A method and system for reading and writing RFID tag data

Abstract

The application relates to the technical field of data reading and writing, and particularly discloses a reading and writing method and a reading and writing system for RFID tag data. The method comprises the following steps: obtaining task parameters of a high-density deployment target tag in a shelf scene, and extracting cluster interference tags; extracting the digital field physical characteristics of the cluster interference tags, dividing interference dominant types, extracting fault-tolerant sensitive segments through fault-tolerant adaptive analysis; performing multi-round instant read-back on the fault-tolerant sensitive segments, extracting read-back difference characteristics, calculating consistency reliability, and determining a sensitive tendency category; constructing a write compensation strategy according to the sensitive tendency category and the consistency reliability grading, and realizing adaptive write closed-loop adjustment. The reading and writing system comprises a cluster analysis module, a segment extraction module, a category splitting module and a closed-loop adjustment module. Through the analysis of the cluster interference area and the targeted adaptive compensation strategy, the application improves the accuracy and stability of the RFID tag data reading and writing under the high-density deployment of the shelf scene.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a method and system for reading and writing RFID tag data. Background Technology

[0002] With the continuous upgrading of the demand for refined asset and merchandise management in logistics warehousing, intelligent manufacturing, and retail supermarkets, RFID (Radio Frequency Identification) technology, with its core advantages of non-contact identification and batch reading, is gaining traction. In practical applications, high-density parallel deployment of RFID tags is used on multi-level shelves in large logistics warehouses and merchandise display shelves in supermarkets to achieve traceability and dynamic control of massive amounts of goods or merchandise.

[0003] However, the high-density deployment of shelf spaces results in limited shelf space and extremely small physical spacing between labels. The electromagnetic signals of adjacent labels overlap, creating strong label coupling. In particular, the dense display of warehouse and supermarket shelves exacerbates signal interference between labels. At the same time, the superposition of environmental electromagnetic noise creates cluster interference.

[0004] Cluster interference can cause bit differences between the inventory information and batch number written to the target and the readback data during inventory counting. It can also damage the integrity of key fields such as the electronic coding area and user area, leading to inventory statistics deviations, gaps in goods traceability, and affecting the efficiency of inbound and outbound verification and inventory turnover. In supermarket shelf management, interference can easily cause errors in reading and writing price information and sales status on product labels, resulting in abnormal barcode scanning and settlement, and delayed replenishment reminders, which can affect the accuracy of identification and information traceability in shelf scenarios.

[0005] Therefore, the present invention provides a method and system for reading and writing RFID tag data. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for reading and writing RFID tag data to solve the aforementioned background problems.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A method for reading and writing RFID tag data includes the following steps:

[0009] Acquire target tags deployed in high density and in parallel, perform cluster interference analysis on the target tags to determine the cluster interference region of the target tags, and extract the cluster interference tags in the cluster interference region.

[0010] Obtain the numerical fields of cluster interference labels and perform functional partitioning to obtain the field physical characteristics; classify the interference dominant type of cluster interference labels; combine the field physical characteristics with the interference dominant type and field characteristics to perform fault tolerance adaptation analysis to obtain fault tolerance sensitive segments;

[0011] Perform immediate readback after writing to the fault-tolerant sensitive segment, extract the readback difference features of the readback data, perform segment verification processing on all fault-tolerant sensitive segments, obtain the consistency reliability and perform reverse deconstruction processing to determine the sensitivity tendency category of the corresponding tag of the fault-tolerant sensitive segment;

[0012] Based on the sensitivity category and consistency reliability of the corresponding labels of fault-tolerant sensitive segments, a write compensation strategy is constructed, and an adaptive write closed-loop adjustment with continuous convergence is performed.

[0013] As a further technical solution of the present invention: the method for determining the cluster interference region is as follows:

[0014] Obtain the RFID tag number and the three-dimensional coordinate range of the area to which the tag belongs, and perform virtual meshing based on the three-dimensional coordinate range to divide the target area into M1 mesh units;

[0015] Obtain the signal attenuation gradient and tag coupling coefficient for each target tag in the grid cell;

[0016] The criteria for determining cluster interference areas are set, and the cluster interference units are obtained by comparing and analyzing the signal attenuation gradient and tag coupling coefficient within the grid cell with the criteria.

[0017] Physically adjacent cluster interference units are merged to obtain the cluster interference region.

[0018] As a further technical solution of the present invention: the method for extracting the cluster interference label is as follows:

[0019] The ratio of the signal attenuation gradient of each target tag to the average signal attenuation gradient within the cluster interference area is calculated to obtain the attenuation gradient coefficient.

[0020] The coupling deviation coefficient is obtained by calculating the ratio of the tag coupling coefficient of each target tag to the average tag coupling coefficient within the cluster interference area.

[0021] The attenuation gradient coefficient and coupling deviation coefficient are summed to obtain the perturbation determination value of the target tag;

[0022] The target labels within the cluster interference area are sorted in descending order according to the disturbance judgment value, and the top M3 target labels in the sorting result are selected as the cluster interference labels.

[0023] As a further technical solution of the present invention: the fault-tolerant adaptation analysis is performed as follows:

[0024] Extract the cluster interference labels, coupling deviation coefficients, and attenuation gradient coefficients from the mixed-dominant type of interference.

[0025] Weight separation is performed based on the coupling deviation coefficient and the decay gradient coefficient to obtain the coupling effect weight and the decay effect weight;

[0026] The redundancy is processed by complementary transformation to obtain the redundancy sensitivity coefficient;

[0027] Calculate the product of the redundancy sensitivity coefficient and the attenuation effect weight, and the product of the effective field ratio and the coupling effect weight. Sum the two products to obtain the perturbation sensitivity coefficient.

[0028] Based on the perturbation sensitivity coefficient, fault-tolerant sensitive segments are obtained by performing fault-tolerant screening of cluster interference labels.

[0029] As a further technical solution of the present invention, the weight separation is performed as follows:

[0030] Calculate the sum of the coupling deviation coefficient and the attenuation gradient coefficient to obtain the deviation coefficient sum;

[0031] Calculate the ratio of the coupling deviation coefficient to the sum of deviation coefficients to obtain the coupling influence weight;

[0032] Calculate the ratio of the attenuation gradient coefficient to the sum of the deviation coefficients to obtain the attenuation influence weight.

[0033] As a further technical solution of the present invention, the fault-tolerant screening process is performed as follows:

[0034] Density clustering algorithm is used to cluster the perturbation sensitivity coefficients of all fields in the standard functional partition of all cluster interference labels, and identify the high-sensitivity clusters containing the highest sensitivity coefficients.

[0035] Extract fragments of data fields corresponding to high-sensitivity clusters, and extract the start to end addresses of the corresponding storage address segments; determine that the data fields corresponding to the storage address segments are fault-tolerant sensitive fragments.

[0036] As a further technical solution of the present invention: based on the readback difference features, redundancy sensitivity coefficient and tag coupling coefficient, feature index reconstruction is performed to obtain positive and negative indices of fault-tolerant sensitive segments;

[0037] The negative indicators are processed by complementary transformation, and the negative and positive indicators of all fault-tolerant sensitive segments are integrated to construct a standard evaluation matrix.

[0038] An ideal solution sorting algorithm is used to determine the positive and negative ideal solution vectors;

[0039] Calculate the Euclidean distance from the consistency evaluation vector to the positive ideal solution vector for each fault-tolerant sensitive segment to obtain the negative distance, and the distance from the consistency evaluation vector to the negative ideal solution vector to obtain the positive distance;

[0040] The sum of the positive and negative distances is calculated, and the ratio of the negative distance to the sum of the distances is calculated to obtain the consistency confidence of the segment.

[0041] As a further technical solution of the present invention, the method for reconstructing the feature indicators is as follows:

[0042] Extract the readback difference coefficient and difference instability coefficient from the readback difference features;

[0043] Calculate the mean of the tag coupling coefficient and the mean of the redundancy sensitivity coefficient of the cluster interference tag corresponding to the fault-tolerant sensitive segment in N rounds of reading;

[0044] The readback difference coefficient and the difference instability coefficient are used as negative indicators, while the mean of the tag coupling coefficient and the mean of the redundancy sensitivity coefficient are used as positive indicators.

[0045] As a further technical solution of the present invention: the method for determining the sensitive tendency category is as follows:

[0046] Sort the data in descending order according to the consistency confidence level, and calculate the projection component of each negative index of the fault-tolerant sensitive segment in the direction of the negative ideal solution vector.

[0047] Calculate the ratio of the projected component to the negative distance to obtain the individual risk contribution rate;

[0048] Based on the individual risk contribution rate of each negative indicator, the corresponding sensitive tendency categories are obtained.

[0049] An RFID tag data reading and writing system includes the following modules:

[0050] Cluster Analysis Module: Used to acquire target tags deployed in high density and in parallel, perform cluster interference analysis on the target tags to determine the cluster interference region of the target tags, and extract the cluster interference tags in the cluster interference region.

[0051] The fragment extraction module is used to obtain the numerical fields of cluster interference labels and perform functional partitioning to obtain the field physical characteristics; classify the interference dominant type of cluster interference labels; and perform fault tolerance adaptation analysis on the interference dominant type and field characteristics in combination with the field physical characteristics to obtain fault-tolerant sensitive fragments.

[0052] Category splitting module: used to perform immediate readback after writing to fault-tolerant sensitive segments, extract the readback difference features of the readback data, perform segment verification processing on all fault-tolerant sensitive segments, obtain consistency credibility and perform reverse deconstruction processing to determine the sensitivity tendency category of the corresponding tag of the fault-tolerant sensitive segment;

[0053] Closed-loop adjustment module: Based on the sensitivity tendency category and consistency reliability of the corresponding labels of fault-tolerant sensitive segments, a write compensation strategy is constructed, and adaptive write closed-loop adjustment with continuous convergence is performed.

[0054] The beneficial effects of this invention are as follows:

[0055] 1. To address the strong tag coupling caused by limited space and extremely small tag spacing in high-density deployment scenarios, as well as the cluster interference caused by dense display of warehouse and supermarket shelves and the superposition of environmental electromagnetic noise, a virtual mesh division based on three-dimensional coordinate range is used. This is combined with the signal attenuation gradient and tag coupling coefficient collected by RFID readers, and cluster interference units are screened and merged into cluster interference areas. At the same time, cluster interference tags are extracted by sorting according to the disturbance judgment value. This is beneficial for locating the core interference objects in high-density parallel deployment scenarios and determining the interference processing targets.

[0056] 2. According to the standard partitioning of the RFID tag storage area, the digital fields are deconstructed to obtain the physical characteristics of the fields. Combined with the numerical relationship between the attenuation gradient coefficient and the coupling deviation coefficient, the interference-dominant type is divided into attenuation-dominant, coupling-dominant, and hybrid-dominant types. By identifying fault-tolerant sensitive segments, it is beneficial to identify the correlation between the field characteristics of the cluster interference tags and the interference, providing key data segments for subsequent segment verification and write compensation, and reducing the scope and complexity of subsequent processing.

[0057] 3. Based on the complex characteristics of cluster interference in shelf scenarios, multiple rounds of real-time readback are performed on fault-tolerant sensitive segments to extract readback difference characteristics such as readback difference coefficient and difference instability coefficient. A consistency evaluation system is constructed by combining positive indicators and negative indicators after complementary transformation, and the consistency credibility is calculated. Then, the sensitive tendency category is determined based on the individual risk contribution rate and the positive indicator pulling effect. This is conducive to identifying the read / write quality and interference impact tendency of fault-tolerant sensitive segments, and is conducive to formulating corresponding write compensation strategies in the future.

[0058] 4. Based on sensitivity category and consistency reliability, a read / write compensation strategy for RFID tags under cluster interference on shelves is constructed. The compensation strategy uses micro-cycles to adjust the compensation action. The compensation parameters of the RFID tag are updated by judging the convergence trend of continuous micro-cycles. This is conducive to dynamically optimizing the writing process according to the actual interference characteristics of fault-tolerant sensitive segments, improving the stability of RFID tag data read / write under the high-density deployment characteristics of shelves, and alleviating the cluster interference problem faced by RFID tags. Attached Figure Description

[0059] The invention will now be further described with reference to the accompanying drawings.

[0060] Figure 1 This is a flowchart of a method for reading and writing RFID tag data according to the present invention;

[0061] Figure 2 This is a flowchart illustrating the determination process for continuing the compensation action in this invention.

[0062] Figure 3 This is a functional module diagram of an RFID tag data reading and writing system according to the present invention. Detailed Implementation

[0063] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0064] Example 1

[0065] like Figure 1 As shown, an RFID tag data reading and writing method is applied to high-density RFID tag deployment scenarios such as logistics warehouse shelves and supermarket shelves. This method addresses tag coupling interference caused by extremely small physical spacing between tags in these scenarios, as well as cluster interference caused by unstable RSSI values ​​due to environmental factors such as shelf pillar obstruction, goods packaging barriers, and electromagnetic reflection from pedestrian traffic and metal shopping carts. By acquiring high-density parallel-deployed target tags, performing cluster interference analysis to identify interference areas and core interfering tags, and then deconstructing the digital fields of the interfering tags to obtain physical characteristics, classifying the dominant interference types, and conducting fault-tolerant adaptation analysis to extract fault-tolerant sensitive segments, the method locates the core interference and high-risk data segments. This provides targets for subsequent read / write optimization, alleviating the problems of low accuracy and poor stability in RFID tag data reading and writing in high-density shelf scenarios. The method includes the following steps:

[0066] S10. Obtain target labels deployed in high density and in parallel, perform cluster interference analysis on the target labels to determine the cluster interference region of the target labels, and extract the cluster interference labels in the cluster interference region.

[0067] The method for obtaining target labels for high-density parallel deployment is as follows:

[0068] In some embodiments, in warehouse racking scenarios, RFID tag readers obtain high-density writing task instructions (such as batch information writing tasks for goods on an entire shelf) through the warehouse management system (WMS), while in supermarket racking scenarios, they obtain instructions (such as price update tasks for goods on a certain shelf) through the supermarket retail system and parse task parameters from the writing task instructions.

[0069] The task parameters include: a list of RFID tag IDs (e.g., IDs of goods on shelves in a warehouse scenario, and IDs of product tags in a supermarket scenario), the three-dimensional coordinate range of the area to which the tag belongs, and the working frequency corresponding to the writing task;

[0070] Virtual meshing is performed based on the three-dimensional coordinate range, dividing the target area into M1 mesh cells (mesh side length set to M1). , The signal wavelength corresponding to the operating frequency is approximately 11–16 cm.

[0071] The parallel deployment of RFID tags in the grid cells is used as the target tags, and the tags in the RFID ID list are mapped to the corresponding grid cells. The ID, grid coordinates (e.g., the 4th cell in the 2nd column of the 3rd layer of a warehouse shelf, the 5th cell in the 1st layer of the 2nd row of a supermarket shelf), and operating frequency of the target tags are extracted.

[0072] Preferably, M1=20;

[0073] The method for determining the cluster interference region of the target tag through cluster interference analysis is as follows:

[0074] Preferably, the signal processing module built into the RFID reader works in conjunction with the spectrum analyzer module to collect signal parameters on the channel where the target tag is located;

[0075] Combine any two target labels within a grid cell to form a label pair;

[0076] Among them, the signal acquisition parameters include the signal attenuation gradient of a single target tag and the tag coupling coefficient;

[0077] The signal attenuation gradient is obtained by calculating the average difference between the RSSI (Signal Strength Indication) values ​​collected continuously for M2 consecutive times, which reflects the signal attenuation rate.

[0078] It should be noted that the RSSI (Signal Strength Indication) value is obtained through the RFID tag reader;

[0079] Preferably, M2=50;

[0080] Through the formula: Get the label coupling coefficient of label i ;

[0081] Where i and j are the numbers of the two target tags in the tag pair. The antenna polarization angle between tags i and j in the tag pair is collected in real time by the polarization angle detection module built into the reader, combined with the antenna orientation parameters after the tag is deployed. Let Euclidean distance be the distance between the label pairs. The number of tags for the target label within the grid cell. The total number of labels within a grid cell. Within the grid cell The maximum value;

[0082] It should be noted that the tag coupling coefficient is applicable to high-density RFID tag deployment scenarios on shelves such as logistics warehousing and supermarket goods. It needs to meet the constraints of the same type of tag, fixed working frequency, and parallel dense deployment within the virtual grid of the shelf. The tag coupling coefficient is a relative tag coupling coefficient, not an absolute electromagnetic coupling strength. Rather, it reflects the relative strength of the target tag i being affected by the coupling interference of adjacent tags in the same shelf environment. It is used to filter out cluster interference tags with strong coupling interference and provide target objects for subsequent interference suppression.

[0083] If a grid cell contains only one label (no label pair), it will not be included in the analysis;

[0084] Preferably, the spatial distribution density of tags is calculated through the data analysis module of the reader, and the criteria for determining cluster interference areas are set:

[0085] Calculate the signal attenuation gradient of the target tag within the grid cell, as well as the mean value of the tag coupling coefficient;

[0086] Judgment condition 1: Within the same grid cell, the Euclidean distance between any two target tags is ≤ the preset maximum spacing of the two-dimensional grid, and the number of tags in the grid cell is ≥ 8;

[0087] Judgment Condition 2: For the grid cell of Judgment Condition 1, whether the average signal attenuation gradient of the target tag within the judgment grid cell is >0.5dB / time (high attenuation) and whether the average coupling coefficient of neighboring tags is >0.3 (high coupling), if both judgment conditions are met, the grid cell is confirmed as a cluster interference cell;

[0088] It should be noted that the average signal attenuation gradient is >0.5dB / time: based on test data of 10 common UHF RFID tags (such as ImpinjMonzaR6 and AlienH3) in warehouse racking scenarios, the average adjacent tag coupling coefficient is >0.3, which is set by those skilled in the art with reference to the ISO / IEC18000-6C standard;

[0089] If a grid cell meets the criteria for determining a cluster interference region, i.e., both criteria one and two are met, then the grid cell is marked as a cluster interference cell, and physically adjacent cluster interference cells are merged to obtain a cluster interference region.

[0090] If a grid cell does not meet the criteria for determining a cluster interference area, no action will be taken.

[0091] The method for extracting cluster interference labels from cluster interference regions is as follows:

[0092] The ratio of the signal attenuation gradient of each target tag to the average signal attenuation gradient within the cluster interference area is calculated to obtain the attenuation gradient coefficient. A value greater than 1 indicates that the tag attenuates faster than the regional average, and the larger the value, the stronger the attenuation-related interference.

[0093] The coupling deviation coefficient is obtained by calculating the ratio of the tag coupling coefficient of each target tag to the average tag coupling coefficient within the cluster interference area.

[0094] The attenuation gradient coefficient and coupling deviation coefficient are summed without weights to obtain the disturbance judgment value of the RFID tag.

[0095] It should be noted that the quantitative indicator for measuring the degree of interference to a single RFID tag within a cluster interference area is obtained by unweighted summation of the attenuation gradient coefficient and the coupling deviation coefficient. The attenuation gradient coefficient reflects the degree of deviation of the tag signal attenuation rate from the average level of the area, while the coupling deviation coefficient reflects the degree of deviation of the coupling strength between the tag and surrounding tags from the average level of the area. The larger the disturbance judgment value after the two are superimposed, the more significant the combined impact of attenuation and coupling interference on the tag.

[0096] In the summation process, both the decay gradient coefficient and the coupling deviation coefficient are dimensionless values.

[0097] The target labels within the cluster interference area are sorted in descending order according to the disturbance judgment value, and the top M3 target labels in the sorting result are selected as the cluster interference labels.

[0098] Preferably, M3=10;

[0099] Summarize all eligible labels, associate label IDs, grid coordinates, and individual parameters with the regional mean data, and establish a cluster interference label set.

[0100] S20. Obtain the numerical fields of the cluster interference labels and perform functional partitioning deconstruction to obtain the field physical characteristics; classify the interference dominant type of the cluster interference labels; combine the field physical characteristics to perform fault tolerance adaptation analysis on the interference dominant type and field characteristics to obtain fault tolerance sensitive segments.

[0101] The method for obtaining the numerical field of the cluster interference label and performing functional partitioning to obtain the field's physical characteristics is as follows:

[0102] The target tag storage area is structured and decomposed according to the standard partitions. The standard functional partitions are numeric fields. The physical characteristics of the numeric fields in the standard functional partitions are extracted, including the effective field ratio and redundancy (i.e., the ratio of the length of the check bit in the numeric field to the total length of the numeric field).

[0103] It should be noted that the standard functional partitions of the target tag include: reserved area, electronic coding area, manufacturer coding area, and user area; the effective field ratio is obtained by dividing the length of the data field excluding check bits and error correction codes by the length of the entire digital field; the redundancy value ranges from 0.05 to 0.5 (based on the RFID tag storage area standard, the check bit ratio is usually 5%-50%). If the calculated redundancy is <0.05 or >0.5, the boundary value is taken (0.05 when <0.05, 0.5 when >0.5) and then a complementary transformation is performed;

[0104] Preserve the physical characteristics of the fields in the electronic coding area, user area, and manufacturer coding area;

[0105] The method for classifying the dominant interference type of cluster interference labels is as follows:

[0106] If the attenuation gradient coefficient is higher than the coupling deviation coefficient and higher than 1, it is determined to be attenuation-dominated.

[0107] If the coupling deviation coefficient is higher than the attenuation gradient coefficient and higher than 1, it is determined to be a coupling-dominated type;

[0108] If the coupling deviation coefficient is greater than 1 and the attenuation gradient coefficient is greater than 1, and the absolute value of the difference between the two is less than or equal to 0.02, it is determined to be a hybrid dominant type.

[0109] Among them, the method of obtaining fault-tolerant sensitive segments by combining the field physical characteristics with the fault tolerance adaptation analysis of the interference-dominant type and field characteristics is as follows:

[0110] Obtain the mixed-dominant cluster interference labels, as well as the coupling deviation coefficient and decay gradient coefficient;

[0111] Calculate the sum of the coupling deviation coefficient and the attenuation gradient coefficient to obtain the deviation coefficient sum;

[0112] Calculate the ratio of the coupling deviation coefficient to the sum of deviation coefficients to obtain the coupling influence weight;

[0113] Calculate the ratio of the attenuation gradient coefficient to the sum of the deviation coefficients to obtain the attenuation influence weight;

[0114] The redundancy is processed by complementary transformation to obtain the redundancy sensitivity coefficient. The product of the redundancy sensitivity coefficient and the attenuation influence weight, and the product of the effective field ratio and the coupling influence weight are calculated. The two products are summed without weight to obtain the perturbation sensitivity coefficient.

[0115] It should be noted that the redundancy sensitivity coefficient is essentially the field's ability to resist attenuation interference (the larger the coefficient, the weaker the resistance to attenuation). Multiplying it by the attenuation impact weight (representing the proportion of attenuation interference in the current mixed interference) calculates the actual impact of attenuation interference on the field. The effective field ratio is the degree of loss of the field due to coupling interference (the larger the ratio, the denser the key information, and the heavier the loss under coupling interference). Multiplying it by the coupling impact weight (representing the proportion of coupling interference in the current mixed interference) determines the actual impact of coupling interference on the field. The two correspond to the matching relationship between different interferences and field characteristics.

[0116] Among them, the disturbance sensitivity coefficient is used to quantify the sensitivity of RFID cluster interference tag data fields to mixed interference. Combining the field's own characteristics with the influence weights of the two types of interference, it reflects the field's ability to withstand interference: the larger the value, the higher the possibility of data integrity being damaged after the field is interfered with.

[0117] It should be noted that the complementary transformation is to subtract the redundancy from 1 to obtain the redundancy sensitivity coefficient;

[0118] Based on the perturbation sensitivity coefficient, fault-tolerant sensitive segments are obtained by performing fault-tolerant screening of cluster interference labels.

[0119] The method for fault tolerance screening is as follows:

[0120] Density clustering algorithm is used to cluster the perturbation sensitivity coefficients of all fields in the standard functional partition of all cluster interference labels, and identify the high-sensitivity clusters containing the highest sensitivity coefficients.

[0121] It should be noted that when using the density clustering algorithm (DBSCAN), the neighborhood radius is 0.2 and the minimum number of points is 5 (to ensure that highly sensitive clusters contain at least 5 field fragments and avoid interference from isolated points).

[0122] Extract fragments of data fields corresponding to high-sensitivity clusters, and extract the start and end addresses of the corresponding storage address segments; determine that the data fields corresponding to the storage address segments are fault-tolerant sensitive fragments;

[0123] It is understandable that the purpose of extracting fault-tolerant sensitive segments is:

[0124] Function 1: To identify the key data fields with the weakest tolerance to interference in the cluster interference tags, thereby reducing the need for indiscriminate processing of all fields of the tags in the future, reducing the unnecessary computing power consumption and time cost of the reader during the data reading and writing process, and allowing processing resources to be concentrated on high-risk fields;

[0125] Secondly, it identifies the segments in the tag data most susceptible to integrity issues due to coupling and attenuation interference, and designs protection schemes that better suit the characteristics of these segments to reduce the probability of data errors during reading and writing of high-risk segments, thereby ensuring the quality of reading and writing the core information of the tags.

[0126] Example 2

[0127] Please see Figure 1 As shown, a method for reading and writing RFID tag data further includes the following steps:

[0128] S30. Perform immediate readback after writing to the fault-tolerant sensitive segment, extract the readback difference features of the readback data, perform segment verification processing on all fault-tolerant sensitive segments, obtain the consistency credibility and perform reverse deconstruction processing to determine the sensitivity tendency category of the corresponding tag of the fault-tolerant sensitive segment.

[0129] The method for performing immediate readback after writing to fault-tolerant sensitive segments is as follows:

[0130] Perform N rounds of real-time readback on the fault-tolerant sensitive segments and extract the readback difference features of the data in each round of readback;

[0131] Preferably, the number of target tags in the grid cell where the cluster interference tag corresponding to the fault-tolerant sensitive segment is located is obtained, and the square root of the number of tags is taken as the integer part to obtain the value of N;

[0132] It should be noted that if the number of labels in a grid cell is 0, the cell is skipped; if the calculated value of N is less than 4, N is set to 4 for at least 4 rounds of reading to extract differential features.

[0133] The method for extracting the readback difference features is as follows:

[0134] Calculate the average number of bit differences between the target written content and the read data in N rounds of readback data, and obtain the bit length of the fault-tolerant sensitive segment. Ratio the average number of bit differences to the bit length to obtain the readback difference coefficient.

[0135] Obtain the repetition rate of the same bit in N rounds of reading relative to the total number of N rounds of reading, calculate the average repetition rate of all bits, and obtain the difference instability coefficient.

[0136] The readback difference coefficient and the difference instability coefficient are used as readback difference characteristics;

[0137] Understandably, the readback difference coefficient quantifies the overall difference between the target written content and the actual readback data in multiple readbacks of a fault-tolerant segment. It is obtained by calculating the average number of bit differences in multiple readbacks and then comparing it with the total bit length of the segment. The size of the ratio directly reflects the overall proportion of data deviation during the read and write process. The larger the ratio, the more significant the deviation between the overall data of the segment and the target written content, and the more prominent the overall impact of data integrity interference.

[0138] The instability coefficient reflects the stability of bit differences within a fault-tolerant sensitive segment, i.e., whether there are repeatedly erroneous bits. It is obtained by calculating the proportion of the frequency of repeated differences of the same bit in multiple reads to the total number of reads, and then taking the average of this proportion for all bits. The larger the coefficient, the more bits with repeated differences in the segment, the stronger the persistence of local deviations during data reading and writing, and the weaker the stability of data reading and writing.

[0139] The method for obtaining the consistency reliability by performing segment verification processing on all fault-tolerant sensitive segments is as follows:

[0140] Calculate the mean of the tag coupling coefficient and the mean of the redundancy sensitivity coefficient of the cluster interference tag corresponding to the fault-tolerant sensitive segment in N rounds of reading;

[0141] The reread difference coefficient and the difference instability coefficient are used as negative indicators;

[0142] The mean of the tag coupling coefficient and the mean of the redundancy sensitivity coefficient are used as positive indicators.

[0143] Negative indicators are processed by complementary transformation, which means that the larger the value of the indicator, the better the consistency, while positive indicators remain unchanged.

[0144] The negative and positive indices of each fault-tolerant sensitive segment after complementary transformation are integrated to construct a consistency evaluation vector;

[0145] Obtain the consistency evaluation vectors of all fault-tolerant sensitive segments and construct the consistency evaluation matrix;

[0146] The consistency evaluation matrix is ​​normalized using Min-Max to establish a standard evaluation matrix.

[0147] An ideal solution sorting algorithm is used to determine the positive and negative ideal solution vectors;

[0148] Calculate the Euclidean distance from the consistency evaluation vector to the positive ideal solution vector for each fault-tolerant sensitive segment to obtain the negative distance, and the distance from the consistency evaluation vector to the negative ideal solution vector to obtain the positive distance;

[0149] Calculate the sum of the positive and negative distances, and then calculate the ratio of the negative distance to the sum of the distances to obtain the consistency confidence of the segment.

[0150] The method for determining the sensitivity tendency of the corresponding tags of fault-tolerant sensitive segments through reverse deconstruction is as follows:

[0151] Sort the data in descending order according to the consistency confidence level, and calculate the projection component of each negative index of the fault-tolerant sensitive segment in the direction of the negative ideal solution vector.

[0152] Calculate the ratio of the projected component to the negative distance to obtain the individual risk contribution rate;

[0153] If the risk contribution rate of the readback difference coefficient is the largest, then the sensitivity tendency of the fault-tolerant sensitive segment is: logical storage impairment;

[0154] If the risk contribution rate of the differential instability coefficient is the largest, then the sensitivity tendency of the fault-tolerant sensitive segment is: sensitive to random noise;

[0155] If the pulling effect of the tag coupling coefficient on the positive distance is lower than the preset pulling threshold, then the sensitivity tendency of the fault-tolerant sensitive segment is environmental suppression type.

[0156] It should be noted that forward distance refers to the maximum distance at which an RFID reader can stably read back data (readback success rate ≥98%) for a tag corresponding to a fault-tolerant sensitive segment under the rated transmission power P0 (e.g., 30dBm).

[0157] Under standard conditions (same as the forward distance measurement environment), adjust the layout of surrounding tags of tag i so that the coupling coefficient of tag i changes sequentially from the initial value by ΔUh (ΔUh=0.1, i.e., Uh1=Uh0+0.1, Uh2=Uh0+0.2…); for each coupling coefficient value, measure the forward distance of tag i to obtain the distance change ΔD (e.g., when Uh increases from 0.3 to 0.4, the forward distance increases from 40cm to 55cm, ΔD=15cm); the pulling effect = ΔD / ΔUh, that is, the change in forward distance corresponding to a unit change in coupling coefficient;

[0158] The preset pulling threshold is determined by conducting at least 30 sets of "coupling coefficient-forward distance" tests under standard conditions, calculating the pulling effect of each set, and taking the minimum value among all test results as the preset pulling threshold.

[0159] Understandably, the purpose of reverse destructuring is:

[0160] Objective 1: To identify the core risk factors affecting read / write quality from the consistency reliability results of fault-tolerant sensitive segments. By calculating the projection components of negative indicators in the direction of the negative ideal solution vector and the contribution rate of individual risks, we can identify whether overall data bias or localized persistent instability dominates the read / write anomalies of the segments.

[0161] Objective 2: Based on the risk contribution characteristics and the driving effect of positive indicators, the sensitivity tendency category of the segment can be identified. Through this targeted decomposition, the abstract consistency evaluation results can be transformed into specific problem type judgments, which will help select the appropriate writing compensation method and reduce the situation where the compensation strategy does not match the actual problem of the segment.

[0162] Integrate the sensitivity bias and consistency reliability of fault-tolerant sensitive segments and output them.

[0163] S40. Based on the sensitivity tendency category and consistency reliability of the corresponding labels of fault-tolerant sensitive segments, construct a write compensation strategy and perform adaptive write closed-loop adjustment with continuous convergence.

[0164] The core compensation writing strategy is constructed as follows:

[0165] S401. Based on the sensitivity tendency category of fault-tolerant sensitive segments and the consistency credibility of the segments, the compensation actions are classified to determine the dimensions of the compensation strategy.

[0166] Preferably, a compensation strategy is set for three dimensions: write power, encoding redundancy, and write stability time slot;

[0167] The method for classifying compensation actions based on consistency reliability is as follows:

[0168] Consistency reliability grading standard: Consistency reliability ≥ 0.8 is Level 1 (low risk), 0.5 ≤ consistency reliability < 0.8 is Level 2 (medium risk), and consistency reliability < 0.5 is Level 3 (high risk); Level 1 corresponds to basic compensation parameters, Level 2 corresponds to enhanced compensation parameters, and Level 3 corresponds to reinforced compensation parameters;

[0169] Among them, logical damage tends to correspond to a slight increase in write power and a short-window repeated write mechanism;

[0170] For example, if the cluster interference tag has a logical impairment tendency, a consistency confidence level of 0.65 (Level 2 medium risk), a tag rated write power of 20dBm, and an original write window duration of 10ms:

[0171] The base write power is 20 × 1.2 = 24 dBm, increasing by 0.2 times at level 2, resulting in a final power of 24 × (1 + 0.2) = 28.8 dBm.

[0172] It should be noted that, based on the power tolerance test of 10 common UHF RFID tags (such as ImpinjMonzaR6 and AlienH3), 1.2 times the rated power can improve the signal strength without exceeding the tag's maximum power tolerance.

[0173] Set the number of repeated writes to 3, keep the window duration of each write to 10ms, and the interval between adjacent writes to 5ms. Cover the damaged logical memory area by multiple small power increments.

[0174] Random noise tends to correspond to redundancy ratio improvement mechanisms;

[0175] For example, suppose a cluster's interfering labels tend to be random noise, with a consistency confidence level of 0.42, corresponding to a level 3 high risk, and the original label redundancy is 0.15, meaning the checksum percentage is 15%.

[0176] The base value of coding redundancy = 0.15 × 1.5 = 0.225, the level 3 improvement is 0.6 times, and the final redundancy ratio = 0.225 × (1 + 0.6) = 0.36;

[0177] Add a CRC-16 checksum bit to the user area data fields;

[0178] Environmental suppression tendencies correspond to stable time slot stretching and the addition of a link observation window;

[0179] For example, suppose a cluster's interference label indicates an environmental suppression tendency, its consistency confidence level is 0.78, and the original stable write time slot duration is 50μs:

[0180] The base duration of writing to the stable time slot is 50 × 2 = 100 μs. After secondary stretching by 1, the final time slot is 100 × (1 + 1) = 200 μs.

[0181] Link observation window: Two additional windows are added at the second level, each with a duration of 20μs. Signal monitoring is initiated 10μs before and 10μs after writing, respectively, to capture signal fluctuations caused by environmental interference in real time. Data writing integrity is ensured through time slot stretching, and the observation window helps to avoid sudden environmental suppression interference.

[0182] S402. Based on the dimensions of the compensation strategy and by monitoring the readback data after writing in real time, the write compensation strategy is constructed.

[0183] During the execution of writing to fault-tolerant sensitive segments, the write-back data of sensitive segments is collected in real time, and the read-back difference features are extracted.

[0184] The control cycle is based on a segment-level micro-cycle, and each micro-cycle allows only one compensation action update.

[0185] The adaptive write closed-loop adjustment method for continuous convergence is as follows:

[0186] Throughout the entire write cycle, the convergence trend of the readback difference characteristics after each adjustment action is judged;

[0187] like Figure 2 As shown, if the readback difference feature corresponding to the compensation action produces positive convergence for two consecutive micro-cycles, then the compensation action will continue to be implemented.

[0188] The positive convergence is defined as follows: the difference coefficient decreases by ≥0.003 compared to the previous micro-cycle and the difference instability coefficient decreases by ≥0.001.

[0189] If the compensation action fails to converge or drifts backward twice consecutively, the compensation action is adjusted and recorded as a low-sensitivity matching action. For example, the compensation dimension is changed first (e.g., the original write power adjustment is changed to code redundancy adjustment). If the change of dimension is still ineffective, the parameter magnitude is increased or decreased within the original dimension (e.g., the original increase of 0.2 times power is changed to increase of 0.3 times or decrease of 0.1 times), and recorded as a low-sensitivity matching action, forming a complete operation logic chain.

[0190] All micro-cycle regulation and feedback results are organized into a convergence trajectory, and the compensation parameter set is updated for the next round of writing, realizing a closed loop of gradually adjusted writing strategy. This is used to improve the reading and writing stability of RFID tags when they are subject to cluster interference in high-density deployment scenarios on shelves.

[0191] Example 3

[0192] like Figure 3 As shown, an RFID tag data reading and writing system includes the following modules:

[0193] Cluster Analysis Module: Used to acquire target tags deployed in high density and in parallel, perform cluster interference analysis on the target tags to determine the cluster interference region of the target tags, and extract the cluster interference tags in the cluster interference region.

[0194] The fragment extraction module is used to obtain the numerical fields of cluster interference labels and perform functional partitioning to obtain the field physical characteristics; classify the interference dominant type of cluster interference labels; and perform fault tolerance adaptation analysis on the interference dominant type and field characteristics in combination with the field physical characteristics to obtain fault-tolerant sensitive fragments.

[0195] Category splitting module: used to perform immediate readback after writing to fault-tolerant sensitive segments, extract the readback difference features of the readback data, perform segment verification processing on all fault-tolerant sensitive segments, obtain consistency credibility and perform reverse deconstruction processing to determine the sensitivity tendency category of the corresponding tag of the fault-tolerant sensitive segment;

[0196] Closed-loop adjustment module: Based on the sensitivity tendency category and consistency reliability of the corresponding labels of fault-tolerant sensitive segments, a write compensation strategy is constructed, and adaptive write closed-loop adjustment with continuous convergence is performed.

[0197] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A method for reading and writing RFID tag data, characterized in that: Includes the following steps: Acquire target tags deployed in high density and in parallel, perform cluster interference analysis on the target tags to determine the cluster interference region of the target tags, and extract the cluster interference tags in the cluster interference region. Obtain the numerical fields of the cluster interference labels and perform functional partitioning to deconstruct the fields and obtain their physical characteristics; Classify the dominant interference type of cluster interference labels; By combining the physical characteristics of the fields, a fault-tolerant adaptation analysis is performed on the dominant interference type and field characteristics to obtain fault-tolerant sensitive segments; Perform immediate readback after writing to the fault-tolerant sensitive segment, extract the readback difference features of the readback data, perform segment verification processing on all fault-tolerant sensitive segments, obtain the consistency reliability and perform reverse deconstruction processing to determine the sensitivity tendency category of the corresponding tag of the fault-tolerant sensitive segment; Based on the sensitivity category and consistency reliability of the corresponding labels of fault-tolerant sensitive segments, a write compensation strategy is constructed, and an adaptive write closed-loop adjustment with continuous convergence is performed.

2. The method for reading and writing RFID tag data according to claim 1, characterized in that: The method for determining the cluster interference area is as follows: Obtain the RFID tag number and the three-dimensional coordinate range of the area to which the tag belongs, and perform virtual meshing based on the three-dimensional coordinate range to divide the target area into M1 mesh units; Obtain the signal attenuation gradient and tag coupling coefficient for each target tag in the grid cell; The criteria for determining cluster interference areas are set, and the cluster interference units are obtained by comparing and analyzing the signal attenuation gradient and tag coupling coefficient within the grid cell with the criteria. Physically adjacent cluster interference units are merged to obtain the cluster interference region.

3. The method for reading and writing RFID tag data according to claim 1, characterized in that: The method for extracting the cluster interference tags is as follows: The ratio of the signal attenuation gradient of each target tag to the average signal attenuation gradient within the cluster interference area is calculated to obtain the attenuation gradient coefficient. The coupling deviation coefficient is obtained by calculating the ratio of the tag coupling coefficient of each target tag to the average tag coupling coefficient within the cluster interference area. The attenuation gradient coefficient and coupling deviation coefficient are summed to obtain the perturbation determination value of the target tag; The target labels within the cluster interference area are sorted in descending order according to the disturbance judgment value, and the top M3 target labels in the sorting result are selected as the cluster interference labels.

4. The method for reading and writing RFID tag data according to claim 1, characterized in that: The fault tolerance adaptation analysis is performed as follows: Extract the cluster interference labels, coupling deviation coefficients, and attenuation gradient coefficients from the mixed-dominant type of interference. Weight separation is performed based on the coupling deviation coefficient and the decay gradient coefficient to obtain the coupling effect weight and the decay effect weight; The redundancy is processed by complementary transformation to obtain the redundancy sensitivity coefficient; Calculate the product of the redundancy sensitivity coefficient and the attenuation effect weight, and the product of the effective field ratio and the coupling effect weight. Sum the two products to obtain the perturbation sensitivity coefficient. Based on the perturbation sensitivity coefficient, fault-tolerant sensitive segments are obtained by performing fault-tolerant screening of cluster interference labels.

5. The method for reading and writing RFID tag data according to claim 4, characterized in that: The weight separation is performed as follows: Calculate the sum of the coupling deviation coefficient and the attenuation gradient coefficient to obtain the deviation coefficient sum; Calculate the ratio of the coupling deviation coefficient to the sum of deviation coefficients to obtain the coupling influence weight; Calculate the ratio of the attenuation gradient coefficient to the sum of the deviation coefficients to obtain the attenuation influence weight.

6. The method for reading and writing RFID tag data according to claim 4, characterized in that: The fault-tolerant filtering process is performed as follows: Density clustering algorithm is used to cluster the perturbation sensitivity coefficients of all fields in the standard functional partition of all cluster interference labels, and identify the high-sensitivity clusters containing the highest sensitivity coefficients. Extract fragments of data fields corresponding to high-sensitivity clusters, and extract the start to end addresses of the corresponding storage address segments; determine that the data fields corresponding to the storage address segments are fault-tolerant sensitive fragments.

7. The method for reading and writing RFID tag data according to claim 1, characterized in that: Based on the readback difference characteristics, redundancy sensitivity coefficient and label coupling coefficient, feature indicators are reconstructed to obtain positive and negative indicators of fault-tolerant sensitive segments; The negative indicators are processed by complementary transformation, and the negative and positive indicators of all fault-tolerant sensitive segments are integrated to construct a standard evaluation matrix. An ideal solution sorting algorithm is used to determine the positive and negative ideal solution vectors; Calculate the Euclidean distance from the consistency evaluation vector to the positive ideal solution vector for each fault-tolerant sensitive segment to obtain the negative distance, and the distance from the consistency evaluation vector to the negative ideal solution vector to obtain the positive distance; The sum of the positive and negative distances is calculated, and the ratio of the negative distance to the sum of the distances is calculated to obtain the consistency confidence of the segment.

8. The method for reading and writing RFID tag data according to claim 7, characterized in that: The method for reconstructing the aforementioned feature indicators is as follows: Extract the readback difference coefficient and difference instability coefficient from the readback difference features; Calculate the mean of the tag coupling coefficient and the mean of the redundancy sensitivity coefficient of the cluster interference tag corresponding to the fault-tolerant sensitive segment in N rounds of reading; The readback difference coefficient and the difference instability coefficient are used as negative indicators, while the mean of the tag coupling coefficient and the mean of the redundancy sensitivity coefficient are used as positive indicators.

9. The method for reading and writing RFID tag data according to claim 1, characterized in that: The method for determining the category of the sensitivity tendency is as follows: Sort the data in descending order according to the consistency confidence level, and calculate the projection component of each negative index of the fault-tolerant sensitive segment in the direction of the negative ideal solution vector. Calculate the ratio of the projected component to the negative distance to obtain the individual risk contribution rate; Based on the individual risk contribution rate of each negative indicator, the corresponding sensitive tendency categories are obtained.

10. An RFID tag data reading and writing system, used to implement the RFID tag data reading and writing method according to any one of claims 1-9, characterized in that: Includes the following modules: Cluster Analysis Module: Used to acquire target tags deployed in high density and in parallel, perform cluster interference analysis on the target tags to determine the cluster interference region of the target tags, and extract the cluster interference tags in the cluster interference region. Fragment extraction module: used to obtain the numerical fields of cluster interference labels and perform functional partitioning to obtain the physical characteristics of the fields; Classify the dominant interference type of cluster interference labels; By combining the physical characteristics of the fields, a fault-tolerant adaptation analysis is performed on the dominant interference type and field characteristics to obtain fault-tolerant sensitive segments; Category splitting module: used to perform immediate readback after writing to fault-tolerant sensitive segments, extract the readback difference features of the readback data, perform segment verification processing on all fault-tolerant sensitive segments, obtain consistency credibility and perform reverse deconstruction processing to determine the sensitivity tendency category of the corresponding tag of the fault-tolerant sensitive segment; Closed-loop adjustment module: Based on the sensitivity tendency category and consistency reliability of the corresponding labels of fault-tolerant sensitive segments, a write compensation strategy is constructed, and adaptive write closed-loop adjustment with continuous convergence is performed.

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