RFID-based label card issuing device management method and system

Abstract

The invention discloses an RFID-based label card issuing device management method and system, and relates to the technical field of RFID label card issuing device management, and the method comprises the steps: based on an effective label, executing elliptic curve zero-knowledge proof authentication, obtaining a label authentication result, calculating the optimal working frequency and transmitting power of an RFID label card issuing device according to the label authentication result in combination with the signal intensity, and sending the optimal working frequency and transmitting power to the RFID label card issuing device. And executing data writing according to the optimal working frequency and the transmitting power of the RFID tag card issuing device, obtaining an error rate, actual transmitting power and a channel state index, and predicting the service life and maintenance suggestions of the RFID tag card issuing device by using an LSTM model based on the error rate, the actual transmitting power and the channel state index. According to the management method of the RFID tag card sender, the management process of the RFID tag card sender is optimized, and the maintenance cost and the fault risk of the RFID tag card sender are greatly reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of RFID tag card dispenser management, in particular to a tag card dispenser management method and system based on RFID. BACKGROUND

[0002] With the rapid development of information technology, radio frequency identification (RFID) as a core technology in the field of Internet of Things (IoT) is gradually becoming a bridge connecting the physical world and the digital world. Since the first proposal of RFID in the 20th century, it has experienced a development process from low frequency to high frequency and even ultra-high frequency. RFID tag card dispenser, as an important device for data writing, plays a crucial role in realizing intelligent management of goods, especially in modern logistics and warehouse management. By using RFID tag card dispenser, the entry and update of goods information can be quickly and accurately completed, greatly improving work efficiency and accuracy, and showing broad application prospects and development potential.

[0003] However, the existing RFID tag card dispenser management method still needs improvement. Although ALOHA algorithm is used for time slot allocation to reduce the collision problem between tags, the influence of environmental noise on communication quality is not fully considered. In addition, most of the current management units lack effective predictive maintenance mechanism and cannot timely warn potential equipment failure, which not only affects work efficiency but also increases maintenance cost. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a tag card dispenser management method based on RFID to solve the problems of environmental noise influence and lack of fault warning of RFID tag card dispenser.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In the first aspect, the present application provides a tag card dispenser management method based on RFID, which includes,

[0008] Collecting environmental noise signals and using a filtering algorithm to calculate the communication frequency of RFID tag card dispenser, collecting tags to be issued, and calculating the number of time slots through a time slot optimization algorithm;

[0009] Based on the communication frequency of RFID tag card dispenser and the number of time slots, the EPC code of the tag to be issued is grouped by hash, and the effective tag and corresponding signal strength are identified through binary tree search;

[0010] Based on the effective tag, elliptic curve zero-knowledge proof authentication is performed to obtain the tag authentication result;

[0011] According to the tag authentication result, in combination with the signal strength, the optimal working frequency and transmission power of the RFID tag card issuer are calculated;

[0012] According to the optimal working frequency and transmission power of the RFID tag card issuer, data writing is performed to obtain the bit error rate, actual transmission power and channel state index;

[0013] Based on the bit error rate, actual transmission power and channel state index, the life and maintenance suggestions of the RFID tag card issuer are predicted using an LSTM model.

[0014] As a preferred scheme of the RFID-based tag card issuer management method, the environmental noise signal is collected, and a filtering algorithm is used to calculate the communication frequency of the RFID tag card issuer; the tags to be issued are collected, and the time slot number is calculated through a time slot optimization algorithm, specifically,

[0015] The collected noise signal is converted into a digital signal through an analog-to-digital converter;

[0016] The digital signal is processed in real time to obtain a filtered digital signal;

[0017] The filtered digital signal is subjected to frequency spectrum analysis to identify the frequency band in which the communication frequency of the RFID tag card issuer is located, and the communication frequency of the RFID tag card issuer is calculated through frequency estimation;

[0018] The total number of tags to be issued and the historical conflict coefficient of the tags to be issued are collected, and the total number of tags and the historical conflict coefficient are calculated according to the modified frame time slot ALOHA to obtain the time slot number.

[0019] As a preferred scheme of the RFID-based tag card issuer management method, the EPC code of the tag to be issued is subjected to hash grouping based on the communication frequency and time slot number of the RFID tag card issuer, and the valid tags and corresponding signal strengths are identified through binary tree search, specifically,

[0020] The EPC code of the tag to be issued is processed to generate a hash value of the tag to be issued;

[0021] The hash value of the tag to be issued is subjected to a modulo operation to divide the tag to be issued into N groups;

[0022] Based on the communication frequency and time slot number of the RFID tag card issuer, a query instruction of a group identifier is sent to the N groups, and the valid tags and corresponding signal strengths in the N groups are identified in turn.

[0023] As a preferred scheme of the RFID-based tag card dispenser management method, wherein: based on the valid tag, an elliptic curve zero-knowledge proof authentication is performed to obtain a tag authentication result, specifically,

[0024] The RFID tag card dispenser generates a random challenge value and sends it to the valid tag, and the valid tag performs elliptic curve cryptographic operation through key material to generate a response value;

[0025] After the response value is returned to the RFID tag card dispenser through backscatter communication, the RFID tag card dispenser performs elliptic curve zero-knowledge proof verification operation using the material to obtain the tag authentication result;

[0026] The tag authentication result includes tags that pass the authentication and tags that fail the authentication.

[0027] As a preferred scheme of the RFID-based tag card dispenser management method, wherein: according to the tag authentication result, the optimal working frequency and transmission power of the RFID tag card dispenser are calculated based on the signal strength, specifically,

[0028] For tags that pass the authentication, the corresponding signal strength is extracted, and the frequency point with the smallest signal strength attenuation is selected as the optimal working frequency, and the transmission power is calculated according to the signal strength attenuation and the power compensation algorithm;

[0029] For tags that fail the authentication, a conservative working frequency and a basic transmission power are used.

[0030] As a preferred scheme of the RFID-based tag card dispenser management method, wherein: according to the optimal working frequency and transmission power of the RFID tag card dispenser, data writing is performed to obtain the bit error rate, actual transmission power and channel state index, specifically,

[0031] The RFID tag card dispenser sends an encoded data packet to the valid tag according to the optimal working frequency and transmission power, and the RFID tag card dispenser receives the response signal returned by the valid tag, calculates the bit error rate by counting the number of decoding failures through cyclic redundancy check;

[0032] The output level of the power amplifier of the RFID tag card dispenser is monitored in real time to obtain the actual transmission power;

[0033] The pilot sequence in the response signal is extracted to analyze the multipath effect and generate the channel state index.

[0034] As a preferred scheme of the RFID-based tag card dispenser management method, wherein: based on the bit error rate, actual transmission power and channel state index, an LSTM model is used to predict the service life and maintenance recommendations of the RFID tag card dispenser, specifically,

[0035] Collecting power amplifier historical cumulative working time, working temperature curve and voltage fluctuation data of the RFID tag card dispenser and dividing into a training set and a test set;

[0036] The RFID tag card dispenser comprises a power amplifier, a low noise amplifier and a frequency synthesizer;

[0037] The training set is input into an LSTM model for time series pattern learning, the LSTM model adopts a three-layer hidden layer structure, each layer has 128 neurons, the Adam optimizer is used to minimize the prediction error, and after the training is completed, the test set is used to verify the accuracy of the LSTM model, and the trained LSTM model is obtained;

[0038] The bit error rate, actual transmission power and channel state index are input into the trained LSTM model, and the LSTM model calculates the residual life prediction value of the power amplifier, the low noise amplifier and the frequency synthesizer;

[0039] According to the residual life prediction value of the power amplifier, the low noise amplifier and the frequency synthesizer, a three-level maintenance suggestion is generated.

[0040] In a second aspect, the present application provides an RFID-based tag card dispenser management system, comprising,

[0041] The computing module collects environmental noise signals and calculates the RFID tag card dispenser communication frequency using a filtering algorithm, collects tags to be issued, and calculates the time slot number through a time slot optimization algorithm;

[0042] The identification module performs hash grouping on the EPC code of the tag to be issued based on the RFID tag card dispenser communication frequency and the time slot number, and identifies the effective tag and the corresponding signal strength through binary tree search;

[0043] The authentication module performs elliptic curve zero-knowledge proof authentication based on the effective tag to obtain a tag authentication result;

[0044] The optimization module calculates the optimal working frequency and transmission power of the RFID tag card dispenser according to the tag authentication result and the signal strength;

[0045] The index module performs data writing according to the optimal working frequency and transmission power of the RFID tag card dispenser to obtain the bit error rate, actual transmission power and channel state index;

[0046] The maintenance suggestion module uses an LSTM model to predict the life and maintenance suggestion of the RFID tag card dispenser based on the bit error rate, actual transmission power and channel state index.

[0047] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program, when executed by the processor, implements any step of the RFID-based label card dispenser management method according to the first aspect of the present application.

[0048] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any step of the RFID-based label card dispenser management method according to the first aspect of the present application.

[0049] The present application has the following beneficial effects: by using the hash grouping and binary tree search technology to efficiently identify effective labels and corresponding signal strengths, the misidentification rate caused by label conflicts is greatly reduced, the identification efficiency and reliability are enhanced, the elliptic curve zero-knowledge proof authentication mechanism is used to ensure the security of label information, which provides strong protection for the safe access of Internet of Things devices, the LSTM model is used to analyze working parameters to predict the service life of all RFID label card dispenser devices and give maintenance suggestions, realizing predictive maintenance, which not only optimizes the RFID label card dispenser management process, but also greatly reduces the maintenance cost and RFID label card dispenser failure risk. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0051] Fig. 1 The flowchart of the RFID-based label card dispenser management method.

[0052] Fig. 2 The flowchart of generating a three-level maintenance suggestion.

[0053] Fig. 3 The flowchart of the architecture of the RFID-based label card dispenser management method.

[0054] Fig. 4 The flowchart of the LSTM model training and prediction. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0056] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0057] Secondly, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic under at least one implementation of the present application. The "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is independent of or mutually exclusive with other embodiments.

[0058] Reference Figs. 1-4 For one embodiment of the present application, the embodiment provides an RFID-based tag card dispenser management method, comprising the following steps:

[0059] S1, collect the environmental noise signal, and use the filtering algorithm to calculate the RFID tag card dispenser communication frequency, collect the tag to be sent, and calculate the time slot number through the time slot optimization algorithm;

[0060] The collected noise signal is converted into a digital signal by an analog-to-digital converter;

[0061] It should be noted that the radio frequency front end of the RFID tag card dispenser receives the environmental noise signal, and the environmental noise signal is limited to the 840-960MHz operating frequency band through a band-pass filter; the filtered environmental noise signal is input to a 16-bit analog-to-digital converter, the analog-to-digital converter converts the environmental noise signal into a digital signal at a sampling rate of 2MSPS, and at the same time, a built-in reference voltage source is used to ensure conversion accuracy, and the digital signal is transmitted to a digital signal processor through an SPI interface.

[0062] The digital signal is processed in real time to obtain the filtered digital signal;

[0063] It should be noted that the digital signal processor inputs the digital signal output by the analog-to-digital converter into the Kalman filtering algorithm; the Kalman filtering algorithm executes a prediction-update cycle according to a preset noise process covariance matrix and an observation noise covariance matrix: in the prediction stage, the current signal prediction value is calculated based on the previous state estimation value, and in the update stage, the Kalman gain is calculated and the state estimation is corrected in combination with the current actual sampling value; After iterative calculation, the filtered digital signal is output, and the filtered digital signal is transmitted to a spectrum analysis unit through a DMA channel.

[0064] The filtered digital signal is subjected to spectrum analysis, the frequency band in which the RFID tag card dispenser communication frequency is located is identified, and the RFID tag card dispenser communication frequency is calculated through frequency estimation;

[0065] It should be noted that the filtered digital signal is input into a fast Fourier transform unit for spectral analysis to generate power spectral density distribution data; the power spectral density distribution data is input into a peak detection algorithm, and the power spectral density distribution data traverses all frequency points through a sliding window, while detecting local minimum points of all frequency points whose power spectral density values are simultaneously less than those of the previous and next frequency points; all local minimum points are sorted in ascending order of power spectral density values, and the three frequency bands corresponding to the three local minimum points with the smallest power spectral density values are selected as the three candidate frequency bands with the lowest power spectral density; the three candidate frequency bands with the lowest power spectral density are input into a frequency estimation algorithm, and the frequency estimation algorithm calculates the center frequency and noise fluctuation coefficient of each candidate frequency band based on a weighted least squares method, wherein the center frequency of each candidate frequency band is represented by the formula,

[0066]

[0067] wherein f represents the center frequency of the candidate frequency band, n represents the number of frequency points in the candidate frequency band, w represents the reciprocal of the power spectral density value of the frequency point, a represents the frequency value of the frequency point in the candidate frequency band, and i represents the serial number index of the frequency point in the candidate frequency band;

[0068] The noise fluctuation coefficient of each candidate frequency band is represented by the formula,

[0069]

[0070] wherein σ represents the noise fluctuation coefficient of the candidate frequency band, n represents the number of frequency points in the candidate frequency band, w represents the reciprocal of the power spectral density value of the frequency point, a represents the frequency value of the frequency point in the candidate frequency band, i represents the serial number index of the frequency point in the candidate frequency band, and f represents the center frequency of the candidate frequency band;

[0071] The frequency band with a noise fluctuation coefficient less than (for example, 0.1) and a center frequency within the hardware support range is selected as the RFID tag dispenser communication frequency.

[0072] The total number of tags to be dispensed and the historical conflict coefficient are collected, and the total number of tags and the historical conflict coefficient are calculated according to the modified frame time slot ALOHA.

[0073] Further, the RFID tag dispenser reads the total number of tags to be dispensed and the historical conflict coefficient from the batch information database; the total number of tags and the historical conflict coefficient are transmitted to the time slot optimization algorithm as input parameters; the time slot optimization algorithm performs calculation based on the modified frame time slot ALOHA formula; and the final determined time slot number is written into the time slot allocation register of the RFID tag dispenser. The specific formula for calculating the time slot number is,

[0074]

[0075] Wherein, B represents the number of time slots, A represents the total amount of labels to be issued, δ represents the number of redundant time slots (default value 5), and α represents the historical conflict coefficient.

[0076] Wherein, the RFID label issuing device creates a batch information database in the FRAM memory and establishes a table structure including batch ID, EPC code, scanning timestamp, environmental parameter and radio frequency noise intensity; the RFID label issuing device scans the labels to be issued through the multi-antenna interface, combines the collected EPC code and environmental parameter into a complete record, and writes it into the batch information database after being packaged into a 128-byte data block by the DMA controller, forming a complete batch information database; the historical conflict coefficient is obtained by analyzing the past issuing task data stored in the batch information database.

[0077] S2, based on the communication frequency and the number of time slots of the RFID label issuing device, the EPC code of the label to be issued is grouped by hash, and the effective label and the corresponding signal strength are identified by binary tree search;

[0078] The EPC code of the label to be issued is processed to generate the hash value of the label to be issued.

[0079] It should be noted that the RFID label issuing device reads the EPC code of the label to be issued, and inputs the binary data of the EPC code into the SHA-256 hash algorithm; the SHA-256 hash algorithm performs 64 rounds of encryption transformation on the EPC code, including round constant XOR, bit rotation and modulo addition operation, to generate a 256-bit hash value; the hash value is calculated by a hardware accelerator; the generated hash value is temporarily stored in the cache area of the RFID label issuing device, which is used for subsequent hash grouping operation. The operation process of the SHA-256 hash algorithm conforms to the FIPS180-4 standard, ensuring that the hash values of different EPC codes have uniform distribution characteristics. After the hash calculation is completed, the verification mechanism is automatically triggered to verify the correspondence between the hash value and the original EPC code.

[0080] The hash value of the label to be issued is subjected to a modulo operation to divide the label to be issued into N groups.

[0081] It should be noted that the RFID label issuing device obtains the 256-bit hash value of the label to be issued and converts it into a decimal value; the converted decimal value is subjected to a modulo N operation (the modulo N operation process is realized by a 32-bit arithmetic logic unit, and the time consumption of a single operation is not more than 50ns); N is the number of groups; the result of the modulo N operation is used as a group identifier, and the value range is an integer from 0 to N-1; the labels to be issued with the same group identifier are divided into the same group, and the group information is written into the group management table of the RFID label issuing device.

[0082] Based on the RFID tag card generator communication frequency and time slot number, the query instruction of group identifier is sent to N groups, and the valid tags in N groups and the corresponding signal strength are identified in turn;

[0083] Further, the RFID tag card generator sends the query instruction of group identifier to N groups in turn according to the determined communication frequency and time slot number; the query instruction of each group is transmitted to the tag through the radio frequency front end within the specified time slot number, and the tag returns a response signal after receiving the matching group identifier query instruction; the response signal is input into the binary tree search algorithm after digital demodulation, and the binary tree search algorithm identifies each valid tag in the group by iteratively segmenting the conflict tag set; the EPC code and the corresponding signal strength value of each valid tag are recorded during the identification process, and the signal strength value is measured and calibrated by the received signal strength indicator.

[0084] S3, based on the valid tag, an elliptic curve zero-knowledge proof authentication is performed to obtain a tag authentication result;

[0085] The RFID tag card generator generates a random challenge value and sends it to the valid tag, and the valid tag generates a response value by performing an elliptic curve cryptographic operation using the key material;

[0086] It should be noted that a 32-byte random number is generated by the RFID tag card generator as a challenge value, which is sent to the valid tag through the radio frequency link; after receiving the challenge value, the valid tag performs scalar multiplication operation using the elliptic curve private key material, and the challenge value is multiplied with the private key to calculate the elliptic curve point, and the calculation process is completed in the secure encryption coprocessor of the tag, and the finally generated response value contains the x coordinate and y coordinate of the elliptic curve point.

[0087] After the response value is returned to the RFID tag card generator through backscattering communication, the RFID tag card generator performs an elliptic curve zero-knowledge proof verification operation using the material to obtain a tag authentication result;

[0088] Further, the RFID tag card dispenser receives the response value returned by the valid tag, performs Base64 decoding to restore the elliptic curve point coordinates, and simultaneously inputs the public key material into the elliptic curve verification operation. The verification operation checks the mathematical relationship between the response value and the challenge value through a bilinear pairing algorithm. When the mathematical relationship between the response value and the challenge value meets the IEEE 1363.3 standard, it is a successfully authenticated tag. When the mathematical relationship between the response value and the challenge value does not meet the IEEE 1363.3 standard, it is a failed tag. The tag authentication status, the timestamp, and the tag EPC code jointly constitute a complete tag authentication result, which is written into the authentication log of the RFID tag card dispenser. The IEEE 1363.3 standard is an identification password system based on bilinear pairing, and the IEEE 1363.3 standard clearly defines the identification password algorithm using the bilinear pairing function, including the mathematical principles and implementation specifications of key generation, encryption and decryption, signature verification, which can be obtained from the IEEE standard association website.

[0089] S4, according to the tag authentication result, combining the signal strength, calculating the optimal working frequency and transmission power of the RFID tag card dispenser;

[0090] For the successfully authenticated tag, the corresponding signal strength is extracted, and the frequency point with the smallest signal strength attenuation degree is selected as the optimal working frequency. According to the signal strength attenuation degree, the transmission power is calculated according to the power compensation algorithm;

[0091] It should be noted that the RFID tag card dispenser extracts the signal strength value from the successfully authenticated tag record, and compares the signal strength attenuation amplitudes of the successfully authenticated tag in each frequency band to select the candidate frequency point with the smallest signal strength attenuation degree. The candidate frequency point is input into the frequency selection algorithm, which determines the final optimal working frequency according to the environmental noise signal. After the optimal working frequency is determined, the RFID tag card dispenser calculates the transmission power through the power compensation algorithm according to the signal strength attenuation degree. The output value of the power compensation algorithm is limited within the power range supported by the hardware, ensuring that the reverse link signal strength is maintained above the minimum receiving sensitivity specified in the protocol. The finally determined optimal working frequency and transmission power are immediately configured to the radio frequency front-end register of the RFID tag card dispenser.

[0092] For the failed tag, a conservative working frequency and a basic transmission power are used;

[0093] It should be noted that after the RFID tag card dispenser detects that the tag authentication fails, it automatically switches to a conservative working frequency, the conservative working frequency selects the middle frequency point in the ISM frequency band and is far away from the known interference source; the transmission power is set to the minimum basic transmission power allowed by the protocol, and the minimum basic transmission power is read from the security configuration area of the non-volatile memory; the conservative working frequency and the basic transmission power are written into the frequency synthesizer and power amplifier control register of the RFID tag card dispenser radio frequency front end through the SPI interface.

[0094] S5, data writing is performed according to the optimal working frequency and transmission power of the RFID tag card dispenser, and the bit error rate, actual transmission power and channel state index are obtained;

[0095] The RFID tag card dispenser sends an encoded data packet to the valid tag according to the optimal working frequency and transmission power, and at the same time, the RFID tag card dispenser receives the response signal returned by the valid tag, and calculates the bit error rate by counting the number of decoding failures through cyclic redundancy check;

[0096] It should be noted that the RFID tag card dispenser configures the radio frequency front end according to the determined optimal working frequency and transmission power, and sends a data packet encoded by Fletcher-16 to the valid tag; the valid tag performs decoding operation after receiving the data packet, and returns a response signal containing CRC check code through backscatter modulation; the RFID tag card dispenser extracts the CRC check code after receiving the response signal, and counts the number of check failures as the number of decoding failures; the bit error rate is calculated by the ratio of the number of decoding failures to the total number of Fletcher-16 encoded data packets transmitted.

[0097] The power amplifier output level of the RFID tag card dispenser is monitored in real time to obtain the actual transmission power;

[0098] It should be noted that the directional coupler of the RFID tag card dispenser collects the radio frequency signal output by the power amplifier in real time, and the collected radio frequency signal is input into the logarithmic detector to convert it into a direct current voltage signal; the direct current voltage signal is sampled by a 16-bit analog-to-digital converter to obtain a sampling voltage value, and the sampling voltage value is input into a power calculation unit; the power calculation unit converts the sampling voltage value into the actual transmission power according to the voltage-power calibration curve.

[0099] The pilot sequence in the response signal is extracted to analyze the multipath effect, and the channel state index is generated;

[0100] It should be pointed out that the RFID tag card dispenser separates the pilot sequence from the response signal returned by the effective tag, and the pilot sequence is converted into a frequency domain signal through an orthogonal frequency division multiplexing demodulator; the frequency domain signal is input into a multipath analysis algorithm, the multipath analysis algorithm calculates the multipath time delay spread and channel coherence bandwidth by comparing the phase offset and amplitude attenuation of the received pilot sequence, and finally generates channel state indicators including time delay spectrum, Doppler shift and signal-to-noise ratio matrix; after noise suppression by a Kalman filter, the channel state indicators are written into the channel state register of the RFID tag card dispenser.

[0101] S6, based on the bit error rate, the actual transmission power and the channel state indicators, using the LSTM model to predict the life and maintenance suggestion of the RFID tag card dispenser;

[0102] The power amplifier historical cumulative working time, working temperature curve and voltage fluctuation data of the RFID tag card dispenser are collected and divided into training set and test set;

[0103] It should be pointed out that the RFID tag card dispenser reads the power amplifier historical cumulative working time, working temperature curve and voltage fluctuation data from the non-volatile memory, and the historical data contains time stamp and working condition label; after the historical data is arranged in time sequence format, a stratified sampling method is used to divide them into training set and test set, the training set contains seventy percent of the historical data, and the test set contains the remaining thirty percent of the historical data; the divided training set and test set are stored in different memory areas respectively, the training set is used for LSTM model parameter training, and the test set is used for LSTM model precision verification. The data division process ensures the time continuity and working condition coverage integrity of the training set and test set, and avoids data leakage.

[0104] The training set is input into the LSTM model for time series pattern learning, the LSTM model adopts a three-layer hidden layer structure, each layer has 128 neurons, and the Adam optimizer is used to minimize the prediction error, after training, the test set is used to verify the precision of the LSTM model, and the trained LSTM model is obtained;

[0105] It should be pointed out that the training set is formatted according to the time window length and input into the LSTM model, the three-layer hidden layer structure of the LSTM model processes the training set in turn, and each layer has 128 neurons which extract the performance attenuation characteristics of the power amplifier through the gating mechanism; the Adam optimizer adjusts the LSTM model parameters with a learning rate of 0.001 to minimize the mean square error between the predicted output and the actual value of the training set; the training process adopts an early stopping strategy, which terminates the training when the validation loss does not decrease for 10 consecutive epochs; the trained LSTM model is executed on the test set to calculate the mean absolute percentage error between the LSTM model prediction value and the actual value of the training set as the model precision indicator, and finally the trained LSTM model is obtained.

[0106] Input the bit error rate, actual transmit power and channel state indicators into the trained LSTM model, and the LSTM model calculates the remaining lifetime predictions of the power amplifier, low noise amplifier and frequency synthesizer.

[0107] It should be noted that the RFID tag issuer inputs the bit error rate, actual transmit power, and channel state indicators into the trained LSTM model in a time-series format. The LSTM model analyzes the correlation between the bit error rate, actual transmit power, and channel state indicators and historical performance degradation patterns layer by layer through a three-layer hidden layer structure, and calculates the remaining lifetime prediction values ​​of the power amplifier, low-noise amplifier, and frequency synthesizer respectively. The remaining lifetime prediction values ​​are output in hours, and a confidence score is generated and written into the lifetime prediction register of the RFID tag issuer, and simultaneously stored in the device health database of non-volatile memory.

[0108] Three-level maintenance recommendations are generated based on the predicted remaining lifetime values ​​of the power amplifier, low-noise amplifier, and frequency synthesizer.

[0109] Furthermore, based on the predicted remaining lifespan of the RFID tag issuer's power amplifier, low-noise amplifier, and frequency synthesizer, a three-tiered maintenance recommendation is generated. For example, when the predicted remaining lifespan of all three components is greater than 800 hours, a "continue operation" recommendation is generated; when the predicted remaining lifespan of any component is between 300 and 800 hours, a "planned maintenance" recommendation is generated, listing the specific component name; when the predicted remaining lifespan of any component of the RFID tag issuer is less than 300 hours, an "immediate replacement" recommendation is generated, indicating the component name and its predicted remaining lifespan. The maintenance recommendations are displayed through the RFID tag issuer's human-machine interface and simultaneously stored in an encrypted maintenance log in non-volatile memory. The maintenance recommendation generation process is executed automatically every 24 hours, triggering an immediate warning when a sudden change in the predicted remaining lifespan exceeding 20% ​​is detected.

[0110] This embodiment also provides an RFID-based tag issuing system management system, including:

[0111] The calculation module collects environmental noise signals and uses a filtering algorithm to calculate the communication frequency of the RFID tag issuer. It also collects tags to be issued and calculates the number of time slots using a time slot optimization algorithm.

[0112] The identification module hashes and groups the EPC codes of the tags to be issued based on the communication frequency and number of time slots of the RFID tag issuer, and identifies valid tags and their corresponding signal strengths through binary tree retrieval.

[0113] The authentication module performs elliptic curve zero-knowledge proof authentication based on valid labels and obtains the label authentication results.

[0114] An optimization module calculates optimal working frequency and transmission power of the RFID tag card dispenser according to the tag authentication result and in combination with the signal strength;

[0115] An index module performs data writing according to the optimal working frequency and transmission power of the RFID tag card dispenser, and obtains bit error rate, actual transmission power and channel state index;

[0116] A maintenance suggestion module predicts the service life and maintenance suggestion of the RFID tag card dispenser using an LSTM model based on the bit error rate, actual transmission power and channel state index.

[0117] The embodiment also provides a computer device suitable for the RFID-based tag card dispenser management method, which comprises a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the RFID-based tag card dispenser management method proposed in the above embodiment.

[0118] The computer device can be a terminal, and the computer device comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to perform wired or wireless communication with external terminals. The wireless communication can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0119] The embodiment also provides a storage medium on which a computer program is stored, the program being executed by a processor to implement the RFID-based tag card dispenser management method proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disk.

[0120] To sum up, the application efficiently identifies valid tags and corresponding signal strengths by using hash grouping and binary tree retrieval technology, greatly reduces the misidentification rate caused by tag conflicts, enhances the identification efficiency and reliability, adopts an elliptic curve zero-knowledge proof authentication mechanism to ensure the security of tag information, provides a strong guarantee for the secure access of Internet of Things devices, uses an LSTM model to analyze working parameters to predict the service life of all devices of the RFID tag card dispenser and give maintenance suggestions, realizes predictive maintenance, optimizes the RFID tag card dispenser management process, and greatly reduces the maintenance cost and RFID tag card dispenser failure risk.

[0121] It should be noted that the above embodiments are only used to illustrate the technical solutions of the application rather than limit the application. Although the application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the application, and all should be covered in the scope of the claims of the application.

Claims

1. An RFID-based label card dispenser management method, characterized by: The method comprises the following steps: Collecting environmental noise signals and calculating the RFID tag dispenser communication frequency using a filtering algorithm, collecting tags to be dispensed, and calculating the time slot number using a time slot optimization algorithm; Based on the RFID tag dispenser communication frequency and the time slot number, the EPC code of the tag to be dispensed is hashed and grouped, and the valid tags and corresponding signal strengths are identified through binary tree search; Based on the valid tags, elliptic curve zero-knowledge proof authentication is performed to obtain the tag authentication result; According to the tag authentication result, the optimal working frequency and transmission power of the RFID tag dispenser are calculated based on the signal strength; According to the optimal working frequency and transmission power of the RFID tag dispenser, data writing is performed to obtain the bit error rate, actual transmission power and channel state index; Based on the bit error rate, actual transmission power and channel state index, an LSTM model is used to predict the service life and maintenance recommendations of the RFID tag dispenser.

2. The RFID-based label card dispenser management method according to claim 1, characterized by: The method comprises the following steps: The collected noise signals are converted into digital signals through an analog-to-digital converter; The digital signals are processed in real time to obtain filtered digital signals; The filtered digital signals are subjected to frequency spectrum analysis to identify the frequency band where the RFID tag dispenser communication frequency is located, and the RFID tag dispenser communication frequency is calculated through frequency estimation; The total number of tags to be dispensed and the historical conflict coefficient are collected, and the total number of tags and the historical conflict coefficient are calculated according to the modified frame time slot ALOHA.

3. The RFID-based label card dispenser management method according to claim 2, characterized by: The method comprises the following steps: The EPC code of the tag to be dispensed is processed to generate the hash value of the tag to be dispensed; The hash value of the tag to be dispensed is subjected to a modulo operation to divide the tag to be dispensed into N groups; Based on the RFID tag dispenser communication frequency and the time slot number, the query instruction of the group identifier is sent to the N groups, and the valid tags in the N groups and the corresponding signal strengths are identified in turn.

4. The RFID-based label card dispenser management method according to claim 3, characterized by: The method comprises the following steps: The RFID tag dispenser generates a random challenge value and sends it to the valid tags, and the valid tags perform elliptic curve cryptographic operation through key materials to generate a response value; After the response value is returned to the RFID tag dispenser through backscattering communication, the RFID tag dispenser performs elliptic curve zero-knowledge proof verification operation using the material to obtain the tag authentication result; The tag authentication result includes the tags that pass the authentication and the tags that fail the authentication.

5. The RFID-based label card dispenser management method according to claim 4, characterized by: The method comprises the following steps: For the tags that pass the authentication, the corresponding signal strength is extracted, the frequency point with the smallest signal strength attenuation degree is selected as the optimal working frequency, and the transmission power is calculated according to the signal strength attenuation degree. For the tag of authentication failure, the conservative working frequency and the basic transmission power are adopted.

6. The RFID-based label card dispenser management method according to claim 5, characterized by: The data write is performed according to the optimal working frequency and the transmission power of the RFID tag card dispenser, the error code rate, the actual transmission power and the channel state index are obtained, and specifically, The RFID tag card dispenser sends the encoded data packet to the effective tag according to the optimal working frequency and the transmission power, and meanwhile the RFID tag card dispenser receives the response signal returned by the effective tag, and the error code rate is calculated by the number of decoding failure times counted through the cyclic redundancy check; The power amplifier output level of the RFID tag card dispenser is monitored in real time, and the actual transmission power is obtained; The pilot sequence in the response signal is extracted to analyze the multipath effect, and the channel state index is generated.

7. The RFID-based label card dispenser management method according to claim 6, characterized by: The life and the maintenance suggestion of the RFID tag card dispenser are predicted by using the LSTM model based on the error code rate, the actual transmission power and the channel state index, and specifically, The power amplifier historical cumulative working time, the working temperature curve and the voltage fluctuation data of the RFID tag card dispenser are collected and divided into a training set and a test set; The RFID tag card dispenser comprises a power amplifier, a low noise amplifier and a frequency synthesizer; The training set is input into the LSTM model for time sequence mode learning, the LSTM model adopts a three-layer hidden layer structure, each layer has 128 neurons, the prediction error is minimized through the Adam optimizer, the LSTM model accuracy is verified after the training is completed by using the test set, and the trained LSTM model is obtained; The error code rate, the actual transmission power and the channel state index are input into the trained LSTM model, and the residual life prediction values of the power amplifier, the low noise amplifier and the frequency synthesizer are calculated by the LSTM model; The three-level maintenance suggestions are generated according to the residual life prediction values of the power amplifier, the low noise amplifier and the frequency synthesizer.

8. An RFID tag card dispenser management system based on the RFID tag card dispenser management method according to any one of claims 1 to 7, characterized by: It comprises, The calculation module collects the environmental noise signal, calculates the RFID tag card dispenser communication frequency by using a filtering algorithm, collects the tags to be carded, and calculates the time slot number by using a time slot optimization algorithm; The identification module groups the EPC codes of the tags to be carded by using a hash algorithm based on the RFID tag card dispenser communication frequency and the time slot number, and identifies the effective tags and corresponding signal strengths by using a binary tree search; The authentication module performs elliptic curve zero-knowledge proof authentication based on the effective tags, and obtains the tag authentication result; The optimization module calculates the optimal working frequency and the transmission power of the RFID tag card dispenser according to the tag authentication result and the signal strength; The index module performs data write according to the optimal working frequency and the transmission power of the RFID tag card dispenser, and obtains the error code rate, the actual transmission power and the channel state index; The maintenance suggestion module predicts the life and the maintenance suggestion of the RFID tag card dispenser by using the LSTM model based on the error code rate, the actual transmission power and the channel state index. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the RFID-based tag card dispenser management method in any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the RFID-based tag card dispenser management method in any one of claims 1-7.

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