Multi-level reading method, system, device and medium based on RFID and barcode

Abstract

The present invention discloses a multi-level identification method, system, device, and medium based on RFID and barcodes. The method comprises the following steps: using an RFID reader / writer to perform long-distance, contactless batch scanning of items within a target area to obtain macroscopic information about the items; based on the macroscopic information, filtering a subset of targets for further identification from the batch scanning results; performing close-range identification of individual items within the target subset using a barcode scanner to obtain detailed information about the individual items; and storing the macroscopic information in association with the detailed information, and outputting a complete multi-level identification result. This invention improves identification efficiency and resource scheduling accuracy, and is suitable for a variety of application scenarios with differentiated task priorities.

Description

Technical Field

[0001] The present invention belongs to the field of information management technology, and in particular relates to a multi-level reading method, system, device and medium based on RFID and barcode. Background Art

[0002] With the continuous improvement of intelligent technologies in logistics, warehousing, manufacturing, and retail, object recognition technology has been widely used as a key means of achieving intelligent identification and traceability management. Existing recognition technologies mainly include barcode recognition (including one-dimensional and two-dimensional barcodes) and radio frequency identification (RFID).

[0003] Barcode technology, due to its simple structure and low cost, is widely used in product identification and inbound and outbound management. However, its reading process requires close proximity and visual operation, and it is difficult to achieve rapid batch reading, limiting its application in high-efficiency work scenarios. RFID technology, on the other hand, offers the advantages of non-contact, long-range, penetrable, and multi-tag parallel recognition, making it particularly suitable for large-scale item identification, inventory counting, and flow tracking. However, RFID technology also faces the following challenges in practical deployment: First, the RFID reading process is easily affected by factors such as tag obstruction, metal interference, and multiple tag collisions, resulting in reading failures or reduced accuracy. Second, RFID recognition results are often batched and macroscopic data, making it difficult to accurately obtain the characteristic information of individual items.

[0004] To address the technical shortcomings of both RFID and barcodes, some systems attempt to combine the two, using RFID for large-scale initial screening and then performing detailed verification through barcodes. For example, Chinese invention application publication number CN118229200A discloses a warehouse management method, device, and equipment based on RFID and barcodes, which includes scanning RFID tags within an identification area to obtain RFID information of items within the identification area; installing the RFID tags on the items; querying the warehouse database based on the RFID information to obtain item information; the database includes information about all items in the warehouse; adaptively adjusting camera parameters and the light intensity of the light source system to obtain a barcode image within the identification area; decoding the barcode image to obtain barcode information; matching the item information with the barcode information, and synchronizing the matching results to the warehouse database.

[0005] However, existing technologies mostly rely on the simple superposition of secondary reading processes, lack a unified reading strategy control and status management mechanism, and are unable to dynamically adjust the reading depth or strategy according to the complexity of the recognition task and the real-time environment, resulting in waste of system resources or reduced reading accuracy. Summary of the Invention

[0006] The present invention provides a multi-level reading method, system, device and medium based on RFID and barcode, aiming to solve the problems of unclear reading levels, lack of dynamic control mechanism, waste of system resources and reduced reading accuracy in the existing technology.

[0007] To solve the above technical problems, the first aspect of the present invention proposes a multi-level reading method based on RFID and barcode, comprising the following steps:

[0008] Use RFID readers to perform long-distance, contactless batch scanning of items in the target area to obtain macro information of the items;

[0009] Based on the macro information, a subset of targets to be further identified is selected from the batch scanning results;

[0010] For each item in the target subset, a barcode scanner is used to perform close-range reading to obtain detailed information of the item;

[0011] The macro information is associated with the detail information and stored, and a complete multi-level reading result is output.

[0012] Preferably, the method further comprises the following steps before the associated storage:

[0013] Real-time monitoring of RFID signal strength or reading success rate. When it falls below the preset threshold or when multiple tags collide, the barcode scanning mode is triggered to supplement or verify the data.

[0014] Preferably, the preset thresholds include a signal strength threshold and a reading success rate threshold, and are generated by:

[0015] The reading monitoring module records the RSSI (Received Signal Strength Indication) value of each reading round, the number of successful / failed tags, the number of collision tags, and the average reading time;

[0016] Collect external environmental parameters and perform quantitative scoring;

[0017] Assign task priorities based on the recognition accuracy required for the current reading task;

[0018] Input RSSI mean, reading success rate, number of collision tags, environmental score, and task priority into the threshold calculation engine, execute the adaptive threshold algorithm, and output the recommended minimum signal strength threshold and minimum reading success rate threshold;

[0019] Update system operating parameters and apply them to the current or next reading cycle;

[0020] If the next round of recognition deviates from expectations, feedback will be sent to the feedback optimization module to perform fine-tuning / rollback / model update.

[0021] Preferably, the threshold calculation engine adopts a reinforcement learning model, the input state of the reinforcement learning model includes RSSI mean, reading success rate, number of collision tags, environmental score, task priority, and the output action includes increasing or decreasing the RSSI threshold or success rate threshold. The reward function Expressed as:

[0022]

[0023] Where, is the preset recognition rate weight, is the reading rate in the current time window, is the historical average literacy rate, The increase in energy consumption.

[0024] Preferably, the task priority is divided into multiple levels according to the task type, including priority strategies of high security verification, normal inventory taking, fast sorting, and random inspection and verification, and weight factors are set according to different levels to participate in threshold calculation.

[0025] Preferably, the associated storage includes:

[0026] Bind the batch number in the macro information with the product serial number in the detail information and generate a binding log;

[0027] Compare the binding log with the pre-stored database to verify the legitimacy of the batch number and the serial number of the single product;

[0028] If the legitimacy check passes, a one-to-many mapping relationship table between RFID batch number and item serial number will be established for all items in the same target subset; if the legitimacy check fails, an abnormal alarm will be triggered and the location coordinates of the item will be recorded.

[0029] Preferably, the screening strategy for the target subset includes:

[0030] Sort based on read failure rate;

[0031] Filter based on tag type or product category;

[0032] Based on the spatial distribution density between tags;

[0033] Based on task priority;

[0034] Or a combination of the above strategies.

[0035] In a second aspect of the present invention, a multi-level identification and reading system based on RFID and barcodes is further provided. The system is used to implement the multi-level identification and reading method based on RFID and barcodes, comprising:

[0036] RFID reading module, used for long-distance, non-contact batch scanning of items in the target area to obtain macro information of the items;

[0037] A target screening module is used to screen out a subset of targets that need to be further identified from the batch scanning results based on the macro information;

[0038] A barcode reader module is used to scan the barcode of a single item in the target subset at close range to obtain detailed information of the single item;

[0039] The reading monitoring module is used to monitor the RFID signal strength and reading success rate in real time, and trigger barcode scanning when it falls below a preset threshold or a multi-tag collision occurs;

[0040] The threshold calculation engine is used to input information such as RSSI average, number of collision tags, reading success rate, environmental parameters, task priority, etc., and execute the adaptive threshold algorithm to output the currently recommended minimum threshold;

[0041] Feedback optimization module, used to make fallback adjustments or model optimization when actual reading results deviate;

[0042] The data association and storage module is used to associate macro information with detailed information, establish a one-to-many mapping relationship table between RFID batch numbers and single product serial numbers, and output multi-level reading results.

[0043] In the third aspect of the present invention, an electronic device is also proposed, comprising: one or more processors; a storage device for storing one or more programs; and when the one or more programs are executed by the one or more processors, the above-mentioned multi-level reading method based on RFID and barcode is implemented.

[0044] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the multi-level recognition method based on RFID and barcode is implemented.

[0045] Compared with the prior art, the present invention has the following technical effects:

[0046] 1. The multi-level recognition method proposed in this invention obtains macro-batch information through RFID, and then uses barcode close-range recognition to obtain detailed information on the selected target subset, realizing hierarchical control of the recognition depth, effectively avoiding repeated recognition of non-key targets, improving recognition efficiency and resource scheduling accuracy, and is suitable for application scenarios with differentiated task priorities.

[0047] 2. The multi-level recognition method proposed in this invention monitors the RSSI value, recognition success rate, and multi-tag collision situation in real time through the recognition monitoring module. Combined with the environmental score and task priority, the reinforcement learning model is used to dynamically output the optimal threshold, realizing real-time fine-tuning and optimization of the recognition strategy, significantly improving the recognition accuracy and energy efficiency in complex environments.

[0048] 3. During the information processing process, the multi-level reading method proposed in this invention establishes a one-to-many mapping relationship between the batch number identified by RFID and the serial number of the single product identified by barcode, and verifies the legitimacy, which facilitates the rapid tracing and verification from macro to micro in the subsequent links such as logistics management, inventory tracing, and quality inspection sampling.

[0049] 4. When the RFID recognition quality falls below a dynamic threshold or a collision occurs, the multi-level reading method proposed in this invention can automatically switch to barcode recognition mode to supplement or verify data to avoid information omissions. If the recognition information is inconsistent with the database, an alarm can be triggered and the problem item can be located, facilitating rapid troubleshooting of anomalies.

[0050] 5. The target subset screening strategy in the multi-level recognition method proposed in this invention is flexible and can be screened based on multiple dimensions such as recognition failure rate, tag type, and spatial distribution density. It can adapt to different application requirements, such as rapid sorting, warehouse inventory, security verification, sampling inspection and other tasks, and has wide applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of the multi-level reading method of the present invention. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in combination with specific embodiments of the present application and with reference to the accompanying drawings.

[0053] Example 1

[0054] This embodiment is a multi-level reading method based on RFID and barcode, such as Figure 1 As shown, the following steps are included from step 1 to step 4:

[0055] Step 1: Use the RFID reader to perform long-distance, contactless batch scanning of items in the target area to obtain macro information of the items.

[0056] In this step, at least one RFID reader is first placed in the area to be read, with its antenna covering the effective recognition range of the target area. The target area can be warehouse shelves, sorting areas, temporary storage areas on production lines, logistics import and export buffers, etc.

[0057] The RFID reader / writer uses an ultra-high frequency (UHF) device, enabling long-range, contactless multi-tag recognition at distances of up to 3 to 10 meters. RFID tags are passive electronic tags attached to items or packaging. They contain pre-encoded item identification information, such as batch number, category identifier, and supplier code.

[0058] The reader initiates a scan at a set interval (e.g., every three seconds), emitting an excitation signal through its antenna to wake up RFID tags within the target area. All awakened tags upload their electronically encoded information via TDMA, ALOHA, or other anti-collision protocols. The system records the information of each tag that successfully responds and caches or preliminarily processes the responding tag's RSSI (Received Signal Strength Indicator), EPC (Electronic Product Code), and read timestamp as macro-information.

[0059] To avoid misreading or missing information, the reader can be configured with the following parameters:

[0060] Polling number threshold: perform 3 to 5 rounds of polling in a multi-tag environment and count the number of tags successfully read;

[0061] RSSI screening threshold, set the minimum signal strength threshold to filter out unstable tags caused by too far distance or poor posture;

[0062] A tag cache mechanism deduplicates multiple reads of the same tag within a short period of time, retaining only the first or highest-quality reading.

[0063] The tag recognition completion judgment condition is as follows: if no new tag is recognized in two consecutive rounds of scanning, the scanning is considered completed.

[0064] Through this scanning method, the system can quickly obtain macro information about all items in the target area, including item-level batches, types, quantities, preliminary spatial distribution, etc. This information will serve as the basis for subsequent screening of target subsets.

[0065] In some other embodiments of the present invention, it is also possible to combine the deployment of multi-antenna arrays for partitioned scanning, and roughly estimate the approximate spatial orientation of each tag through RSSI differences or multi-antenna collaborative positioning, providing a spatial distribution reference for the screening of target subsets.

[0066] Step 2: Based on the macro information, a subset of targets for further identification is screened from the batch scan results. Strategies for screening the target subsets include: sorting based on read failure rate; screening based on tag type or product category; screening based on spatial distribution density between tags; screening based on task priority; or a combination of the above four strategies.

[0067] Specifically, the macro information obtained in step 1 includes: the RFID tag's electronic product code (EPC code), received signal strength indicator (RSSI) value, response timestamp, number of successful tag reads, collision detection information, and possible spatial location information.

[0068] Therefore, in this step, the system implements the following strategy based on the feature data in the macro information to filter out the target subset that needs further identification:

[0069] Based on the read failure rate policy:

[0070] The system counts the number of successful readings of the RFID tag corresponding to each item in multiple rounds of scanning. If a tag fails to be stably identified within a set number of times (for example, 5 rounds) (for example, the number of successful readings is less than 3 times), the tag is considered to be unstable and the item it is on is included in the target subset for subsequent barcode supplementary recognition.

[0071] Based on tag type or product category strategy:

[0072] The system identifies the label types that require special attention in the current task based on the label's EPC prefix, product category information, and supplier code. For example, if the identification task is "Hazardous Goods Incoming Verification," only labels with an EPC prefix that matches the hazardous goods code will be selected.

[0073] Based on the spatial distribution strategy between tags:

[0074] If the system supports RSSI positioning or multi-antenna deployment, it can make a preliminary estimate of the tag distribution density in space based on the tag's RSSI strength and its changing trend. For areas where tags are concentrated, which may be subject to occlusion or signal interference, the system can select objects in areas with excessive distribution density as a target subset for further refined identification.

[0075] Task priority drives the strategy:

[0076] When an identification task has a clear priority (such as inventory sampling and safety verification), the system will prioritize the selection of target items related to the task for supplementary identification, such as products from a specific supplier, some samples within a certain batch number, or items with historical identification anomalies.

[0077] Comprehensive multi-strategy integration:

[0078] In a preferred embodiment, the system builds a target subset screening scoring model that integrates dimensions such as reading stability (failure rate), item type matching, spatial distribution score, and task priority weight to generate a comprehensive recognition score for each item and set a screening threshold. For example:

[0079]

[0080] Where S is the comprehensive recognition score, F is the reading failure rate score, T is the tag type matching degree, D is the distribution density score, P is the task priority score, and is the weight coefficient of each dimension. It can be preset by the system or adjusted dynamically by tasks.

[0081] Items with scores below the threshold are screened into a target subset and serve as candidates for subsequent barcode precision reading. This approach enables the system to implement an adaptive screening mechanism driven by multi-dimensional information, improving the targeted nature of reading while avoiding resource waste and effectively supporting subsequent fine-grained recognition and data fusion processing.

[0082] Step three: Use a barcode scanning device to perform close-range reading on individual items in the target subset to obtain detailed information about the individual items.

[0083] For the target subset screened in step 2, the present invention uses a barcode scanning device to perform close-range reading on each target item one by one to supplement and obtain detailed information about the item.

[0084] In this embodiment, the barcode reading mode can be triggered automatically or manually in the following ways:

[0085] Automatic triggering: When the system detects that a target subset has a reading failure, a high collision risk, or an abnormal positioning situation, it automatically controls the barcode scanning device to start the reading program;

[0086] Manual confirmation triggers, in scenarios where manual review is required (such as warehouse review), the operator scans the item barcode with a handheld device;

[0087] Collaborative triggering automatically completes the reading operation through the scanning equipment equipped with smart terminals, robotic systems or AGVs (Automated Guided Vehicles).

[0088] Barcode reading can be achieved through various types of scanning devices, including but not limited to: handheld barcode scanners; industrial-grade fixed scanners (installed on assembly lines and shelves); visual recognition modules configured on robotic arms or automated mobile devices; and visual-assisted recognition systems used in conjunction with smart glasses or AR headsets.

[0089] To improve reading efficiency and accuracy, this embodiment adopts the following reading optimization strategy:

[0090] The barcode is read multiple times. If the content is consistent, it is considered a valid result. If it is inconsistent, the "image cache + manual assisted recognition" mode is triggered.

[0091] Supports multiple barcode formats (such as EAN-13, Code128, QR code, etc.), and the system can automatically identify and decode the code;

[0092] Combined with the image acquisition module, it performs distortion correction, illumination enhancement, tilt correction and other processing on the barcode image to improve the recognition success rate of low-quality barcodes;

[0093] When multiple items in the target subset are closely arranged, the device can continuously read multiple barcodes and establish a mapping in high frame rate scanning mode.

[0094] The information carried in the barcode may include: single product serial number, product model or version number, production date / expiration date, manufacturer code, quality inspection code, area code, etc.; after the barcode is read, the system transmits the detailed information to the information processing module through the data structured interface, and compares and binds it with the RFID macro information in the previous step.

[0095] If the barcode cannot be read successfully, the system automatically marks the reading failure and records the reason (such as barcode damage, obstruction, duplication, etc.), and captures the current barcode image through the designed image acquisition module for subsequent manual review; it issues an on-site abnormality prompt sound or light to remind the operator to handle it; it can optionally execute the RFID tag to scan again for confirmation, and try to correct the failure result based on environmental changes.

[0096] Through this step, the system can achieve fine-grained identification of objects, significantly enhancing the ability to supplement accurate information that RFID macro scanning cannot provide, thereby supporting the object identification needs of higher security levels and more complex business scenarios.

[0097] Step 4: Associate and store the macro information with the detail information, and output a complete multi-level reading result. The associated storage includes:

[0098] Bind the batch number in the macro information with the product serial number in the detail information and generate a binding log;

[0099] Compare the binding log with the pre-stored database to verify the legitimacy of the batch number and the serial number of the single product;

[0100] If the legitimacy check passes, a one-to-many mapping relationship table between RFID batch number and item serial number will be established for all items in the same target subset; if the legitimacy check fails, an abnormal alarm will be triggered and the location coordinates of the item will be recorded.

[0101] This step aims to integrate and store the macro information and detailed information obtained in steps one to three, so as to build a complete multi-level recognition data structure, which can be used for subsequent tracing, verification, analysis and business processing.

[0102] During implementation, precise information association is achieved through the following methods: Binding relationships are established, such as using the batch number extracted from the RFID tag as an anchor to search and match the corresponding barcode information. Matching is performed based on rule-based mapping or database lookup. Multiple barcode item numbers can be bound to a single batch number, generating a "batch number - multiple serial numbers" mapping table.

[0103] After the system completes barcode reading, it matches the item's barcode information with the corresponding RFID scan record. Successful binding generates a reading and binding log record, containing information such as the RFID tag ID, batch number, corresponding barcode serial number, reading timestamp, scanning device number, operator information, and location information. This binding log is stored in the local system or cloud database and can be pushed to business systems such as ERP / WMS through an API.

[0104] To ensure data accuracy, some other embodiments of the present invention also incorporate a validity verification mechanism that verifies the validity of the batch number and serial number combination by consulting a pre-set database or rule base. Verification methods include, but are not limited to, format verification, database comparison verification, and historical reading conflict verification.

[0105] If the legitimacy check fails, the exception handling process will be executed, and the system will automatically trigger the exception alarm module to perform the following operations: pop up or record the error message, add the exception barcode number to the "exception mark queue", capture the positioning coordinates of the item at the reading site, set the reading status of the record to "exception" and enter the manual review pending pool.

[0106] If the system supports positioning function, the spatial coordinates of the item can be recorded for subsequent tracing and retrieval.

[0107] After completing the above process, the system will output structured multi-level reading results, including: RFID macro information layer (batch layer), barcode detail information layer (single product layer), scanning metadata layer (time, equipment, personnel), status label layer (normal / abnormal / pending confirmation), and other optional content such as spatial positioning layer.

[0108] The reading results can be used for business system synchronization, product traceability and anti-counterfeiting verification, anomaly detection and quality analysis, multi-dimensional statistics and visual display, etc.

[0109] In some embodiments of the present invention, the multi-level reading method further includes step A between step 3 and step 4, that is, step A is included before the associated storage. Then, the implementation steps of the multi-level reading method are step 1, step 2, step 3, step A, and step 4; step A is specifically:

[0110] Real-time monitoring of RFID signal strength or reading success rate. When it falls below the preset threshold or when multiple tags collide, the barcode scanning mode is triggered to supplement or verify the data.

[0111] The preset thresholds include a signal strength threshold and a reading success rate threshold, and the generation method includes the following steps S1 to S6:

[0112] S1: The reading monitoring module records the RSSI value, number of successful / failed tags, number of collision tags, and average reading time for each round of reading.

[0113] S2: Collect external environmental parameters and perform quantitative scoring.

[0114] S3: Assign task priority based on the recognition accuracy required by the current reading task.

[0115] S4: Input the RSSI mean, reading success rate, number of collision tags, environmental score, and task priority into the threshold calculation engine, execute the adaptive threshold algorithm, and output the recommended minimum signal strength threshold and minimum reading success rate threshold.

[0116] S5: Update system operating parameters and apply them to the current or next reading cycle.

[0117] S6: If the next round of recognition deviates from expectations, feedback is sent to the feedback optimization module to perform fine-tuning / rollback / model update.

[0118] The threshold calculation engine adopts a reinforcement learning model. The input state of the reinforcement learning model includes RSSI mean, reading success rate, number of collision tags, environmental score, and task priority. The output action includes increasing or decreasing the RSSI threshold or success rate threshold. The reward function Expressed as:

[0119]

[0120] Where, is the preset recognition rate weight, is the reading rate in the current time window, is the historical average literacy rate, The increase in energy consumption.

[0121] In a preferred embodiment of the present invention, the reinforcement learning model employs a Q-Learning model. The Q-table is updated after every 100 scans, prioritizing the action combination with the highest cumulative reward. In other embodiments of the present invention, the reinforcement learning model employs a Deep Q-Network model or a Proximal Policy Optimization (PPO) model.

[0122] Action space (A):

[0123] {Increase signal threshold by 5dBm, decrease signal threshold by 5dBm, increase success rate threshold by 5%, decrease success rate threshold by 5%}

[0124] The task priority is divided into multiple levels according to the task type, including priority strategies of high security verification, normal inventory taking, fast sorting, and random inspection and verification, and weight factors are set according to different levels to participate in threshold calculation.

[0125] Example 2

[0126] This embodiment is a multi-level identification and reading system based on RFID and barcodes. The system is used to implement the multi-level identification and reading method based on RFID and barcodes as described in the first embodiment, including:

[0127] RFID reading module, used for long-distance, non-contact batch scanning of items in the target area to obtain macro information of the items;

[0128] A target screening module is used to screen out a subset of targets that need to be further identified from the batch scanning results based on the macro information;

[0129] A barcode reader module is used to scan the barcode of a single item in the target subset at close range to obtain detailed information of the single item;

[0130] The reading monitoring module is used to monitor the RFID signal strength and reading success rate in real time, and trigger barcode scanning when it falls below a preset threshold or a multi-tag collision occurs;

[0131] The threshold calculation engine is used to input information such as RSSI mean, reading success rate, environmental parameters, and task priority, execute the adaptive threshold algorithm, and output the currently recommended minimum threshold;

[0132] Feedback optimization module, used to make fallback adjustments or model optimization when actual reading results deviate;

[0133] The data association and storage module is used to associate macro information with detailed information, establish a one-to-many mapping relationship table between RFID batch numbers and single product serial numbers, and output multi-level reading results.

[0134] Example 3

[0135] This embodiment is an electronic device, comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, a multi-level reading method based on RFID and barcode as described in Example 1 is implemented.

[0136] Example 4

[0137] This embodiment is a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the multi-level reading method based on RFID and barcode as described in the first embodiment is implemented.

[0138] The above description is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the creative concept of the present invention, which all fall within the scope of protection of the present invention.

Claims

1. A multi-level reading method based on RFID and barcode, characterized in that: The following steps are involved: Use RFID readers to perform long-distance, contactless batch scanning of items in the target area to obtain macro information of the items; Based on the macro information, a subset of targets to be further identified is selected from the batch scanning results; For each item in the target subset, a barcode scanner is used to perform close-range reading to obtain detailed information of the item; Real-time monitoring of RFID signal strength or reading success rate. When it falls below a preset threshold, the barcode scanning mode is triggered to supplement or verify data. The preset thresholds include a signal strength threshold and a reading success rate threshold, and are generated by: The reading monitoring module records the RSSI value, number of successful / failed tags, and number of collision tags in each round of reading; Collect external environmental parameters and perform quantitative scoring; Assign task priorities based on the recognition accuracy required for the current reading task; Input RSSI mean, reading success rate, number of collision tags, environmental score, and task priority into the threshold calculation engine, execute the adaptive threshold algorithm, and output the recommended minimum signal strength threshold and minimum reading success rate threshold; The threshold calculation engine adopts a reinforcement learning model. The input state of the reinforcement learning model includes RSSI mean, reading success rate, number of collision tags, environmental score, and task priority. The output action includes increasing or decreasing the RSSI threshold or success rate threshold. The reward function Expressed as: ， Where, is the preset recognition rate weight, is the reading rate in the current time window, is the historical average literacy rate, is the energy consumption increment; Update system operating parameters and apply them to the current or next reading cycle; If the next round of recognition deviates from expectations, feedback is sent to the feedback optimization module to perform model updates; The macro information is associated with the detail information and stored, and a complete multi-level reading result is output.

2. The method according to claim 1, characterized in that The task priority is divided into multiple levels according to the task type, and weight factors are set according to different levels to participate in the threshold calculation.

3. The method according to claim 1, characterized in that The associated storage includes: Bind the batch number in the macro information with the product serial number in the detail information and generate a binding log; Compare the binding log with the pre-stored database to verify the legitimacy of the batch number and the serial number of the single product; If the legitimacy check passes, a one-to-many mapping relationship table between RFID batch number and item serial number will be established for all items in the same target subset; if the legitimacy check fails, an abnormal alarm will be triggered and the location coordinates of the item will be recorded.

4. The method according to claim 1, wherein The target subset screening strategy adopts any one of the following strategies or a combination of all the following strategies: A strategy based on read failure rate sorting; Strategies that filter based on tag type or product category; Strategies based on the spatial distribution density between tags; Strategy based on task priority.

5. The multi-level reading system based on RFID and barcode is characterized by: The system is used to implement the multi-level reading method based on RFID and barcode according to any one of claims 1 to 4, comprising: RFID reading module, used for long-distance, non-contact batch scanning of items in the target area to obtain macro information of the items; A target screening module, configured to screen out a subset of targets to be further identified from the batch scanning results based on the macro information; A barcode reader module is used to scan the barcode of a single item in the target subset at close range to obtain detailed information of the single item; The reading monitoring module is used to monitor the RFID signal strength and reading success rate in real time, and trigger barcode scanning when it falls below a preset threshold; The threshold calculation engine is used to input the RSSI average, reading success rate, number of collision tags, environmental score, and task priority, execute the adaptive threshold algorithm, and output the currently recommended minimum threshold; Feedback optimization module, used to update the model according to the deviation of actual reading results; The data association and storage module is used to associate macro information with detailed information, establish a one-to-many mapping relationship table between RFID batch numbers and single product serial numbers, and output multi-level reading results.

6. An electronic device comprising: one or more processors; A storage device for storing one or more programs; characterized in that when the one or more programs are executed by the one or more processors, the multi-level reading method based on RFID and barcode as described in any one of claims 1-4 is implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-level reading method based on RFID and barcode is implemented as described in any one of claims 1 to 4.

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