Goods management method and device based on label checking, medium and product

Abstract

The invention discloses a goods management method and device based on label checking, a medium and a product, and relates to the field of data processing. In the method, a scanning session is started, and a temporary storage label set and a confirmation label set are created; monitoring and receiving data packets broadcasted by the Bluetooth tags, and extracting a tag ID and a signal strength indication value of the Bluetooth tag corresponding to each data packet; in the temporary storage label set, creating a signal characteristic file for each label ID; if it is determined that the tag ID passes the reading quality verification, moving the tag ID from the temporary storage tag set to a confirmation tag set; when the preset scanning period is ended, the scanning session is terminated, all the label IDs in the confirmation label set are used as a successful reading list, and the label IDs in the temporary storage label set are identified as weak signal labels; and uploading the successfully read list and the weak signal label to a central management platform to carry out cargo management and re-scanning reminding. By implementing the technical scheme provided by the invention, the accuracy of cargo scanning is improved.

Description

Technical Field

[0001] This application relates to the field of data processing, specifically to a method, apparatus, medium, and product for goods management based on tag inventory. Background Technology

[0002] Traditional inventory management methods, such as manual recording and barcode scanning, have inherent drawbacks such as low efficiency, high error rates, and limited line-of-sight operation. They are particularly problematic when dealing with large quantities of complexly stacked items, which severely restricts management efficiency and data real-time performance.

[0003] To address the aforementioned shortcomings, the industry has proposed an IoT solution that uses active Bluetooth tags and mobile terminals as reading and writing devices. Operators can use mobile terminals to collect data from tags within range in batches without contact and upload the data to a central management platform in real time. This platform can update inventory and achieve intelligent management, significantly improving inventory efficiency and automation levels.

[0004] However, this Bluetooth solution revealed a problem in practical applications: during the scanning cycle, normal operator movement causes changes in the directionality of the mobile terminal's antenna, resulting in dynamic fluctuations in the signal reception strength of tags at different locations. Consequently, some tags are missed because they happen to be in a weak signal reception area at the moment of broadcast. This probabilistic nature of signal acquisition cannot guarantee the stable reading of all target tags within a single scanning cycle, creating a technical bottleneck for inventory scenarios requiring high accuracy. Summary of the Invention

[0005] This application provides a label-based inventory management method, apparatus, medium, and product that improves the accuracy of goods scanning.

[0006] A first aspect of this application provides a cargo management method based on tag inventory, the method comprising: in response to an operator's cargo scanning operation, initiating a scanning session and creating a temporary tag set and a confirmed tag set for the scanning session; within a preset scanning period of the scanning session, listening to and receiving data packets broadcast by Bluetooth tags within the communication range, and extracting the tag ID of the Bluetooth tag corresponding to each data packet and the signal strength indication value when the data packet was received; in the temporary tag set, creating a signal feature profile for each tag ID, the signal feature profile including various signal strength indication values ​​associated with the tag ID; for each tag ID in the temporary tag set, after each new data packet is received and the signal feature profile is updated, performing a read quality verification on the tag ID and determining whether the tag ID passes the read quality verification; if it is determined that the tag ID passes the read quality verification, moving the tag ID from the temporary tag set to the confirmed tag set; At the end of the preset scanning cycle, the scanning session is terminated, and all tag IDs in the confirmed tag set are used as a successful read list, while the tag IDs in the temporary tag set are identified as weak signal tags. The successful read list and the weak signal tags are uploaded to the central management platform so that the central management platform can manage goods based on the successful read list and provide rescan reminders based on the weak signal tags.

[0007] By adopting the above technical solution, a temporary tag set and a confirmation tag set are dynamically created during the scanning session, enabling real-time acquisition and classified storage of Bluetooth tag data. During the scanning cycle, the system continuously listens for and receives data packets broadcast by Bluetooth tags, extracts the tag ID and corresponding signal strength indicator value, and creates a signal feature profile for each tag ID, recording its signal strength changes in detail. Simultaneously, the system performs real-time read quality verification on each tag ID, dynamically updating the temporary tag set and the confirmation tag set based on the verification results, achieving instant filtering and classification of tag data. At the end of the scanning session, the system automatically generates a successful read list and a weak signal tag list, facilitating subsequent cargo management and rescanning operations. By uploading to the central management platform, the results of each scanning session are aggregated, further improving the efficiency of cargo inventory and tracking. The dynamic data organization and real-time quality analysis of this technical solution significantly improve the accuracy and reliability of cargo scanning. This technical solution, by creating a temporary tag set and a confirmation tag set, combined with signal feature profiles and read quality verification, dynamically tracks tag signal strength and judges the reliability of tag reads in real time during the scanning session, achieving phased tag management. After the scanning cycle is completed, successfully read tags are distinguished from weak signal tags to ensure the accuracy of inventory data, and a rescan reminder mechanism reduces missed reads. This solution effectively solves the problem of missed reads caused by changes in antenna directivity and signal fluctuations, significantly improving the efficiency and accuracy of inventory counting, while also providing guidance for subsequent rescan optimization.

[0008] Optionally, the step of performing read quality verification on the tag ID and determining whether the tag ID passes the read quality verification specifically includes: obtaining all signal strength indication values ​​recorded in the signal feature file of the tag ID, and calculating the sampling number of all the signal strength indication values; determining whether the sampling number reaches a preset minimum sampling threshold; if the sampling number reaches the preset minimum sampling threshold, obtaining a purified signal sequence, and calculating the statistical mean and statistical standard deviation of the signal strength indication values ​​based on the purified signal sequence; determining whether the signal strength meets a preset strength condition based on the statistical mean, and determining whether the signal fluctuation meets a preset stability condition based on the statistical standard deviation; if the signal strength meets the preset strength condition and the signal fluctuation meets the preset stability condition, then determining that the tag ID passes the read quality verification.

[0009] By employing the above technical solution, the system analyzes the sampling quantity, signal mean, and fluctuation of tag IDs to evaluate the reliability of tag reading from multiple dimensions. First, the system determines the sufficiency of the sampled data based on a preset minimum sampling threshold to ensure the statistical significance of the analysis. Then, the system cleanses the original signal sequence, removing potential outliers and improving data quality. Based on this, the system calculates the statistical mean and standard deviation of the cleaned signal to quantitatively assess the signal strength and stability. Finally, the system compares the calculation results with preset strength and stability conditions to comprehensively determine whether the tag ID passes the reading quality verification. This multi-parameter quality assessment mechanism effectively identifies anomalies such as signal instability and insufficient strength, improves the discovery and re-scanning efficiency of weak signal tags, and ensures the overall quality of the inventory results.

[0010] Optionally, the step of acquiring the purified signal sequence and calculating the statistical mean and statistical standard deviation of the signal strength indication values ​​based on the purified signal sequence specifically includes: arranging all signal strength indication values ​​recorded in the signal feature file in chronological order of reception to form an original signal sequence; traversing the original signal sequence using a sliding window to calculate the local median of the signal strength indication values ​​within each sliding window, wherein the window length of the sliding window is a preset number of local analysis points; calculating the absolute deviation between each signal strength indication value in the original signal sequence and the local median of the corresponding sliding window; marking signal strength indication values ​​whose absolute deviation exceeds a preset deviation as outliers; removing the outliers from the original signal sequence to obtain the purified signal sequence; and calculating the statistical mean and statistical standard deviation based on the signal strength indication values ​​in the purified signal sequence.

[0011] By employing the above technical solution, a sliding window median filtering algorithm is introduced for the purification of the original signal sequence, effectively removing interference from outliers. By sorting the signal strength indicators by reception time, the system obtains the original sequence reflecting the signal change trend. Then, the system uses a fixed-length sliding window to perform local analysis of the sequence, calculating the median of the signal within each window, representing the normal intensity level within that interval. By comparing the absolute deviation of the original signal value with the median of the corresponding window, the system identifies outliers with excessive deviations and marks them as outliers. After removing these outliers, the resulting purified signal sequence is smoother and more stable, reducing the impact of abnormal fluctuations. Calculating the statistical mean and standard deviation based on the purified sequence yields more reliable and accurate results, improving the robustness and effectiveness of readout quality verification. This technical solution cleverly utilizes sliding windows and median filtering to adaptively correct signal deviations, achieving data purification simply and efficiently.

[0012] Optionally, determining whether the signal strength meets the preset strength condition based on the statistical mean specifically includes: obtaining target cargo area information associated with the current scanning session from the central management platform, the target cargo area information including the spatial range of the cargo area and the shelf level configuration; calculating a theoretical signal attenuation reference value based on the spatial range, the theoretical signal attenuation reference value being determined according to the propagation attenuation model of Bluetooth signals in free space and the maximum diagonal distance of the spatial range; determining an obstruction attenuation compensation amount based on the shelf level configuration, the obstruction attenuation compensation amount being calculated through the number of levels and the material properties of the level interval in the shelf level configuration; subtracting the theoretical signal attenuation reference value and the obstruction attenuation compensation amount from a preset reference signal threshold to obtain a lower limit threshold for strength; determining whether the statistical mean is greater than or equal to the lower limit threshold for strength; if yes, determining that the signal strength meets the preset strength condition; if no, determining that the signal strength does not meet the preset strength condition.

[0013] By adopting the above technical solution, when judging whether the signal strength meets the preset conditions, the spatial layout of the goods area and the influence of obstructions are fully considered, and an adaptive signal threshold adjustment method is proposed. The system first acquires the spatial range parameters of the target goods area and the shelf hierarchy configuration information, accurately describing the physical characteristics of the inventory environment. Then, the system calculates the theoretical signal attenuation reference value based on the spatial range, quantifying the impact of scanning distance on signal strength. Simultaneously, the system determines the obstruction attenuation compensation amount according to the shelf hierarchy configuration, estimating the contribution of shelf material and quantity to signal penetration attenuation. Finally, the system combines the preset benchmark signal threshold with the theoretical attenuation value and the obstruction compensation amount to dynamically adjust the lower limit threshold of the strength, forming an adaptive strength condition judgment standard. By comparing with the statistical mean, the system reliably determines whether the signal strength meets the inventory requirements. This technical solution introduces an environmental adaptive mechanism in quality judgment, overcoming the limitations of fixed thresholds and improving inventory reliability.

[0014] Optionally, the step of calculating the theoretical signal attenuation reference value based on the spatial range specifically includes: extracting the length, width, and height of the cargo area from the spatial range; calculating the maximum diagonal distance of the cargo area using the spatial diagonal formula based on the length, width, and height; obtaining the parameters of the propagation attenuation model of the Bluetooth signal in free space, wherein the propagation attenuation model parameters include the signal strength reference value and path loss index at the reference distance; substituting the maximum diagonal distance into the propagation attenuation model to calculate the signal attenuation amount relative to the reference distance at the maximum diagonal distance, and using the signal attenuation amount as the theoretical signal attenuation reference value.

[0015] By adopting the above technical solution, a theoretical signal attenuation calculation method based on the spatial diagonal length is proposed, quantitatively evaluating the impact of cargo area size on signal propagation. The system extracts length, width, and height parameters from the spatial range of the cargo area, and calculates the maximum diagonal distance of the area using the spatial diagonal formula, characterizing the maximum straight-line distance between the scanning device and the tag. Then, the system obtains the propagation attenuation model parameters of the Bluetooth signal in free space, including the baseline signal strength value at a reference distance and the path loss exponent, depicting the general law of signal attenuation with increasing distance. Finally, the system substitutes the maximum diagonal distance into the propagation attenuation model to calculate the theoretical signal attenuation at that distance relative to the reference distance, quantitatively estimating the impact of cargo area size on signal strength. This technical solution utilizes simple geometric calculations and an empirical propagation model to quickly evaluate the theoretical attenuation, providing an important reference for adaptive threshold adjustment and improving inventory reliability.

[0016] Optionally, determining the occlusion attenuation compensation amount based on the shelf layer configuration specifically includes: obtaining the number of shelf layers and the material attributes of the layer intervals between each shelf layer from the shelf layer configuration; querying a preset material attenuation coefficient table based on the layer interval material attributes to obtain the single-layer penetration attenuation value corresponding to each material; calculating the maximum number of penetration layers between the operator's maximum scanning path and the shelf layer, wherein the maximum number of penetration layers is determined based on the number of layers and a preset standard scanning position; multiplying the maximum number of penetration layers by the corresponding single-layer penetration attenuation value to obtain a cumulative penetration attenuation value; and adding a preset environmental interference margin to the cumulative penetration attenuation value to obtain the occlusion attenuation compensation amount.

[0017] By adopting the above technical solution, a method for calculating the occlusion attenuation compensation considering shelf levels and materials is proposed, quantifying the impact of complex shelf environments on signal penetration attenuation. The system first obtains the number of shelf levels and inter-level material attributes from the shelf level configuration, precisely describing the physical structural characteristics of the shelf. Then, the system queries a preset material attenuation coefficient table based on the material attributes to obtain the single-layer penetration attenuation value of different materials, quantitatively characterizing the attenuation characteristics of various materials. Next, the system calculates the maximum number of penetration layers between the operator's maximum scanning path and the shelf level, estimating the longest path for signal propagation within the shelf. Finally, the system multiplies the maximum number of penetration layers by the corresponding single-layer attenuation value of the material and adds a preset environmental interference margin to obtain the occlusion attenuation compensation amount that comprehensively considers the shelf structure and materials. This technical solution meticulously models the signal attenuation effect of the shelf environment, provides accurate compensation estimates, and further improves the environmental adaptability and reliability of inventory counting.

[0018] Optionally, before uploading the successful read list and the weak signal tags to the central management platform, the method further includes: for each tag ID in the weak signal tags, obtaining the corresponding target signal feature file from the temporary tag set; analyzing the reasons for failure to pass the read quality verification based on the target signal feature file, the reasons for failure including insufficient sampling, low signal strength, or excessive signal fluctuation; generating a rescan priority score for each weak signal tag according to the reasons for failure, and sorting the weak signal tags based on the rescan priority scores to generate a rescan priority list; generating targeted rescan suggestions according to the reasons for failure, wherein for failure due to insufficient sampling, it is recommended to extend the scan dwell time; for failure due to low signal strength, it is recommended to shorten the scan distance; and for failure due to excessive signal fluctuation, it is recommended to keep the scanning device stable; and pushing the rescan priority list and the targeted rescan suggestions to the operator terminal.

[0019] By adopting the above technical solution, an optimized rescanning assistance mechanism is proposed for weak signal tags. By analyzing the reasons for failure to pass quality verification, differentiated rescanning priorities and suggestions are generated to guide operators in efficiently completing rescanning tasks. The system first obtains the target signal feature profile corresponding to each weak signal tag ID, recording in detail the signal changes during the reading process. Then, based on the feature profile, the system intelligently analyzes the specific reasons for reading failures, such as insufficient sampling, low signal strength, or excessive fluctuations, accurately locating the root cause of the problem. Based on this, the system generates a rescanning priority score for each weak signal tag according to the severity of the failure reason and the difficulty of rescanning, and sorts the tags accordingly to form a rescanning priority list. Simultaneously, the system provides corresponding rescanning operation suggestions for different failure reasons, such as extending the dwell time, shortening the scanning distance, or maintaining equipment stability, effectively guiding operators to optimize rescanning actions. Finally, the system pushes the rescanning priority list and targeted suggestions to the operator terminal, providing clear and explicit guidance for on-site operations. This technical solution utilizes signal features to intelligently generate differentiated rescanning schemes, improving the targeting and efficiency of rescanning and ensuring the overall quality and speed of inventory counting.

[0020] Secondly, embodiments of this application provide a tag-based inventory management device, which includes one or more processors and a memory; the memory is coupled to the one or more processors and is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the tag-based inventory management device to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a tag-based inventory management device, cause the tag-based inventory management device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a tag-based inventory management device, cause the tag-based inventory management device to perform the method described in the first aspect and any possible implementation thereof.

[0023] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: 1. This application achieves refined management of Bluetooth tag signals by creating a temporary tag set and a confirmation tag set, combined with signal feature files and a read quality verification mechanism. By dynamically updating the signal feature files, eliminating outliers using purified signal sequences, and performing statistical analysis based on signal strength and fluctuations, this scheme can effectively reduce tag missed reads and improve the reliability and accuracy of tag reading.

[0024] 2. This application comprehensively considers the spatial range of the target goods area and the configuration of shelf levels. By dynamically adjusting the judgment conditions for signal strength through theoretical signal attenuation reference values ​​and occlusion attenuation compensation, it further enhances adaptability in complex stacking scenarios. The classification and analysis of weak signal tags, as well as the generation and sorting of targeted rescan suggestions, improve the efficiency of inventory rescanning and guide operators to optimize scanning operation paths.

[0025] 3. This application improves the automation and intelligence level of large-scale inventory counting, solves problems such as Bluetooth tag signal fluctuation, missed reading and insufficient sampling, and realizes efficient and accurate inventory management. At the same time, it further optimizes the actual operation process through rescan priority and operation suggestions. Attached Figure Description

[0026] Figure 1 This is a schematic flowchart of a goods management method based on tag inventory disclosed in an embodiment of this application; Figure 2 This is another schematic diagram of a cargo management method based on tag inventory disclosed in an embodiment of this application; Figure 3 This is a schematic diagram of a cargo management device based on tag inventory provided in an embodiment of this application.

[0027] Explanation of reference numerals in the attached drawings: 301, Central Processing Unit; 302, Read-Only Memory; 303, Random Access Memory; 304, Bus; 305, Input / Output Interface; 306, Input Section; 307, Output Section; 308, Storage Section; 309, Communication Section; 310, Driver; 311, Removable Media. Detailed Implementation

[0028] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0029] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0030] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple system devices refer to two or more system devices, and multiple screen terminals refer to two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0031] This application provides a cargo management method based on tag inventory, referring to... Figure 1 , Figure 1 This is a flowchart illustrating a tag-based inventory management method according to an embodiment of this application. The method is applied to a device, specifically a mobile terminal, which can execute a tag-based inventory management program. The method includes steps S101 to S107, as follows: Step S101: In response to the operator's cargo scanning operation, start a scanning session and create a temporary tag set and a confirmation tag set for the scanning session.

[0032] In step S101, the operator's goods scanning operation refers to the inventory instruction initiated by warehouse or store staff using a handheld mobile terminal; the scanning session is used to represent a complete inventory task process from start to finish, which has a unique identifier and is bound to a specific operator and device; the temporary tag set is a dynamic data list used to temporarily store the identification information of all Bluetooth tags that are scanned for the first time or have not yet passed quality verification; the confirmation tag set is a data list specifically used to store the identification information of Bluetooth tags that have passed the read quality verification and have been confirmed as valid inventory results.

[0033] Specifically, when an operator presses the inventory button on their mobile terminal in a designated goods area, the built-in application captures this event. The application first obtains the operator's identification credentials and the mobile terminal's unique device identifier, along with the current system timestamp. This information is then encapsulated into a session initialization request and sent to the central management platform. Upon receiving the request, the central management platform verifies the operator's identity and permissions and returns the parameters required for the scanning session based on the inventory task configuration, such as the specific duration of the preset scanning cycle. After receiving the configuration parameters, the mobile terminal generates a unique scanning session identifier locally and creates two initially empty data sets in its local storage space: a temporary tag set and a confirmation tag set. These two sets are then associated with the scanning session identifier to ensure data isolation and accuracy.

[0034] Step S102: Within the preset scanning period of the scanning session, listen for and receive data packets broadcast by Bluetooth tags within the communication range, and extract the tag ID of the Bluetooth tag corresponding to each data packet and the signal strength indication value when the data packet is received.

[0035] In step S102, the preset scanning period refers to a fixed scanning duration set when the scanning session starts; the data packet broadcast by the Bluetooth tag refers to a wireless signal frame containing its own information actively sent by the Bluetooth tag attached to the goods at predetermined intervals; the tag ID refers to the identity code that can uniquely identify a Bluetooth tag, usually a MAC address or UUID; the signal strength indicator value, usually called the RSSI value, is used to indicate the power of the signal when the mobile terminal receives the data packet, and is a key indicator for judging the signal strength and distance.

[0036] Specifically, after the scanning session is successfully initiated and the timer starts, the mobile terminal's Bluetooth receiver enters a continuous listening state. In this state, the receiver continuously captures data packets on all Bluetooth broadcast channels within its physical communication range. For each successfully received data packet, the mobile terminal's processor parses it, extracts the pre-formatted tag ID from the packet payload, and simultaneously records the signal strength indicator value at the moment the data packet is received. This process continues until the preset scanning cycle ends, during which all captured tag IDs and corresponding signal strength indicator values ​​are processed in real time.

[0037] Step S103: In the temporary tag set, create a signal feature profile for each tag ID. The signal feature profile includes each signal strength indication value associated with the tag ID.

[0038] In step S103, the signal feature profile refers to a data structure established for each independent tag ID in the temporary tag set, used to systematically record and manage all signal strength indication values ​​received by the tag during the scanning session, forming a time series dataset.

[0039] Specifically, in step S102, each time the mobile terminal extracts a tag ID and its signal strength indicator value, it first checks whether the tag ID exists in the current temporary tag set. If the tag ID is being discovered for the first time, the system creates a new entry for the tag ID in the temporary tag set and initializes a signal feature file, storing the first received signal strength indicator value as the first record in the signal feature file. If the tag ID has already been recorded in the temporary tag set, the system directly locates the signal feature file associated with the tag ID and appends the newly received signal strength indicator value to the end of the record list, thereby continuously enriching the signal data sample of the tag.

[0040] Step S104: For each tag ID in the temporary tag set, after each new data packet is received and the signal feature profile is updated, perform a read quality verification on the tag ID and determine whether the tag ID passes the read quality verification.

[0041] In step S104, the read quality verification refers to a comprehensive evaluation process that uses a series of preset quantitative standards to determine whether the signal of a Bluetooth tag is reliable enough, thereby determining whether the tag should be considered a valid inventory object.

[0042] Specifically, for any tag ID in the temporary tag set, whenever its corresponding signal feature file is updated (i.e., a new data packet is received), the mobile terminal immediately triggers a read quality verification process for that tag ID. The system checks the total number of signal strength indicators recorded in the signal feature file of that tag ID to determine if the minimum sample size required for effective statistical analysis has been reached. If the sample size is sufficient, the system further executes a data cleaning algorithm to remove obvious abnormal jumps, and then calculates the mean and standard deviation of the signal strength based on the cleaned data. Finally, the system compares the calculated mean with a preset strength threshold and the standard deviation with a preset stability threshold. Only when both signal strength and signal stability indicators meet the conditions is the tag ID determined to have passed the read quality verification.

[0043] like Figure 2 As shown, in one possible implementation, a read quality verification is performed on the tag ID, and it is determined whether the tag ID passes the read quality verification. Specifically, this includes steps S201-S217, which are as follows: Step S201: Obtain all signal strength indication values ​​recorded in the signal feature file of the tag ID, and calculate the number of samples for all signal strength indication values.

[0044] In step S201, the number of samples refers to the total number of data packets that the mobile terminal successfully receives and records from a specific Bluetooth tag during the current scanning session. This value is the basic basis for measuring whether the data samples required for subsequent signal quality analysis are sufficient.

[0045] Specifically, when a read quality verification process for a specific tag ID is triggered, the mobile terminal's processing unit first accesses the signal characteristic file associated with that tag ID. This signal characteristic file is typically a list or array in its data structure, storing the signal strength indication value for every data packet received since the tag ID was first discovered. The processing unit iterates through this data structure and counts the total number of entries for the recorded signal strength indication values. The result of this counting operation is an integer value, which represents the total number of signal strength indication values ​​sampled.

[0046] Step S202: Determine whether the number of samples has reached the preset minimum sampling threshold.

[0047] In step S202, the preset minimum sampling threshold refers to a minimum sample quantity standard set in the system configuration in order to ensure the effectiveness and reliability of signal statistical analysis. Subsequent signal quality calculations can only be started when the number of received data packets reaches or exceeds this standard.

[0048] Specifically, after calculating the number of samples, the mobile terminal reads the preset minimum sampling threshold from the session configuration parameters either locally or from the central management platform. This preset minimum sampling threshold is a fixed integer, for example, set to 10. The processing unit compares the number of samples obtained in step S201 with this preset minimum sampling threshold. If the number of samples is greater than or equal to the preset minimum sampling threshold, the result is "yes," and the process continues with subsequent steps; if the number of samples is less than the preset minimum sampling threshold, the result is "no," the current read quality verification process is prematurely terminated, and verification is retried only after the tag ID receives more data packets.

[0049] Step S203: If the number of samples reaches the preset minimum sampling threshold, then all signal strength indication values ​​recorded in the signal feature file are arranged in the order of reception time to form the original signal sequence.

[0050] In step S203, the receiving time order refers to the order in which the signal strength indication values ​​are recorded in the signal feature file. This order strictly corresponds to the time point when the mobile terminal receives the corresponding data packet. The original signal sequence refers to an ordered time series data formed by organizing all the discrete signal strength indication values ​​stored in the signal feature file according to the receiving time order. This sequence provides the basis for subsequent signal fluctuation analysis and outlier removal.

[0051] Specifically, if the judgment result of step S202 is yes, indicating that the data sample is sufficient, the processing unit will access the signal feature file of the tag ID again. Since each newly received signal strength indication value is sequentially appended to the end of the storage list when creating and updating the signal feature file, the list itself implicitly contains the reception time order. The processing unit will directly read all the signal strength indication values ​​sequentially from the first element to the last element according to the storage order of the list, and construct these values ​​into a new, ordered array or list. This newly generated data sequence is the original signal sequence, and the arrangement of data in this sequence faithfully reflects the original dynamic process of signal strength changing over time.

[0052] Step S204: Use a sliding window to traverse the original signal sequence and calculate the local median of the signal intensity indicator value within each sliding window. The window length of the sliding window is the preset number of local analysis points.

[0053] In step S204, the sliding window refers to a fixed-length subset of data that moves sequentially from beginning to end on the original signal sequence as the calculation progresses; the local median is used to represent the median value of all signal strength indicators within the coverage area of ​​the current sliding window after sorting. Since the median is not sensitive to extreme values, it can robustly reflect the local central trend of the signal; the preset number of local analysis points refers to a pre-set integer value that determines the size of the sliding window, which is usually an odd number to ensure the uniqueness of the median.

[0054] Specifically, the processing unit of the mobile terminal obtains a preset number of local analysis points from the system configuration, for example, set to 3. The processing unit places a sliding window of length 3 at the beginning of the original signal sequence, covering the first, second, and third signal strength indicators in the sequence. The processing unit sorts these three values ​​and takes the value at the middle position as the first local median. Then, the processing unit moves the sliding window one data point backward, covering the second, third, and fourth signal strength indicators in the sequence, and repeats the sorting and median calculation. This process is repeated until the sliding window has traversed the entire original signal sequence, thus calculating a corresponding local median for each signal strength indicator in the sequence.

[0055] Step S205: Calculate the absolute deviation between each signal intensity indication value in the original signal sequence and the local median of the corresponding sliding window.

[0056] In step S205, the absolute deviation value refers to the absolute value of the difference between a signal strength indicator value in the original signal sequence and its corresponding local median. This value is used to quantify the degree to which a single signal point deviates from its local stable trend.

[0057] Specifically, after the calculation is completed in step S204, the processing unit of the mobile terminal obtains a local median sequence with the same or approximately the same length as the original signal sequence. The processing unit iterates through each signal strength indicator value in the original signal sequence. For each signal strength indicator value, the processing unit finds its corresponding local median at the same location, performs a subtraction operation, calculates the difference between the two, and takes the absolute value of the difference. This calculated non-negative value is the absolute deviation value of the signal strength indicator value. The processing unit repeats this operation for all points in the original signal sequence to generate a completely new deviation value sequence composed of all absolute deviation values.

[0058] Step S206: Mark the signal strength indication value whose absolute deviation value exceeds the preset deviation value as an outlier.

[0059] In step S206, the preset deviation value refers to a threshold value used as a judgment standard to define the normal fluctuation range and abnormal jumps of the signal; outliers refer to signal strength indication values ​​whose absolute deviation values ​​exceed the preset deviation value. These points are considered unreliable data caused by transient interference or measurement errors.

[0060] Specifically, the mobile terminal's processing unit reads a preset deviation value from the system configuration parameters. This value is set based on historical data analysis or experience, for example, 10. The processing unit then iterates through the deviation value sequence generated in the previous step. For each absolute deviation value in the sequence, the processing unit compares it with the preset deviation value. If an absolute deviation value is greater than the preset deviation value of 10, the processing unit marks the corresponding signal strength indicator value in the original signal sequence as an outlier. This process continues until all values ​​in the deviation value sequence have been checked.

[0061] Step S207: Remove outliers from the original signal sequence to obtain a cleaned signal sequence.

[0062] In step S207, the purified signal sequence refers to the new sequence formed by the remaining data points after removing all signal strength indication values ​​marked as outliers from the original signal sequence. This sequence is more representative of the true and stable strength level of the Bluetooth tag signal.

[0063] Specifically, after marking all outliers, the mobile terminal's processing unit creates a new empty data sequence. Then, the processing unit iterates through the original signal sequence again from beginning to end. During this iteration, the processing unit checks if each signal strength indicator value has an associated outlier label. If a signal strength indicator value is not marked as an outlier, the processing unit copies the value and adds it to the newly created data sequence. If a signal strength indicator value is marked as an outlier, the processing unit skips that value without performing any operation. Once the original signal sequence has been traversed, the newly created data sequence contains only the signal strength indicator values ​​of all non-outliers. This new sequence is the cleaned signal sequence and is used for subsequent calculations of the statistical mean and standard deviation.

[0064] Step S208: Calculate the statistical mean and statistical standard deviation based on the signal strength indication values ​​in the purified signal sequence.

[0065] In step S208, the statistical mean refers to the arithmetic mean of all signal strength indication values ​​in the clean signal sequence. This value reflects the central strength level of the tag signal after eliminating transient interference. The statistical standard deviation is used to represent the degree of dispersion of all signal strength indication values ​​in the clean signal sequence relative to the statistical mean. It is a key indicator for measuring signal stability or volatility.

[0066] Specifically, after obtaining the purified signal sequence, the mobile terminal's processing unit calls a built-in mathematical calculation function. First, the processing unit adds up all signal strength indicators in the purified signal sequence, then divides the sum by the number of elements in the sequence; the result is the statistical mean. Next, the processing unit calculates the square of the difference between each signal strength indicator in the sequence and this statistical mean, adds up all these squared differences, divides them by the number of elements, and finally takes the square root of the result to obtain the statistical standard deviation. These two calculated values, the statistical mean and the statistical standard deviation, are temporarily stored for subsequent strength and stability verification.

[0067] Step S209: Obtain the target cargo area information associated with the current scanning session from the central management platform. The target cargo area information includes the spatial range of the cargo area and the shelf level configuration.

[0068] In step S209, the target cargo area information refers to structured data that describes the physical environment characteristics of the inventory work and is bound to the current scanning session; the spatial range refers to the specific dimensions of the target cargo area in three-dimensional space, which is usually defined by length, width and height; the shelf hierarchy configuration refers to the description of the physical structure of the shelves in the area, including the total number of shelves and the material type that makes up each shelf.

[0069] Specifically, when the mobile terminal initiates the quality verification process, it sends a request to the central management platform via the network. This request contains the current scanning session identifier. The central management platform queries the database based on this identifier to find the target goods area information corresponding to this inventory task. For example, for shelf area A, the central management platform might return data such as: spatial dimensions of 5 meters long, 1 meter wide, and 2 meters high; shelf layer configuration of 4 layers; and material of metal. After receiving this information, the mobile terminal parses it and loads it into memory for subsequent dynamic threshold calculations.

[0070] Step S210: Calculate the theoretical signal attenuation reference value based on the spatial range. The theoretical signal attenuation reference value is determined according to the propagation attenuation model of Bluetooth signal in free space and the maximum diagonal distance of the spatial range.

[0071] In step S210, the theoretical signal attenuation reference value refers to the signal strength loss that a Bluetooth signal would experience when it propagates from an ideal transmission source to the farthest point of the target cargo area, as predicted by physical laws; the propagation attenuation model in free space is a mathematical formula for calculating the decrease in the intensity of radio waves as the distance increases in an unobstructed space; the maximum diagonal distance refers to the straight-line distance between the two farthest vertices within a cuboid defined by the spatial range.

[0072] Specifically, the mobile terminal first uses the spatial range size data obtained in step S209 to calculate the maximum diagonal distance of the cuboid region using the three-dimensional Pythagorean theorem. Then, the mobile terminal uses this maximum diagonal distance as an input parameter and substitutes it into the built-in Bluetooth signal free-space propagation attenuation model formula, such as a simplified form of the Friis transmission equation. This calculation yields a value representing the signal strength attenuation in decibels; this value is the theoretical signal attenuation reference value, quantifying the maximum signal loss caused solely by distance.

[0073] In one possible implementation, the theoretical signal attenuation reference value is calculated based on the spatial range, specifically including steps S2101-S2104, as follows: Step S2101: Extract the length, width, and height of the cargo area from the spatial range.

[0074] In step S2101, length, width, and height refer to the specific dimensional values ​​of the three-dimensional cuboid space used to describe the target cargo area in three basic dimensions.

[0075] Specifically, the processing unit of the mobile terminal accesses the target cargo area information obtained and stored from the central management platform in step S209. This information is structured data, from which the processing unit parses out fields related to the spatial range and reads out the specific values ​​representing the length, width, and height of the cargo area, such as a length of 5 meters, a width of 1 meter, and a height of 2 meters, and temporarily stores these three values ​​for subsequent calculations.

[0076] Step S2102: Based on the length, width, and height, calculate the maximum diagonal distance of the cargo area using the spatial diagonal formula.

[0077] In step S2102, the spatial diagonal formula is a mathematical expression used to calculate the straight-line distance between the two farthest vertices inside a cuboid. This expression is the square root of the sum of the squares of the length, width, and height.

[0078] Specifically, the processing unit of the mobile terminal uses the length, width, and height values ​​extracted in step S2101. The processing unit squares the length, width, and height values. Then, it adds these three squared results to obtain a sum. Finally, the processing unit performs a square root operation on this sum, and the final result is the maximum diagonal distance of the cargo area, which will be used for subsequent signal attenuation calculations.

[0079] Step S2103: Obtain the parameters of the propagation attenuation model of the Bluetooth signal in free space. The propagation attenuation model parameters include the signal strength reference value at the reference distance and the path loss index.

[0080] In step S2103, the propagation attenuation model parameters refer to a set of preset constants used in a specific wireless signal propagation theoretical model to describe the law of signal strength change with distance; the signal strength reference value at the reference distance refers to the signal strength value measured at a specific short distance from the Bluetooth signal transmitter, usually 1 meter, in a standard unobstructed environment; the path loss index is a value that reflects the characteristics of the signal propagation environment and is used to represent the rate at which the signal strength attenuates with increasing distance.

[0081] Specifically, the mobile terminal's processing unit reads the Bluetooth signal propagation attenuation model parameters in free space from a local system configuration file or a pre-set constant library. These parameters are typically pre-set based on empirical testing or Bluetooth technical specifications; for example, the processing unit might obtain a reference signal strength baseline of -50dBm and a path loss index of 2.0 at a reference distance. These two parameters are loaded into memory as the basis for calculating the theoretical attenuation.

[0082] Step S2104: Substitute the maximum diagonal distance into the propagation attenuation model, calculate the signal attenuation at the maximum diagonal distance relative to the reference distance, and use the signal attenuation as the theoretical signal attenuation reference value.

[0083] In step S2104, the signal attenuation refers to the strength loss of the Bluetooth signal as it propagates from the reference distance to the maximum diagonal distance.

[0084] Specifically, the mobile terminal's processing unit substitutes the maximum diagonal distance calculated in step S2102 and the path loss exponent obtained in step S2103 into the formula of the logarithmic distance path loss model. This calculation typically involves first finding the ratio of the maximum diagonal distance to the reference distance of 1 meter, then taking the logarithm of that ratio, and multiplying it by 10 times the path loss exponent. Through this calculation, the processing unit obtains a value in decibels, which represents the signal attenuation during its propagation from 1 meter to the farthest point in the cargo area. The processing unit then stores this calculated signal attenuation directly as a theoretical signal attenuation reference value for use in subsequent steps.

[0085] Step S211: Determine the occlusion attenuation compensation amount based on the shelf level configuration. The occlusion attenuation compensation amount is calculated using the number of levels and the material properties of the level interval in the shelf level configuration.

[0086] In step S211, the obstruction attenuation compensation amount refers to the additional signal strength loss caused by the absorption and reflection of Bluetooth signals by physical obstacles such as shelf partitions; the material properties of the layer spacing are a key parameter, and different materials have significantly different attenuation effects on Bluetooth signals.

[0087] Specifically, the mobile terminal performs calculations based on the shelf level configuration information obtained from the central management platform. The system maintains a material attenuation coefficient lookup table, which records the typical signal attenuation decibel values ​​for different materials, such as metal, wood, and plastic. The mobile terminal looks up the material attribute in the shelf level configuration and finds the corresponding attenuation coefficient in the lookup table. Then, it multiplies the attenuation coefficient of that unit level by the number of levels in the shelf level configuration minus one, because the maximum number of partitions the signal needs to penetrate is the total number of levels minus one. The calculation result is the total obstruction attenuation compensation.

[0088] In one possible implementation, the amount of shading attenuation compensation is determined based on the shelf hierarchy configuration, specifically including steps S2111-S2115, as follows: Step S2111: Obtain the number of shelf levels and the material properties of the level interval between each shelf level from the shelf level configuration.

[0089] In step S2111, the number of levels refers to the total number of partitions that make up the entire shelf; the material attribute of the level partitions refers to the specific material type used to manufacture the shelf partitions, such as metal, wood or plastic.

[0090] Specifically, the processing unit of the mobile terminal accesses the shelf layer configuration information acquired and stored in step S209. The processing unit parses this data structure, precisely extracting two key numerical fields: an integer value representing the number of layers, and a string or enumeration value representing the material attribute of the layer interval. For example, the processing unit might read that the number of layers is 5 and the layer interval material attribute is metal. These two pieces of information will be stored separately for subsequent occlusion attenuation calculations.

[0091] Step S2112: Query the preset material attenuation coefficient table according to the layer interval material attributes to obtain the single-layer penetration attenuation value corresponding to each material.

[0092] In step S2112, the preset material attenuation coefficient table refers to a data lookup table built into the mobile terminal system, which records the typical attenuation decibel values ​​of different materials for Bluetooth signals; the single-layer penetration attenuation value refers to the signal strength lost by the Bluetooth signal when penetrating a shelf partition of a specific material.

[0093] Specifically, the processing unit of the mobile terminal uses the layer spacing material attribute obtained in step S2111 as a query keyword. The processing unit searches and matches in a preset material attenuation coefficient table stored locally. For example, if the layer spacing material attribute is metal, the processing unit will find the entry corresponding to the metal material and read the single-layer penetration attenuation value recorded in that entry, such as 8dB. This retrieved value represents the fixed loss of the signal passing through a single-layer metal plate and will be used to calculate the total penetration attenuation.

[0094] Step S2113: Calculate the maximum number of penetration layers between the operator's maximum scanning path and the shelf level. The maximum number of penetration layers is determined based on the number of levels and the preset standard scanning position.

[0095] In step S2113, the maximum number of penetration layers refers to the maximum possible number of shelf partitions that the Bluetooth signal needs to pass through from the tag to the mobile terminal under the most unfavorable scanning conditions; the preset standard scanning position refers to the assumption that the operator is always in a relative position that can cause the maximum signal blockage, such as scanning at the bottom or top of the shelf, in order to simplify the calculation.

[0096] Specifically, the mobile terminal's processing unit calculates the maximum number of penetration layers based on the number of layers obtained in step S2111. According to the worst-case principle of the preset standard scanning position—that is, the signal needs to penetrate from one end of the shelf to the other—the processing unit performs a simple subtraction operation, subtracting 1 from the total number of layers. For example, if the shelf has 5 layers, the calculated maximum number of penetration layers is 4. This value represents the upper limit of the number of obstacles encountered on the signal path.

[0097] Step S2114: Multiply the maximum number of penetration layers by the corresponding single-layer penetration attenuation value to obtain the cumulative penetration attenuation value.

[0098] In step S2114, the cumulative penetration attenuation value refers to the total signal strength loss caused by all the shelf partitions that need to be penetrated.

[0099] Specifically, the processing unit of the mobile terminal performs a multiplication operation. The processing unit multiplies the maximum number of penetration layers calculated in step S2113 with the single-layer penetration attenuation value corresponding to the material obtained in step S2112. For example, if the maximum number of penetration layers is 4 and the single-layer penetration attenuation value is 8dB, then the cumulative penetration attenuation value calculated by the processing unit is 32dB. This result quantifies the core signal attenuation caused solely by the physical obstruction of the shelf partition.

[0100] Step S2115: Add a preset environmental interference margin to the cumulative penetration attenuation value to obtain the occlusion attenuation compensation amount.

[0101] In step S2115, the preset environmental interference margin refers to a pre-set fixed value used to compensate for additional signal attenuation caused by unforeseen environmental factors other than the shelf itself, such as goods stacking and personnel movement.

[0102] Specifically, the mobile terminal's processing unit reads a fixed preset environmental interference margin value, such as 3dB, from the local configuration. Then, the processing unit performs an addition operation, adding the cumulative penetration attenuation value calculated in step S2114 to this preset environmental interference margin. For example, adding a cumulative penetration attenuation value of 32dB to the preset environmental interference margin of 3dB yields 35dB. This final calculation result is the amount of obstruction attenuation compensation required for this task, taking into account the impact of the shelf structure and potential environmental interference on the signal.

[0103] Step S212: Subtract the theoretical signal attenuation reference value and the occlusion attenuation compensation amount from the preset reference signal threshold to obtain the intensity lower limit threshold.

[0104] In step S212, the preset reference signal threshold is a fixed reference value, which usually represents the ideal signal strength of the Bluetooth tag at a distance of 1 meter without obstruction; the lower limit threshold refers to the minimum average signal strength required for a tag to be considered a valid signal, which is dynamically calculated for a specific cargo area after taking into account distance and obstruction factors.

[0105] Specifically, the mobile terminal reads a fixed preset reference signal threshold, such as -50dBm, from its local configuration. Then, the processing unit performs a subtraction operation, subtracting the theoretical signal attenuation reference value calculated in step S210 from the preset reference signal threshold, and then subtracting the occlusion attenuation compensation amount calculated in step S211. The final result of this calculation is a customized lower limit threshold for the intensity of the target cargo area.

[0106] Step S213: Determine whether the statistical mean is greater than or equal to the lower limit threshold of intensity.

[0107] In step S213, determining whether the statistical mean is greater than or equal to the lower limit threshold of intensity is a core comparison operation used to verify whether the actual average signal strength of the tag has reached the minimum requirement for effective identification in the current environment.

[0108] Specifically, the processing unit of the mobile terminal compares the statistical mean calculated in step S208 with the intensity lower limit threshold calculated in step S212. The result of this comparison is a Boolean value, either true or false. If the value of the statistical mean is greater than or equal to the value of the intensity lower limit threshold, the comparison result is true.

[0109] Step S214: If yes, then determine that the signal strength meets the preset strength condition.

[0110] In step S214, determining that the signal strength meets the preset strength condition means drawing a positive conclusion for the signal strength verification of the current tag based on the comparison result of step S213.

[0111] Specifically, if the judgment result of step S213 is true, the processing unit of the mobile terminal will set an internal status flag to indicate that the strength is sufficient. This result indicates that despite the attenuation caused by distance and obstruction, the average signal strength of the tag is still strong enough, initially meeting the characteristics of a valid tag.

[0112] Step S215: If not, then determine that the signal strength does not meet the preset strength condition.

[0113] In step S215, determining that the signal strength does not meet the preset strength condition means, based on the comparison result of step S213, making a negative conclusion for the signal strength verification of the current tag.

[0114] Specifically, if the judgment result of step S213 is false, that is, the statistical mean is less than the lower limit threshold of strength, the processing unit will set the internal status flag to "strength not satisfied". This result means that the signal of the tag is too weak to meet the standard for reliable identification in the current complex environment, and the tag will fail this reading quality verification.

[0115] Step S216: Determine whether the signal fluctuation meets the preset stability conditions based on the statistical standard deviation.

[0116] In step S216, signal fluctuation refers to the amplitude of the signal strength indicator value in the purified signal sequence changing around its statistical mean. This fluctuation is quantified by the statistical standard deviation. The preset stability condition refers to setting an acceptable upper limit for signal fluctuation in advance in order to ensure the reliability of the signal source. It is usually expressed as a specific statistical standard deviation threshold.

[0117] Specifically, the processing unit of the mobile terminal obtains a specific value used to define the preset stability condition from the scanning session parameters configured locally or issued by the central management platform, namely a standard deviation upper limit threshold, for example, 5. Then, the processing unit compares the statistical standard deviation calculated in step S208 with this standard deviation upper limit threshold. If the statistical standard deviation is less than or equal to the standard deviation upper limit threshold, the processing unit determines that the signal fluctuation meets the preset stability condition and records a positive judgment result. Conversely, if the statistical standard deviation is greater than the standard deviation upper limit threshold, the processing unit determines that the signal fluctuation does not meet the preset stability condition and records a negative judgment result.

[0118] Step S217: If the signal strength meets the preset strength condition and the signal fluctuation meets the preset stability condition, then the tag ID is determined to have passed the reading quality verification.

[0119] In step S217, the read quality verification refers to a comprehensive judgment process used to confirm that the signal of a Bluetooth tag is not only strong enough to be reliably received, but also stable enough to be continuously tracked, thereby determining whether the tag belongs to a valid inventory target.

[0120] Specifically, the mobile terminal's processing unit checks two internal state flags recorded in previous steps: one regarding whether the signal strength meets a preset strength condition, and the other regarding whether the signal fluctuation meets a preset stability condition. The processing unit performs a logical AND operation. The result of the logical AND operation is true only if both the signal strength and signal fluctuation conditions are met. If the result is true, the processing unit ultimately determines that the tag ID has passed the read quality verification and marks it as a valid tag. If at least one of the two states is not met, the result of the logical AND operation is false, and the processing unit determines that the tag ID has failed the read quality verification and marks it as an invalid or unobservable tag.

[0121] Step S105: If it is determined that the tag ID passes the read quality verification, then move the tag ID from the temporary tag set to the confirmed tag set.

[0122] In step S105, the temporary tag set and the confirmed tag set are two mutually exclusive data containers. The movement of tag IDs reflects the change in tag inventory status, migrating from the unconfirmed state to the confirmed state.

[0123] Specifically, after the read quality verification process in step S104 is completed, the system will obtain a verification result. If the result indicates that the specified tag ID has passed the read quality verification, the mobile terminal will perform a data migration operation. This operation first searches for and removes the tag ID and its associated signal feature file from the temporary tag set, and then adds the tag ID to the confirmed tag set. This operation ensures that any tag ID will only exist in either the temporary tag set or the confirmed tag set in the same scanning session, clearly distinguishing between confirmed valid tags and tags that are still under observation or have poor signal quality.

[0124] Step S106: At the end of the preset scanning cycle, terminate the scanning session, and use all tag IDs in the confirmed tag set as the successful read list, and identify the tag IDs in the temporary tag set as weak signal tags.

[0125] In step S106, the successful read list refers to the final list of all goods tags that have been confirmed as valid in the warehouse during this inventory task; weak signal tags refer to tags that failed to pass the read quality verification within the entire preset scanning cycle. These tags may have substandard signal quality due to reasons such as excessive distance, severe obstruction, or environmental interference.

[0126] Specifically, when the countdown of the preset scanning period ends, the mobile terminal automatically stops Bluetooth listening and officially terminates the current scanning session. At this time, the system confirms that all tag IDs stored in the tag set are compiled into a final successful read list. Simultaneously, the system checks the temporary tag set; all tag IDs still retained in this set, regardless of whether they are due to insufficient sampling, substandard signal strength, or inadequate stability, are uniformly identified as weak signal tags. Thus, the local processing phase of the inventory data is completed, producing two core results: the successful read list and the weak signal tags.

[0127] Step S107: Upload the successful read list and weak signal tags to the central management platform so that the central management platform can manage goods based on the successful read list and issue rescan reminders based on the weak signal tags.

[0128] In step S107, the central management platform is the backend server system responsible for storing, analyzing and managing all inventory data; goods management refers to a series of backend operations such as updating the inventory database and adjusting the storage location information based on the uploaded inventory results; and the rescan reminder is an inventory instruction pushed by the central management platform to the operator based on the weak signal tag information, indicating that a second verification or key attention is required.

[0129] Specifically, after the scanning session ends, the mobile terminal connects to the central management platform via wireless network and uploads the successful read list generated in step S106 and all identified weak signal tag data. Upon receiving the data, the central management platform parses the successful read list and compares and updates the tag IDs in the list with the backend inventory database, completing automated inventory calibration. For the simultaneously uploaded weak signal tag data, the central management platform records the location of these tags and, according to preset business rules, generates a rescan reminder on the operator's next task interface, clearly indicating which areas or tags require the operator to return for closer or longer scanning to improve inventory coverage and accuracy.

[0130] In one possible implementation, before uploading the successful read list and weak signal tags to the central management platform, the method further includes: for each tag ID in the weak signal tags, obtaining the corresponding target signal feature file from the temporary tag set; analyzing the reasons for failure to pass the read quality verification based on the target signal feature file, the reasons for failure include insufficient sampling, low signal strength, or excessive signal fluctuation; generating a rescan priority score for each weak signal tag according to the reasons for failure, and sorting the weak signal tags according to the rescan priority scores to generate a rescan priority list; generating targeted rescan suggestions according to the reasons for failure, wherein for failure due to insufficient sampling, it is recommended to extend the scan dwell time; for failure due to low signal strength, it is recommended to shorten the scan distance; and for failure due to excessive signal fluctuation, it is recommended to keep the scanning equipment stable; and pushing the rescan priority list and targeted rescan suggestions to the operator terminal.

[0131] Specifically, in this embodiment, after the scanning session ends, the mobile terminal will first preprocess the scanning results and then upload the processed data to the central management platform.

[0132] The first step in preprocessing is to analyze each weak signal tag individually. The mobile terminal extracts the signal feature profile corresponding to each weak signal tag ID from the temporary tag set, which serves as the target data for analysis. The signal feature profile actually records the signal strength changes of that tag ID throughout the entire scanning cycle, including the sampling time point and the signal strength indication value for each sample. Based on this raw data, the mobile terminal assesses the specific reasons why the tag failed the read quality verification.

[0133] Typically, read quality verification failures can be categorized into three types: insufficient sampling, low signal strength, and excessive signal fluctuation. Insufficient sampling means that the number of valid samples for the tag ID within the scanning cycle does not reach the minimum threshold required by the verification algorithm, resulting in insufficient data to support reliable statistical analysis. Low signal strength indicates that the tag may be too far from the inventory equipment, or that there are obstructions hindering signal propagation. Excessive signal fluctuation indicates that the relative position between the equipment and the tag may have changed significantly during the inventory process, causing drastic fluctuations in signal strength. Mobile terminals can automatically determine these three failure causes by analyzing statistical indicators such as the number of samples, signal mean, and variance.

[0134] After identifying the cause of the failure, the mobile terminal generates a rescan priority score. This score considers both the severity of the failure and the urgency of the rescan. For example, insufficient sampling can usually be remedied by slightly extending the scan time, so its priority is relatively low; while low signal strength may indicate a misplacement of goods, requiring immediate re-checking, so its priority is higher. The mobile terminal then sorts all weak signal tags based on this score, forming a rescan priority list.

[0135] Meanwhile, the mobile terminal automatically generates targeted rescanning suggestions based on the reason for each tag's failure. If the reading failure is due to insufficient sampling, the operator is advised to appropriately extend the dwell time on the tag during the next scan to obtain more sampling data points. If the failure is due to low signal strength, the operator is advised to shorten the scanning distance to the goods or try adjusting the inventory angle to reduce obstruction. For cases of excessive signal fluctuations, the operator is prompted to maintain the stability of the scanning equipment and reduce unnecessary shaking. These suggestions can guide the operator to optimize inventory operations and improve rescanning efficiency.

[0136] Finally, the mobile terminal pushes the rescan priority list and corresponding rescan suggestions to the operator's terminal interface. This allows the operator to conduct targeted rescanning based on the system's intelligent analysis results, prioritizing labels with more serious problems. Simultaneously, the intuitive rescan suggestions help operators quickly adjust their inventory strategies and procedures, improving the reading quality of labels that failed the initial scan, thereby enhancing overall inventory efficiency and accuracy.

[0137] After completing the above preprocessing steps, the mobile terminal will upload the priority list of weak signal tags and rescan suggestions, along with the successful read list of the entire scanning session, to the central management platform for subsequent data aggregation and business process scheduling. This demonstrates that by analyzing on-site data, the mobile terminal fully leverages the advantages of front-end intelligence, reducing the processing pressure on the central system and providing managers with more accurate and practical auxiliary decision-making information.

[0138] The following describes a tag-based inventory management device according to an embodiment of the present invention from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of a cargo management device based on tag inventory in an embodiment of this application.

[0139] It should be noted that, Figure 3 The structure of a tag-based inventory management device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0140] like Figure 3As shown, a tag-based inventory management device includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage section 308 into a random access memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for device operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0141] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0142] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0143] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0144] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0145] Specifically, a goods management device based on tag inventory in this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements a goods management method based on tag inventory provided in the above embodiment.

[0146] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the tag-based inventory management device described in the above embodiments; or it may exist independently and not assembled into the tag-based inventory management device. The storage medium carries one or more computer programs, which, when executed by a processor of the tag-based inventory management device, cause the tag-based inventory management device to implement the tag-based inventory management method based on IoT data encryption transmission provided in the above embodiments.

Claims

1. A cargo management method based on tag inventory, characterized in that, The method includes: In response to the operator's cargo scanning operation, a scanning session is initiated, and a temporary tag set and a confirmation tag set are created for the scanning session; Within a preset scanning period of the scanning session, listen for and receive data packets broadcast by Bluetooth tags within the communication range, and extract the tag ID of the Bluetooth tag corresponding to each data packet and the signal strength indication value when the data packet is received; In the temporary tag set, a signal feature profile is created for each tag ID, the signal feature profile including each signal strength indication value associated with the tag ID; For each tag ID in the temporary tag set, after each new data packet is received and the signal feature profile is updated, a read quality verification is performed on the tag ID, and it is determined whether the tag ID passes the read quality verification. If it is determined that the tag ID passes the read quality verification, then the tag ID is moved from the temporary tag set to the confirmed tag set; At the end of the preset scanning cycle, the scanning session is terminated, and all tag IDs in the confirmed tag set are used as a successful read list, while the tag IDs in the temporary tag set are identified as weak signal tags. The successful read list and the weak signal tag are uploaded to the central management platform so that the central management platform can manage goods based on the successful read list and issue rescan reminders based on the weak signal tag.

2. The method according to claim 1, characterized in that, The step of performing read quality verification on the tag ID and determining whether the tag ID passes the read quality verification specifically includes: Obtain all signal strength indication values ​​recorded in the signal feature file of the tag ID, and calculate the number of samples of all the signal strength indication values; Determine whether the number of samples has reached a preset minimum sampling threshold; If the number of samples reaches the preset minimum sampling threshold, a purified signal sequence is obtained, and the statistical mean and statistical standard deviation of the signal strength indication value are calculated based on the purified signal sequence. Based on the statistical mean, it is determined whether the signal strength meets the preset strength condition, and based on the statistical standard deviation, it is determined whether the signal fluctuation meets the preset stability condition. If the signal strength meets the preset strength condition and the signal fluctuation meets the preset stability condition, then the tag ID is determined to have passed the read quality verification.

3. The method according to claim 2, characterized in that, The step of acquiring the purified signal sequence and calculating the statistical mean and statistical standard deviation of the signal strength indication value based on the purified signal sequence specifically includes: All signal strength indication values ​​recorded in the signal feature file are arranged in chronological order of reception time to form the original signal sequence; The original signal sequence is traversed using a sliding window, and the local median of the signal intensity indicator value within each sliding window is calculated. The window length of the sliding window is a preset number of local analysis points. Calculate the absolute deviation between each signal strength indication value in the original signal sequence and the local median of the corresponding sliding window; The signal strength indication value whose absolute deviation value exceeds the preset deviation value is marked as an outlier. The outlier is removed from the original signal sequence to obtain the purified signal sequence; The statistical mean and the statistical standard deviation are calculated based on the signal strength indication values ​​in the purified signal sequence.

4. The method according to claim 2, characterized in that, The step of determining whether the signal strength meets the preset strength condition based on the statistical mean specifically includes: The target cargo area information associated with the current scanning session is obtained from the central management platform. The target cargo area information includes the spatial range of the cargo area and the shelf level configuration. The theoretical signal attenuation reference value is calculated based on the spatial range. The theoretical signal attenuation reference value is determined according to the propagation attenuation model of Bluetooth signal in free space and the maximum diagonal distance of the spatial range. The occlusion attenuation compensation amount is determined based on the shelf hierarchy configuration, and the occlusion attenuation compensation amount is calculated through the number of shelves and the material properties of the shelf spacing in the shelf hierarchy configuration; Subtracting the theoretical signal attenuation reference value and the occlusion attenuation compensation amount from the preset reference signal threshold yields the lower intensity threshold. Determine whether the statistical mean is greater than or equal to the intensity lower limit threshold; If so, then the signal strength is determined to meet the preset strength condition; If not, then it is determined that the signal strength does not meet the preset strength condition.

5. The method according to claim 4, characterized in that, The calculation of the theoretical signal attenuation reference value based on the spatial range specifically includes: Extract the length, width, and height of the cargo area from the spatial range; Based on the length, width, and height, the maximum diagonal distance of the cargo area is calculated using the spatial diagonal formula. Obtain the parameters of the propagation attenuation model of Bluetooth signal in free space, wherein the propagation attenuation model parameters include the signal strength reference value at a reference distance and the path loss index; Substitute the maximum diagonal distance into the propagation attenuation model to calculate the signal attenuation at the maximum diagonal distance relative to the reference distance, and use the signal attenuation as the theoretical signal attenuation reference value.

6. The method according to claim 4, characterized in that, The determination of the occlusion attenuation compensation amount based on the shelf hierarchy configuration specifically includes: Obtain the number of shelf levels and the material properties of the shelf spacing between each shelf level from the shelf level configuration; Based on the material properties of the layer interval, query the preset material attenuation coefficient table to obtain the single-layer penetration attenuation value corresponding to each material; Calculate the maximum number of layers that can be penetrated between the operator's maximum scanning path and the shelf level, the maximum number of layers being determined based on the number of levels and a preset standard scanning position; Multiply the maximum number of penetration layers by the corresponding single-layer penetration attenuation value to obtain the cumulative penetration attenuation value; The occlusion attenuation compensation amount is obtained by adding a preset environmental interference margin to the cumulative penetration attenuation value.

7. The method according to claim 1, characterized in that, Before uploading the successfully read list and the weak signal tag to the central management platform, the method further includes: For each tag ID in the weak signal tags, obtain the corresponding target signal feature file from the temporary tag set; Based on the analysis of the target signal feature profile, the reasons for failure to pass the read quality verification include insufficient sampling, low signal strength, or excessive signal fluctuation. Based on the reasons for failure, a rescan priority score is generated for each weak signal tag, and the weak signal tags are sorted based on the rescan priority scores to generate a rescan priority list. Based on the reasons for failure, targeted rescan suggestions are generated. For failures due to insufficient sampling, it is recommended to extend the scan dwell time; for failures due to low signal strength, it is recommended to shorten the scan distance; and for failures due to excessive signal fluctuations, it is recommended to keep the scanning equipment stable. The rescan priority list and the targeted rescan suggestions are pushed to the operator terminal.

8. A goods management device based on tag inventory, characterized in that, The tag-based inventory management device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the tag-based inventory management device to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on a tag-based inventory management device, the tag-based inventory management device performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on a tag-based inventory management device, the tag-based inventory management device performs the method as described in any one of claims 1-7.

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