Label checking method and device, storage medium and computer program product

Abstract

The embodiment of the invention provides a label checking method and device, a storage medium and a computer program product. The method comprises the steps of obtaining first data corresponding to each first antenna; wherein the first data comprises identification information of one or more first tags checked by the first antenna; determining a first parameter corresponding to each first antenna based on the first data; wherein the first parameter is at least used for representing first residence time required by the first antenna to check the first tag; determining a corresponding first inventory strategy based on the first parameter and a preset inventory scene, and performing label inventory through the first inventory strategy; wherein the first inventory strategy comprises one or more of an optimized polling strategy and an intelligent concurrency strategy, and the optimized polling strategy is at least used for adjusting the working sequence of each first antenna to realize the performance equalization of each antenna.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, and in particular to a tag inventory method, device, storage medium, and computer program product. Background Technology

[0002] Passive Internet of Things (IoT) technology enables automatic identification and management of items through wireless communication between tags and readers, and is widely used in warehousing, retail, and other fields. In scenarios requiring rapid inventory of a large number of tags, a multi-device network approach is typically employed to improve coverage and identification efficiency. In related technical solutions, readers activate different antennas sequentially according to a fixed polling order and perform tag inventory based on mechanisms such as Session 2. While this method can alleviate signal collision problems to some extent, its reliance on a fixed polling strategy means that after the first activated antenna completes its inventory, most tags within its coverage area become silent. Subsequent antennas struggle to effectively identify tags within their own coverage areas, resulting in significant differences in inventory performance among antennas and impacting overall system efficiency.

[0003] Furthermore, the number of tags (i.e., tag density) varies greatly under the coverage of different antennas. However, related technical solutions typically configure all antennas with uniform, static parameters, such as the same dwell time. This "one-size-fits-all" configuration strategy is indiscriminate; it ignores the actual workload of each antenna and easily leads to incomplete data collection.

[0004] In summary, the fixed polling strategy in related technologies affects the speed and balance of inventory counting in scenarios with large areas and massive amounts of tags. Furthermore, the "one-size-fits-all" static parameter configuration leads to resource waste and incomplete inventory counting, thereby reducing inventory counting efficiency. Summary of the Invention

[0005] To address the aforementioned technical problems, embodiments of this application provide a tag inventory method, device, storage medium, and computer program product, which can achieve performance equalization of each antenna and improve tag inventory efficiency.

[0006] The technical solution of this application embodiment is implemented as follows: In a first aspect, embodiments of this application provide a label inventory method, the method comprising: Obtain first data corresponding to each first antenna; wherein, the first data includes the identification information of one or more first tags detected by the first antenna; Based on the first data, a first parameter corresponding to each of the first antennas is determined; wherein, the first parameter is at least used to characterize the first dwell time required for the first antenna to inventory the first tag; Based on the first parameter and the preset inventory scenario, a corresponding first inventory strategy is determined to perform tag inventory. The first inventory strategy includes one or more of the following: an optimized polling strategy and an intelligent concurrency strategy. The optimized polling strategy is used to adjust the working order of each first antenna.

[0007] Secondly, embodiments of this application provide a label inventory device, which includes: an acquisition unit and a determination unit; wherein, The acquisition unit is used to acquire first data corresponding to each first antenna; wherein, the first data includes identification information of one or more first tags detected by the first antenna; The determining unit is configured to determine a first parameter corresponding to each of the first antennas based on the first data; wherein the first parameter is at least used to characterize the first dwell time required for the first antenna to inventory the first tags; and to determine a corresponding first inventory strategy based on the first parameter and a preset inventory scenario, so as to perform tag inventory through the first inventory strategy; wherein the first inventory strategy includes one or more of an optimized polling strategy and an intelligent concurrency strategy, and the optimized polling strategy is at least used to adjust the working order of each of the first antennas.

[0008] Thirdly, embodiments of this application provide a label inventory device, which includes: a processor and a memory; wherein, The memory is used to store computer programs that can run on the processor; The processor is configured to execute the label inventory method as described above when running the computer program.

[0009] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program code, which, when executed by a computer, implements the label inventory method described above.

[0010] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the label inventory method described above.

[0011] This application provides a tag inventory method, device, storage medium, and computer program product. The method includes: acquiring first data corresponding to each first antenna; wherein the first data includes identification information of one or more first tags inventoried by the first antenna; determining a first parameter corresponding to each first antenna based on the first data; wherein the first parameter is at least used to characterize the first dwell time required for the first antenna to inventory the first tags; determining a corresponding first inventory strategy based on the first parameter and a preset inventory scenario, so as to perform tag inventory through the first inventory strategy; wherein the first inventory strategy includes one or more of an optimized polling strategy and an intelligent concurrency strategy. Therefore, this application embodiment can first acquire the tag information corresponding to each antenna and calculate its key characteristic parameters, such as the first dwell time required for each first antenna to inventory the first tag, thereby adaptively setting different dwell times for different first antennas, improving inventory efficiency, and laying the foundation for subsequent adaptive inventory strategy development. Then, combined with a preset inventory scenario such as conventional inventory or high real-time inventory, an optimal inventory execution plan is generated. On the one hand, compared with the fixed polling order and static configuration in related technologies, the embodiments of this application can dynamically adjust the antenna working order and parameters, effectively alleviate the first-mover suppression effect, and achieve equalization of the performance of each antenna; on the other hand, by introducing an intelligent concurrency mechanism, the overall inventory cycle is significantly shortened and the system response speed and resource utilization are improved under the premise of low interference. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the label inventory method proposed in the embodiments of this application. Figure 1 ; Figure 2 This is a schematic diagram of the label inventory method proposed in the embodiments of this application. Figure 2 ; Figure 3 This is a schematic diagram of the composition and structure of the label inventory device proposed in the embodiments of this application. Figure 1 ; Figure 4 This is a schematic diagram of the composition and structure of the label inventory device proposed in the embodiments of this application. Figure 2 . Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for explaining the relevant application and not for limiting the application. Furthermore, it should be noted that, for ease of description, only the parts related to the relevant application are shown in the accompanying drawings.

[0014] Passive Internet of Things (IoT) technology enables automatic identification and management of items through wireless communication between tags and readers, and is widely used in warehousing, retail, and other fields. In scenarios requiring rapid inventory of a large number of tags, the system typically employs a multi-device network approach to improve coverage and identification efficiency. In related technologies, to reduce signal collisions and improve recognition rates, the Session mode from the ISO / IEC 18000-6C protocol is often used, especially Session 2 or Session 3, which puts identified tags into a silent state, thereby increasing the chance of response from unidentified tags.

[0015] In related technical solutions, the reader activates different antennas sequentially according to a fixed polling order and performs tag inventory based on mechanisms such as Session 2. While this method can alleviate the signal collision problem to some extent, its reliance on a fixed polling strategy means that after the first activated antenna completes its inventory, most of the tags within its coverage area become silent. Subsequent antennas then struggle to effectively identify tags within their own coverage areas, resulting in significant differences in inventory performance among antennas and impacting overall system efficiency.

[0016] Furthermore, the number of tags (i.e., tag density) varies greatly under the coverage of different antennas. However, related technical solutions typically configure all antennas with uniform, static parameters, such as the same dwell time. This "one-size-fits-all" configuration strategy is indiscriminate; it ignores the actual workload of each antenna and easily leads to incomplete data collection.

[0017] The main drawbacks of the aforementioned technologies are as follows: due to the combination of fixed polling strategy and session mechanism, some antennas are highly efficient due to "first-mover advantage", while other antennas cannot work effectively because a large number of tags are in a silent state. As a result, the synergistic effect of the entire system is not fully utilized, which affects the inventory speed and balance in large-area, massive tag scenarios. In addition, the "one-size-fits-all" static parameter configuration leads to resource waste and incomplete inventory, resulting in a decrease in inventory efficiency.

[0018] To address the current inability to achieve balanced performance across antennas and improve tag inventory efficiency, this application provides a tag inventory method, device, storage medium, and computer program product. The method includes: acquiring first data corresponding to each first antenna; wherein the first data includes identification information of one or more first tags inventoried by the first antenna; determining first parameters corresponding to each first antenna based on the first data; wherein the first parameters at least characterize the first dwell time required for the first antenna to inventory the first tags; determining a corresponding first inventory strategy based on the first parameters and a preset inventory scenario, and performing tag inventory through the first inventory strategy; wherein the first inventory strategy includes one or more of an optimized polling strategy and an intelligent concurrency strategy. Therefore, this application embodiment can first acquire tag information corresponding to each antenna and calculate its key characteristic parameters, such as the first dwell time required for each first antenna to inventory the first tag. This allows for adaptively setting different dwell times for different first antennas, improving inventory efficiency and laying the foundation for subsequent adaptive inventory strategy development. Then, combined with a preset inventory scenario such as conventional inventory or high real-time inventory, an optimal inventory execution plan is generated. On the one hand, compared with the fixed polling order and static configuration in related technologies, the embodiments of this application can dynamically adjust the antenna working order and parameters, effectively alleviate the first-mover suppression effect, and achieve equalization of the performance of each antenna; on the other hand, by introducing an intelligent concurrency mechanism, the overall inventory cycle is significantly shortened and the system response speed and resource utilization are improved under the premise of low interference.

[0019] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0020] This application provides a label inventory method. Figure 1 This is a schematic diagram of the label inventory method proposed in the embodiments of this application. Figure 1 ,like Figure 1 As shown, the label inventory method may include the following steps: Step 101: Obtain the first data corresponding to each first antenna; wherein, the first data includes the identification information of one or more first tags detected by the first antenna.

[0021] In the embodiments of this application, the tag inventory device can acquire first data corresponding to each first antenna.

[0022] It should be noted that, in the embodiments of this application, the label inventory device may include a business platform or other devices, and this application does not specifically limit the type of label inventory device.

[0023] It should be noted that, in the embodiments of this application, the first data may include the identification information of one or more first tags detected by the first antenna. This application does not specifically limit the data type and the amount of data included in the first data.

[0024] It should be noted that, in the embodiments of this application, the identification information of the first tag can refer to the data content used to uniquely identify a first tag in a Radio Frequency Identification (RFID) system, typically including an Electronic Product Code (EPC), a first tag serial number, etc. An EPC code may be a 96-bit binary number, representing information such as the batch, production date, and serial number of a specific product, thereby enabling accurate tracking of each first tag in the logistics chain.

[0025] For example, in an embodiment of this application, assume that a warehouse has three first antennas, ANT_0, ANT_1, and ANT_2, located in different locations within the warehouse. These three first antennas can be activated sequentially to perform a preliminary serial inventory count. During this process, ANT_0 may identify first tags A001, A002, and A003; ANT_1 may identify first tags A003 and A004; and ANT_2 may identify first tags A002 and A005. At this point, the first data acquired by each first antenna is the tag EPC list corresponding to that first antenna. This first data is not only used for subsequent analysis of the workload and spatial relationships of each first antenna, but also provides a basis for optimizing the inventory count strategy.

[0026] Step 102: Determine the first parameter corresponding to each first antenna based on the first data; wherein the first parameter is used to characterize at least the first dwell time required for the first antenna to count the first tag.

[0027] In the embodiments of this application, after the tag inventory device acquires the first data corresponding to each first antenna, it can determine the first parameter corresponding to each first antenna based on the first data.

[0028] It should be noted that, in the embodiments of this application, the first parameter refers to a set of key indicators calculated based on the first data obtained in the first step, including but not limited to: first tag density, first spatial overlap, first session mode, first encoding method (i.e., physical layer encoding method), and first dwell time. The first tag density is an indicator that measures the number of independent first tags under a certain first antenna, reflecting the actual workload faced by a certain antenna; the first spatial overlap is used to assess the degree of overlap in the coverage areas between two first antennas, thereby determining whether there are potential conflicts or interference between the two first antennas; and the first dwell time refers to the theoretically shortest working time required to ensure that all first tags under a certain antenna are effectively identified.

[0029] It should be noted that, in the embodiments of this application, the first session mode determines under what conditions the first tag responds to commands issued by the RFID reader. Common session modes include Session 0, Session 1, Session 2, and Session 3, each suitable for different application scenarios. For example, Session 0 is suitable for low-density first tag environments and can quickly complete first tag identification; Session 2 is suitable for high-density first tag environments and has stronger anti-interference capabilities. The choice of session mode determines the response behavior of the first tag and the identification efficiency of the entire system.

[0030] It should be noted that, in the embodiments of this application, the physical layer coding method refers to the coding standard and modulation method used for data transmission between the first tag and the reader in RFID communication, which determines the stability and speed of data transmission. Common physical layer coding methods include Miller-4, FM0, M2, and Miller-2. Different coding methods are suitable for different communication distances and environmental conditions. For example, Miller-4 coding exhibits better anti-interference performance in long-distance communication, while FM0 coding is more suitable for short-distance, high-speed communication scenarios. The choice of physical layer coding method affects the accuracy of first tag identification and the overall performance of the system.

[0031] Optionally, in embodiments of this application, when the tag inventory device determines the first parameter corresponding to each first antenna based on the first data, it can perform summary processing on each first data, and calculate the first tag density and first spatial overlap corresponding to each first antenna based on the summarized data; wherein, the first tag density is used to characterize the number of tags counted by the first antenna; the first spatial overlap is used to characterize the number of tags jointly counted between any two first antennas; for each first antenna, the first session mode and first encoding method corresponding to the first antenna can be determined based on the first tag density.

[0032] For example, in an embodiment of this application, the tag inventory device can record all tag EPC codes (i.e., first data) counted by each first antenna, as well as the number of times each EPC is counted and the Received Signal Strength Indicator (RSSI). The collected data is summarized to form a global data view, providing a basis for subsequent analysis, as shown in Table 1 below.

[0033] Table 1

[0034] It should be noted that, in the embodiments of this application, the tag density of each antenna can be obtained by analyzing Table 1 above. For example, the tag density of antenna 101_0 is 3 (because it has acquired three independent tags A001, A002, and A003), the tag density of antenna 101_1 is 2 (it has acquired tags A003 and A004), and so on. Spatial overlap analysis can also be performed. For example, the spatial overlap between antennas 101_0 and 101_1 is 1 because they have both acquired tag A003; the spatial overlap between antennas 101_0 and 101_2 is 1 because they have both acquired tag A002; the spatial overlap between antennas 101_1 and 101_2 is 0 because they have no tags acquired in common. In this way, the number of tags acquired in common between any two antennas can be obtained.

[0035] For example, in an embodiment of this application, for each first antenna, when the tag inventory device determines the first session mode and the first encoding method corresponding to the first antenna based on the first tag density, it can determine whether the first tag density is less than a first preset threshold; if the first tag density is less than the first preset threshold, it determines the second session mode and the second encoding method corresponding to the first antenna; wherein, the first session mode includes the second session mode, and the first encoding method includes the second encoding method.

[0036] It should be noted that, in the embodiments of this application, the first preset threshold can be any number of tags set based on historical experience, such as the first preset threshold being set to 100. This application does not specifically limit the size of the first preset threshold.

[0037] It should be noted that, in the embodiments of this application, the second session mode may include Session 0 or Session 1. In these two modes, the tags will not enter a silent state after being inventoried, and fast repeated confirmation is supported. They are suitable for scenarios with a small number of tags. This application does not specifically limit the type of the second session mode.

[0038] It should be noted that, in the embodiments of this application, the second encoding method may include FM0 or Miller-2 encoding. FM0 or Miller-2 encoding has lower decoding complexity and faster processing speed, making it suitable for efficient inventory counting in low-density areas. This application does not specifically limit the type of the second encoding method.

[0039] For example, in an embodiment of this application, assuming that the first tag density corresponding to the first antenna is 98, which is less than the first preset threshold (100), it can be determined that the session mode corresponding to the first antenna is Session 0 or Session 1, and the encoding method is FM0 or Miller-2.

[0040] It should be noted that, in the embodiments of this application, by selecting lighter communication parameters for low-density areas, resource consumption can be significantly reduced while ensuring inventory quality.

[0041] Optionally, in embodiments of this application, when the first tag density is greater than or equal to a first preset threshold, a third session mode and a third encoding method corresponding to the first antenna can be determined; wherein, the first session mode includes the third session mode, and the first encoding method includes the third encoding method.

[0042] It should be noted that in the embodiments of this application, the third session mode may include Session 2 or Session 3. In the third session mode, after the tag is successfully inventoried, the tag will enter a silent state for a period of time, thereby reducing the effect of repeated inventory. This feature makes it suitable for high-density application scenarios. This application does not specifically limit the type of the third session mode.

[0043] It should be noted that, in the embodiments of this application, the third encoding method refers to the data transmission method used at the physical layer, such as Miller-4 or Miller-8 encoding. These two encoding methods are more suitable for high-density environments because they have higher anti-interference capabilities and higher data transmission efficiency. This application does not specifically limit the type of the third encoding method.

[0044] In other words, in the embodiments of this application, when the first tag density is greater than or equal to the first preset threshold, the session mode can be selected as Session 2 or Session 3, and the physical layer encoding method can be selected as Miller-4 or Miller-8 encoding. It should be noted that, in the embodiments of this application, the tag density and spatial overlap of each first antenna are calculated based on the first data, and the optimal session mode and encoding method are determined based on the calculation results. This ensures that each antenna in the network obtains a set of core parameters best suited to its initial workload, thereby optimizing the performance of the first antenna in a multi-device network and significantly improving the inventory efficiency and balance in scenarios with massive tags.

[0045] It should be noted that, in the embodiments of this application, the tag inventory device can also accurately estimate the total time (i.e., the first dwell time) theoretically required to inventory all tags within its coverage area based on the tag density of each antenna, thereby adaptively adjusting the antenna dwell time.

[0046] Optionally, in embodiments of this application, when determining the first dwell time corresponding to each first antenna, the tag inventory device can determine the first number of first tags to be inventoried for each first antenna based on the first tag density; then, it can determine the number of time slots consumed in the Nth round of inventorying the second tags based on the second number of remaining second tags to be inventoried and the first preset function; wherein, the second tags are some or all of the first tags; N is a positive integer; furthermore, it can determine the third number of remaining third tags to be inventoried based on the second number, the number of tags successfully inventoried in the Nth round, and the first session mode, wherein the second tags include the third tags; if the third number is equal to the first value, the first dwell time is determined based on the total number of time slots consumed in the Nth round of inventorying the first tags.

[0047] It should be noted that, in the embodiments of this application, the first quantity can be the total number of tags that theoretically need to be inventoried, estimated based on the first tag density. This application does not specifically limit the size of the first quantity.

[0048] It should be noted that, in the embodiments of this application, the second quantity represents the number of second tags still to be counted at a certain moment, that is, the total number of tags that have not yet been identified. The second quantity changes dynamically with each round of counting, reflecting the current counting progress of the system. This application does not specifically limit the size of the second quantity.

[0049] It should be noted that in the embodiments of this application, the second label is some or all of the labels in the first label. For example, in the case of the first round of inventory, the second label is the first label. In the case of other rounds of inventory, such as the third round of inventory, the second label is some of the labels in the first label.

[0050] It should be noted that, in the embodiments of this application, the first preset function may include a rounding up function, and this application does not specifically limit the function type of the first preset function.

[0051] For example, in an embodiment of this application, when the tag inventory device determines the number of time slots consumed in the Nth round of inventorying the second tags based on the second number of the remaining second tags to be inventoried and the first preset function, it can first calculate the Q value of this round based on the second number of the remaining second tags to be inventoried and the rounding up function, as shown in the following formula (1). Then, it can determine the number of time slots consumed in the Nth round of inventorying the second tags based on the determined Q value. For example, if the Q value is 7, then the determined number of time slots consumed in the Nth round of inventorying the second tags can be... (i.e., 128) time slots.

[0052] (1) in, Represents the Q value. This represents the floor function. Indicates the second quantity of the second label.

[0053] For example, in an embodiment of this application, when the tag inventory device determines the third quantity of the remaining third tags to be inventoried based on the second quantity, the number of tags successfully inventoried in the Nth round, and the first session mode, it can determine the number of the remaining third tags to be inventoried according to the session mode adopted. For example, if the current mode is Session2, the tags successfully inventoried in the Nth round will be flipped to side B and will not participate in subsequent inventory. Therefore, the number of tags successfully inventoried in the Nth round needs to be subtracted from the second quantity to obtain the number of remaining third tags to be inventoried (i.e., the third quantity). If it is in Session0 / Session1 mode, the number of inventoried tags (i.e., tags successfully inventoried in the Nth round) will no longer be subtracted from the second quantity during iterative calculation. If the third quantity is equal to the first value, the first dwell time is determined based on the total number of time slots consumed in the Nth round of inventorying the first tags.

[0054] It should be noted that in the embodiments of this application, the first value is a preset threshold used to determine whether the number of remaining tags to be inventoried has reached an acceptable minimum, such as 0. When the third value equals the first value, it indicates that the tags under the antenna being operated on have been basically identified, and the current inventory process can be ended. This application does not specifically limit the size of the first value.

[0055] For example, in an embodiment of this application, if the third quantity is equal to the first value, when the tag inventory device determines the first dwell time based on the total number of time slots consumed by the first tag in the Nth round of inventory, assuming N is 6, it can obtain the total number of time slots consumed by the first tag in the 6th round of inventory based on the number of time slots consumed by the first tag in the 1st round of inventory + the number of time slots consumed by the second tag in the 2nd round of inventory + ... + the number of time slots consumed by the sixth tag in the 6th round of inventory. Then, the total number of time slots consumed (S_total) can be multiplied by the empirical value of the average single time slot consumption T_slot_avg (usually taken as 1.0ms to 2.0ms, for example 1.5ms). The calculation formula is shown in the following formula (2). Then, the calculation result ( Then multiply by a redundancy factor Factor_redundancy (usually 1.2) to deal with real-world environmental interference, and obtain the first dwell time as shown in the following formula (3).

[0056] (2) in, This indicates the total number of time slots consumed. This represents an empirical value indicating the average time spent per time slot. (3) in, Indicates the first length of stay. This represents the redundancy factor.

[0057] Optionally, in embodiments of this application, if the third quantity is greater than the first value, the number of time slots consumed in the (N+1)th round of inventorying the third tag is determined based on the third quantity and the first preset function; then, the fourth quantity of the remaining fourth tags to be inventoryed can be determined based on the third quantity, the number of tags successfully inventoryed in the (N+1)th round, and the first session mode, where the third tag includes the fourth tag; if the fourth quantity is equal to the first value, the first dwell time can be determined based on the total number of time slots consumed in the (N+1)th round of inventorying the first tag; or, If the fourth quantity is greater than the first value, the loop continues until the number of remaining tags to be inventoried is equal to the first value, and the first dwell time is determined based on the total number of time slots consumed by the first tag in the (N+i)th round of inventory; where i is a positive integer.

[0058] For example, in an embodiment of this application, the tag inventory device can calculate the Q value for this round based on the third quantity of the remaining third tags to be inventoried and a rounding function, as shown in formula (1) above. Then, based on the determined Q value, the number of time slots consumed in the (N+1)th round of inventorying the third tags can be determined. For example, if the Q value is 6, then the determined number of time slots consumed in the (N+1)th round of inventorying the third tags can be... (i.e., 64) time slots; then the fourth number of the remaining fourth tags to be inventoried can be determined based on the third number, the number of tags successfully inventoried in the N+1th round and the first session mode. If the fourth number is equal to the first value, the first dwell time can be determined based on the total number of time slots consumed by the first tag in the N+1th round. Assuming that the N+1th round is the 7th round, the total number of time slots consumed by the first tag in the 7th round can be obtained based on the number of time slots consumed by the tags in the 1st round + the number of time slots consumed by the tags in the 2nd round + ... + the number of time slots consumed by the tags in the 7th round. Then the first dwell time can be calculated using the above formulas (2) and (3).

[0059] It should be noted that, in the embodiments of this application, if the fourth quantity is greater than the first value, the loop continues until it is determined that the number of remaining tags to be inventoried is equal to the first value, and the first dwell time is determined based on the total number of time slots consumed by the first tag in the (N+i)th round of inventory; where i is a positive integer.

[0060] It should be noted that, in the embodiments of this application, the first quantity is dynamically determined based on the first tag density, and the number of time slots required for each round of inventory is estimated by combining a first preset function. Then, the remaining tag quantity is evaluated according to the first session mode, and finally, the dwell time is accurately calculated when the termination condition is met. In this way, the resource waste and missed reading problems caused by fixed parameters can be effectively avoided, thereby enabling differentiated configuration and efficient collaborative work for different antennas, and thus significantly improving the overall inventory performance in scenarios with massive tags.

[0061] Step 103: Based on the first parameter and the preset inventory scenario, determine the corresponding first inventory strategy to perform tag inventory; wherein, the first inventory strategy includes one or more of the optimized polling strategy and the intelligent concurrency strategy, and the optimized polling strategy is used to adjust the working order of each first antenna at least.

[0062] In the embodiments of this application, after determining the first parameter corresponding to each first antenna based on the first data, the tag inventory device can determine the corresponding first inventory strategy based on the first parameter and the preset inventory scenario, so as to perform tag inventory through the first inventory strategy.

[0063] It should be noted that, in the embodiments of this application, the preset inventory scenario refers to the inventory targets and constraints set according to actual application needs, which determine the priority and execution strategy during the inventory process. Common preset inventory scenarios can include time-insensitive regular inventory scenarios and scenarios requiring rapid inventory. For example, in regular inventory scenarios, due to the more lenient time constraints, more detailed identification strategies can be adopted; while in rapid inventory scenarios, time efficiency is prioritized, employing high-frequency concurrent operations and simplified communication protocols to accelerate the overall inventory speed. This application does not specifically limit the type and number of inventory scenarios included in the preset inventory scenarios.

[0064] It should be noted that, in the embodiments of this application, the tag inventory device can dynamically generate an optimal first inventory strategy based on a preset inventory scenario and the first parameters of each first antenna. For example, in a high real-time scenario, an intelligent concurrency strategy will be prioritized, enabling multiple first antennas to work simultaneously through reasonable grouping, thereby significantly shortening the overall inventory time. In a conventional inventory scenario, an optimized polling strategy can be used to balance the load on the first antennas and improve the recognition success rate. By flexibly switching between different first inventory strategies, efficient tag recognition capabilities can be maintained in various application scenarios.

[0065] Optionally, in the embodiments of this application, when the tag inventory device determines the corresponding first inventory strategy based on the first parameter and the preset inventory scenario, it can construct a node undirected graph based on each first antenna, where the preset inventory scenario represents the first inventory scenario; wherein, the node undirected graph contains one or more edges, each edge represents that the first spatial overlap between two connected nodes is greater than a second preset threshold, and the node represents the first antenna; then, the optimized polling strategy can be determined based on the node undirected graph.

[0066] It should be noted that, in the embodiments of this application, the first inventory scenario can be a time-insensitive conventional inventory scenario, and this application does not specifically limit the type of the first inventory scenario.

[0067] It should be noted that, in the embodiments of this application, the second preset threshold can be any value set based on experience, and this application does not specifically limit the size of the second preset threshold.

[0068] It should be noted that, in the embodiments of this application, the undirected node graph can be a data structure used to describe the spatial relationships between antennas. The undirected node graph can consist of several nodes, each node representing an antenna. If the spatial overlap between any two antennas (i.e., the number of tags jointly counted by any two antennas) exceeds a second preset threshold, an edge is established between the nodes corresponding to any two antennas. The undirected node graph can intuitively reflect the overlap of coverage areas between antennas, which helps in the optimization decision of subsequent polling order or concurrent grouping.

[0069] For example, in an embodiment of this application, in a warehouse environment, if two antennas ANT_1 and ANT_2 identify a large number of identical tags (such as A001, A002, etc.) in multiple inventory cycles, it indicates that the spatial coverage of the two antennas highly overlaps. In this case, the two antennas ANT_1 and ANT_2 can be treated as strongly correlated and connected by edges in the undirected graph of nodes. Conversely, if the two antennas hardly identify any identical tags, no edges will be established, indicating that the two antennas have strong spatial independence.

[0070] It should be noted that, in the embodiments of this application, after the tag inventory device constructs an undirected graph of nodes based on each first antenna, it can determine an optimized polling strategy based on the undirected graph of nodes.

[0071] Optionally, in embodiments of this application, when the tag inventory device determines the optimized polling strategy based on the undirected graph of nodes, it can determine the corresponding access path based on the undirected graph of nodes; wherein, the access path includes a path that visits all nodes only once, and the number of edges between all adjacent nodes in the path is minimized; then, a first node list can be determined based on the access path; wherein, the first node list includes M first antennas arranged in order, where M is a positive integer; and then, the optimized polling strategy can be determined based on the first node list and the first parameters corresponding to the first antennas in the first node list.

[0072] For example, in an embodiment of this application, the access path can refer to a Hamiltonian path in an undirected graph of nodes, that is, a path that passes through all nodes exactly once. The goal of the access path is to minimize the number of edges between adjacent nodes, thereby reducing the spatial overlap between two adjacent working antennas and mitigating the first-mover suppression effect. By reducing the number of overlapping tags between adjacent antennas, the impact of preceding antenna inventory on subsequent antennas can be avoided, improving the overall system balance and efficiency.

[0073] For example, in the embodiments of this application, the minimum conflict path problem can be solved using heuristic algorithms (such as greedy algorithms, simulated annealing, etc.). For instance, the antenna with the smallest total overlap can be selected as the starting point, and in each iteration, another antenna with no edge connection to the currently selected antenna or the smallest edge weight can be selected as the next node, thereby gradually constructing a complete access path. By selecting antennas and constructing access paths in the above manner, a more reasonable polling order can be generated, minimizing the interference between antennas in adjacent operating states.

[0074] Furthermore, in the embodiments of this application, the tag inventory device can determine a first node list based on the access path. The first node list is an ordered antenna working sequence generated according to the access path. The list contains M first antennas, where M is a positive integer. The first node list determines the working order of the antennas.

[0075] It should be noted that, in the embodiments of this application, after determining the first node list, the tag inventory device can determine an optimized polling strategy based on the first node list and the first parameter corresponding to the first antenna in the first node list.

[0076] For example, in the embodiments of this application, when generating the optimized polling strategy, the first node list can be mapped one-to-one with the first parameter of each antenna to form a complete execution plan. For example, if the first node list is [ANT_101_0, ANT_106_1, ANT_103_1], and their corresponding parameters are Session 2, Miller-4, DwellTime=684ms (i.e., the first dwell time); Session 2, Miller-8, DwellTime=550ms; Session 2, Miller-4, DwellTime=620ms, then the antennas will be activated sequentially in this order, and inventory operations will be performed according to their respective parameters.

[0077] In other words, in the embodiments of this application, an optimized polling order is generated by constructing an undirected graph of nodes and solving for the minimum conflict path, thereby alleviating the performance imbalance problem caused by spatial overlap between antennas. Simultaneously, resource utilization is maximized by adaptively configuring the communication parameters of each antenna. Through the above methods, the inventory efficiency in multi-device networking can be effectively improved, thereby achieving a balance in the performance of each antenna and meeting the real-time inventory requirements in large-scale, high-density tag scenarios.

[0078] Optionally, in the embodiments of this application, when the tag inventory device determines the corresponding first inventory strategy based on the first parameter and the preset inventory scenario, it can also divide all the first antennas based on the first tag density corresponding to each first antenna to obtain L regions, where the preset inventory scenario represents the second inventory scenario; wherein each region includes one or more first antennas, each region corresponds to a different tag density level, and L is a positive integer; then, for each region, the first antennas in the region can be grouped to obtain Q groups; wherein each group includes one or more second antennas, the first antennas contain the second antennas, and Q is a positive integer; and then, an intelligent concurrency strategy can be generated based on the Q groups and the first parameter corresponding to the second antennas.

[0079] It should be noted that, in the embodiments of this application, the second inventory scenario can be a scenario that requires rapid inventory, and this application does not specifically limit the type of the second inventory scenario.

[0080] It should be noted that, in the embodiments of this application, the partitioning process refers to dividing all the first antennas into several regions according to the tag density of the first antenna, with the first antennas in each region having a similar tag density level. This partitioning method helps improve the efficiency of subsequent grouping processing and allows for more accurate inventory strategies for different regions. For example, in high-density regions, the system may use Session 2 or Session 3 mode to reduce duplicate inventory and signal collisions; while in low-density regions, Session 0 or Session 1 mode may be used to improve response speed.

[0081] It should be noted that, in the embodiments of this application, for each area, when the tag inventory device groups the first antenna in the area to obtain Q groups, it can group the first antenna in the area based on a preset clustering algorithm to obtain Q groups; wherein, the spatial overlap between any two second antennas in a group is less than a third preset threshold, and the difference between the first dwell time corresponding to any two second antennas in a group is less than a fourth preset threshold.

[0082] It should be noted that, in the embodiments of this application, the preset clustering algorithm can refer to a calculation method used to divide multiple antennas into several groups according to their spatial distribution, signal coverage, and other characteristics. The preset clustering algorithm can be a traditional clustering method such as K-Means, DBSCAN, or hierarchical clustering, or a custom algorithm combining spatial distance and tag density. The role of the preset clustering algorithm is to optimize the work scheduling of antennas and avoid performance degradation caused by signal interference between antennas or uneven workload. By reasonably grouping them, the overall system efficiency can be improved and resource waste reduced. This application does not specifically limit the type of preset clustering algorithm.

[0083] For example, in an embodiment of this application, taking a greedy algorithm as an example, grouping the first antenna in a region may include the following steps: Step a. For each region, select one from the ungrouped antennas in that region as the "seed" node of the new concurrent group `G_new` (e.g., select the one with the longest DwellTime among the remaining antennas); Step b. Traverse all other ungrouped antennas ANT_j and check whether it can be added to G_new; wherein, the necessary and sufficient condition for ANT_j to be added is that the spatial overlap (Overlap(j, m)) between it and each existing member ANT_m in G_new is less than Threshold_concurrent (i.e., the third preset threshold). And the difference between its theoretical dwell time DwellTime_j and the DwellTime_seed of the "seed" node of G_new is within the allowable range (i.e., the difference is less than the fourth preset threshold); Step c. Add all ANT_j that meet the conditions to G_new; Step d. G_new is completed. Repeat the above steps until all antennas are assigned to concurrent groups. The final output is a list consisting of multiple concurrent groups, such as `[[101_0, 108_2, 115_3], [102_1, 109_0], ...]`; at the same time, the polling order between the groups is determined.

[0084] In other words, in the embodiments of this application, by grouping the first antenna in the region based on a preset clustering algorithm and setting restrictions on the spatial overlap and dwell time difference of the antennas within the group, signal interference and resource waste can be effectively avoided. As a result, the system can improve the overall inventory efficiency and stability, and ultimately meet the application requirements in high-density, large-scale passive IoT scenarios.

[0085] It should be noted that, in the embodiments of this application, by finely grouping the first antennas in the area and combining their respective parameter information, a set of efficient inventory solutions suitable for the current business scenario is generated, which compresses the originally long serial inventory time into the total time of several concurrent groups, thereby achieving an order-of-magnitude improvement in inventory speed and meeting the stringent requirements of high real-time business scenarios.

[0086] Furthermore, in the embodiments of this application, when the tag inventory device performs tag inventory through the first inventory strategy, it can perform tag inventory sequentially based on the M first antennas arranged in the first node list and the first parameters corresponding to the first antennas; or, it can perform tag inventory concurrently based on the second second antenna in each group and the first parameters corresponding to the second second antenna.

[0087] In summary, the embodiments of this application provide two optional tag inventory methods: one is sequential execution, suitable for scenarios with high stability requirements; the other is concurrent execution within a group, suitable for scenarios with high real-time requirements. By providing these two tag inventory methods, the system can flexibly adapt to different business needs, thereby enabling the system to fully leverage the advantages of multi-device networking and improve overall system performance.

[0088] This application provides a tag inventory method, which includes: acquiring first data corresponding to each first antenna; wherein the first data includes identification information of one or more first tags inventoried by the first antenna; determining a first parameter corresponding to each first antenna based on the first data; wherein the first parameter is at least used to characterize the first dwell time required for the first antenna to inventory the first tag; determining a corresponding first inventory strategy based on the first parameter and a preset inventory scenario, so as to perform tag inventory through the first inventory strategy; wherein the first inventory strategy includes one or more of an optimized polling strategy and an intelligent concurrency strategy. Therefore, this application embodiment can first acquire the tag information corresponding to each antenna and calculate its key characteristic parameters, such as the first dwell time required for each first antenna to inventory the first tag, thereby adaptively setting different dwell times for different first antennas, improving inventory efficiency, and laying the foundation for subsequent adaptive inventory strategy development. Then, combined with a preset inventory scenario such as conventional inventory or high real-time inventory, an optimal inventory execution plan is generated. On the one hand, compared with the fixed polling order and static configuration in related technologies, the embodiments of this application can dynamically adjust the antenna working order and parameters, effectively alleviate the first-mover suppression effect, and achieve equalization of the performance of each antenna; on the other hand, by introducing an intelligent concurrency mechanism, the overall inventory cycle is significantly shortened and the system response speed and resource utilization are improved under the premise of low interference.

[0089] Based on the above embodiments, another embodiment of this application provides a tag inventory method. This method can achieve balanced inventory performance, maximize resource utilization, and significantly improve inventory speed by dynamically optimizing the antenna working sequence, adaptively adjusting the antenna dwell time, and introducing an antenna concurrent working mechanism.

[0090] It should be noted that, in the embodiments of this application, Figure 2 This is a schematic diagram of the label inventory method proposed in the embodiments of this application. Figure 2 ,like Figure 2 As shown, the tag inventory method may include the following steps: Step 1. The service platform (i.e., the tag inventory device) can send service instructions to the passive system management node, carrying the polling mode and initial configuration parameters; Step 2. The passive system management node sends the device inventory instructions to the passive system distribution node; Step 3. The passive system distribution node connects one or more antennas, activates each antenna, and performs one or more complete inventory checks; Step 4. The passive system distribution node receives tag data (i.e., raw EPC data); Step 5. The passive system management node receives the tag data sent by the passive system distribution node and sends it to the service platform; Step 6. The service platform performs adaptive parameter (i.e., first parameter) configuration and intelligent scheduling, and sends service instructions to the passive system management node, carrying the inventory mode (i.e., first inventory strategy) and parameter configuration (i.e., first parameter); Step 7. The tags are inventoried according to the corresponding inventory strategy (i.e., first inventory strategy) and first parameter.

[0091] It should be noted that, in the embodiments of this application, the tag inventory method may include the following steps: Stage 1: Initialization diagnosis and antenna dwell time confirmation (this part is performed after the initial system deployment or significant environmental changes, aiming to generate the optimal inventory plan), Step 1: Perform baseline inventory and establish a global data view, including the following specific content, Step 1: The system (i.e., the tag inventory device) uses a traditional serial polling mode (e.g., 101_0 -> 101_1 -> ... ->134 (last device)_3), activate each antenna in the network in sequence, and perform one or more complete inventory checks, and collect the original EPC data (i.e., the first data) of all antennas. Step 2: The system will summarize the collected data and calculate the following core features for each antenna ANT_i: tag density Density_i (i.e., the first tag density), which represents the number of independent tags under antenna ANT_i, and spatial overlap Overlap(i,j) (i.e., the first spatial overlap), which represents the number of tags jointly counted between any two antennas ANT_i and ANT_j. After initializing the inventory check, record in detail all tag EPC codes counted by each antenna, as well as the number of times each EPC is counted and the signal strength (RSSI). Summarize the collected data to form a global data view, which provides a basis for subsequent analysis, as shown in Table 1 above. From Table 1, we can intuitively analyze: (1) Tag density analysis: The tag density of antenna 101_0 is 3 (because it counted A001, A002, A003). (3 independent tags); the tag density of antenna 101_1 is 2 (A003, A004 are listed); (2) Spatial overlap analysis: the spatial overlap of antennas 101_0 and 101_1 is 1 because they have both listed tag A003; the spatial overlap of antennas 101_0 and 101_2 is 1 because they have both listed tag A002; the spatial overlap of antennas 101_1 and 101_2 is 0 because they have no tags listed together; Step 2: Determine the core listing parameter configuration of each antenna: This step aims to tailor the optimal core communication parameters for each antenna ANT_i (i.e., the first antenna) in the network based on its tag density Density_i obtained in step 1; Configuration principle: Based on the "load matching" principle, select the most suitable listing mode for the working load of antennas with different tag densities;(1) For low-density areas: If the tag density Density_i of antenna ANT_i is less than a preset low-density threshold Threshold_low_density (e.g., Threshold_low_density = 100 tags) (i.e., the first preset threshold), select an inventory mode suitable for "small number of tags, fast response" for the antenna. For example: Session can be selected as Session 0 or Session 1. In these two session modes, the tags will return to state A immediately after being inventoried, which is suitable for quickly and repeatedly confirming a small number of tags present; Physical layer coding (PML): FM0 or Miller-2 can be selected. These coding methods have low decoding complexity and are suitable for scenarios with a small number of tags; (2) For high-density / massive tag areas: If the tag density Density_i of antenna ANT_i is greater than or equal to Threshold_low_density (i.e., the first preset threshold), an inventory mode suitable for "massive tags, avoid repeated inventory" can be selected for the antenna. Session: Session 2 or Session 3 must be selected, utilizing its "silent after disk" A->B state flipping mechanism to concentrate communication resources on unidentified tags; Physical Layer Coding (PML): Miller-4 or Miller-8 coding, more suitable for dense environments, should be selected. Through this step, each antenna in the network obtains a set of core parameters best suited to its initial workload, laying a solid foundation for subsequent dwell time calculation and scheduling execution.

[0092] It should be noted that, in the embodiments of this application, the tag density and spatial overlap of each first antenna are calculated based on the first data, and the optimal session mode and encoding method are determined based on the calculation results. This ensures that each antenna in the network obtains a set of core parameters best suited to its initial workload, thereby optimizing the performance of the first antenna in a multi-device network and significantly improving the inventory efficiency and balance in scenarios with massive tags.

[0093] It should be noted that, in the embodiments of this application, the initialization diagnosis and antenna dwell time confirmation (i.e., stage one) may further include the following detailed steps: Step three: Antenna dwell time estimation based on iterative performance model (this step aims to accurately estimate the total time theoretically required to inventory all tags within its coverage area for each antenna ANT_i, based on its unique tag density Density_i). Antenna dwell time estimation may include the following: Step 1: In order to obtain the highest inventory efficiency in a single Query (query signal), the inventory frame length is... It should be slightly larger than the number of tags to be inventoried, Density_i, as shown in formula (1) above. Assuming Density_i = 100, Rounding up, we get Q_opt = 7, at which point the frame length is... One slot; In practice, in some time-insensitive scenarios (allowing for a longer inventory time), Q_opt may be incremented by 1 to ensure a lower collision rate; Step 2: Estimate the total time (DwellTime_i) required for all tags under full antenna coverage (i.e., the first dwell time). This estimation is performed by an iterative model to calculate the complete inventory process of a reader with a dynamic Q-value adjustment mechanism in Session 2 mode, and to accumulate the number of slots consumed in this process; The model input is Density_i (i.e., the first tag density), which represents the initial tag density under this antenna (obtained from the tag density analysis above), and the model output is DwellTime_i, which represents the theoretical shortest antenna dwell time; The iterative calculation process is as follows: 1. Initialization: Set a variable N_remaining, representing the number of tags remaining to be inventoried (the second number of the second tags to be inventoried), with an initial value of Density_i, and set a variable S_total, representing the total number of slots consumed, with an initial value of 0; 2. The system starts inventory (as long as N_remaining>0) (1) Determine the Q value for this round: The reader dynamically calculates the optimal Q value based on the current remaining number of tags N_remaining (i.e., the second quantity). The calculation rules are shown in step 1. The optimal Q value (Q_opt) is determined. (2) Accumulate the time slots consumed in this round: This round of inventory will consume Each time slot is accumulated into S_total, as shown in the following formula (4); (3) Count the number of tags successfully counted in this round N_success_this_round (i.e., the number of tags successfully counted in the Nth round); (4) Update the number of remaining tags to be counted. If the Session 2 mode is used, these N_success_this_round tags will be flipped to the B side and will no longer participate in the subsequent count. Therefore, subtract this number (i.e., N_success_this_round) from N_remaining (i.e., the second number) to obtain the number of remaining tags to be counted (i.e., the third number of the third tags to be counted); In the S0 / S1 mode, the counted tags will no longer be subtracted from N_remaining during the iterative calculation; Step 3: The multi-round count ends. When the value of N_remaining decreases to 0 (or close to 0), the count ends; At this time, S_total is the total number of time slots required to count all tags. Step 4. Calculate the final dwell time (i.e. the first dwell time), multiply the total number of time slots consumed S_total by an empirical value of average single time slot consumption T_slot_avg (usually taken as 1.0ms to 2.0ms, for example 1.5ms) to obtain the basic dwell time, and then multiply it by a redundancy factor Factor_redundancy (which can be set, usually taken as 1.2) to cope with real-world environmental interference, as shown in the above formulas (2) and (3).

[0094] (4) in, This indicates the number of time slots consumed in this round of inventory counting. This indicates the total number of time slots consumed.

[0095] For example, in an embodiment of this application, it is assumed that Density_i (i.e., the first label density) = 100. First round query: N_rem=100, Q=7, S_total=128, N_succ=36, at this time N_rem becomes 64; where N_rem (i.e. N_remaining) represents the number of tags remaining to be inventoried, and N_succ (i.e. N_success_this_round) represents the number of tags successfully inventoried in this round; Second round of query: N_rem=64, Q=6, S_total=128+64=192, N_succ=23, at this time N_rem becomes 41; Third round of query: N_rem=41, Q=6, S_total=192+64=256, N_succ=15, at this time N_rem becomes 26; ...The inventory continues... Finally, assuming that S_total converges to 380 when N_rem=0, then DwellTime_i = 380 * 1.5ms * 1.2≈ 684ms.

[0096] It should be noted that, in the embodiments of this application, the above steps calculate a precise dwell time for each antenna that matches its workload, providing core data support for subsequent adaptive scheduling and concurrent packetization.

[0097] It should be noted that, in the embodiments of this application, the tag inventory method may further include the following detailed steps: Stage Two: Selecting and executing an optimized inventory strategy (i.e., the first inventory strategy) based on the business scenario. After completing the initialization diagnosis in the first stage and calculating the accurate dwell time and optimal inventory parameters for each antenna, this stage aims to select and execute the optimal inventory strategy from the two strategies of "optimized polling" and "intelligent concurrency" based on the real-time requirements of the business scenario; Strategy One: Optimized Polling (applicable to routine inventory scenarios that are not time-sensitive). 1. Applicable Scenarios and Problem Definition: For routine business scenarios where time requirements are not stringent but the stability and integrity of inventory need to be guaranteed (such as nighttime unmanned warehouse inventory), an optimized polling strategy can be adopted to solve the "first-mover suppression" effect caused by antenna spatial overlap in the traditional fixed-order polling in Session 2 mode, which results in severe inventory performance imbalance. This strategy aims to alleviate this problem by optimizing the working order of the antennas. 2. Algorithm for Optimizing Polling Order Generation: The goal of this algorithm is to generate a reasonable serial working order, such that the spatial overlap between any two adjacent antennas in the new order is minimized. The specific steps are as follows: Step 1: Construct an antenna collision graph. First, define the adjacent collision threshold Threshold_adjacent (i.e., the second preset threshold): This is a configurable integer representing the maximum number of overlapping tags allowed between two adjacent antennas; for example, it can be set to 10 or 5% of the total number of tags. Then, create an undirected graph with all antennas ANT_i as nodes. For any pair of antennas (ANT_i... If the spatial overlap (Overlap(i,j)) calculated in Phase 1 is greater than Threshold_adjacent (i.e., the second preset threshold), then connect an edge between these two nodes. This edge means that antennas i and j have a significant adjacent working conflict, and their inventory order should not be directly connected. Step 2: Solve the "minimum conflict path". On the constructed "antenna conflict graph", find a path that visits all nodes once and only once (i.e., Hamiltonian path) and minimizes the number of edges between all adjacent nodes on the path. This path is the optimal polling order. This is a classic combinatorial optimization problem. It can be solved by heuristic algorithms (such as nearest neighbor algorithm, simulated annealing) or backtracking search. For example, a simplified implementation idea (greedy algorithm) is provided: starting from a node (such as the antenna with the smallest total overlap), at each step, select a node that has not been visited and is not directly connected to the current node (i.e., the overlap is below the threshold) as the next visit object. If all unvisited nodes are connected to the current node, then select the node with the smallest edge weight (i.e., overlap).Finally, an optimized antenna operating sequence list is output, for example, ["118_1", "106_1", "103_1", "132_1", "133_0", "122_0"... "103_2", "132_2", "129_1"]; 3. Execute the optimized polling scheme: Load this newly generated operating sequence list. At the same time, for each antenna in the list, load its dedicated dwell time DwellTime_i calculated in Phase 1 and the optimal inventory parameter (i.e., the first parameter). After starting the inventory, the system will strictly follow the optimized sequence and the dedicated configuration of each antenna, thereby ensuring the integrity of the inventory while maximizing the balance of antenna performance.

[0098] It should be noted that in the embodiments of this application, Strategy 2: Intelligent Concurrency (applicable to scenarios requiring rapid inventory checks), 1. Applicable Scenarios and Problem Definitions Applicable Scenarios: For business scenarios with high real-time requirements, such as rapid inbound and outbound channels, dynamic tracking of production lines, and personnel positioning during peak customer traffic in retail stores. In these scenarios, the traditional serial polling mechanism cannot meet the requirements due to its excessively long inventory check cycle; Problem to be solved: How to shorten the total time required to complete a full-scale inventory check to orders of magnitude by using antenna concurrency while ensuring inventory check quality, thereby achieving a "near real-time" snapshot of the entire field status; 2. Intelligent Concurrency Group Generation Algorithm: The goal of this algorithm is to divide all antennas into k "conflict-free groups" that can work in parallel, achieving an upgrade from "antenna serial polling" to an efficient working mode of "serial between groups, concurrent within groups". The core of the grouping is to strictly follow the following dual constraints: Constraint 1: Low spatial overlap. Only antennas with sufficiently low spatial overlap can be assigned to the same concurrent group. This is to fundamentally avoid severe radio frequency interference caused by concurrent operation, and logical conflicts caused by state flipping in Session 2 / 3 modes. A concurrency conflict threshold, Threshold_concurrent (i.e., the third preset threshold), is defined. Antennas i and j are considered concurrent only when Overlap(i,j) is less than this threshold. This threshold is typically stricter than `Threshold_adjacent` in polling scenarios. Constraint two: Similar tag density and dwell time. All antennas within the same concurrent group should have similar workloads. This is to avoid the "barrel effect" in concurrent mode—that is, the concurrency time of the entire group is determined by the "slow" antenna that requires the longest time, while other "fast" antennas remain idle for a long time after completing their tasks, resulting in a new type of time waste. A density similarity threshold, Threshold_density_similarity, is defined. For example, the standard deviation of the tag density (Density) of all antennas within the same group cannot exceed a certain specific value.More directly, it can be required that the difference in theoretical dwell time (DwellTime) calculated in Phase 1 for all antennas within the same group cannot exceed an allowable range (e.g., |DwellTime_i - DwellTime_j| < ΔT_max); 3. Algorithm implementation steps: A constrained multi-objective clustering algorithm is used to automatically generate the optimal concurrent grouping scheme; Step 1: Candidate antenna screening and pre-classification; First, all antennas can be pre-divided into several coarse levels (i.e., L regions) according to the label density (Density), such as "high density region", "medium density region", and "low density region". This ensures that subsequent grouping will only be performed between antennas with similar densities; Step 2: Perform constrained clustering grouping: An iterative clustering algorithm is used (which can be modified based on the classic K-Means, DBSCAN, or a custom greedy algorithm); Iterative logic (greedy algorithm example): a. From the ungrouped antennas, select one as the "seed" node of the new concurrent group `G_new` (e.g., select the one with the longest DwellTime among the remaining antennas); b. Iterate through all other ungrouped antennas ANT_j and check if they can be added to G_new. The necessary and sufficient condition for ANT_j to be added is that: its spatial overlap (Overlap(j, m)) with each existing member ANT_m in G_new is less than Threshold_concurrent (i.e., the third preset threshold); and the difference between its theoretical dwell time DwellTime_j and the DwellTime_seed of G_new's "seed" node is within the allowed range; c. Add all ANT_j that meet the conditions to G_new; d. G_new is now complete. Repeat steps 1-3 until all antennas are assigned to concurrent groups; Step 3: Output concurrent execution plan: Finally, output a list consisting of multiple concurrent groups, such as `[[101_0, 108_2, 115_3], [102_1, 109_0], ...]`; At the same time, determine the polling order between groups; Step 4: Execute intelligent concurrency scheme: Load this concurrent execution plan. After the inventory starts, first activate all antennas in the first concurrent group, allowing them to work simultaneously with their respective optimal parameters (i.e., the first parameter). The overall working time of this group is determined by the antenna with the longest DwellTime in the group; When the first group is completed, the system shuts down all antennas in the group and immediately activates the next concurrent group, and so on, until all groups have completed execution. Through this intelligent concurrency strategy, the originally long serial inventory time is compressed into the total time of several concurrent groups, thereby achieving an order-of-magnitude improvement in inventory speed and meeting the stringent requirements of high real-time business scenarios.

[0099] In summary, the embodiments of this application have successfully solved the core technical bottlenecks of related technologies in scenarios with massive tags and multi-device networking, such as unbalanced inventory performance, resource waste, and excessively long inventory cycles caused by blindly adopting fixed polling and static configuration, through an automated, data-driven three-in-one optimization process of "diagnosis-configuration-scheduling".

[0100] This application provides a tag inventory method, which includes: acquiring first data corresponding to each first antenna; wherein the first data includes identification information of one or more first tags inventoried by the first antenna; determining a first parameter corresponding to each first antenna based on the first data; wherein the first parameter is at least used to characterize the first dwell time required for the first antenna to inventory the first tag; determining a corresponding first inventory strategy based on the first parameter and a preset inventory scenario, so as to perform tag inventory through the first inventory strategy; wherein the first inventory strategy includes one or more of an optimized polling strategy and an intelligent concurrency strategy. Therefore, this application embodiment can first acquire the tag information corresponding to each antenna and calculate its key characteristic parameters, such as the first dwell time required for each first antenna to inventory the first tag, thereby adaptively setting different dwell times for different first antennas, improving inventory efficiency, and laying the foundation for subsequent adaptive inventory strategy development. Then, combined with a preset inventory scenario such as conventional inventory or high real-time inventory, an optimal inventory execution plan is generated. On the one hand, compared with the fixed polling order and static configuration in related technologies, the embodiments of this application can dynamically adjust the antenna working order and parameters, effectively alleviate the first-mover suppression effect, and achieve equalization of the performance of each antenna; on the other hand, by introducing an intelligent concurrency mechanism, the overall inventory cycle is significantly shortened and the system response speed and resource utilization are improved under the premise of low interference.

[0101] Based on the above embodiments, this application provides a label inventory device. Figure 3 Schematic diagram of the composition structure of the label inventory equipment Figure 1 ,like Figure 3 As shown, device 10 includes: an acquisition unit 11 and a determination unit 12; wherein, The acquisition unit 11 is used to acquire first data corresponding to each first antenna; wherein, the first data includes the identification information of one or more first tags detected by the first antenna; The determining unit 12 is configured to determine a first parameter corresponding to each of the first antennas based on the first data; wherein the first parameter is at least used to characterize the first dwell time required for the first antenna to inventory the first tag; and to determine a corresponding first inventory strategy based on the first parameter and a preset inventory scenario, so as to perform tag inventory through the first inventory strategy; wherein the first inventory strategy includes one or more of an optimized polling strategy and an intelligent concurrency strategy, and the optimized polling strategy is at least used to adjust the working order of each of the first antennas.

[0102] In the embodiments of this application, further, Figure 4 Schematic diagram of the composition structure of the label inventory equipment Figure 2 ,like Figure 4 As shown, the label inventory device 10 proposed in this application embodiment may further include a processor 13, a memory 14 storing instructions executable by the processor 13, and further, the device 10 may also include a communication interface 15 and a bus 16 for connecting the processor 13, the memory 14 and the communication interface 15.

[0103] In the embodiments of this application, the processor 13 can be at least one of the following: Application-Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field-Programmable Gate Array (FPGA), Central Processing Unit (CPU), Controller, Microcontroller, and Microprocessor. It is understood that for different devices, the electronic device used to implement the above-mentioned processor function can also be other, and this application embodiment does not specifically limit it. Device 10 may also include a memory 14, which can be connected to the processor 13. The memory 14 is used to store executable program code, which includes computer operation instructions. The memory 14 may include high-speed RAM memory and may also include non-volatile memory, such as at least two disk drives.

[0104] In embodiments of this application, bus 16 is used to connect communication interface 15, processor 13 and memory 14 and the mutual communication between these devices.

[0105] In embodiments of this application, memory 14 is used to store instructions and data.

[0106] Further, in an embodiment of this application, the processor 13 is configured to acquire first data corresponding to each first antenna; wherein the first data includes identification information of one or more first tags detected by the first antenna; determine a first parameter corresponding to each first antenna based on the first data; wherein the first parameter is at least used to characterize the first dwell time required for the first antenna to detect the first tags; determine a corresponding first inventory strategy based on the first parameter and a preset inventory scenario, so as to perform tag inventory through the first inventory strategy; wherein the first inventory strategy includes one or more of an optimized polling strategy and an intelligent concurrency strategy, and the optimized polling strategy is at least used to adjust the working order of each first antenna.

[0107] In practical applications, the aforementioned memory 14 can be volatile memory, such as random-access memory (RAM); or non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); or a combination of the above types of memory, and provide instructions and data to the processor 13.

[0108] This application provides a tag inventory device that acquires first data corresponding to each first antenna. The first data includes identification information of one or more first tags acquired by the first antenna. Based on the first data, a first parameter is determined for each first antenna. The first parameter at least characterizes the first dwell time required for the first antenna to acquire the first tag. Based on the first parameter and a preset inventory scenario, a corresponding first inventory strategy is determined to perform tag inventory. The first inventory strategy includes one or more of an optimized polling strategy and an intelligent concurrency strategy. Therefore, this application can first acquire the tag information corresponding to each antenna and calculate its key characteristic parameters, such as the first dwell time required for each first antenna to acquire the first tag. This allows for adaptively setting different dwell times for different first antennas, improving inventory efficiency and laying the foundation for subsequent adaptive inventory strategies. Then, combined with a preset inventory scenario such as regular inventory or high real-time inventory, an optimal inventory execution plan is generated. On the one hand, compared with the fixed polling order and static configuration in related technologies, the embodiments of this application can dynamically adjust the antenna working order and parameters, effectively alleviate the first-mover suppression effect, and achieve equalization of the performance of each antenna; on the other hand, by introducing an intelligent concurrency mechanism, the overall inventory cycle is significantly shortened and the system response speed and resource utilization are improved under the premise of low interference.

[0109] This application provides a computer-readable storage medium storing a program that, when executed by a processor, implements the label inventory method described above.

[0110] Specifically, the program instructions corresponding to a tag inventory method in this embodiment can be stored on storage media such as optical discs, hard disks, and USB flash drives. When the program instructions corresponding to a tag inventory method in the storage media are read or executed by an electronic device, the following steps are included: Obtain first data corresponding to each first antenna; wherein, the first data includes the identification information of one or more first tags detected by the first antenna; Based on the first data, a first parameter corresponding to each of the first antennas is determined; wherein, the first parameter is at least used to characterize the first dwell time required for the first antenna to inventory the first tag; Based on the first parameter and the preset inventory scenario, a corresponding first inventory strategy is determined to perform tag inventory. The first inventory strategy includes one or more of the following: an optimized polling strategy and an intelligent concurrency strategy. The optimized polling strategy is used to adjust the working order of each first antenna.

[0111] This application also provides a computer program product, including a computer program that can be executed by the processor 13 of the tag inventory device 10 to complete the steps described in any of the foregoing methods.

[0112] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0113] This application is described with reference to schematic and / or block diagrams of implementations of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the schematic and / or block diagrams can be implemented by computer program instructions, and combinations of blocks in the schematic and / or block diagrams can be implemented. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the schematic and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0114] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in the implementation flow diagram. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0116] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application.

Claims

1. A label inventory method, characterized in that, The method includes: Obtain first data corresponding to each first antenna; wherein, the first data includes the identification information of one or more first tags detected by the first antenna; Based on the first data, a first parameter corresponding to each of the first antennas is determined; wherein, the first parameter is at least used to characterize the first dwell time required for the first antenna to inventory the first tag; Based on the first parameter and the preset inventory scenario, a corresponding first inventory strategy is determined to perform tag inventory. The first inventory strategy includes one or more of the following: an optimized polling strategy and an intelligent concurrency strategy. The optimized polling strategy is used to adjust the working order of each first antenna.

2. The method according to claim 1, characterized in that, The first parameter includes one or more of the following: first tag density, first spatial overlap, first session mode, and first encoding method corresponding to the first antenna. Determining the first parameter corresponding to each first antenna based on the first data includes: Each of the first data points is aggregated, and based on the aggregated data, the first tag density and the first spatial overlap corresponding to each first antenna are calculated; wherein, The first tag density is used to characterize the number of tags counted by the first antenna; the first spatial overlap is used to characterize the number of tags jointly counted by any two first antennas. For each of the first antennas, the first session mode and the first encoding method corresponding to the first antenna are determined based on the first tag density.

3. The method according to claim 2, characterized in that, The step of determining the first session mode and first encoding method corresponding to the first antenna based on the first tag density includes: Determine whether the density of the first tag is less than a first preset threshold; When the first tag density is less than the first preset threshold, the second session mode and the second encoding method corresponding to the first antenna are determined; wherein, the first session mode includes the second session mode, and the first encoding method includes the second encoding method.

4. The method according to claim 3, characterized in that, The method further includes: When the first tag density is greater than or equal to the first preset threshold, the third session mode and the third encoding method corresponding to the first antenna are determined; wherein, the first session mode includes the third session mode, and the first encoding method includes the third encoding method.

5. The method according to claim 1 or 2, characterized in that, The first parameter also includes the first dwell time, and the step of determining the first parameter corresponding to each of the first antennas based on the first data includes: For each of the first antennas, determine the first number of first tags to be inventoried based on the first tag density; The number of time slots consumed in the Nth round of inventorying the second tags is determined based on the second number of remaining second tags to be inventoried and the first preset function; wherein, the second tags are some or all of the first tags; and N is a positive integer; Based on the second quantity, the number of tags successfully counted in the Nth round, and the first session mode, a third quantity of the remaining third tags to be counted is determined, wherein the second tag includes the third tag; If the third quantity is equal to the first value, then the first dwell time is determined based on the total number of time slots consumed by the first tag in the Nth round of inventory.

6. The method according to claim 5, characterized in that, The method further includes: If the third quantity is greater than the first value, then the number of time slots consumed by counting the third tag in the N+1th round is determined based on the third quantity and the first preset function. Based on the third quantity, the number of tags successfully counted in the N+1th round, and the first session mode, a fourth quantity of the remaining fourth tags to be counted is determined, wherein the third tag includes the fourth tag; If the fourth quantity equals the first value, the first dwell time is determined based on the total number of time slots consumed by the first tag in the (N+1)th round of inventory counting; or... If the fourth quantity is greater than the first value, the loop continues until the number of remaining tags to be inventoried is equal to the first value, and the first dwell time is determined based on the total number of time slots consumed in the (N+i)th round of inventorying the first tag; where i is a positive integer.

7. The method according to claim 1, characterized in that, The step of determining the corresponding first inventory strategy based on the first parameter and the preset inventory scenario includes: When the preset inventory scenario represents the first inventory scenario, an undirected graph of nodes is constructed based on each of the first antennas; wherein, the undirected graph of nodes contains one or more edges, each edge represents that the first spatial overlap between two connected nodes is greater than a second preset threshold, and the node represents the first antenna; The optimized polling strategy is determined based on the undirected graph of the nodes.

8. The method according to claim 7, characterized in that, The step of determining the optimized polling strategy based on the undirected graph of nodes includes: The corresponding access path is determined based on the undirected graph of the nodes; wherein the access path includes a path that visits all nodes only once, and the number of edges between all adjacent nodes in the path is minimized; A first node list is determined based on the access path; wherein the first node list includes M first antennas arranged in order, where M is a positive integer; The optimized polling strategy is determined based on the first node list and the first parameter corresponding to the first antenna in the first node list.

9. The method according to claim 1, characterized in that, The step of determining the corresponding first inventory strategy based on the first parameter and the preset inventory scenario includes: When the preset inventory scenario represents the second inventory scenario, all first antennas are divided based on the first tag density corresponding to each first antenna to obtain L regions; wherein, each region includes one or more first antennas, each region corresponds to a different tag density level, and L is a positive integer; For each region, the first antenna in the region is grouped to obtain Q groups; wherein each group includes one or more second antennas, the first antenna includes the second antenna, and Q is a positive integer; The intelligent concurrency strategy is generated based on the Q groups and the first parameters corresponding to the second antenna.

10. The method according to claim 9, characterized in that, The first antenna in the region is grouped to obtain Q groups, including: The first antenna in the region is grouped based on a preset clustering algorithm to obtain the Q groups; wherein the spatial overlap between any two second antennas in the group is less than a third preset threshold, and the difference between the first dwell time corresponding to any two second antennas in the group is less than a fourth preset threshold.

11. The method according to claim 8 or 9, characterized in that, The tag inventory process using the first inventory strategy includes: Tag inventory is performed sequentially based on the M first antennas arranged in the first node list and the first parameters corresponding to the first antennas. or, Tag inventory is performed concurrently based on the second antenna in each group and the first parameter corresponding to the second antenna.

12. A label inventory device, characterized in that, The label inventory device includes: an acquisition unit and a determination unit; wherein... The acquisition unit is used to acquire first data corresponding to each first antenna; wherein, the first data includes the identification information of one or more first tags detected by the first antenna; The determining unit is configured to determine a first parameter corresponding to each of the first antennas based on the first data; wherein the first parameter is at least used to characterize the first dwell time required for the first antenna to inventory the first tags; and to determine a corresponding first inventory strategy based on the first parameter and a preset inventory scenario, so as to perform tag inventory through the first inventory strategy; wherein the first inventory strategy includes one or more of an optimized polling strategy and an intelligent concurrency strategy, and the optimized polling strategy is at least used to adjust the working order of each of the first antennas.

13. A label inventory device, characterized in that, The label inventory device includes: a processor and a memory; wherein... The memory is used to store computer programs that can run on the processor; The processor is configured to perform the method as described in any one of claims 1-11 when running the computer program.

14. A computer-readable storage medium, characterized in that, The storage medium stores computer program code, which, when executed by a computer, performs the method described in any one of claims 1-11.

15. A computer program product, comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method according to any one of claims 1-11.

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