmental physical parameters to be fully integrated into the comprehensive evaluation. S64 uses scenario-based weighting coefficient settings to linearly fuse the standardized normalized values ​​of the three dimensions, obtaining a frequency point comprehensive score that fits the actual application requirements, realizing the quantitative ranking of frequency point comprehensive performance, and providing a precise and objective decision-making basis for determining the optimal operating frequency. The entire process eliminates the differences in dimensions and numerical scales between heterogeneous parameters through standardized normalization, enabling fair and scientific fusion calculation of multi-dimensional frequency performance indicators. Simultaneously, through scenario-based weighted design, the comprehensive frequency evaluation can adapt to the performance requirements of different application scenarios, overcoming the problems of one-sided frequency evaluation and disconnect from actual scenarios in existing technologies. The resulting comprehensive score can fully and objectively reflect the overall performance of each frequency point in complex environments, making the selection of the optimal operating frequency point more scientific, targeted, and environmentally adaptable. This effectively improves the rationality of frequency selection for RFID temperature measurement systems in complex scenarios such as power equipment inspection, cold chain logistics, and industrial monitoring, ensuring the stability of the system's communication link and solving the core technical problems of fixed frequencies being unable to adapt to complex environments and having poor communication quality.

[0053] In one embodiment, step S7, which establishes a communication link with the RFID temperature measurement tag based on the current optimal operating frequency to adaptively read the temperature data collected by the RFID temperature measurement tag, includes: S71. While continuously reading temperature data at the current optimal operating frequency, periodically collect the real-time received signal strength indication value and the real-time bit error rate. S72. Obtain the historical average received signal strength indication value of the current optimal operating frequency point, and calculate the link quality score based on the real-time received signal strength indication value, the real-time bit error rate, and the historical average received signal strength indication value. S73. Determine whether the link quality score is lower than a preset threshold and the duration exceeds a set window. If the link quality score is lower than the preset threshold and the duration exceeds the set window, the communication is immediately interrupted and the entire process is reset. The process returns to the step of obtaining the environmental data of the RFID temperature measurement tag in the target monitoring area at the current moment, re-sensing the environment, and scanning the frequency point. If the link quality score is not lower than the preset threshold and the duration exceeds the set window, the current working frequency is maintained, and the temperature data, environmental feature data, working frequency and link quality score read this time are associated and stored in the database to update the mapping relationship between environmental features and the optimal frequency, so as to adaptively read the temperature data collected by the RFID temperature measurement tag.

[0054] As described in steps S71-S73 above, step S71 of this invention involves periodically collecting the real-time received signal strength indicator and the real-time bit error rate while continuously reading temperature data at the current optimal operating frequency. This step is the basic data collection link for real-time monitoring of the communication link quality. Its physical meaning is to capture the impact of dynamic environmental changes on the communication performance at the optimal operating frequency by periodically collecting core parameters that reflect the real-time performance of the communication link. The received signal strength indicator directly reflects the real-time signal transmission energy intensity of the link, and the bit error rate directly reflects the real-time data transmission accuracy of the link. These two are the core physical quantities for evaluating the quality of the communication link. Periodic collection can ensure timely perception of changes in link performance and avoid communication failures caused by sudden environmental changes. The technical implementation of this step involves the reader switching to the current optimal operating frequency and establishing a communication link. It then continuously reads the temperature data from the RFID temperature measurement tag at a preset acquisition cycle. Simultaneously, during each data reading, the radio frequency signal processing module extracts the received signal strength indicator value carried by the physical layer in real time, obtaining the real-time RSSI value. A cyclic redundancy check algorithm is then used to verify each received response signal data packet, counting the number of failed verification packets and calculating the real-time bit error rate. The acquisition cycle can be set according to the rate of environmental change in the actual application scenario. In industrial monitoring and outdoor inspection scenarios with rapid environmental changes, the cycle can be set to 1-5 seconds; in relatively stable cold chain storage scenarios, the cycle can be set to 10-30 seconds. All acquired real-time received signal strength indicators and real-time bit error rates are temporarily stored in the system cache in real time, providing continuous real-time data for subsequent link quality score calculations. For example, in a power equipment inspection scenario, the data acquisition cycle is set to 2 seconds. The reader collects the real-time RSSI value and real-time bit error rate every 2 seconds at the optimal operating frequency of 915MHz. When the metal equipment in the monitoring area undergoes slight displacement, the fluctuation of link performance can be captured in a timely manner through changes in real-time parameters. The technical effect of this step is to achieve real-time and continuous monitoring of the communication link performance at the optimal operating frequency. By periodically collecting core communication performance parameters, the system can promptly perceive the impact of dynamic environmental changes on the link, providing a real and continuous real-time data foundation for subsequent link quality assessment. At the same time, the flexibly configurable acquisition cycle allows the system to adapt to application scenarios with different rates of environmental change, ensuring a balance between the timeliness and efficiency of monitoring.

[0055] S72 involves obtaining the historical average received signal strength indicator value for the current optimal operating frequency and calculating a link quality score based on the real-time received signal strength indicator value, the real-time bit error rate, and the historical average received signal strength indicator value. This step is a core component for quantitatively evaluating the real-time quality of the communication link. Its physical meaning is to compare the real-time collected communication performance parameters with the historical performance benchmark of the frequency point. The quantitative score directly reflects the current quality status of the communication link. The historical average received signal strength indicator value serves as the communication performance benchmark for the frequency point, effectively reflecting its stable communication level under normal conditions. Combined with the comparison calculation of real-time parameters, the degree of link performance attenuation caused by environmental changes can be accurately quantified. This transforms the evaluation of link quality from scattered parameter values ​​into a unified and comparable quantitative score. Furthermore, the integration of historical environmental data makes the quantitative score of the current optimal operating frequency point more accurate and avoids extreme fluctuations. The technical implementation of this step involves the system retrieving received signal strength indication values ​​(RSSIDs) from the database for the current optimal operating frequency point under similar or identical conditions. The historical average RSSID is calculated using an arithmetic mean. This historical data originates from the system's previous environment learning and data storage processes, providing reliable environmental reference. Subsequently, a reasonable weighting coefficient is set, and the real-time RSSID, real-time bit error rate (BER), and historical average RSSID are fused to calculate a link quality score. Specifically, the ratio of the real-time RSSID to the historical average RSSID is first calculated. The signal strength relative value is obtained. The closer this value is to 1, the closer the real-time signal strength is to the historical baseline. Then, the real-time bit error rate is reverse-normalized to obtain the bit error rate impact value. The closer this value is to 1, the lower the real-time bit error rate. Finally, the signal strength relative value and the bit error rate impact value are weighted and summed according to preset weights to obtain the link quality score. The score range is mapped to [0,1]. The closer the score is to 1, the better the link quality. The weighting coefficient can be set according to the application scenario requirements. For example, the weight of the signal strength relative value can be increased in scenarios with high requirements for signal stability, and the weight of the bit error rate impact value can be increased in scenarios with high requirements for data accuracy.For example, the historical average received signal strength indication value at a certain optimal operating frequency is -58dBm, and the real-time received signal strength indication value at a certain moment is -65dBm. The relative signal strength value is (-58) / (-65)≈0.892, and the real-time bit error rate is 8%. After inverse normalization, the bit error rate impact value is 0.85. If both have a weight of 0.5, the link quality score is 0.892×0.5+0.85×0.5=0.871, indicating that the link quality is at a good level at this time. If the real-time parameters continue to deteriorate, the score will decrease accordingly. The technical effect of this step is to realize the quantitative and standardized evaluation of communication link quality. By introducing the historical average received signal strength indication value as a performance benchmark, the evaluation of link quality is made more objective and referential, avoiding the one-sidedness of evaluation based on a single real-time parameter. At the same time, the quantitative scoring form makes the link quality status intuitive and identifiable, providing a clear and quantitative basis for subsequent dynamic decision-making and adjustment. The flexible setting of the weighting coefficient also allows the scoring calculation to adapt to the performance requirements of different application scenarios.

[0056] S73 determines whether the link quality score is lower than a preset threshold and the duration exceeds a set window. If the link quality score is lower than the preset threshold and the duration exceeds the set window, communication is immediately interrupted and a full process reset is triggered. The process returns to the step of obtaining the environmental data of the RFID temperature tag in the target monitoring area at the current moment to re-perceive the environment and scan the frequency point. If the link quality score is not lower than the preset threshold and the duration exceeds the set window, the current working frequency point is maintained. The temperature data, environmental feature data, working frequency point, and link quality score read this time are associated and stored in the database to update the mapping relationship between environmental features and the optimal frequency point, so as to adaptively read the temperature data collected by the RFID temperature tag. This step is a dynamic decision-making, adjustment, and environmental learning iteration link after the communication link quality assessment. Its physical meaning is to avoid misjudgment caused by instantaneous environmental fluctuations by setting a score threshold and time window, to achieve accurate judgment of link quality deterioration, and to perform differentiated operations according to the judgment result. This ensures that the optimal frequency point can be reselected in time when the environment changes significantly, and that environmental learning can be achieved through data storage when the environment is relatively stable, updating the mapping relationship between environmental features and the optimal frequency point, so that the frequency point selection capability of the system can be continuously optimized with data accumulation. The technical implementation of this step involves first setting a link quality score threshold based on the communication stability requirements of the actual application scenario, typically between 0.6 and 0.8. Simultaneously, a time window is set, representing the duration for which the score remains below the threshold, typically 3-10 data collection cycles. The system continuously monitors changes in the link quality score and determines whether it meets the condition of "below the threshold and lasting longer than the time window." If this condition is met, it indicates a significant and continuous change in the environment of the target monitoring area, rendering the original optimal operating frequency unsuitable for the current environment. In this case, the system immediately interrupts the current communication link, triggering a full process reset and returning to step S1. The system acquires new environmental data, scans frequency points, and selects a new optimal operating frequency point. If this condition is not met, it indicates that the environment has only experienced instantaneous fluctuations or minor changes, and the current optimal operating frequency point can still adapt to the environment. The system then maintains the current operating frequency point and continues to read temperature data. At the same time, it integrates and correlates the temperature data read this time, the environmental characteristic data at the current moment (environmental state parameters, environmental physical parameters), the current operating frequency point, and the link quality score, and stores them in the system database. The database will update the mapping relationship between the original environmental characteristics and the optimal frequency point based on the newly stored data, optimize the reference basis for subsequent frequency point selection, and realize continuous iteration of environmental learning.For example, in a cold chain logistics transportation scenario, the link quality score threshold is set to 0.7, and the time window is set to 5 collection cycles. When the transport vehicle passes through a tunnel, the environment changes significantly, and the link quality score drops to 0.65 and remains there for 6 collection cycles, meeting the judgment condition. The system immediately triggers a reset, re-sensing the environment and selecting a new optimal frequency. If, during transportation, the score only drops briefly to 0.68 due to slight vehicle bumps, and recovers to above 0.8 after 2 collection cycles, not meeting the judgment condition, the system maintains the original frequency and stores the environmental and link data in the database, updating the mapping relationship. The technical effect of this step is... It achieves precise dynamic decision-making and adjustment of communication links. Through the dual judgment of scoring threshold and time window, it effectively avoids erroneous operation caused by instantaneous environmental fluctuations and ensures the stability of system operation. At the same time, the differentiated operation strategy not only solves the problem of optimal frequency point failure when the environment changes significantly, but also realizes the system's environmental learning capability iteration through data storage and mapping relationship updates. This allows subsequent frequency point selection to be based on richer environmental data, improving the accuracy and efficiency of frequency point selection. In addition, the relational data storage method allows environmental characteristics and frequency point performance to be accurately correlated, providing a structured and traceable data source for environmental learning.

[0057] In summary, compared with the existing RFID temperature measurement data reading methods that use fixed frequencies, lack link monitoring, and lack environmental learning, this invention constructs a complete communication link management and temperature data adaptive reading system of "real-time monitoring - quantitative evaluation - dynamic decision-making - learning iteration," which effectively solves the problems of unstable communication and unreliable temperature data reading in RFID temperature measurement systems caused by dynamic environmental changes in the technical background. S71 achieves real-time and continuous monitoring of the communication link at the optimal operating frequency by periodically collecting real-time received signal strength indication values ​​and real-time bit error rate. The flexibly configurable collection period allows the system to adapt to application scenarios with different environmental change rates, providing accurate real-time data for link quality assessment. S72 introduces historical average received signal strength indication values ​​as a performance benchmark, and calculates a quantitative link quality score by fusing real-time parameters with historical benchmarks, achieving standardized and objective assessment of link quality. The flexible setting of weighting coefficients allows the score to adapt to the performance requirements of different scenarios, providing a clear quantitative basis for dynamic decision-making. S73 achieves accurate judgment of link quality deterioration through dual judgment of scoring threshold and time window. At the same time, it performs differentiated operations such as resetting and reselecting the frequency point or maintaining the frequency point and storing data based on the judgment result. This ensures timely adaptation of the communication link when the environment changes significantly, and achieves continuous iteration of the system's environmental learning capability through related data storage and mapping relationship updates, allowing the accuracy of frequency point selection to continuously improve with data accumulation. The entire process forms a complete closed loop, organically combining real-time management of the communication link, adaptation to dynamic environmental changes, and continuous iteration of environmental learning. This overcomes the limitations of existing RFID temperature measurement systems, which suffer from poor adaptability to dynamic environmental changes and lack self-learning capabilities. The system can maintain a stable communication link in application scenarios with dynamic environmental changes, such as power equipment inspection, cold chain logistics, and industrial monitoring, enabling adaptive and highly reliable reading of temperature data. At the same time, continuous environmental learning optimizes the system's frequency adaptation capability, further enhancing the applicability and practical application value of the RFID temperature measurement system in complex dynamic environments.

[0058] like Figure 2 As shown, this application also provides an RFID temperature measurement frequency adaptive system that integrates environmental learning, comprising: The data acquisition module is used to acquire the environmental data of the RFID temperature measurement tag in the target monitoring area at the current moment, wherein the environmental data includes environmental state parameters and environmental physical parameters; The scanning module is used to generate a frequency point scanning sequence according to the preset initial frequency band range and scanning step size, and control the reader to transmit reading commands on each frequency point in the frequency point scanning sequence in sequence. The reader is used to connect the RFID temperature measurement tag and the back-end system in the RFID system. The communication quality acquisition module is used to collect the initial communication quality parameters returned by the RFID temperature measurement tag at each frequency point in response to the reading command, wherein the initial communication quality parameters include the received signal strength indication value, the bit error rate, and the reading success rate; The obstacle acquisition module is used to acquire the distribution location area of ​​metal obstacles within the target monitoring area based on the environmental state parameters, and to acquire multiple obstacle influence indices based on the distribution location area and the frequency point scanning sequence. An environmental parameter module is used to obtain multiple environmental impact characteristic values ​​based on the environmental physical parameters and the frequency point scanning sequence. The calculation module is used to obtain the comprehensive score of each frequency point based on the initial communication quality parameters, multiple obstacle impact indices and multiple environmental impact characteristic values, and to determine the frequency point with the highest comprehensive score as the current optimal working frequency point; The temperature data acquisition module is used to control the reader to switch to the current optimal operating frequency and establish a communication link with the RFID temperature measurement tag based on the current optimal operating frequency, so as to adaptively read the temperature data collected by the RFID temperature measurement tag.

[0059] In one embodiment, the scanning module includes: The signal receiving unit is used to control the reader to continuously transmit K reading commands at each frequency point in the frequency scanning sequence, where K is a preset integer greater than 1, and to record whether the response signal returned by the RFID temperature measurement tag is successfully received after each transmission. The acquisition unit is used to acquire the number of successful responses and the total number of transmissions on the frequency point, and to acquire the reading success rate based on the number of successful responses and the total number of transmissions; The signal strength acquisition unit is used to extract the received signal strength indication value carried by the physical layer for each successfully received response signal, obtain multiple RSSI values, and calculate the average received signal strength indication value based on the multiple RSSI values. The bit error rate acquisition unit is used to perform cyclic redundancy check on each successfully received acknowledgment signal, obtain the number of data packets that failed the check, and compare the number of data packets with the number of successful acknowledgments to obtain the bit error rate. The quality parameter acquisition unit is used to acquire initial communication quality parameters based on the read success rate, the average received signal strength indication value, and the bit error rate.

[0060] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0061] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0062] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0063] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0064] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An adaptive method for RFID temperature measurement frequency points that integrates environmental learning, characterized in that, include: Acquire environmental data of the RFID temperature measurement tag in the target monitoring area at the current moment, wherein the environmental data includes environmental state parameters and environmental physical parameters; Based on the preset initial frequency band range and scanning step size, a frequency point scanning sequence is generated, and the reader is controlled to transmit reading commands on each frequency point in the frequency point scanning sequence in sequence. The reader is used to connect the RFID temperature measurement tag and the back-end system in the RFID system. The initial communication quality parameters returned by the RFID temperature measurement tag at each frequency point in response to the read command are collected, wherein the initial communication quality parameters include the received signal strength indication value, the bit error rate, and the read success rate; The distribution area of ​​metal obstacles within the target monitoring area is obtained based on the environmental state parameters, and multiple obstacle influence indices are obtained based on the distribution area and the frequency scanning sequence. Multiple environmental impact characteristic values ​​are obtained based on the environmental physical parameters and the frequency point scanning sequence; The comprehensive score of each frequency point is obtained based on the initial communication quality parameters, multiple obstacle impact indices, and multiple environmental impact characteristic values, and the frequency point with the highest comprehensive score is determined as the current optimal working frequency point. The reader is controlled to switch to the current optimal operating frequency, and a communication link with the RFID temperature measurement tag is established based on the current optimal operating frequency to adaptively read the temperature data collected by the RFID temperature measurement tag.

2. The RFID temperature measurement frequency adaptive method based on integrated environment learning according to claim 1, characterized in that, The step of collecting the initial communication quality parameters returned by the RFID temperature measurement tag at each frequency point in response to the read command includes: For each frequency point in the frequency scanning sequence, the reader is controlled to continuously transmit K reading commands at that frequency point, where K is a preset integer greater than 1, and it is recorded whether the response signal returned by the RFID temperature measurement tag is successfully received after each transmission. The number of successful responses and the total number of transmissions on the frequency point are obtained, and the reading success rate is obtained based on the number of successful responses and the total number of transmissions. For each successfully received response signal, the received signal strength indication value carried by its physical layer is extracted to obtain multiple RSSI values, and the average received signal strength indication value is calculated based on the multiple RSSI values. Cyclic redundancy check is performed on each successfully received response signal to obtain the number of data packets that failed the check, and the bit error rate is obtained by comparing the number of data packets with the number of successful responses. The initial communication quality parameters are obtained based on the read success rate, the average received signal strength indicator value, and the bit error rate.

3. The RFID temperature measurement frequency adaptive method based on integrated environment learning according to claim 1, characterized in that, The step of obtaining the distribution location area of ​​metal obstacles within the target monitoring area based on the environmental state parameters, and obtaining multiple obstacle influence indices based on the distribution location area and the frequency point scanning sequence, includes: The environmental state parameters are obtained, including the echo signal dataset obtained by the reader through ultra-wideband radar detection, wherein the echo signal dataset contains the flight time and echo intensity of multiple sampling points; Using the flight time and echo intensity, a three-dimensional spatial reflection intensity distribution map of the target monitoring area is constructed using a back projection algorithm; Threshold segmentation and connected component analysis are performed on the three-dimensional spatial reflection intensity distribution map to extract the contour information of at least one metal obstacle. Each metal obstacle corresponds to a connected component, and the connected component is used as the distribution location region. For each frequency point in the frequency scanning sequence, calculate the direct path of the electromagnetic wave from the reader antenna to the RFID temperature measurement tag at that frequency point, and determine in turn whether the direct path intersects with multiple distribution location areas. If the direct path intersects with the distribution area, it is determined that it is blocked by a metal obstacle, and multiple reflection coefficients and multiple blocking area ratios of multiple metal obstacles are obtained. Multiple blocking loss factors are calculated based on the multiple reflection coefficients and multiple blocking area ratios. If the direct path does not intersect with the distribution area, it is determined that it is not blocked by metal obstacles, and the multipath reflection interference of multiple metal obstacles on the direct path is obtained, and multiple interference factors are calculated based on the multipath reflection interference. Multiple obstacle influence indices for a frequency point are obtained based on multiple occlusion loss factors and multiple interference factors.

4. The RFID temperature measurement frequency adaptive method based on integrated environment learning according to claim 1, characterized in that, The step of obtaining multiple environmental impact feature values ​​based on the environmental physical parameters and the frequency point scanning sequence includes: The ambient temperature and humidity are obtained, and the relative permittivity and conductivity of the current air are obtained based on the ambient temperature and humidity. The distance between the reader and the RFID temperature measurement tag is obtained, and the propagation attenuation factor of the electromagnetic wave in the air is obtained based on the distance, the relative permittivity and the conductivity. The input impedance of the RFID temperature measurement tag due to thermal expansion is obtained based on the ambient temperature, and the impedance mismatch factor is calculated based on the difference between the input impedance and the preset reader characteristic impedance. The environmental impact characteristic value corresponding to each frequency point is obtained based on the propagation attenuation factor, the impedance mismatch factor, and the frequency point scanning sequence.

5. The RFID temperature measurement frequency adaptive method based on integrated environment learning according to claim 1, characterized in that, The step of obtaining a comprehensive score for each frequency point based on the initial communication quality parameters, multiple obstacle impact indices, and multiple environmental impact characteristic values ​​includes: The initial communication quality parameters are normalized to obtain normalized initial communication quality values; The multiple obstacle impact indices are sequentially normalized to obtain multiple obstacle impact normalized values; The environmental impact feature values ​​are normalized sequentially to obtain multiple normalized values ​​of environmental impact features; The comprehensive score for each frequency point is calculated based on the normalized value of the initial communication quality, the normalized values ​​of multiple obstacle effects, and the normalized values ​​of multiple environmental impact characteristics.

6. The RFID temperature measurement frequency adaptive method based on integrated environment learning according to claim 1, characterized in that, The step of establishing a communication link with the RFID temperature measurement tag based on the current optimal operating frequency to adaptively read the temperature data collected by the RFID temperature measurement tag includes: While continuously reading temperature data at the current optimal operating frequency, the real-time received signal strength indicator and real-time bit error rate are periodically collected. Obtain the historical average received signal strength indication value of the current optimal operating frequency, and calculate the link quality score based on the real-time received signal strength indication value, the real-time bit error rate, and the historical average received signal strength indication value; Determine whether the link quality score is lower than a preset threshold and the duration exceeds a set window; If the link quality score is lower than the preset threshold and the duration exceeds the set window, the communication is immediately interrupted and the entire process is reset. The process returns to the step of obtaining the environmental data of the RFID temperature measurement tag in the target monitoring area at the current moment, re-sensing the environment, and scanning the frequency point. If the link quality score is not lower than the preset threshold and the duration exceeds the set window, the current working frequency is maintained, and the temperature data, environmental feature data, working frequency and link quality score read this time are associated and stored in the database to update the mapping relationship between environmental features and the optimal frequency, so as to adaptively read the temperature data collected by the RFID temperature measurement tag.

7. An RFID temperature measurement frequency adaptive system integrating environmental learning, characterized in that, include: The data acquisition module is used to acquire the environmental data of the RFID temperature measurement tag in the target monitoring area at the current moment, wherein the environmental data includes environmental state parameters and environmental physical parameters; The scanning module is used to generate a frequency point scanning sequence according to the preset initial frequency band range and scanning step size, and control the reader to transmit reading commands on each frequency point in the frequency point scanning sequence in sequence. The reader is used to connect the RFID temperature measurement tag and the back-end system in the RFID system. The communication quality acquisition module is used to collect the initial communication quality parameters returned by the RFID temperature measurement tag at each frequency point in response to the reading command, wherein the initial communication quality parameters include the received signal strength indication value, the bit error rate, and the reading success rate; The obstacle acquisition module is used to acquire the distribution location area of ​​metal obstacles within the target monitoring area based on the environmental state parameters, and to acquire multiple obstacle influence indices based on the distribution location area and the frequency point scanning sequence. An environmental parameter module is used to obtain multiple environmental impact characteristic values ​​based on the environmental physical parameters and the frequency point scanning sequence. The calculation module is used to obtain the comprehensive score of each frequency point based on the initial communication quality parameters, multiple obstacle impact indices and multiple environmental impact characteristic values, and to determine the frequency point with the highest comprehensive score as the current optimal working frequency point; The temperature data acquisition module is used to control the reader to switch to the current optimal operating frequency and establish a communication link with the RFID temperature measurement tag based on the current optimal operating frequency, so as to adaptively read the temperature data collected by the RFID temperature measurement tag.

8. The RFID temperature measurement frequency adaptive system integrating environmental learning according to claim 7, characterized in that, The scanning module includes: The signal receiving unit is used to control the reader to continuously transmit K reading commands at each frequency point in the frequency scanning sequence, where K is a preset integer greater than 1, and to record whether the response signal returned by the RFID temperature measurement tag is successfully received after each transmission. The acquisition unit is used to acquire the number of successful responses and the total number of transmissions on the frequency point, and to acquire the reading success rate based on the number of successful responses and the total number of transmissions; The signal strength acquisition unit is used to extract the received signal strength indication value carried by the physical layer for each successfully received response signal, obtain multiple RSSI values, and calculate the average received signal strength indication value based on the multiple RSSI values. The bit error rate acquisition unit is used to perform cyclic redundancy check on each successfully received acknowledgment signal, obtain the number of data packets that failed the check, and compare the number of data packets with the number of successful acknowledgments to obtain the bit error rate. The quality parameter acquisition unit is used to acquire initial communication quality parameters based on the read success rate, the average received signal strength indication value, and the bit error rate.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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