Multi-level reading method, system and equipment based on RFID (Radio Frequency Identification) and bar code, and medium

Abstract

The invention discloses a multi-level reading method, system and device based on RFID and bar codes and a medium, and the method comprises the following steps: carrying out the long-distance and non-contact batch scanning of articles in a target area through an RFID reader-writer, and obtaining the macroscopic information of the articles; according to the macroscopic information, screening out a target subset which needs to be further identified from batch scanning results; performing close-range reading on the single article in the target subset through bar code scanning equipment to obtain detail information of the single article; and storing the macroscopic information and the detailed information in an associated manner, and outputting a complete multi-level reading result. The method improves the recognition efficiency and the resource scheduling precision, and is suitable for application scenarios with various task priorities differentiated.

Description

Technical Field

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

[0002] With the continuous improvement of the intelligent level in fields such as logistics, warehousing, manufacturing and retail, item identification technology, as an important means to achieve intelligent identification and traceability management, has been widely applied. The existing identification technologies mainly include two categories: barcode (including one-dimensional barcode and two-dimensional barcode) identification and radio frequency identification (RFID).

[0003] Barcode technology is widely used in product identification and in-out warehouse management due to its simple structure and low cost. However, its reading process requires close-range and visual operation, and it is difficult to achieve batch and fast reading, which limits its application in high-efficiency operation scenarios. RFID technology has the advantages of non-contact, long-distance, penetrability, multi-tag parallel identification, etc., and is especially suitable for application scenarios such as large-scale item identification, inventory counting, and transfer tracking. However, RFID technology also has the following problems in actual deployment: First, the RFID reading process is easily affected by factors such as tag occlusion, metal interference, and multi-tag collision, resulting in reading failure or decreased accuracy; Second, the RFID identification results are often batch and macroscopic data, and it is difficult to accurately obtain the characteristic information of single items.

[0004] In order to make up for the respective technical shortcomings of RFID and barcodes, some systems attempt to combine the two, that is, to achieve a large-scale preliminary screening through RFID and then conduct detailed verification through barcodes. For example, Chinese invention application with publication number CN118229200A discloses a warehouse management method, device and equipment based on RFID and barcodes, including scanning RFID tags in the identification area to obtain RFID information of items in the identification area; the RFID tags are installed on the items; querying the database of the warehouse according to the RFID information to obtain item information; the database includes all item information of the warehouse; adaptively adjusting the parameters of the camera and the light intensity of the light source system to obtain a barcode image in the identification area; decoding the barcode image to obtain barcode information; matching the item information and the barcode information, and synchronizing the matching result to the database of the warehouse.

[0005] However, most of the existing technologies are simple superpositions of secondary reading processes, lacking a unified reading strategy control and status management mechanism, and unable to dynamically adjust the reading depth or strategy according to the complexity of the identification task and the real-time environment, resulting in waste of system resources or decreased reading accuracy. Summary of the Invention

[0006] The present invention provides a multi-level reading method, system, device and medium based on RFID and barcodes, aiming to solve the problems in the prior art such as unclear reading levels, lack of dynamic control mechanism, system resource waste and decline in reading accuracy.

[0007] To solve the above technical problems, in the first aspect of the present invention, a multi-level reading method based on RFID and barcodes is proposed, including the following steps:

[0008] Use an RFID reader to perform long-distance and non-contact batch scanning on items in the target area to obtain the macroscopic information of the items;

[0009] According to the macroscopic information, screen out the target subset to be further identified from the batch scanning results;

[0010] For a single item in the target subset, perform close-range reading through a barcode scanning device to obtain the detailed information of a single item;

[0011] Associate and store the macroscopic information and the detailed information, and output the complete multi-level reading result.

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

[0013] Real-time monitor the RFID signal strength or reading success rate. When it is lower than the preset threshold or multi-tag collision is detected, trigger the barcode scanning mode to supplement or verify the data.

[0014] Preferably, the preset threshold includes a signal strength threshold and a reading success rate threshold, and the generation method is as follows:

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

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

[0017] Assign a task recognition priority according to the required recognition accuracy of the current reading task;

[0018] Input the RSSI data, reading success rate, environmental score, and task type 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 the system operation parameters and apply them to the current or next reading cycle;

[0020] If the next round of reading deviates from the expectation, it is fed back to the dynamic feedback optimization module to perform fine-tuning / rollback / model update.

[0021] Preferably, the threshold calculation engine adopts a reinforcement learning model. The input states of the reinforcement learning model include the RSSI mean, the number of collision tags, the environment score, and the task priority. The output actions include increasing or decreasing the RSSI threshold or the success rate threshold, and the reward function is expressed as:

[0022]

[0023] In the formula, is the preset reading rate weight, is the reading rate within the current time window, is the historical average reading rate, is the energy consumption increment.

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

[0025] Preferably, the associated storage includes:

[0026] Bind the batch number in the macro information with the single-item serial number in the detailed information to generate a binding log;

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

[0028] If the legality verification passes, establish a one-to-many mapping relationship table between the RFID batch number and the single-item serial number for all single items within the same target subset; if the legality verification fails, trigger an exception alarm and record the positioning coordinates of the item.

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

[0030] Sort based on the reading failure rate;

[0031] Screen based on the tag type or product category;

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

[0033] Based on the task priority;

[0034] Or a combination of the above strategies.

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

[0036] An RFID reading module for performing long-distance and non-contact batch scanning on items in a target area to obtain macroscopic information of the items;

[0037] A target screening module for screening out a target subset to be further identified from the batch scanning results according to the macroscopic information;

[0038] A barcode reading module for performing close-range barcode scanning on individual items in the target subset to obtain detailed information of a single item;

[0039] A reading monitoring module for real-time monitoring of RFID signal strength and reading success rate, and triggering barcode scanning when the signal strength is lower than a preset threshold or multi-tag collision occurs;

[0040] A threshold calculation engine for recording information such as RSSI value, reading success rate, environmental parameters, task priority, etc., and executing an adaptive threshold algorithm to output the currently recommended minimum threshold;

[0041] A feedback optimization module for performing fallback adjustment or model optimization on the deviation of the actual reading result;

[0042] A data association and storage module for associating macroscopic information with detailed information, and establishing a one-to-many mapping relationship table between RFID batch numbers and single-item serial numbers, and outputting multi-level reading results.

[0043] In a third aspect of the present invention, an electronic device is further proposed, including: 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, the above-mentioned multi-level reading method based on RFID and barcodes is implemented.

[0044] In a fourth aspect of the present invention, a computer-readable storage medium is further proposed, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned multi-level reading method based on RFID and barcodes is implemented.

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

[0046] 1. The multi-level reading method proposed by the present invention obtains macroscopic batch information through RFID, and then uses barcode close-range identification for the screened target subset to obtain detailed information, realizing hierarchical control of the reading depth, effectively avoiding repeated identification of non-key targets, improving the identification efficiency and resource scheduling accuracy, and being applicable to various application scenarios with different task priorities.

[0047] 2. The multi-level reading method proposed by the present invention realizes real-time fine-tuning and optimization of the reading strategy by using a reinforcement learning model to dynamically output the optimal threshold by monitoring the RSSI value, reading success rate, and multi-tag collision situation through the reading monitoring module, in combination with the environmental score and task priority, significantly improving the recognition accuracy and energy efficiency ratio in complex environments.

[0048] 3. In the information processing process of the multi-level reading method proposed by the present invention, a one-to-many mapping relationship is established between the batch number identified by RFID and the single-item serial number identified by barcodes, and the legitimacy is verified, facilitating subsequent rapid implementation of traceability and verification from macro to detail in links such as logistics management, inventory traceability, and quality inspection sampling.

[0049] 4. When the RFID recognition quality is lower than the dynamic threshold or a collision occurs in the multi-level reading method proposed by the present invention, the system can automatically switch to the barcode recognition mode to supplement or verify data, avoiding information omission; if the recognized information is inconsistent with the database, an alarm can also be triggered and the problematic item can be located, facilitating rapid troubleshooting of anomalies.

[0050] 5. The target subset screening strategy in the multi-level reading method proposed by the present invention is flexible and can be screened by multi-dimensional combinations such as recognition failure rate, tag type, and spatial distribution density to adapt to different application requirements, such as tasks like rapid sorting, warehouse inventory, security verification, and sampling inspection, with wide applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a schematic flow chart of the multi-level reading method described in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present application and with reference to the accompanying drawings.

[0053] Embodiment 1

[0054] This embodiment is a multi-level reading method based on RFID and barcodes. As Figure 1 shown, it includes the following steps 1 to 4:

[0055] Step 1, use an RFID reader to perform long-distance and non-contact batch scanning on items in the target area to obtain the macro information of the items.

[0056] In this step, first, at least one RFID reader is arranged in the area to be read, and its antenna covers the effective recognition range of the target area. The target area can be a warehouse shelf, sorting area, temporary storage area on the production line, logistics import and export buffer area, etc.

[0057] The RFID reader selects a device that supports Ultra High Frequency (UHF), which can achieve long-distance and contactless multi-tag identification, with an identification distance of up to 3 to 10 meters. The RFID tag is a passive electronic tag attached to the outside of the item or packaging, and its internal pre-coded item identification information, such as batch number, category identification, supplier code, etc.

[0058] The reader starts a scanning task at a set period (such as once every 3 seconds), sends an excitation signal through the antenna to wake up the RFID tags in the target area. All the awakened tags upload their own electronic coding information through TDMA, ALOHA or other anti-collision protocols. The system records the information of each successfully responded tag, and caches or preliminarily processes the RSSI (Received Signal Strength Indicator value), EPC code (Electronic Product Code), read timestamp, etc. of the responded tags as macroscopic information.

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

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

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

[0062] Tag caching mechanism, de-duplicate the results of reading the same tag multiple times within a short time, and only retain the first reading or the reading with the highest quality;

[0063] Tag identification completion judgment condition, for example, if no new tags are identified in two consecutive rounds of scanning, it is judged that the scanning is completed.

[0064] Through the above scanning method, the system can obtain the macroscopic information of all items in the target area in a short time, that is, the batch, type, quantity, preliminary spatial distribution, etc. at the item level. This information will be used as the basis for subsequent screening of the target subset.

[0065] In some other embodiments of the present invention, multi-antenna arrays can also be deployed in combination for zonal scanning. Through RSSI differences or multi-antenna collaborative positioning, the approximate spatial orientation of each tag can be roughly estimated, providing a spatial distribution reference for the screening of the target subset.

[0066] Step 2: Based on the macroscopic information, screen out the target subset that needs to be further identified from the batch scanning results. The screening strategies for the target subset include: sorting based on the read failure rate; screening based on the tag type or product category; based on the spatial distribution density between tags; based on the task priority; or a combination of the above four strategies.

[0067] Specifically, the macroscopic information obtained in Step 1 includes: the Electronic Product Code (EPC code) of the RFID tag, the Received Signal Strength Indicator (RSSI) value, the response timestamp, the number of successful tag reads, the collision detection information, and the possible spatial location information.

[0068] Therefore, in this step, the system executes the following strategies based on the characteristic data in the macroscopic information to screen out the target subset that needs to be further identified:

[0069] Based on the read failure rate strategy:

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

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

[0072] The system identifies the tag types that need to be key processed in the current task based on the EPC coding prefix, category information, supplier code, etc. contained in the tag. For example, if the identification task is "verifying the incoming dangerous goods", only the tags with EPC prefixes matching the dangerous goods codes are screened out.

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

[0074] If the system supports RSSI positioning or multi-antenna layout, it can preliminarily estimate the spatial distribution density of the tags based on the RSSI intensity of the tags and its change trend. For areas where there may be occlusion or signal interference in the tag concentration area, the system can select the items in the area with too high distribution density as the target subset for further fine identification.

[0075] Task priority-driven strategy:

[0076] When the identification task has a clear priority (such as inventory sampling inspection, safety verification), the system will preferentially select the target items related to the task for supplementary identification, such as products of specific suppliers, some samples within a certain batch number, or items with abnormal identification history.

[0077] Comprehensive multi-strategy integration:

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

[0079]

[0080] In the formula, S is the comprehensive recognition score, F is the reading failure rate score, T is the label type matching degree, D is the distribution density score, P is the task priority score, and are the weight coefficients of each dimension, which can be preset by the system or dynamically adjusted by the task.

[0081] Items with scores lower than the threshold will be screened into the target subset and used as candidates for subsequent barcode precise reading. Through the above method, the system can implement an adaptive screening mechanism driven by multi-dimensional information, which not only improves the pertinence of reading but also avoids waste of resources, effectively supporting subsequent fine-grained recognition and data fusion processing.

[0082] Step 3: For a single item in the target subset, perform close-range reading through a barcode scanning device to obtain detailed information of the single item.

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

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

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

[0086] Manually confirmed trigger: In scenarios where manual review is required (such as warehousing review scenarios), the operator scans the item barcode with a handheld device;

[0087] Collaborative trigger: Through the smart terminal, the robot system, or the scanning device equipped with an AGV (Automated Guided Vehicle), the reading operation is automatically completed.

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

[0089] To improve the reading efficiency and accuracy, the following reading optimization strategies are adopted in this embodiment:

[0090] Read the barcode multiple times. If the content is the same, it is judged as a valid result; if it is inconsistent, the "image caching + manual assisted recognition" mode is triggered;

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

[0092] Combined with the image acquisition module, perform distortion correction, illumination enhancement, skew correction, etc. 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 in the high-frame-rate scanning mode and establish a mapping.

[0094] The information carried in the barcode can include: single-item serial number, product model or version number, production date / expiry date, manufacturer code, quality inspection code, regional code, etc.; after the barcode reading is completed, the system will transmit the detailed information to the information processing module through the data structuring interface and compare and bind it with the RFID macroscopic information in the previous step.

[0095] If the barcode cannot be successfully read, the system automatically marks the failed reading item and records the reason (such as barcode damage, occlusion, duplication, etc.), and captures the current barcode image through the designed image acquisition module for subsequent manual review; emits an on-site abnormal prompt sound or light to remind the operator to handle it; optionally, perform a re-scan of the RFID tag to confirm and try to correct the failed result in combination with environmental changes.

[0096] Through this step, the system can achieve fine-grained recognition of items, significantly enhance the ability to supplement precise information that cannot be provided by RFID macroscopic scanning, and thus support the item recognition requirements for higher security levels and more complex business scenarios.

[0097] Step Four, associate and store the macroscopic information and the detailed information, and output the complete multi-level reading result. The associated storage includes:

[0098] Bind the batch number in the macroscopic information with the single-item serial number in the detailed information to generate a binding log;

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

[0100] If the legitimacy verification passes, establish a one-to-many mapping relationship table between the RFID batch number and the single-item serial number for all single items in the same target subset; if the legitimacy verification fails, trigger an abnormal alarm and record the positioning coordinates of the item.

[0101] This step aims to uniformly integrate and store the macroscopic information and detailed information obtained in Steps 1 to 3 respectively, so as to construct a complete multi-level reading data structure, which can be used for subsequent traceability, verification, analysis and business processing.

[0102] In the implementation process, the precise association of information is achieved in the following ways: Establish a binding relationship. For example, taking the batch number extracted from the RFID tag as an anchor point, search for and match the corresponding barcode information. The matching is carried out based on rule mapping or database table lookup. Multiple barcode single item numbers can be bound under one batch number to generate a mapping relationship table of "batch number - multiple serial numbers".

[0103] After the system completes barcode reading, it pairs the barcode information of the item with the corresponding RFID scan record. After successful binding, a reading binding log record is generated. The log structure includes RFID tag ID, batch number, corresponding barcode serial number, reading timestamp, scanning device number, operator information, location information, etc. The said binding log will be stored in the local system or cloud database, and supports pushing to business systems such as ERP / WMS through the call interface.

[0104] In some other embodiments of the present invention to ensure data accuracy, a legality verification mechanism is also introduced. By referring to a preset database or rule library, the legality of the combination of batch number and serial number is verified. The verification methods include but are not limited to: format verification, database comparison verification, and historical reading conflict verification.

[0105] If the legality verification fails, an exception handling process is executed. The system will automatically trigger the exception warning module and perform the following operations: pop up or record the error information, add the abnormal barcode number to the "abnormal mark queue", capture the positioning coordinates of the item at the reading site, set the reading status of this record to "abnormal" and enter the manual review pending processing pool.

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

[0107] After completing the above process, the system will output a structured multi-level reading result, including: RFID macroscopic information layer (batch layer), barcode detailed information layer (single item layer), scanning metadata layer (time, device, personnel), status label layer (normal / abnormal / pending confirmation), and other optional contents such as spatial positioning layer.

[0108] The reading result can be used for business system synchronization, product traceability and anti-counterfeiting verification, exception troubleshooting and quality analysis, and multi-dimensional statistics and visualization display, etc.

[0109] In some embodiments of the present invention, the multi-level reading method further includes step A located between step three and step four, that is, step A is also included before associated storage. Then, the implementation steps of the multi-level reading method are step one, step two, step three, step A, and step four; specifically, step A is as follows:

[0110] Real-time monitor the RFID signal strength or reading success rate. When it is lower than the preset threshold or multi-tag collision is detected, trigger the barcode scanning mode to supplement or verify data.

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

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

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

[0114] S3: Assign a task recognition priority according to the recognition accuracy requirement of the current reading task.

[0115] S4: Input the RSSI data, reading success rate, environmental score, and task type 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 the system operation parameters and apply them to the current or next reading cycle.

[0117] S6: If the next round of reading deviates from the expectation, feedback to the dynamic feedback optimization module to execute fine-tuning / rollback / model update.

[0118] The threshold calculation engine adopts a reinforcement learning model. The input states of the reinforcement learning model include the RSSI mean, the number of collision tags, the environmental score, and the task priority. The output actions include increasing or decreasing the RSSI threshold or success rate threshold. The reward function is expressed as:

[0119]

[0120] In the formula, is the preset reading rate weight, is the reading rate within the current time window, is the historical average reading rate, is the energy consumption increment.

[0121] In a preferred embodiment of the present invention, the reinforcement learning model adopts the Q-Learning model, updates the Q-table after every 100 scans, and preferentially selects the action combination with the highest cumulative reward. In some other embodiments of the present invention, the reinforcement learning model adopts the Deep Q-Network model or the PPO (Proximal Policy Optimization) model.

[0122] Action space (A):

[0123] {Increase the signal threshold by 5 dBm, Decrease the signal threshold by 5 dBm, Increase the success rate threshold by 5%, Decrease the success rate threshold by 5%}

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

[0125] Embodiment 2

[0126] This embodiment is a multi-level reading system based on RFID and barcodes. The system is used to implement the multi-level reading method based on RFID and barcodes as described in Embodiment 1, including:

[0127] The RFID reading module is used to perform long-distance and non-contact batch scanning on the items in the target area to obtain the macroscopic information of the items;

[0128] The target screening module is used to screen out the target subset to be further identified from the batch scanning results according to the macroscopic information;

[0129] The barcode reading module is used to perform close-range barcode scanning on a single item in the target subset to obtain the 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 the signal is lower than the preset threshold or multi-tag collision occurs;

[0131] The threshold calculation engine is used to record information such as RSSI value, reading success rate, environmental parameters, task priority, etc., and execute the adaptive threshold algorithm to output the currently recommended lowest threshold;

[0132] The feedback optimization module is used to perform backtracking adjustment or model optimization on the deviation of the actual reading result;

[0133] The data association and storage module is used to associate the macroscopic information with the detailed information, establish a one-to-many mapping relationship table between the RFID batch number and the single-item serial number, and output the multi-level reading result.

[0134] Embodiment III

[0135] This embodiment is an electronic device, including: 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, the multi-level reading method based on RFID and barcodes as described in Embodiment I is implemented.

[0136] Embodiment IV

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

[0138] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the inventive concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention.

Claims

1. A multi-level reading method based on RFID and barcodes, characterized in that It includes the following steps: Use an RFID reader to perform long-distance, non-contact batch scanning on items in the target area to obtain the macroscopic information of the items; According to the macroscopic information, screen out the target subset to be further identified from the batch scanning results; For a single item in the target subset, perform close-range reading through a barcode scanning device to obtain the detailed information of a single item; Associate and store the macroscopic information and the detailed information, and output the complete multi-level reading result.

2. The method according to claim 1, characterized in that, Before the associated storage, the method further includes the following steps: Real-time monitor the RFID signal strength or reading success rate. When it is lower than the preset threshold or multi-tag collision is detected, trigger the barcode scanning mode to supplement or verify data.

3. The method according to claim 2, wherein The preset threshold includes a signal strength threshold and a reading success rate threshold, and the generation method is: The reading monitoring module records the RSSI value, the number of successful / failed tags, the number of collided tags, and the average reading time for each round of reading; Collect external environment parameters and perform quantitative scoring; Assign a task recognition priority according to the required recognition accuracy of the current reading task; Input the RSSI data, reading success rate, environmental score, and task type into the threshold calculation engine, execute the adaptive threshold algorithm, and output the recommended minimum signal strength threshold and minimum reading success rate threshold; Update the system operation parameters and apply them to the current or next reading cycle; If the next round of reading deviates from the expectation, feedback it to the dynamic feedback optimization module to perform fine-tuning / rollback / model update.

4. The method according to claim 3, wherein The threshold calculation engine adopts a reinforcement learning model. The input states of the reinforcement learning model include the RSSI mean, the number of collision labels, the environmental score, and the task priority. The output actions include increasing or decreasing the RSSI threshold or the success rate threshold, and the reward function is expressed as: Wherein, is the preset recognition rate weight, is the recognition rate within the current time window, is the historical average recognition rate, is the energy consumption increment.

5. The method according to claim 3, characterized in that The task recognition priority is divided into multiple levels according to the task type, including the priority strategies of high-security verification, normal warehousing inventory, fast sorting, and sampling verification, and weight factors are set according to different levels to participate in the threshold calculation.

6. The method according to claim 1, wherein The associated storage includes: Bind the batch number in the macroscopic information to the single-item serial number in the detailed information to generate a binding log; Compare the binding log with the pre-stored database to verify the legitimacy of the batch number and the single-item serial number; If the legitimacy verification passes, establish a one-to-many mapping relationship table between the RFID batch number and the single-item serial number for all single items within the same target subset; if the legitimacy verification fails, trigger an exception alarm and record the positioning coordinates of the item.

7. The method according to claim 1, wherein The screening strategy of the target subset includes: Sort based on the reading failure rate; Screen based on the tag type or product category; Based on the spatial distribution density between tags; Based on the task priority; Or a combination of the above strategies.

8. A multi-level reading system based on RFID and barcodes, characterized in that, The system is used to implement the multi-level reading method based on RFID and barcode as described in any one of claims 1-7, including: An RFID reading module for performing long-distance, non-contact batch scanning on items in the target area to obtain the macroscopic information of the items; A target screening module for screening out the target subset to be further identified from the batch scanning results according to the macroscopic information; A barcode reading module for performing close-range barcode scanning on a single item in the target subset to obtain the detailed information of a single item; The reading and monitoring module is used to monitor the RFID signal strength and reading success rate in real time, and trigger barcode scanning when the signal strength is lower than the preset threshold or multi-tag collision occurs; The threshold calculation engine is used to record information such as RSSI value, reading success rate, environmental parameters, task priority, etc., and execute an adaptive threshold algorithm to output the currently recommended lowest threshold; The feedback optimization module is used to perform fallback adjustment or model optimization on the deviation of the actual reading result; The data association and storage module is used to associate macro information with detailed information, establish a one-to-many mapping relationship table between the RFID batch number and the single-item serial number, and output multi-level reading results.

9. 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 1-7 is implemented.

10. 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 as described in any one of claims 1-7 is implemented.

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