A Smart State Management Method and System for RFID Communication Antennas

Abstract

This invention provides an intelligent state management method and system for RFID communication antennas, belonging to the field of Internet of Things (IoT) communication technology. The method includes generating an initial power configuration table based on the metal density distribution of a 3D rasterized warehouse map, and constructing a dynamic impedance compensation parameter table using temperature and humidity sensor data; collecting AGV position event data, predicting target shelf coordinates using a federated learning historical trajectory library, and activating beamforming; parsing the RSSI phase difference data matrix returned by the antenna, and triggering closed-loop control commands when the phase difference is ≥15° using GNN confidence analysis; adjusting the transmission power using a distance power compensation formula, and performing redundant jumps based on ring bus fault detection; the system includes an environment adaptation module, a spatial pre-activation module, a state monitoring module, and a system maintenance module. This invention improves signal strength in densely metal areas, reduces tilt response speed, effectively reduces ineffective radiation, and improves antenna performance and reliability in complex environments.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) communication technology, and in particular to an intelligent state management method and system for RFID communication antennas. Background Technology

[0002] In the current era of booming Internet of Things (IoT) technology, Radio Frequency Identification (RFID) technology, as a key support, has been widely penetrated into numerous fields, playing an irreplaceable and vital role. From precise tracking of goods in logistics management to efficient inventory control in the retail industry; from strict supervision of pharmaceuticals and equipment in the healthcare sector to convenient ticketing in the transportation system, RFID technology, with its unique advantages, has significantly improved the operational efficiency and management level of various industries. Within the entire RFID system, the RFID communication antenna, as the core component for signal transmission, has a crucial impact on the effective identification range, identification accuracy, and the stability and reliability of data transmission. In practical applications, RFID communication antennas face extremely complex and harsh working environments. For example, in large warehousing and logistics centers, the dense stacking of large quantities of goods, along with various metal shelves and equipment, not only causes strong reflection, scattering, and absorption of antenna signals but also leads to extremely complex signal propagation paths, forming multipath effects. This causes mutual interference between the signals received by the antenna, severely degrading signal quality and consequently affecting the accurate identification of goods tags. For example, in densely populated intelligent transportation hubs such as airports and train stations, the electronic devices carried by many passengers, such as mobile phones, tablets, and smartwatches, emit electromagnetic waves of different frequencies, which overlap with the operating frequency band of RFID communication antennas, causing serious electromagnetic interference. This greatly increases the bit error rate of signal transmission, making it difficult for the system to operate stably.

[0003] Faced with such a complex working environment, existing RFID communication antennas have revealed numerous problems in state management. Traditional RFID communication antennas mostly lack effective environmental perception capabilities and cannot monitor the dynamic changes in the surrounding electromagnetic environment in real time. This leads to a situation where, when faced with complex and ever-changing interference sources, they are like "blind men touching an elephant," only able to react passively and unable to proactively take effective measures to adjust. For example, when a new strong interference source appears, the antenna cannot detect and respond in time, continuing to operate according to preset fixed parameters, resulting in a sharp decline in communication quality, or even communication interruption. In terms of frequency adjustment, existing technologies exhibit significant lag and inflexibility. When a decline in communication quality is detected, manual intervention is often required, involving cumbersome procedures to adjust the antenna's operating frequency. This method is not only inefficient but also prone to errors during manual judgment and operation, making it difficult to quickly and accurately find the optimal operating frequency. Especially in applications with extremely high real-time requirements, such as material tracking on automated production lines, any communication delay or interruption will cause the entire production process to halt, resulting in significant economic losses. Existing RFID communication antennas also have considerable room for improvement in power consumption management. Many antennas cannot dynamically adjust their power consumption according to actual workload and communication needs, often operating in a high-power state. This not only results in significant energy waste and increased operating costs, but also causes the antennas to overheat due to prolonged high-load operation, thus affecting their lifespan and stability. In some applications requiring long-term continuous operation, such as long-term cargo monitoring in intelligent logistics warehousing, the high power consumption problem is particularly prominent, posing a serious challenge to the long-term stable operation of the system.

[0004] To address the aforementioned issues, some existing technologies attempt to employ adaptive antenna technology. By utilizing a combination of antenna array elements to perform signal processing and automatically adjusting the transmit and receive patterns, signal transmission quality is improved to some extent. However, this technology still faces numerous limitations in practical applications. On one hand, adaptive antenna technology places extremely high demands on hardware, requiring a large number of antenna array elements and high-performance digital signal processors (DSPs) or field-programmable gate arrays (FPGAs), significantly increasing system costs and limiting its widespread application in large-scale scenarios. On the other hand, the adaptability of adaptive antenna technology in complex environments needs improvement. Faced with the combined effects of multipath effects and complex interference sources, its performance improvement is not ideal and fails to meet the stringent requirements of practical applications. Furthermore, existing automatic tuning methods, such as manually changing tuning devices or using trimming methods to complete tuning in the initial stage, suffer from poor tuning flexibility and difficulty in guaranteeing accuracy. In actual working environments, tag impedance changes in real time with various factors such as distance, humidity, curvature, and environmental variations. Existing tuning technologies cannot make timely and accurate adjustments based on these dynamic changes, thus affecting the identification and communication functions of RFID tags.

[0005] In view of this, there is an urgent need for an intelligent state management method and system for RFID communication antennas to at least address the above-mentioned shortcomings. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent state management method and system for RFID communication antennas, to solve the problems of signal attenuation in metal shelf scenarios with traditional fixed-power antennas; slow processing speed of anti-collision algorithms; signal attenuation due to temperature drift caused by fixed antenna power / frequency; and 60% redundant energy consumption due to all-time high-power mode. The specific technical solution is as follows:

[0007] This invention provides an intelligent state management method for RFID communication antennas, comprising:

[0008] Step 1: Based on the structured spatial data of the warehouse 3D map, generate an initial power configuration table driven by metal density; based on the metal density partitioning results of the initial power configuration table, perform RF power gradient configuration; and generate a dynamic impedance compensation parameter table based on the ambient temperature and humidity data collected in real time by the sensor.

[0009] Step 2: Generate AGV position event data based on the real-time AGV displacement coordinate signals collected by the buried RFID readers deployed in the work channel. Based on the AGV position event data and the federated learning historical trajectory database, predict the coordinates of the target shelf ahead of the AGV's travel path. Based on the target shelf coordinates, call the initial power configuration table and impedance compensation parameter table to activate the beamforming function of the target antenna associated with the coordinates.

[0010] Step 3: Based on the electronic tag return signal collected by the activated RFID antenna, parse and generate an RSSI phase difference data matrix. Based on the RSSI phase difference data matrix and the preset phase offset threshold, determine the cargo tilt status flag. Based on the cargo tilt status flag and the GNN confidence analysis result, trigger the closed-loop control command set.

[0011] Step 4: Based on the real-time distance measurement data between the antenna and the electronic tag, perform adaptive transmit power adjustment; based on the time period identifier signal triggered by the system clock, switch the antenna working mode; and based on the ring bus continuity detection signal, perform emergency response for the fault node.

[0012] Furthermore, the structured spatial data is a three-dimensional rasterized map containing shelf height and metal density distribution. The initial power configuration table includes a set of radio frequency transmission power reference parameters set based on metal density threshold partitioning. The initial power configuration table stores metal density distribution data, including a coordinate mapping table of dense metal areas.

[0013] Furthermore, the metal density threshold is defined as when the metal content per unit volume is ≥300 kg / m³. 3 The area is marked as a densely populated metal zone, with a power rating ≥1W; when the metal content per unit volume is <300kg / m³. 3 The time stamp indicates it is in the standard shelving area, and the power configuration is 0.5W.

[0014] Furthermore, the dynamic impedance compensation parameter table is a composite compensation coefficient matrix established based on the temperature and humidity-RF attenuation mapping relationship and the metal density correction factor β, which is used to offset signal drift caused by environmental factors.

[0015] Furthermore, the AGV position event data is three-dimensional spatial positioning data containing AGV real-time coordinates, motion vectors, and timestamps; the federated learning historical trajectory library is generated by training LSTM models locally on each AGV and aggregating parameters on a central server to generate a global trajectory probability model; the beamforming function is a directional transmission technology that dynamically adjusts the radio frequency beam towards the target shelf area using a programmable phase array antenna, combined with shelf metal density and environmental impedance parameters.

[0016] Furthermore, the RSSI phase difference data matrix is ​​a data set composed of the difference in tag signal strength received by a multi-antenna array and the phase angle offset of adjacent signals, used to characterize changes in the spatial attitude of goods; the goods tilt status flag is a binary anomaly flag triggered when the phase difference between adjacent tags exceeds the safety tolerance; the GNN confidence analysis result is a tilt judgment reliability weight calculated by constructing the topological relationship of shelf nodes based on a graph neural network and using a neighbor tag data propagation algorithm; the adaptive transmit power adjustment is a technical operation that calculates the antenna transmit power using a distance power compensation formula; and the time period identification signal is a timing control flag generated according to the warehouse operation plan.

[0017] Furthermore, the federated learning history trajectory database is constructed in the following manner:

[0018] 201a. Each AGV terminal trains an LSTM model based on local historical trajectory data and generates encrypted model parameters;

[0019] 201b. The central server uses the FedAvg algorithm to aggregate parameters and update the global trajectory probability model;

[0020] 201c. Perform incremental updates every 2 hours to adapt to dynamic path changes.

[0021] Furthermore, the closed-loop control instruction set includes:

[0022] Step 301: Based on the three-dimensional coordinates of the tilted cargo position, send a deceleration command to the AGV control system;

[0023] Step 302: Call the impedance compensation parameter table generated in Step 1, and increase the antenna transmission power to the upper limit of 1.2W for the tilted cargo position coordinates;

[0024] Step 303: Based on the shelf number associated with the tilted storage location, activate the LED alarm indicator light on the top of that shelf.

[0025] On the other hand, an intelligent state management system for RFID communication antennas in the intelligent state management method for RFID communication antennas is also provided, comprising:

[0026] Environment adaptation module: used to generate radio frequency configuration benchmarks based on the structured spatial data of the warehouse 3D map and real-time environmental parameters;

[0027] Spatial pre-activation module: used to predict the target shelf based on the AGV's movement status and activate the directional communication link;

[0028] Status monitoring module: used to detect the status of goods in real time and trigger safety control commands;

[0029] System maintenance module: Used to ensure the continuous stability of the communication link.

[0030] The present invention also relates to an electronic device, including a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, implements the method as described.

[0031] The beneficial effects of this invention are as follows: the method and system need to have strong environmental perception capabilities, enabling real-time and accurate monitoring of changes in the surrounding electromagnetic environment; possess an efficient and intelligent frequency adjustment mechanism, capable of automatically and quickly adjusting the antenna operating frequency according to environmental changes, ensuring stable and reliable communication quality; and achieve refined power consumption management, dynamically adjusting power consumption according to actual workload and communication needs, reducing energy consumption and operating costs while ensuring system performance, which is beneficial to meeting the growing demand for Internet of Things applications and promoting the in-depth application and development of RFID technology in a wider range of fields.

[0032] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0033] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0034] Figure 1 This is a schematic diagram of an intelligent state management method for RFID communication antennas in an embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram of an intelligent status management system for RFID communication antennas in an embodiment of the present invention. Detailed Implementation

[0036] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0037] Step 1: Generate an initial power configuration table driven by metal density based on the structured spatial data of the warehouse 3D map;

[0038] The structured spatial data is a three-dimensional rasterized map containing shelf height and metal density distribution, with a raster resolution of ≤10cm.

[0039] The initial power configuration table includes a set of radio frequency transmit power reference parameters set based on metal density threshold partitioning. The initial power configuration table stores metal density distribution data, including a coordinate mapping table of dense metal areas.

[0040] Based on the metal density partitioning results of the initial power configuration table, perform RF power gradient configuration;

[0041] The metal density threshold is defined as: when the metal content per unit volume is ≥300 kg / m³. 3 The area is marked as a densely populated metal zone, with a power rating ≥1W; when the metal content per unit volume is <300kg / m³. 3 The time stamp indicates a standard shelving area with a power rating of 0.5W.

[0042] A dynamic impedance compensation parameter table is generated based on the real-time ambient temperature and humidity data collected by the sensor.

[0043] The dynamic impedance compensation parameter table is a composite compensation coefficient matrix established based on the temperature and humidity-RF attenuation mapping relationship and the metal density correction factor β, which is used to offset signal drift caused by environmental factors.

[0044] Step 2:

[0045] AGV position event data is generated based on the real-time collection of AGV displacement coordinate signals by the underground RFID readers deployed in the operation channel.

[0046] Based on the AGV position event data and the federated learning historical trajectory database, predict the coordinates of the target shelf ahead of the AGV's travel path;

[0047] The beamforming function of the target antenna associated with the target shelf is activated by calling the initial power configuration table and impedance compensation parameter table based on the target shelf coordinates.

[0048] The AGV position event data is three-dimensional spatial positioning data containing the AGV's real-time coordinates, motion vectors, and timestamps; the motion vector is a vector tuple containing velocity magnitude, motion direction angle, and vertical tilt angle, used for AGV three-dimensional spatial trajectory modeling.

[0049] The federated learning historical trajectory database is generated by training LSTM models locally on each AGV and then aggregating parameters on a central server to generate a global trajectory probability model.

[0050] The beamforming function is a directional transmission technology that dynamically adjusts the radio frequency beam towards the target shelf area using a programmable phase array antenna, combined with the shelf metal density and environmental impedance parameters. Specifically, based on the initial power configuration table and impedance compensation parameters, the comprehensive penetration loss compensation coefficient S of the radio frequency signal under a specific environment and shelf structure is dynamically calculated to simultaneously compensate for metal absorption loss and environmental phase shift loss. Based on the compensation coefficient S, the antenna phase weight matrix W is dynamically generated and adjusted so that the beam main lobe energy is preferentially focused on areas with a metal density ≥300kg / m². 3In high-loss shelf areas; specifically, by controlling the phase shifter of the target antenna (programmable phase array antenna) through FPGA, the weight matrix W is loaded to achieve precise beam deflection and adaptive energy focusing.

[0051] Step 3:

[0052] Based on the signals returned by the electronic tags collected by the activated RFID antenna, an RSSI phase difference data matrix is ​​generated.

[0053] The RSSI phase difference data matrix is ​​a set of data consisting of the tag signal strength difference (RSSI) received by the multi-antenna array and the phase angle offset of adjacent signals, used to characterize the spatial attitude change of the cargo;

[0054] The cargo tilt status flag is determined based on the RSSI phase difference data matrix and the preset phase offset threshold.

[0055] The cargo tilt status flag is a binary anomaly flag triggered when the phase difference between adjacent tags exceeds the safety tolerance. Its technical effect characterizes the risk level of cargo center of gravity shift.

[0056] For example, the cargo tilt status flag is a binary abnormal status identifier triggered when the phase difference between adjacent tags is ≥15°. Its physical meaning is that the cargo's center of gravity offset exceeds the safety tolerance.

[0057] Based on the cargo tilt status flag and the GNN confidence analysis results, a closed-loop control instruction set is triggered.

[0058] The GNN confidence analysis result is based on the topological relationship of shelf nodes constructed by the graph neural network, and the tilt judgment reliability weight is calculated by the neighbor tag data propagation algorithm. The effective execution threshold of the GNN confidence analysis result is 90% confidence. When the confidence is <90%, the verification mechanism is activated: the adjacent shelf antenna is called to collect tag data a second time.

[0059] Step 4:

[0060] Adaptive transmit power adjustment is performed based on real-time distance measurement data between the antenna and the electronic tag;

[0061] The adaptive transmit power adjustment is a technical operation that calculates the antenna transmit power using a distance power compensation formula.

[0062] The distance power compensation formula is as follows: ×α×β, where To compensate for the subsequent transmission power, The initial reference power is obtained from the initial power configuration table. The preset standard reference distance is usually 1m, d is the distance between the antenna and the electronic tag, α and β are environmental response correction factors. α and β are calculated in real time using the impedance compensation parameter table generated in step 1 to compensate for the RF signal phase shift loss caused by temperature, humidity, and metal distribution. α = α 基 ×K T ×K H , where α 基 =1.0, corresponding to a reference temperature of 25°C; K T K is the temperature compensation coefficient, which is the coefficient of performance when the temperature rises by 10°C. T =1.25; K H K is the humidity compensation coefficient; when humidity > 70%, K H =1.15; β is the metal density correction factor, β=1.8 for dense metal areas and β=1.0 for ordinary shelving areas.

[0063] The antenna operating mode is switched according to the time period identifier signal triggered by the system clock;

[0064] The time period identifier signal is a timing control flag generated according to the warehouse operation plan;

[0065] The working mode switching includes:

[0066] When the identification signal indicates a work period, maintain the antenna in normal power mode;

[0067] When the signal indicates a non-operational period, switch to low-power mode (target power ≤ 0.1mW). In low-power mode, the RF power amplifier module is turned off and the carrier listening circuit is enabled to maintain a 0.1mW-level communication link.

[0068] Based on the ring bus continuity detection signal, execute the emergency response for the faulty node;

[0069] The fault node emergency response is a redundant communication recovery mechanism based on a bus topology; it includes: performing data link switching on nodes with abnormal continuity detection signals; generating encrypted alarm logs containing fault coordinates and timestamps, wherein the encrypted alarm logs are audit files generated by converting bus node numbers into three-dimensional shelf coordinates through a spatial mapping engine and encrypting them using the AES-256 algorithm.

[0070] The beneficial effects of the above technical solution are as follows: This solution significantly improves the performance of industrial RFID systems, specifically as follows: 1. Improved communication stability: Beamforming enhances signal strength in dense metal areas, and adaptive power effectively extends the reading distance; 2. Enhanced safety and protection: Fast tilt detection response and AGV deceleration commands effectively reduce cargo damage accidents; 3. Optimized energy efficiency: Power consumption is effectively reduced during non-operational periods, and dynamic power saves RF energy consumption; 4. Intelligent decision-making: Federated learning models help achieve smaller errors in AGV trajectory prediction, and beamforming reduces a significant amount of ineffective radiation; 5. Industrial robustness: Supports metal compensation strength, and full closed-loop control reduces the need for manual intervention.

[0071] In one embodiment, step 1: Generate a dynamic impedance compensation parameter table based on the ambient temperature and humidity data collected in real time by the sensor;

[0072] The dynamic impedance compensation parameter table is a composite compensation coefficient matrix established based on the temperature and humidity RF attenuation mapping relationship and the metal density correction factor β, used to offset signal drift caused by environmental factors.

[0073] The dynamic impedance compensation parameter table is used to compensate for the combined attenuation of radio frequency signals caused by ambient temperature and humidity and the metal structure of the shelving. The construction methods of the dynamic impedance compensation parameter table include:

[0074] 101a. Establish a lookup table for temperature and humidity attenuation coefficients to obtain compensation coefficients, including temperature compensation coefficients and humidity compensation coefficients. For example, the temperature compensation coefficient is 1.25 for every 10°C increase in temperature.

[0075] 101b. Establish a metal density correction factor lookup table, call the metal density distribution data in the warehouse 3D map, and calculate the metal density correction factor β using the formula β=f(ρ), where ρ is the metal density value. The specific formula is as follows:

[0076] .

[0077] The beneficial effects of the above technical solution are as follows: This embodiment constructs a composite compensation coefficient matrix to synergistically offset the combined radio frequency attenuation caused by ambient temperature and humidity and the metal structure of the shelf. Specifically, temperature and humidity compensation can dynamically suppress signal drift, reducing signal attenuation under high temperature or high humidity environments.

[0078] The metal density correction factor β specifically compensates for absorption losses in densely packed metal areas, at ρ=500kg / m². 3 The regional signal penetration is enhanced, and the dual compensation mechanism improves the tolerance to environmental interference, ensuring communication stability in complex industrial scenarios.

[0079] In one embodiment, step 2: predict the coordinates of the target shelf ahead of the path based on AGV location event data and the federated learning historical trajectory library; the federated learning historical trajectory library is constructed in the following way:

[0080] 201a. Each AGV terminal trains an LSTM model based on local historical trajectory data and generates encrypted model parameters;

[0081] 201b. The central server uses the FedAvg algorithm to aggregate parameters and update the global trajectory probability model; specifically, homomorphic encryption technology is used to transmit model parameters, and the server performs FedAvg aggregation in encrypted form.

[0082] 201c. Perform incremental updates every 2 hours to adapt to dynamic path changes.

[0083] The beneficial effects of the above technical solution are as follows: This embodiment uses distributed learning and encrypted aggregation technology to construct a trajectory prediction model, realizes the transmission of parameters through homomorphic encryption, completes model aggregation under the premise of protecting AGV privacy, improves data security, LSTM local training and global incremental update make the path prediction error ≤0.5m, improves the positioning accuracy of AGV target shelf, adapts to dynamic path changes (such as temporary storage location adjustment), and reduces the frequency of invalid antenna activation.

[0084] In one embodiment, step 3: trigger the closed-loop control instruction set based on the cargo tilt status flag and the GNN confidence analysis results;

[0085] The closed-loop control instruction set includes:

[0086] Step 301: Based on the three-dimensional coordinates of the tilted cargo position, send a speed reduction command (target speed 0.5m / s) to the AGV control system.

[0087] Step 302: Call the impedance compensation parameter table generated in Step 1, and increase the antenna transmission power to the upper limit of 1.2W for the tilted cargo position coordinates;

[0088] Step 303: Based on the shelf number associated with the tilted storage location, activate the LED alarm indicator light on the top of that shelf.

[0089] The beneficial effects of the above technical solution are as follows: This embodiment significantly improves the safety of warehousing operations based on multi-level linkage control in tilted state: the AGV speed is reduced to 0.5m / s to reduce the risk of near-field collisions, the power of the tilted storage location is urgently increased to 1.2W to enhance signal penetration capability to ensure the continuity of real-time monitoring, and the LED alarm indicator is linked to the shelf number to improve the abnormal response speed and reduce the workload of manual inspection.

[0090] Intelligent status management system for RFID communication antennas, such as Figure 2As shown, it includes:

[0091] Environment Adaptation Module: Used to generate RF configuration baselines based on structured spatial data from the warehouse's 3D map and real-time environmental parameters; Specifically, it parses 3D map data with a grid resolution ≤10cm and applies it based on a metal density threshold (≥300kg / m³). 3 An initial power configuration table is generated for densely populated areas, and temperature and humidity sensor data is fused together. A dynamic impedance compensation parameter table is constructed through a composite compensation coefficient matrix.

[0092] Spatial pre-activation module: used to predict the target shelf based on the AGV's motion status and activate the directional communication link; specific execution: parsing the AGV position event data collected by the buried RFID reader, calling the federated learning historical trajectory library, predicting the output parameters of the target shelf coordinate linkage environment adaptation module, and activating the beamforming function of the target antenna.

[0093] Status monitoring module: used to detect the status of goods in real time and trigger safety control commands; specific execution: parsing the RSSI phase difference data matrix collected by the active antenna. When the phase difference is ≥15°, a cargo tilt status flag is generated. Based on the GNN confidence analysis results, a closed-loop control command set is triggered: a deceleration command is sent to the AGV to increase the antenna power of the tilted cargo position to the upper limit of 1.2W and drive the corresponding shelf LED alarm indicator.

[0094] System maintenance module: used to ensure the continuous stability of the communication link; specific execution: executes adaptive transmit power adjustment response system clock signal switching working mode through distance power compensation formula, and executes fault emergency response based on ring bus on / off detection signal: triggers data link jump mechanism to generate AES-256 encrypted fault coordinate alarm log.

[0095] Exception handling mechanism:

[0096] When the status monitoring module detects a low confidence level: If the GNN confidence level is <90%, a verification mechanism is initiated: The adjacent shelf antenna is used to collect tag data a second time. This is done when the metal density of the adjacent shelf is ≥500 kg / m³. 3 When the compensation factor β is increased to 2.0 to enhance signal penetration, if the three verifications fail, it is marked as "untrusted location" and the AGV entry command is suspended. When the system maintenance module detects a continuous fault: if the cumulative fault of a single node is ≥3 times: the faulty antenna is automatically isolated and the redundant array is activated. The beamforming parameters are reconstructed based on the space mapping engine and the encrypted alarm log is pushed to the central operation and maintenance system.

[0097] 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.

Claims

1. A smart state management method for RFID communication antennas, characterized in that, include: Step 1: Generate an initial power configuration table driven by metal density based on the structured spatial data of the warehouse 3D map. Perform RF power gradient configuration according to the metal density partitioning results of the initial power configuration table, and generate a dynamic impedance compensation parameter table based on real-time temperature and humidity data. Step 2: Generate AGV position event data based on the AGV displacement coordinate signal collected by the buried RFID reader, predict the target shelf coordinates by combining the federated learning historical trajectory library, and activate the beamforming function of the target antenna by calling the initial power configuration table and dynamic impedance compensation parameter table. Step 3: Analyze the RSSI phase difference data matrix collected by the active antenna, determine the cargo tilt status flag based on the RSSI phase difference data matrix and the preset phase offset threshold, and trigger the closed-loop control command set in combination with the GNN confidence analysis results; Step 4: Perform adaptive transmit power adjustment based on the real-time distance between the antenna and the tag, switch the antenna operating mode based on the system clock time period identification signal, and perform emergency response for fault nodes based on the ring bus continuity detection signal.

2. The intelligent state management method for RFID communication antennas as described in claim 1, characterized in that, The structured spatial data is a three-dimensional rasterized map containing shelf height and metal density distribution. The initial power configuration table includes a set of radio frequency transmission power reference parameters set based on metal density threshold partitioning. The initial power configuration table stores metal density distribution data, including a coordinate mapping table of dense metal areas.

3. The intelligent state management method for RFID communication antennas as described in claim 2, characterized in that, The metal density threshold is defined as follows: when the metal content per unit volume is ≥300kg / m³, it is marked as a metal-dense area with a power configuration of ≥1W; when the metal content per unit volume is <300kg / m³, it is marked as a normal shelving area with a power configuration of 0.5W.

4. The intelligent state management method for RFID communication antennas as described in claim 1, characterized in that, The dynamic impedance compensation parameter table is a composite compensation coefficient matrix established based on the temperature and humidity-RF attenuation mapping relationship and the metal density correction factor β, which is used to offset signal drift caused by environmental factors.

5. The intelligent state management method for RFID communication antennas as described in claim 1, characterized in that, The AGV position event data is three-dimensional spatial positioning data containing AGV real-time coordinates, motion vectors, and timestamps; the federated learning historical trajectory library is generated by training LSTM models locally on each AGV and aggregating parameters on a central server to generate a global trajectory probability model; the beamforming function is a directional transmission technology that dynamically adjusts the radio frequency beam towards the target shelf area using a programmable phase array antenna, combined with shelf metal density and environmental impedance parameters.

6. The intelligent state management method for RFID communication antennas as described in claim 1, characterized in that, The RSSI phase difference data matrix is ​​a set of data consisting of the difference in tag signal strength received by a multi-antenna array and the phase angle offset of adjacent signals, used to characterize the spatial attitude change of the cargo; the cargo tilt status flag is a binary anomaly flag triggered when the phase difference between adjacent tags exceeds the safety tolerance; the GNN confidence analysis result is a tilt judgment reliability weight calculated by constructing the topological relationship of shelf nodes based on a graph neural network and through a neighbor tag data propagation algorithm. The adaptive transmit power adjustment is a technical operation that calculates the antenna transmit power using a distance power compensation formula; the time period identifier signal is a timing control flag generated according to the warehouse operation plan. When the confidence analysis result of GNN is less than 90%, the verification mechanism is activated: the adjacent shelf antenna is called to collect tag data a second time.

7. The intelligent state management method for RFID communication antennas as described in claim 5, characterized in that, The federated learning history trajectory database is constructed in the following way: 201a. Each AGV terminal trains an LSTM model based on local historical trajectory data and generates encrypted model parameters; 201b. The central server uses the FedAvg algorithm to aggregate parameters and update the global trajectory probability model. The model parameters are transmitted using homomorphic encryption technology, and the server performs aggregation in encrypted form. 201c. Perform incremental updates every 2 hours to adapt to dynamic path changes.

8. The intelligent state management method for RFID communication antennas as described in claim 1, characterized in that, When the cargo tilt status flag is true and the GNN confidence analysis result is ≥90%, the closed-loop control instruction set is triggered. The closed-loop control instruction set includes: Step 301: Based on the three-dimensional coordinates of the tilted cargo position, send a deceleration command to the AGV control system; Step 302: Call the impedance compensation parameter table generated in Step 1, and increase the antenna transmission power to the upper limit of 1.2W for the tilted cargo position coordinates; Step 303: Based on the shelf number associated with the tilted storage location, activate the LED alarm indicator light on the top of that shelf.

9. An intelligent state management system for RFID communication antennas in an intelligent state management method for RFID communication antennas, characterized in that, include: Environment adaptation module: used to generate radio frequency configuration benchmarks based on the structured spatial data of the warehouse 3D map and real-time environmental parameters; Spatial pre-activation module: used to predict the target shelf based on the AGV's movement status and activate the directional communication link; Status monitoring module: used to detect the status of goods in real time and trigger safety control commands; System maintenance module: Used to ensure the continuous stability of the communication link.

10. An electronic device, comprising a processor and a memory, characterized in that, The memory stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-8.

Images