An intelligent article tracking and identifying method and system based on RFID technology

Abstract

The application discloses an intelligent article tracking and identification method and system based on RFID technology, and the method comprises the following steps: collecting an environmental noise intensity value, counting the number of tags in the effective identification range through a tag response signal; inputting the environmental noise intensity value and the number of tags into a power regulation model to output a reader transmission power and generate a reader transmission power instruction; counting the number of collision time slots, the number of successful time slots and the number of idle time slots of the tag response signal within a preset time window; dynamically adjusting the frame time slot number Q value according to the proportional relationship of the number of collision time slots, the number of successful time slots and the number of idle time slots; extracting the EPC code of the successfully identified tag, and deleting the pre-defined fixed bit segment data in the EPC code. The application constructs an optimized system of "environment-data-communication" trinity, improves the dynamic adaptability, data transmission efficiency and data reliability, and improves the identification success rate in a high-noise scene.

Description

Technical Field

[0001] This application relates to the field of smart item tracking technology, and in particular to a smart item tracking and identification method and system based on RFID technology. Background Technology

[0002] With the rapid development of Internet of Things (IoT) technology, RFID technology is being used more and more widely in logistics, warehousing, retail and other fields.

[0003] Chinese Patent CN112258133B discloses a baggage tracking and identification system based on RFID technology. The system includes a conveying mechanism, a logic control system, a baggage location detection device, and an RFID identification system. The conveying mechanism transports baggage affixed with RFID tags. The logic control system drives the conveying mechanism to transport the baggage, receives location information from the baggage location detection device, tracks and controls the baggage, and communicates in real-time with the RFID identification system to automatically identify baggage barcode information. The baggage location detection device detects the baggage's location on the conveying mechanism and sends this information to the logic control system. The RFID identification system receives the baggage tracking signal from the logic control system, scans the RFID tags on the baggage, and marks the baggage tag information. This invention improves baggage recognition rates and achieves accurate binding between baggage tag data and baggage information.

[0004] However, during the implementation of the relevant technical solutions, at least the following technical problems were discovered:

[0005] The aforementioned technologies are based on a method for adjusting the transmission power based on the number of tags (e.g., the number of tags N is linearly related to the power P), but they do not consider the dynamic impact of environmental noise. In high-noise scenarios such as metal shelves, their recognition success rate will decrease significantly compared to the theoretical value. Furthermore, the static EPC compression method used in the aforementioned technologies (e.g., fixing the deletion of 8 bits of the header file) will lead to an inability to adapt to dynamic business requirements (e.g., the need to embed temperature and humidity data in cold chain logistics), resulting in poor compatibility and increased channel load. Summary of the Invention

[0006] To address the aforementioned problems, embodiments of the present invention provide a smart item tracking and identification method based on RFID technology, the method comprising:

[0007] Collect environmental noise intensity values ​​and count the number of tags within the effective identification range based on the tag response signals;

[0008] The environmental noise intensity value and the number of tags are input into the power control model, which outputs the reader transmission power and generates a reader transmission power command.

[0009] Within a preset time window, count the number of collision slots, success slots, and idle slots of the tag response signal;

[0010] The frame time slot number Q value is dynamically adjusted based on the ratio of collision time slots, successful time slots, and idle time slots.

[0011] Extract the EPC code of the successfully identified tag and delete the predefined fixed bit segment data in the EPC code;

[0012] Perform variable-length encoding conversion on the remaining EPC code after deleting fixed-bit data, and output the encoded data;

[0013] The encoded data and spatiotemporal coordinate information are encapsulated into a transmission data packet and sent to the server through a narrowband communication channel.

[0014] Furthermore, the power control model calculates the transmit power P according to the following rules, and the calculation method includes:

[0015]

[0016] Where, N tag N represents the current number of tags. base Preset baseline number of tags, K(N) tag f(S) is a piecewise increasing exponential function based on the number of labels. noise ) is a nonlinear mapping function of environmental noise intensity, and α and β are preset weighting factors.

[0017] Furthermore, the method for dynamically adjusting the number of frame time slots Q includes:

[0018] If the proportion of collision time slots exceeds the preset upper limit threshold, then according to formula Q new =Q current ×γ up Increase the Q value, where γ up >1 is the upward adjustment factor;

[0019] If the proportion of idle time slots exceeds the preset lower threshold, then according to formula Q new =Q current ÷γ up Decrease the Q value, where γ down >1 is the downward adjustment factor.

[0020] Furthermore, the predefined fixed-segment data includes the protocol version identifier in the EPC encoding header and the cyclic check segment in the EPC encoding tail;

[0021] The fixed rules for protocol version identifiers include:

[0022] The protocol version identifier is extracted from the EPC encoding header as a fixed bit segment. The protocol version identifier is located at a preset position in the encoding structure, and its bit length corresponds to the predefined protocol version mapping table. At the decoding end, the corresponding decoding rule set is loaded according to the protocol version identifier.

[0023] The rules for preserving cyclic check code segments include:

[0024] The original cyclic check code segment is extracted from the tail of the EPC code as a fixed segment, and a secondary dynamic check code is inserted into the compressed variable-length encoded data to form a dual check mechanism. The generation algorithm of the secondary dynamic check code is independent of the original cyclic check code, and its check range covers the compressed encoded data and the protocol version identifier.

[0025] Furthermore, the variable-length encoding conversion method includes:

[0026] A dynamic dictionary table is generated based on the historical data statistics of the reader / writer, and the filtering rules for frequently occurring encoded segments are defined. Bitmask matching is performed on the original encoding. Bitmask matching includes: if there is a dictionary table entry whose difference in bits with the original encoding is less than or equal to a preset bitmask threshold, the corresponding entry index and the difference bitmask are output; otherwise, the original encoding is output.

[0027] Furthermore, the spatiotemporal coordinate information generation method includes:

[0028] Compress the geographic location data output by the GNSS module into a binary field of preset width;

[0029] The high-precision timestamp generated by the clock circuit is truncated into a preset bit-width field and concatenated with the geographic location field to form a spatiotemporal tag, which is then embedded into the reserved bits of the EPC encoding.

[0030] Furthermore, the method for generating the dynamic verification sequence includes:

[0031] Based on the preset data volume range to which the compressed encoded data volume belongs, the corresponding parity bit length is matched from a predefined parity bit length mapping table; wherein, the data volume range and parity bit length in the mapping table are inversely related. Based on the Q-value segmentation range to which the current frame slot number Q-value belongs, the corresponding parity polynomial is matched from a predefined parity strategy mapping table; wherein, the Q-value segmentation range in the mapping table is inversely related to the error correction strength of the parity polynomial. Based on the matched parity bit length and parity polynomial, redundancy calculation is performed on the compressed encoded data to generate a dynamic parity sequence and insert it into the transmission data packet.

[0032] A smart item tracking and identification system based on RFID technology, the system includes:

[0033] The acquisition and recognition module is used to acquire environmental noise intensity values ​​and count the number of tags within the effective recognition range based on the tag response signals.

[0034] The power control module is used to input the ambient noise intensity value and the number of tags into the power regulation model, output the reader transmission power, and generate the reader transmission power command.

[0035] The statistics module is used to count the number of collision slots, success slots, and idle slots of the tag response signal within a preset time window.

[0036] The frame time slot number adjustment module is used to dynamically adjust the frame time slot number Q value according to the ratio of collision time slots, success time slots and idle time slots.

[0037] The deletion and modification module is used to extract the EPC code of successfully identified tags and delete the predefined fixed segment data in the EPC code.

[0038] The encoding conversion module is used to perform variable-length encoding conversion on the remaining EPC encoding after deleting fixed-bit data, and output the encoded data.

[0039] The transmission module encapsulates the encoded data and spatiotemporal coordinate information into a transmission data packet, which is then sent to the server via a narrowband communication channel.

[0040] The technical effects and advantages of the intelligent item tracking and identification method and system based on RFID technology provided by this invention are as follows:

[0041] This invention constructs a three-in-one optimization system of "environment-data-communication," improving dynamic adaptability, data transmission efficiency, and data reliability. It also enhances the recognition success rate in high-noise scenarios. Through multi-dimensional parameter linkage control and intelligent encoding compression, it significantly improves the overall system performance, providing a highly robust solution for large-scale IoT deployments. Based on a dual-factor power control model of environmental noise intensity and tag density, combined with dynamic adjustment of frame length using time slot collision rate, this invention achieves precise resource allocation in complex scenarios. By dynamically deleting fixed redundant fields in EPC, using variable-length encoding compression, and embedding spatiotemporal tags, it significantly reduces data volume. A dual verification mechanism is constructed by dynamically selecting verification strategies based on data volume and channel status to avoid resource waste caused by excessive redundancy. Finally, through protocol version identifiers and dynamic dictionary table design, it achieves flexible adaptation of encoding rules. Attached Figure Description

[0042] Figure 1 This is a flowchart of an intelligent item tracking and identification method based on RFID technology, as shown in Example 1.

[0043] Figure 2 This is a flowchart of the method for dynamically adjusting the number of frame time slots Q in Example 1;

[0044] Figure 3 This is a connection diagram of an intelligent item tracking and identification system based on RFID technology in Example 2. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Example 1:

[0047] Please see Figure 1 As shown in this embodiment, a smart item tracking and identification method based on RFID technology is described. The method includes:

[0048] Collect environmental noise intensity values ​​and count the number of tags within the effective identification range based on the tag response signals;

[0049] The environmental noise intensity value and the number of tags are input into the power control model, which outputs the reader's transmission power and generates a reader's transmission power command.

[0050] Within a preset time window, count the number of collision slots, success slots, and idle slots of the tag response signal;

[0051] The frame time slot number Q value is dynamically adjusted based on the ratio of collision time slots, successful time slots, and idle time slots.

[0052] Extract the EPC code of the successfully identified tag and delete the predefined fixed bit segment data in the EPC code;

[0053] Perform variable-length encoding conversion on the remaining EPC code after deleting fixed-bit data, and output the encoded data;

[0054] The encoded data and spatiotemporal coordinate information are encapsulated into a transmission data packet and sent to the server through a narrowband communication channel.

[0055] The ambient noise intensity value is collected in real time by the radio frequency noise sensor built into the reader, and the unit is decibel milliwatt (dBm). It represents the intensity of radio frequency interference signals in the environment where the reader is located, which is usually generated by other wireless devices (such as Wi-Fi, Bluetooth) or electromagnetic noise sources located at the same site.

[0056] The statistical methods for tag response signal statistics include:

[0057] The reader sends a query command containing the Q value (the Q value determines the number of frame slots);

[0058] The tag responds in a randomly selected time slot;

[0059] The reader detects the signal strength of each time slot. If only one tag responds in a time slot, it is counted as a successful time slot; otherwise, it is marked as a collision time slot.

[0060] The number of tags is estimated based on the number of successful time slots (e.g., using the Schoute algorithm: T≈2.39×number of collision time slots).

[0061] For example:

[0062] When Q=16, the number of successful time slots is 10, the number of collision time slots is 3, and the estimated number of tags is ≈10+2.39×3≈17.

[0063] The effective identification range is the maximum distance at which the reader antenna can reliably read the tag, which is affected by the transmission power, antenna gain, tag sensitivity, and environmental attenuation.

[0064] The power control model is a mathematical model that dynamically adjusts the transmission power based on environmental noise and the number of tags. It is used to balance the identification success rate and energy consumption. The transmission power P is calculated using the following rules, and the calculation method includes:

[0065]

[0066] Where, N tag N represents the current number of tags. base Preset baseline number of tags, K(N) tag f(S) is a piecewise increasing exponential function based on the number of labels. noise ) is a nonlinear mapping function of environmental noise intensity, and α and β are preset weighting factors.

[0067] By using weighting factors and function design, power consumption is minimized while ensuring recognition rate, thus achieving the optimal balance between performance and energy efficiency.

[0068] Please see Figure 2 As shown, the methods for dynamically adjusting the frame slot number Q value include:

[0069] If the proportion of collision time slots exceeds the preset upper limit threshold, then according to formula Q new =Q current ×γ up Increase the Q value, where γ up >1 is the upward adjustment factor;

[0070] If the proportion of idle time slots exceeds the preset lower threshold, then according to formula Q new =Q current ÷γ up Decrease the Q value, where γ down >1 is the downward adjustment factor.

[0071] For example:

[0072] Initial parameter settings:

[0073] Current Q value: Q = 5 → Number of frame slots = 2 5 =32 time slots.

[0074] Observation window: K = 8 time slots (simplified calculation).

[0075] The preset upper threshold is 30%, and the preset lower threshold is 40%.

[0076] The upper and lower adjustment coefficients γ = 1.5.

[0077] Phase 1 (High Collision Scenario);

[0078] Monitoring data:

[0079] The number of collision time slots C = 3 (collisions occur in time slots 2, 5, and 7).

[0080] Idle time slot number I = 1 (time slot 4 has no response).

[0081] Successful timeslots S = 4 (timeslots 1, 3, 6, and 8 were successfully identified).

[0082] Proportion calculation:

[0083] Collision ratio = 3 / 8 = 37.5% (>30%).

[0084] Idle rate = 1 / 8 = 12.5% ​​(<40%).

[0085] Q-value adjustment: 5 × 1.5 ≈ 9 → Number of frame slots = 2 8 =256 time slots.

[0086] After adjusting Q=8 in the second stage (high idle scenario), run for a period of time:

[0087] Monitoring data:

[0088] The number of collision slots C = 1 (collision in slot 128).

[0089] Idle time slots I = 5 (time slots 32, 64, 96, 160, and 224 are idle).

[0090] Successful timeslots S = 2 (timeslots 16 and 192 were successful).

[0091] Proportion calculation:

[0092] Idle rate = 5 / 8 = 62.5% (>40%).

[0093] Collision rate = 1 / 8 = 12.5% ​​(<30%).

[0094] Q-value adjustment: 8 / 1.5≈5 → Number of frame slots = 2 5 =32 time slots.

[0095] After adjusting in the third stage (stable scenario), Q=5 is used for operation:

[0096] Monitoring data:

[0097] The number of collision slots C = 2 (collisions at slots 8 and 24).

[0098] Idle time slot number I = 3 (time slots 4, 16, and 28 are idle).

[0099] Successful time slots S = 3 (time slots 12, 20, and 30 were successful).

[0100] Proportion calculation:

[0101] Idle percentage = 25% (<30%), Collision percentage = 37.5% (<40%).

[0102] Adjustment result: → Q value remains unchanged at 5.

[0103] It automatically expands capacity when a high collision rate is detected and shrinks resources when there are a large number of idle time slots, eventually converging to a stable state and improving throughput.

[0104] The predefined fixed-segment data includes the protocol version identifier in the EPC encoding header and the cyclic check segment in the EPC encoding tail;

[0105] EPC codes consist of a string of numbers or letters, containing information such as the item's category, manufacturer, and production date. Different versions of EPC codes may have different rules. The protocol version identifier tells the decoder which version of the rules to use to read the data. The cyclic check code segment is generated through calculation. If the data is tampered with or damaged during transmission (such as by signal interference), the cyclic check code will not match, thus detecting the error.

[0106] The fixed rules for protocol version identifiers include:

[0107] The protocol version identifier is extracted from the EPC encoding header as a fixed bit field. The protocol version identifier is located at a preset position in the encoding structure, and its bit length corresponds to the predefined protocol version mapping table. At the decoding end, the corresponding decoding rule set is loaded according to the protocol version identifier. The decoding rule set is equivalent to a version dictionary table, which can find the corresponding decoding rule according to the protocol version identifier. This makes it possible to identify whether an upgrade is needed even if the old device does not support the new version, and only the "version dictionary table" needs to be updated when a new version is added, without rewriting the entire system.

[0108] The rules for preserving cyclic check code segments include:

[0109] The original cyclic check code segment is extracted from the tail of the EPC code as a fixed segment, and a secondary dynamic check code is inserted into the compressed variable-length encoded data to form a dual verification mechanism. The first lock (cyclic check code) checks for basic errors, and the second lock (dynamic check code) prevents malicious tampering. The generation algorithm of the secondary dynamic check code is independent of the original cyclic check code, and its verification range covers the compressed encoded data and the protocol version identifier. Dual verification can further improve data storage security.

[0110] An example of the EPC code generation process:

[0111] Version identifier:

[0112] Use the "2023 Logistics Coding Rules", corresponding to identifier 011.

[0113] Raw data:

[0114] Includes recipient address, package weight, and shipping route: Shanghai Pudong, 5kg, Beijing-Jinan-Shanghai.

[0115] First-level verification (cyclic checksum):

[0116] Generate a checksum 2F4A and append it to the end of the data.

[0117] Compressed data

[0118] Compress the data into a short string: SHPD5kg-BJ-JN-SH.

[0119] Second layer verification (dynamic code):

[0120] By mixing version number 011 and compressed data, dynamic code 9E3C is generated.

[0121] Final encoding:

[0122] 011SHPD5kg-BJ-JN-SH 2F4A 9E3C.

[0123] Variable-length encoding conversion methods include:

[0124] A dynamic dictionary table is generated based on the historical data statistics of the reader / writer, defining the filtering rules for frequently occurring coded segments. The dynamic dictionary table records frequently occurring coded segments and their corresponding short indexes, which are automatically generated by analyzing historical data, with shorter indexes assigned to more frequently used segments.

[0125] For example:

[0126] Data statistics:

[0127] The system automatically analyzes all the encoded data from the reader over the past month.

[0128] Example: The encoded segment 01001101 was found to appear 100,000 times (accounting for 8% of the total data volume), exceeding the 5% threshold.

[0129] Filtering high-frequency segments:

[0130] Add segments that occur more than 5% of the time to the dictionary.

[0131] Dictionary table structure:

[0132] Frequency of occurrence of indexed encoded fragments

[0133] 01 01001101 8%

[0134] 02 11000011 6%

[0135] Allocate short indexes:

[0136] Replacing 8-bit high-frequency segments with 2-bit codes (such as 01, 02) results in a compression rate of 2 / 8 = 25%.

[0137] Bitmask matching is performed on the original encoding. Bitmask matching includes: if there is a dictionary entry whose difference in bits with the original encoding is less than or equal to a preset bitmask threshold, the corresponding entry index and the difference bitmask are output; otherwise, the original encoding is output.

[0138] For example:

[0139] Preprocessing raw encoding:

[0140] The 64-bit code to be sent is split into eight 8-bit segments:

[0141] [Fragment 1][Fragment 2][Fragment 3][Fragment 4][Fragment 5][Fragment 6][Fragment 7][Fragment 8];

[0142] Differential position detection:

[0143] For each segment, find the most similar entry in the dictionary.

[0144] Matching rule: A maximum difference of 1 bit is allowed (bitmask threshold = 1).

[0145] Example:

[0146] Original fragment: 01001101.

[0147] Dictionary entry: 01001001 (difference in the 7th position).

[0148] Difference bitmask: 10000000 (binary marker for difference positions).

[0149] Encoding conversion:

[0150] If a similar entry is found, output the index plus the mask; otherwise, output the original fragment.

[0151] Conversion result:

[0152] 01 (index) + 10000000 (mask) (total length = 2 + 8 = 10 bits).

[0153] This improves compression efficiency, saving resources and increasing reliability, reducing scanning error alarms and improving inventory efficiency.

[0154] Methods for generating spatiotemporal coordinate information include:

[0155] Compress the geographic location data output by the GNSS module into a binary field with a preset bit width (e.g., 34052 → 100001010100);

[0156] The high-precision timestamp generated by the clock circuit is truncated into a field of preset bit width, for example:

[0157] Base time: 2020-01-01 00:00:00 (Unix timestamp 1577836800)

[0158] Calculate the time difference:

[0159] Target timestamp: 2023-10-05 14:30:45.678 → Unix time 1696523445.678;

[0160] Difference in seconds: 1696523445.678 - 1577836800 = 118686645.678 seconds;

[0161] Maximum representable value: 2 15 -1 = 32767 seconds (approximately 9.1 hours) → requires tiered storage;

[0162] Actual solution: Store the number of milliseconds for the current day (0-86399999);

[0163] Time of day: 14h×3600+30m×60+45.678s=52245.678 seconds;

[0164] Converted to milliseconds: 52245678ms;

[0165] Binary truncation:

[0166] 52245678 → binary 110001110111001111101110 (24 bits);

[0167] Take the high 15 bits: 110001110111001 (decimal 25, 305);

[0168] It is then concatenated with the geolocation field to form a spatiotemporal tag, which is embedded in the reserved bits of the EPC encoding.

[0169] Dynamic check sequences can dynamically generate check codes based on data characteristics, balancing transmission efficiency and reliability.

[0170] Methods for generating dynamic check sequences include:

[0171] Based on the preset data volume range to which the compressed encoded data volume belongs, the corresponding check bit length is matched from a predefined check bit length mapping table; wherein, the data volume range and check bit length in the mapping table have an inverse correlation; based on the Q value segmentation range to which the current frame slot number Q value belongs, the corresponding check polynomial is matched from a predefined check strategy mapping table; for example, CRC-8 (weak error correction): x8+x 2 +x+1; CRC-16 (medium error correction strength): x 16 +x 15 +x 2 +1; wherein, the Q-value segmentation interval in the mapping table is inversely related to the error correction strength of the check polynomial; based on the matching check bit length and check polynomial, redundancy calculation is performed on the compressed encoded data to generate a dynamic check sequence and insert it into the transmission data packet.

[0172] For example:

[0173] The number of bytes in the compressed data is counted. For example, the temperature and humidity data reported by the sensor is compressed to 185 bytes.

[0174] According to the mapping table, 185 bytes belong to the 128-512 range → select a 16-bit checksum.

[0175] Large data blocks (>512B) inherently possess statistical redundancy, and short checksums can save space; small data (<128B) require stronger protection.

[0176] Get the current Q value: Q = 75 (moderate congestion).

[0177] Look up the policy mapping table:

[0178]

[0179] Matching result: Q = 75 → Select CRC-16 polynomial.

[0180] Redundancy calculation (taking CRC-16 as an example):

[0181] Treat the data as a binary stream, such as 1011001100101101...

[0182] CRC-16 polynomial x 16 +x 15 +x 2 +1 → binary 11000000000000101.

[0183] Modulo 2 division:

[0184] Add 16 zeros to the end of the data → 10110011...0000.

[0185] Perform binary division using a polynomial and retain the remainder.

[0186] The remainder is the 16-bit check digit, such as 1100101011001101.

[0187] This approach allows for flexible adaptation to environmental changes while accurately providing the required level of protection, thus avoiding waste of resources.

[0188] Example 2:

[0189] like Figure 3 As shown, based on the same inventive concept as the RFID-based smart item tracking and identification method in the foregoing embodiments, this application provides an RFID-based smart item tracking and identification system. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0190] The acquisition and recognition module is used to acquire environmental noise intensity values ​​and count the number of tags within the effective recognition range based on the tag response signals.

[0191] The power control module is used to input the ambient noise intensity value and the number of tags into the power regulation model, output the reader transmission power, and generate the reader transmission power command.

[0192] The statistics module is used to count the number of collision slots, success slots, and idle slots of the tag response signal within a preset time window.

[0193] The frame time slot number adjustment module is used to dynamically adjust the frame time slot number Q value according to the ratio of collision time slots, success time slots and idle time slots.

[0194] The deletion and modification module is used to extract the EPC code of successfully identified tags and delete the predefined fixed segment data in the EPC code.

[0195] The encoding conversion module is used to perform variable-length encoding conversion on the remaining EPC encoding after deleting fixed-bit data, and output the encoded data.

[0196] The transmission module encapsulates the encoded data and spatiotemporal coordinate information into a transmission data packet, which is then sent to the server via a narrowband communication channel.

[0197] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0198] The above description is merely a preferred embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present application, based on the technical solution and concept of the present application, should be covered within the scope of protection of the present application.

Claims

1. A method for intelligent item tracking and identification based on RFID technology, characterized in that, The methods include: Collect environmental noise intensity values ​​and count the number of tags within the effective identification range based on the tag response signals; The environmental noise intensity value and the number of tags are input into the power control model, which outputs the reader transmission power and generates a reader transmission power command. The power control model calculates the transmit power according to the following rules. The calculation methods include: ； in, This represents the current number of tags. Preset baseline label quantity For tag-based A piecewise increasing exponential function of quantity. This is a nonlinear mapping function for environmental noise intensity. and The weighting factor is preset. Within a preset time window, count the number of collision slots, success slots, and idle slots of the tag response signal; The frame time slot number Q value is dynamically adjusted based on the ratio of collision time slots, successful time slots, and idle time slots. The method for dynamically adjusting the frame time slot number Q value includes: If the proportion of collision time slots exceeds the preset upper limit threshold, then according to the formula... Increase the Q value, where >1 is the upward adjustment factor; If the proportion of idle time slots exceeds the preset lower threshold, then according to the formula... Decrease the Q value, where >1 is the downward adjustment factor; Extract the EPC code of the successfully identified tag and delete the predefined fixed bit segment data in the EPC code; Perform variable-length encoding conversion on the remaining EPC code after deleting fixed-bit data, and output the encoded data; Redundancy calculations are performed on the compressed encoded data to generate a dynamic check sequence; The method for generating the dynamic verification sequence includes: Based on the preset data volume range to which the compressed encoded data volume belongs, the corresponding parity bit length is matched from a predefined parity bit length mapping table; wherein, the data volume range and parity bit length in the mapping table are inversely related. Based on the Q-value segmentation range to which the current frame slot number Q-value belongs, the corresponding parity polynomial is matched from a predefined parity strategy mapping table; wherein, the Q-value segmentation range in the mapping table is inversely related to the error correction strength of the parity polynomial. Based on the matched parity bit length and parity polynomial, redundancy calculation is performed on the compressed encoded data to generate a dynamic parity sequence and insert it into the transmission data packet. The encoded data and spatiotemporal coordinate information are encapsulated into a transmission data packet and sent to the server through a narrowband communication channel.

2. The method according to claim 1, characterized in that, The predefined fixed bit segment data includes the protocol version identifier in the EPC encoding header and the cyclic check code segment in the EPC encoding tail; The fixed rules for protocol version identifiers include: The protocol version identifier is extracted from the EPC encoding header as a fixed bit segment. The protocol version identifier is located at a preset position in the encoding structure, and its bit length corresponds to the predefined protocol version mapping table. At the decoding end, the corresponding decoding rule set is loaded according to the protocol version identifier. The rules for preserving cyclic check code segments include: The original cyclic check code segment is extracted from the tail of the EPC code as a fixed segment, and a secondary dynamic check code is inserted into the compressed variable-length encoded data to form a dual check mechanism. The generation algorithm of the secondary dynamic check code is independent of the original cyclic check code, and its check range covers the compressed encoded data and the protocol version identifier.

3. The method according to claim 1, characterized in that, The variable-length encoding conversion method includes: A dynamic dictionary table is generated based on the historical data statistics of the reader / writer, and the filtering rules for frequently occurring encoded segments are defined. Bitmask matching is performed on the original encoding. Bitmask matching includes: if there is a dictionary table entry whose difference in bits with the original encoding is less than or equal to a preset bitmask threshold, the corresponding entry index and the difference bitmask are output; otherwise, the original encoding is output.

4. The method according to claim 1, characterized in that, The spatiotemporal coordinate information generation method includes: Compress the geographic location data output by the GNSS module into a binary field of preset width; The high-precision timestamp generated by the clock circuit is truncated into a preset bit-width field and concatenated with the geographic location field to form a spatiotemporal tag, which is then embedded into the reserved bits of the EPC encoding.

5. A smart item tracking and identification system based on RFID technology, characterized in that, The system includes: The acquisition and recognition module is used to acquire environmental noise intensity values ​​and count the number of tags within the effective recognition range based on the tag response signals. The power control module is used to input the ambient noise intensity value and the number of tags into the power regulation model, output the reader transmission power, and generate the reader transmission power command. The power control model calculates the transmit power according to the following rules. The calculation methods include: ； in, This represents the current number of tags. Preset baseline label quantity For tag-based A piecewise increasing exponential function of quantity. This is a nonlinear mapping function for environmental noise intensity. and The weighting factor is preset. The statistics module is used to count the number of collision slots, success slots, and idle slots of the tag response signal within a preset time window. The frame time slot number adjustment module is used to dynamically adjust the frame time slot number Q value according to the ratio of collision time slots, success time slots and idle time slots. The method for dynamically adjusting the frame time slot number Q value includes: If the proportion of collision time slots exceeds the preset upper limit threshold, then according to the formula... Increase the Q value, where >1 is the upward adjustment factor; If the proportion of idle time slots exceeds the preset lower threshold, then according to the formula... Decrease the Q value, where >1 is the downward adjustment factor; The deletion and modification module is used to extract the EPC code of successfully identified tags and delete the predefined fixed segment data in the EPC code. The encoding conversion module is used to perform variable-length encoding conversion on the remaining EPC encoding after deleting fixed-bit data, and output the encoded data. Redundancy calculations are performed on the compressed encoded data to generate a dynamic check sequence; The method for generating the dynamic verification sequence includes: Based on the preset data volume range to which the compressed encoded data volume belongs, the corresponding parity bit length is matched from a predefined parity bit length mapping table; wherein, the data volume range and parity bit length in the mapping table are inversely related. Based on the Q-value segmentation range to which the current frame slot number Q-value belongs, the corresponding parity polynomial is matched from a predefined parity strategy mapping table; wherein, the Q-value segmentation range in the mapping table is inversely related to the error correction strength of the parity polynomial. Based on the matched parity bit length and parity polynomial, redundancy calculation is performed on the compressed encoded data to generate a dynamic parity sequence and insert it into the transmission data packet. The transmission module encapsulates the encoded data and spatiotemporal coordinate information into a transmission data packet, which is then sent to the server via a narrowband communication channel.

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