Optimized deployment method and device of RFID antenna, equipment, medium and product

Abstract

The invention relates to an optimized deployment method, device and equipment of an RFID antenna, a medium and a product. The method comprises the following steps: determining an optimization target according to the receiving power of a tag antenna and the receiving power of a reader-writer antenna; the optimization target comprises a label coverage rate target and a performance target, and the performance target comprises at least one of a reader-writer interference target, a distance minimization target and a load balancing target; determining an initial deployment result of each reader-writer to be deployed according to the label coverage rate target; the initial deployment result comprises an initial position and an initial radiation power range; and according to the performance target, optimizing the initial deployment result of each to-be-deployed reader-writer to obtain a target deployment result of each to-be-deployed reader-writer. By adopting the method, iterative optimization can be carried out in a mode that top layer optimization and bottom layer optimization are matched with each other, and the optimal solution of a deployment result can be gradually approached, so that the accuracy of optimal deployment of the RFID antenna can be improved.

Description

Technical Field

[0001] This application relates to the field of radio frequency identification technology, and particularly to an optimized deployment method, device, equipment, medium, and product for RFID antennas. Background Art

[0002] With the rapid development of the Internet of Things technology, radio frequency identification (RFID) technology has been widely used in various application scenarios. To effectively use RFID technology, it is necessary to optimize the deployment method of RFID reader antennas to ensure that each tag can be accurately identified by at least one reader, and at the same time, to avoid collisions between readers.

[0003] However, in the process of deploying RFID reader antennas, when multiple optimization goals need to be satisfied simultaneously, the accuracy of the traditional RFID antenna optimized deployment method is relatively low. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide an optimized deployment method, device, equipment, medium, and product for RFID antennas, which can improve the accuracy of the optimized deployment of RFID antennas.

[0005] In a first aspect, this application provides an optimized deployment method for RFID antennas, including:

[0006] Determine an optimization goal based on the received power of the tag antenna and the received power of the reader antenna; the optimization goal includes a tag coverage rate goal and a performance goal, and the performance goal includes at least one of a reader interference goal, a distance minimization goal, and a load balancing goal;

[0007] Determine the initial deployment result of each to-be-deployed reader according to the tag coverage rate goal; the initial deployment result includes an initial position and an initial radiation power range;

[0008] Optimize the initial deployment results of each to-be-deployed reader according to the performance goal to obtain the target deployment results of each to-be-deployed reader.

[0009] In one of the embodiments, the optimizing the initial deployment results of each to-be-deployed reader according to the performance goal to obtain the target deployment results of each to-be-deployed reader includes:

[0010] Perform a homogenization process on the initial deployment results of each to-be-deployed reader to obtain an initial solution set;

[0011] Perform a local search and / or a global search on the initial solutions in the initial solution set to obtain intermediate solutions;

[0012] Iteratively optimize the initial solution set according to the performance objective and the intermediate solution until the initial solution meets the preset optimization conditions, and obtain the target deployment results of the to-be-deployed readers and writers.

[0013] In one embodiment, the intermediate solution includes a first intermediate solution and a second intermediate solution. The local search and / or global search for the initial solutions in the initial solution set to obtain the intermediate solution includes:

[0014] Perform a local search on the initial solution to obtain the first intermediate solution;

[0015] According to the fitness of each initial solution in the initial solution set, screen out the target solutions with fitness greater than the preset fitness threshold from the initial solution set;

[0016] Perform a global search on the target solutions to obtain the second intermediate solution.

[0017] In one embodiment, the performing a local search on the initial solution to obtain the first intermediate solution includes:

[0018] Screen out the optimal solution with the highest fitness from the initial solution set;

[0019] Determine the first intermediate solution according to the position of the initial solution, the positions of the neighbor solutions of the initial solution, and the optimal solution.

[0020] In one embodiment, the iteratively optimizing the initial solution set according to the performance objective and the intermediate solution includes:

[0021] Determine the first fitness of the intermediate solution and the second fitness of the initial solution according to the performance objective;

[0022] If the first fitness is greater than the second fitness, use the intermediate solution as the new initial solution, and return to perform the local search and / or global search on the initial solutions in the initial solution set;

[0023] If the first fitness is not greater than the second fitness during the iterative optimization process for a preset number of times, adjust the position of the initial solution.

[0024] In one embodiment, the determining the initial deployment results of the to-be-deployed readers and writers according to the tag coverage rate target includes:

[0025] Solve the deployment status corresponding to all readers and writers according to the tag coverage rate target, and determine the initial number of the to-be-deployed readers and writers;

[0026] Determine the initial deployment results of each of the to-be-deployed readers according to the initial quantity of the to-be-deployed readers.

[0027] In a second aspect, the present application further provides an optimized deployment device for an RFID antenna, including:

[0028] An optimization objective determination module, configured to determine an optimization objective according to the received power of the tag antenna and the received power of the reader antenna; the optimization objective includes a tag coverage rate objective and a performance objective, and the performance objective includes at least one of a reader interference objective, a distance minimization objective, and a load balancing objective;

[0029] An initial deployment result determination module, configured to determine the initial deployment results of each of the to-be-deployed readers according to the tag coverage rate objective; the initial deployment results include an initial position and an initial radiation power range;

[0030] An optimization module, configured to optimize the initial deployment results of each of the to-be-deployed readers according to the performance objective to obtain the target deployment results of each of the to-be-deployed readers.

[0031] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method in any one of the embodiments in the first aspect are implemented.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium. On the computer-readable storage medium, a computer program is stored, and when the computer program is executed by a processor, the steps of the method in any one of the embodiments in the first aspect are implemented.

[0033] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the method in any one of the embodiments in the first aspect are implemented.

[0034] The above optimization deployment method, device, equipment, medium and product of the RFID antenna determine an optimization objective according to the received power of the tag antenna and the received power of the reader antenna; the optimization objective includes a tag coverage rate objective and a performance objective, and the performance objective includes at least one of a reader interference objective, a distance minimization objective, and a load balancing objective; according to the tag coverage rate objective, determine the initial deployment result of each reader to be deployed; the initial deployment result includes an initial position and an initial radiation power range; according to the performance objective, optimize the initial deployment result of each reader to be deployed to obtain the target deployment result of each reader to be deployed. In the embodiments of the present application, top-level optimization can be performed according to the tag coverage rate objective, and bottom-level optimization can be performed according to the initial deployment result of each reader to be deployed obtained by the top-level optimization and the performance objective, so that the top-level optimization provides constraint conditions and decision references for the bottom-level optimization, and the bottom-level optimization can more finely adjust the initial deployment result and provide feedback for the top-level optimization. Therefore, iterative optimization is performed by the cooperation of the top-level optimization and the bottom-level optimization, and the optimal solution of the deployment result can be gradually approached, so the accuracy of the optimization deployment of the RFID antenna can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained without creative efforts based on these drawings.

[0036] Figure 1 It is an application environment diagram of the optimization deployment method of the RFID antenna in an embodiment;

[0037] Figure 2 It is a schematic flowchart of the optimization deployment method of the RFID antenna in an embodiment;

[0038] Figure 3 It is a schematic flowchart of the iterative optimization step in an embodiment;

[0039] Figure 4 It is a schematic flowchart of the search step in an embodiment;

[0040] Figure 5 It is a schematic flowchart of the optimization deployment method of the RFID antenna in another embodiment;

[0041] Figure 6 It is a structural block diagram of the optimization deployment device of the RFID antenna in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] In order to make the objectives, technical solutions and advantages of this application more clear and understandable, the following further elaborates on this application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above description of the drawings are intended to cover non-exclusive inclusion.

[0044] In the description of the embodiments of this application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order or primary-secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "a plurality of" is more than two, unless otherwise specifically defined.

[0045] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0046] With the rapid development of Internet of Things technology, radio frequency identification (RFID) technology has been widely used in various application scenarios, especially in retail production monitoring, supply chain management, asset tracking, and positioning navigation. In these applications, it has become a key task to efficiently identify all tags within the working space of RFID readers by reasonably deploying RFID readers and optimizing the working parameters of the readers. This task is usually classified as the RFID network planning (RNP) problem. The core goal of RNP is to minimize the number of readers and optimize the parameter configuration while ensuring system coverage, identification accuracy, and energy consumption efficiency. However, with the expansion of the RFID network scale and the superimposition of complex conditions such as environmental factors, interference between readers, and randomness of tag positions, the RNP problem has gradually evolved into a multi-objective, non-linear, and highly complex optimization problem, making it difficult for traditional RFID network planning methods to provide effective and efficient solutions while meeting multiple constraints. Based on this, in order to use RFID technology more effectively and address the complexity and multi-objective requirements in RFID network planning, it is necessary to optimize the deployment method of RFID reader antennas to ensure that each tag can be accurately identified by at least one reader and avoid collisions between readers.

[0047] However, in the process of deploying RFID reader antennas in traditional technologies, when multiple optimization objectives need to be satisfied simultaneously, multiple objective functions are usually transformed into a single objective function by the weighted summation method, ignoring the complex coupling effects between different priority objectives and variable types. As a result, when the number of objectives and decision variables increases, both accuracy and efficiency are significantly reduced. Therefore, the accuracy of the optimized deployment method of RFID antennas in traditional technologies is relatively low.

[0048] After introducing the background technology of the optimized deployment method of RFID antennas provided by the embodiments of this application as above, below, the implementation environment involved in the optimized deployment method of RFID antennas provided by the embodiments of this application will be briefly described. The optimized deployment method of RFID antennas provided by the embodiments of this application can be applied to, for example Figure 1In the computer device shown. The computer device can be a terminal or a server. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. The computer program, when executed by the processor, implements an optimized deployment method for RFID antennas. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0049] Those skilled in the art can understand that Figure 1 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. A specific terminal may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0050] In one embodiment, as Figure 2 shown, an optimized deployment method for RFID antennas is provided. Taking the method applied to the Figure 1 computer device in as an example, the method includes the following steps:

[0051] S201, determine an optimization target according to the received power of the tag antenna and the received power of the reader antenna; the optimization target includes a tag coverage rate target and a performance target, and the performance target includes at least one of a reader interference target, a distance minimization target, and a load balancing target.

[0052] Among them, the system where RFID technology is located may include but is not limited to tag antennas, reader antennas, etc. The optimization objectives include tag coverage objectives and performance objectives. The performance objectives include at least one of reader interference objectives, distance minimization objectives, and load balancing objectives. The tag coverage objective is that it is expected to identify all target tags in the working area with the least number of readers. The reader interference objective is that it is expected to reduce the collisions between readers in the dense area. The distance minimization objective is that it is expected to minimize the distance from each reader to the center of the coverage area of the reader. The load balancing objective is that it is expected to balance the load distribution between readers.

[0053] In the embodiments of the present application, the computer device may pre-determine the received power of the tag antenna and the received power of the reader antenna. Exemplarily, the received power of the tag antenna is calculated as shown in the following formula (1):

[0054]

[0055] Wherein, represents the output power of the reader, represents the antenna gain of the reader, represents the antenna gain of the tag. represents the path loss, The calculation formula of

[0056]

[0057] Wherein, represents the transmission wavelength, represents the distance between the reader and the tag, represents the path loss exponent, represents other loss factors.

[0058] Exemplarily, the received power of the reader antenna is calculated as shown in the following formula (3):

[0059]

[0060] Thus, the computer device can define multiple optimization objectives according to the received power of the tag antenna and the received power of the reader antenna. Exemplarily, the specific expression of the tag coverage objective is as shown in the following formula (4):

[0061]

[0062] Wherein, represents the tag coverage rate, and the objective is to cover all tags so that each tag is at least covered by one reader Coverage Represents the threshold of the tag received power Represents the threshold of the reader received power Represents the power of the signal transmitted by the reader r1 received by the tag Represents the power of the tag t transmitted received by the reader r1

[0063] Exemplarily, the specific expression of the reader interference target is shown in the following formula (5):

[0064]

[0065] Wherein Represents the reader interference, and the above formula means to minimize the interference between readers

[0066] Exemplarily, the specific expression of the distance minimization target is shown in the following formula (6):

[0067]

[0068] Wherein Represents the distance minimization. The goal of distance minimization is to minimize the distance from each reader to the center of the coverage area of the reader. Wherein Represents the position of the reader Represents the position of the center of its coverage area

[0069] Exemplarily, the specific expression of the load balancing target is shown in the following formula (7):

[0070]

[0071] Wherein Represents the load balancing. The load balancing target is to balance the load distribution between readers. Wherein Represents the number of tags assigned to the reader Represents the maximum number of tags that a single reader can process per unit time

[0072] S202. According to the tag coverage rate target, determine the initial deployment results of each reader to be deployed; the initial deployment results include the initial position and the initial radiation power range

[0073] Wherein, the reader to be deployed is screened from all readers in the system where the RFID technology is located, and the initial deployment results may include at least one of, but not limited to, the initial position and the initial radiation power range, etc

[0074] ​To optimize the multi-objective problem proposed above, the embodiment of the present application proposes a two-level optimization framework, namely top-level optimization and bottom-level optimization. Specifically, in the optimization process, the main objective of top-level optimization is to minimize the number of deployed readers while ensuring that all tags can be covered. That is to say, in the embodiment of the present application, the computer device can first determine the initial deployment results of each reader to be deployed according to the tag coverage target in the top-level optimization. Optionally, the computer device can directly determine the initial deployment results of each reader to be deployed according to the tag coverage target; or, the computer device can first determine the number of readers to be deployed according to the tag coverage target, and then determine the initial deployment results of each reader to be deployed according to the number of readers to be deployed. Of course, the embodiment of the present application does not limit the specific implementation manner of determining the initial deployment results of each reader to be deployed.

[0075] S203. Optimize the initial deployment results of each reader to be deployed according to the performance target to obtain the target deployment results of each reader to be deployed.

[0076] In the embodiment of the present application, in the optimization process, the purpose of bottom-level optimization is to further adjust the continuous parameters of each reader to be deployed on the basis of the initial deployment results of each reader to be deployed provided by the top-level optimization to meet the overall performance requirements. That is to say, the computer device can, according to the initial deployment results of each reader to be deployed and the above performance target, and the initial radiation power range carry out iterative optimization, with the goal of simultaneously and maximally improving multiple performance indicators, so as to obtain the target deployment results of each reader to be deployed. Among them, the target deployment results include the deployment scheme of the reader and the detailed parameter configuration of the reader. The detailed parameter configuration can include but is not limited to at least one of position, radiation power, etc. Exemplarily, the bottom-level optimization problem can be formally expressed as the following formula (8):

[0077]

[0078] In the above method for optimizing the deployment of RFID antennas, an optimization objective is determined based on the received power of the tag antenna and the received power of the reader antenna; the optimization objective includes a tag coverage rate objective and a performance objective, and the performance objective includes at least one of a reader interference objective, a distance minimization objective, and a load balancing objective; according to the tag coverage rate objective, an initial deployment result of each reader to be deployed is determined; the initial deployment result includes an initial position and an initial radiation power range; according to the performance objective, the initial deployment results of each reader to be deployed are optimized to obtain the target deployment results of each reader to be deployed. In the embodiments of the present application, top-level optimization can be performed according to the tag coverage rate objective, and bottom-level optimization can be performed according to the initial deployment results of each reader to be deployed obtained from the top-level optimization and the performance objective, so that the top-level optimization provides constraint conditions and decision references for the bottom-level optimization, and the bottom-level optimization can more finely adjust the initial deployment results and provide feedback for the top-level optimization. Thus, iterative optimization is performed through the cooperation of the top-level optimization and the bottom-level optimization, and the optimal solution of the deployment result can be gradually approached, so the accuracy of the optimized deployment of the RFID antenna can be improved.

[0079] In one embodiment, an implementation manner for determining the initial deployment result of each reader to be deployed is provided, that is, "according to the tag coverage rate objective, determine the initial deployment result of each reader to be deployed" in the above S202, including:

[0080] Solve the deployment status corresponding to all readers according to the tag coverage rate objective to determine the initial number of readers to be deployed.

[0081] Determine the initial deployment result of each reader to be deployed according to the initial number of readers to be deployed.

[0082] In the embodiments of the present application, the computer device can pre-design a variable represented by a binary vector , where represents that the th reader is not deployed, and represents that it is deployed, that is, the above variable is used to characterize the deployment status of each reader in the system where the RFID technology is located, and the deployment status can include not deployed or deployed. Thus, the computer device can solve the deployment status corresponding to all readers according to the tag coverage rate objective, that is, it can be understood as solving the above variable Solve and optimize to determine whether each reader in the system where the RFID technology is located needs to be deployed, so as to determine the initial number of readers to be deployed. After that, the computer device can determine the initial deployment results of each reader to be deployed according to the initial number of readers to be deployed. Exemplarily, the computer device can evenly distribute the initial number of readers to be deployed according to the initial number of readers to be deployed, and then the initial deployment results of each reader to be deployed can be determined. Exemplarily, the top-level optimization problem can be formally expressed as the following formula (9):

[0083]

[0084] Among them, represents the weight of each objective function, is an objective function related to deployment, such as an objective function for minimizing cost or the number of readers.

[0085] In this embodiment, according to the tag coverage rate target, the deployment statuses corresponding to all readers can be solved to determine the initial number of readers to be deployed. Thus, according to the initial number of readers to be deployed, the initial deployment results of each reader to be deployed can be accurately determined.

[0086] In one embodiment, an implementation manner for optimizing the initial deployment result is provided, that is, "optimize the initial deployment results of each reader to be deployed according to the performance target to obtain the target deployment results of each reader to be deployed" in the above S203. As Figure 3 shown, it includes:

[0087] S301, perform homogenization processing on the initial deployment results of each reader to be deployed to obtain an initial solution set.

[0088] Since the artificial bee colony optimization algorithm, as a bionic intelligent optimization method, has strong global search ability and fast convergence characteristics, in the embodiments of this application, the computer device can first perform population initialization (i.e., homogenization processing) on the initial deployment results of each reader to be deployed by using the artificial bee colony optimization algorithm, obtain an initial solution set, and make the solutions evenly distributed in the search space, providing a good starting point for subsequent optimization. Exemplarily, the initial solution set (i.e., the initial population) can be generated by using the orthogonal Latin square method shown in the following formula (10):

[0089]

[0090] Among them, represents the lower bound of the -th variable, represents the upper bound of the -th variable, represents the population size, i.e., the number of initialized solutions, is an index indicating which sampling point it is currently. The solution are points evenly distributed in the search space. By linearly dividing the upper and lower bounds of the variables, it can be ensured that the solutions are evenly distributed in each dimension.

[0091] S302, perform local search and / or global search on the initial solutions in the initial solution set to obtain intermediate solutions.

[0092] In the embodiments of the present application, optionally, the computer device can perform local search on the initial solutions in the initial solution set to obtain intermediate solutions; or, the computer device can also perform global search on the initial solutions in the initial solution set to obtain intermediate solutions; or, the computer device can also perform local search and global search on the initial solutions in the initial solution set to obtain intermediate solutions. Of course, the embodiments of the present application do not limit the specific implementation manners and the order of local search and global search.

[0093] S303, perform iterative optimization on the initial solution set according to the performance objective and the intermediate solutions until the initial solutions meet the preset optimization conditions to obtain the target deployment results of each to-be-deployed reader / writer.

[0094] In the embodiments of the present application, optionally, the computer device can directly perform iterative optimization on the initial solution set according to the performance objective and the intermediate solutions; or, the computer device can first determine the fitness of the intermediate solutions, and then perform iterative optimization on the initial solution set according to the performance objective and the fitness of the intermediate solutions. Of course, the embodiments of the present application do not limit the specific implementation manner of iterative optimization. Thus, until the iterative optimization reaches the initial solutions meeting the preset optimization conditions, the target deployment results of each to-be-deployed reader / writer can be obtained. Among them, the preset optimization condition can be that the number of iterative optimization times reaches the maximum number of iterative optimization times, or the preset optimization condition can also be to obtain a convergent solution. Of course, the embodiments of the present application do not limit the preset optimization conditions.

[0095] In this embodiment, the initial deployment results of each to-be-deployed reader / writer can be homogenized to obtain an initial solution set, and local search and / or global search can be performed on the initial solutions in the initial solution set to obtain intermediate solutions. Thus, iterative optimization can be continuously performed on the initial solution set according to the performance objective and the intermediate solutions until the initial solutions meet the preset optimization conditions. At this time, accurate target deployment results of each to-be-deployed reader / writer can be obtained.

[0096] In one embodiment, the above intermediate solutions include a first intermediate solution and a second intermediate solution. Based on this, an implementation manner of local search and global search is provided, that is, "perform local search and / or global search on the initial solutions in the initial solution set to obtain intermediate solutions" in S302 above, as Figure 4 shown, including:

[0097] S401. Perform a local search on the initial solution to obtain a first intermediate solution.

[0098] In the embodiments of the present application, the computer device can perform a local search on the initial solution through the operations of worker bees, explore the neighborhood of the solution, and find a better solution through interaction with the neighbor solutions, thereby obtaining a first intermediate solution. In one embodiment, S401 includes:

[0099] Select the optimal solution with the highest fitness from the initial solution set.

[0100] Determine the first intermediate solution according to the position of the initial solution, the position of the neighbor solution of the initial solution, and the optimal solution.

[0101] In the embodiments of the present application, the computer device can pre-determine the fitness of each initial solution in the initial solution set, and select the solution with the highest fitness from the initial solution set as the optimal solution. For each worker bee (i.e., initial solution individual) in the initial solution set (i.e., initial population), the computer device can determine the neighbor set of the initial solution individual in the initial population, where the neighbor set can be a preset number of solutions in the initial population that are closest to the initial solution individual. Of course, the embodiments of the present application do not limit the specific value of the preset number. Thus, the computer device can adopt the Neighborhood Discount Information Mechanism (NDI) to select a neighbor solution from the neighbor set of the initial solution individual for learning. That is, it can be understood that by accessing the states of the direct neighbors of the initial solution individual, the best neighbor (i.e., neighbor solution) can be selected according to the quality index of the neighbor (such as fitness, etc.) to update the position of the solution individual based on the best neighbor.

[0102] Thus, the computer device can determine the new position of the initial solution according to the position of the initial solution (i.e., initial solution individual), the position of the neighbor solution of the initial solution, and the optimal solution, and determine the new position of the initial solution as the first intermediate solution. Exemplarily, each worker bee generates a new position according to the following formula (11):

[0103]

[0104] where, represents the -dimensional position of the current solution individual (i.e., worker bee), represents the -dimensional position of the global optimal solution (i.e., optimal solution) in the initial population, which is used to guide the search direction; represents the -dimensional position of the neighbor solution selected from the neighborhood, which is used to provide local optimization information, and is a random coefficient with a range of [0, 1], used to control the search range.

[0105] S402. According to the fitness of each initial solution in the initial solution set, select target solutions from the initial solution set whose fitness is greater than a preset fitness threshold.

[0106] S403. Conduct a global search on the target solutions to obtain second intermediate solutions.

[0107] In the embodiments of the present application, the computer device can achieve global search through the scout bee operation, aiming to select excellent solutions through the information shared by worker bees for further optimization. Different from the local search of worker bee operation, scout bees utilize global fitness information to select target solutions from the initial population for in-depth exploration, enhancing the ability of the optimization algorithm to jump out of local optima.

[0108] First, the computer device can pre-determine the fitness of each initial solution in the initial solution set. Thus, optionally, target solutions whose fitness is greater than a preset fitness threshold can be directly selected from the initial solution set. Of course, the specific value of the preset fitness threshold is not limited in the embodiments of the present application; or, according to the fitness of each initial solution in the initial solution set, the probability of each initial solution being selected by scout bees can be determined, and then target solutions can be selected from the initial solution set according to the probability of each initial solution being selected by scout bees. Exemplarily, the calculation formula for the probability of each initial solution being selected by scout bees is shown in the following formula (12):

[0109]

[0110] where represents the probability that the solution individual is selected by scout bees, represents the solution individual 's fitness value.

[0111] Thus, the computer device can conduct a global search on the target solutions to obtain second intermediate solutions. Specifically, the computer device can determine the new position of the target solution based on the position of the target solution, the positions of the neighbor solutions of the target solution, and the optimal solution, and determine the new position of the target solution as the second intermediate solution. Exemplarily, the formula for determining the new position of the target solution can refer to (11) shown.

[0112] In this embodiment, local search can be performed on the initial solutions to accurately obtain the first intermediate solutions, and, according to the fitness of each initial solution in the initial solution set, target solutions whose fitness is greater than a preset fitness threshold can be selected from the initial solution set, and then global search is performed on the target solutions to accurately obtain the second intermediate solutions.

[0113] In one embodiment, an implementation of iterative optimization is provided, that is, "iteratively optimize the initial solution set according to the performance target and the intermediate solution" in S303 above, including:

[0114] Determine the first fitness of the intermediate solution and the second fitness of the initial solution according to the performance target.

[0115] If the first fitness is greater than the second fitness, then use the intermediate solution as the new initial solution, and return to perform local search and / or global search on the initial solutions in the initial solution set.

[0116] If in the iterative optimization process for a preset number of times, the first fitness is not greater than the second fitness, then adjust the position of the initial solution.

[0117] In the embodiments of the present application, the computer device can determine the first fitness of the intermediate solution and the second fitness of the initial solution according to the performance target, and can compare the first fitness of the intermediate solution with the second fitness of the initial solution. Exemplarily, the formula for calculating the fitness is as shown in the following formula (13):

[0118]

[0119] Wherein, represents the value of the solution on the objective function ; represents the reference solution, and the reference solution is usually the current optimal solution, represents the number of objective functions.

[0120] If it is compared that the first fitness of the intermediate solution is greater than the second fitness of the initial solution, it means that the new solution (i.e., the intermediate solution) is better than the old solution (i.e., the initial solution), then the intermediate solution can be used as the new initial solution, and return to perform local search and / or global search on the initial solutions in the initial solution set, and perform iterative optimization in this way.

[0121] If it is compared that the first fitness of the intermediate solution is not greater than the second fitness of the initial solution, it means that the old solution is better than the new solution, then the old solution can be retained, and the unimproved count of the old solution (i.e., the preset number of times) is increased. Of course, the embodiments of the present application do not limit the specific value of the preset number of times.

[0122] If in the iterative optimization process for a preset number of times, it is compared that the first fitness of the intermediate solution is not greater than the second fitness of the initial solution, that is, the number of times the solution has not been improved reaches the preset number of times, then the position of the initial solution can be adjusted. Specifically, through the operation of scout bees, the positions of the solutions that have not been improved in the iterative optimization process for multiple times can be re-initialized to increase the diversity of the population and avoid falling into the local optimal solution. Exemplarily, the formula for re-initializing the positions of the unimproved solutions is as shown in the following formula (14):

[0123]

[0124] Among them, indicates that the th solution is at the position of the th dimension, and indicate the upper and lower bounds of the th dimension, is a random number whose range is [0, 1].

[0125] In this embodiment, the first fitness of the intermediate solution and the second fitness of the initial solution can be determined according to the performance target. If the first fitness is greater than the second fitness, the intermediate solution is used as the new initial solution, and the process of performing local search and / or global search on the initial solutions in the initial solution set is returned to perform iterative optimization. If, in the iterative optimization process for a preset number of times, the first fitness is not greater than the second fitness, the position of the initial solution is adjusted. In this way, through the operation of the scout bees, the positions of the solutions that have not been improved in the iterative optimization process for multiple times can be re-initialized to increase the diversity of the population and avoid falling into the local optimal solution.

[0126] In an alternative embodiment, as Figure 5 shown, an optimization deployment method for RFID antennas is provided, which is applied to a computer device and includes:

[0127] S21, determining an optimization target according to the received power of the tag antenna and the received power of the reader antenna; the optimization target includes a tag coverage target and a performance target, and the performance target includes at least one of a reader interference target, a distance minimization target, and a load balancing target;

[0128] S22, solving the deployment states corresponding to all readers according to the tag coverage target to determine the initial number of readers to be deployed;

[0129] Determining the initial deployment results of each reader to be deployed according to the initial number of readers to be deployed; the initial deployment results include the initial position and the initial radiation power range;

[0130] S23, performing a homogenization process on the initial deployment results of each reader to be deployed to obtain an initial solution set;

[0131] Screening out the optimal solution with the highest fitness from the initial solution set;

[0132] Determining a first intermediate solution according to the position of the initial solution, the position of the neighbor solution of the initial solution, and the optimal solution;

[0133] Screening out the target solutions with fitness greater than the preset fitness threshold from the initial solution set according to the fitness of each initial solution in the initial solution set;

[0134] Perform a global search on the target solution to obtain a second intermediate solution;

[0135] Determine the first fitness of the intermediate solution and the second fitness of the initial solution according to the performance objectives; the intermediate solution includes the first intermediate solution and the second intermediate solution;

[0136] If the first fitness is greater than the second fitness, then use the intermediate solution as the new initial solution, and return to perform local search and / or global search on the initial solutions in the initial solution set until the initial solution meets the preset optimization conditions to obtain the target deployment results of each reader / writer to be deployed;

[0137] If in the iterative optimization process of the preset number of times, the first fitness is not greater than the second fitness, then adjust the position of the initial solution, and use the adjusted initial solution as the new initial solution, and return to perform local search and / or global search on the initial solutions in the initial solution set until the initial solution meets the preset optimization conditions to obtain the target deployment results of each reader / writer to be deployed.

[0138] In addition, the artificial bee colony optimization algorithm in the embodiments of the present application also includes external archive maintenance. The purpose of external archive maintenance is to manage a set of non-dominated solutions (Pareto front solution set) to record the currently found optimal solutions and ensure the diversity and uniform distribution of these optimal solutions. The core of the external archive maintenance step lies in realizing the storage and update of non-dominated solutions through dynamic crowding distance calculation and archive management mechanism.

[0139] Specifically, first, the computer device can determine non-dominated solutions in the following way: If there exists , but there is no solution that simultaneously satisfies two conditions. Among them, the above two conditions are: For all objectives holds, and for all objectives , then the solution is called a non-dominated solution. Second, each time a new solution is generated, the new solution can be compared with the solutions in the existing archive. If the new solution is dominated by a certain solution in the archive, then discard the new solution; if the new solution dominates some solutions in the archive, then remove these dominated solutions from the archive, and at the same time add the new solution to the archive; if the new solution is not dominated by any solution in the archive and does not dominate any solution in the archive, then directly add the new solution to the archive.

[0140] In addition, in order to maintain the diversity of solutions in the archive, the crowding distance of each non-dominated solution can also be calculated to evaluate the sparsity degree of the distribution of non-dominated solutions in the objective space. The calculation formula of the crowding distance is the following formula (15):

[0141]

[0142] Among them, is the crowding distance, is the number of objective functions, is the normalized value of the solution on the objective The calculation formula of

[0143]

[0144] Thus, if the archive size is set to a fixed value, then when the number of solutions in the archive exceeds this fixed value, the solution with the minimum crowding distance can be deleted to ensure that the archive size meets the requirements.

[0145] In the above RFID antenna optimization deployment method, the optimization objectives are determined according to the received power of the tag antenna and the received power of the reader antenna; the optimization objectives include a tag coverage objective and a performance objective, and the performance objective includes at least one of a reader interference objective, a distance minimization objective, and a load balancing objective; according to the tag coverage objective, the initial deployment results of each reader to be deployed are determined; the initial deployment results include the initial position and the initial radiation power range; according to the performance objective, the initial deployment results of each reader to be deployed are optimized to obtain the target deployment results of each reader to be deployed. The embodiments of the present application can perform top-level optimization according to the tag coverage objective, and perform bottom-level optimization according to the initial deployment results of each reader to be deployed obtained by the top-level optimization and the performance objective, so that the top-level optimization provides constraint conditions and decision references for the bottom-level optimization, and the bottom-level optimization can more finely adjust the initial deployment results and provide feedback for the top-level optimization. Therefore, through the iterative optimization in the way of mutual cooperation between the top-level optimization and the bottom-level optimization, the optimal solution of the deployment result can be gradually approximated, so the accuracy of the RFID antenna optimization deployment can be improved.

[0146] Based on the above embodiments, it can be known that the present application proposes a multi-level RFID network planning (RNP) optimization deployment method based on the principle of distributed decision-making (DDM, Distributed Decision Making), and addresses complex RNP problems through a hierarchical decoupling method. This optimization deployment method has the significant characteristics of centralized control, flexibility, and simplification. Different optimization objectives can be assigned to the corresponding levels, and specific optimization strategies can be adopted according to the type of decision variables and task priorities, thereby significantly improving the accuracy of the deployment and the overall optimization efficiency.

[0147] Specifically, first, the multi-objective artificial bee colony optimization algorithm (H-MOABC) in this application combines reinforcement learning and a dynamic crowding comparison mechanism, which can achieve fast convergence and uniform solution set search in complex multi-objective RFID network planning, significantly improving the optimization efficiency and effect. Through distributed decision-making and an improved fitness evaluation strategy, the global search ability and optimization efficiency of the algorithm are effectively improved, while avoiding being trapped in local optima, significantly enhancing the quality and robustness of the solution. Second, this application adopts a two-level master-slave optimization framework to hierarchically decouple and process target tasks. Among them, the top-level optimization focuses on minimizing the number of readers and maximizing the tag coverage rate, and the bottom-level optimization further adjusts the positions and power configurations of the readers. Through a flexible and efficient multi-objective decoupling framework, the strategy can be dynamically adjusted according to different planning requirements, reducing complexity and achieving balanced optimization of multiple objectives while ensuring the system coverage rate. Third, by dynamically eliminating redundant readers through top-level optimization, the deployment cost can be effectively reduced and the resource utilization rate can be optimized. At the same time, the external archive maintenance mechanism ensures the diversity and uniform distribution of the solution set, making the algorithm applicable to RFID networks of various scales. In this way, the flexible scalability and reliability of this application provide an efficient solution for solving multi-objective planning problems in different application scenarios, with economy, scalability, and adaptability.

[0148] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown sequentially according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps is not strictly restricted by order, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least some of the steps or stages in other steps or other steps.

[0149] Based on the same inventive concept, the embodiments of this application also provide an optimization deployment device for an RFID antenna for implementing the optimization deployment method of the RFID antenna involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the optimization deployment device for the RFID antenna provided below can refer to the limitations on the optimization deployment method of the RFID antenna in the above text, and will not be repeated here.

[0150] In an exemplary embodiment, as Figure 6As shown, an optimized deployment device for an RFID antenna is provided, including: an optimization target determination module 31, an initial deployment result determination module 32, and an optimization module 33, where:

[0151] The optimization target determination module 31 is configured to determine an optimization target according to the received power of the tag antenna and the received power of the reader antenna; the optimization target includes a tag coverage rate target and a performance target, and the performance target includes at least one of a reader interference target, a distance minimization target, and a load balancing target.

[0152] The initial deployment result determination module 32 is configured to determine an initial deployment result of each reader to be deployed according to the tag coverage rate target; the initial deployment result includes an initial position and an initial radiation power range.

[0153] The optimization module 33 is configured to optimize the initial deployment result of each reader to be deployed according to the performance target to obtain a target deployment result of each reader to be deployed.

[0154] In one embodiment, the optimization module 33 includes:

[0155] A homogenization unit configured to perform a homogenization process on the initial deployment result of each reader to be deployed to obtain an initial solution set;

[0156] A search unit configured to perform a local search and / or a global search on the initial solution in the initial solution set to obtain an intermediate solution;

[0157] An optimization unit configured to perform iterative optimization on the initial solution set according to the performance target and the intermediate solution until the initial solution meets a preset optimization condition to obtain a target deployment result of each reader to be deployed.

[0158] In one embodiment, the intermediate solution includes a first intermediate solution and a second intermediate solution, and the search unit includes:

[0159] A local search subunit configured to perform a local search on the initial solution to obtain a first intermediate solution;

[0160] A screening subunit configured to screen out a target solution with a fitness greater than a preset fitness threshold from the initial solution set according to the fitness of each initial solution in the initial solution set;

[0161] A global search subunit configured to perform a global search on the target solution to obtain a second intermediate solution.

[0162] In one embodiment, the local search subunit is specifically configured to:

[0163] Screen out the optimal solution with the highest fitness from the initial solution set;

[0164] Determine a first intermediate solution according to the position of the initial solution, the position of the neighbor solution of the initial solution, and the optimal solution.

[0165] In one embodiment, the optimization unit is specifically configured to:

[0166] Determine the first fitness of the intermediate solution and the second fitness of the initial solution according to the performance target;

[0167] If the first fitness is greater than the second fitness, use the intermediate solution as the new initial solution, and return to perform local search and / or global search on the initial solutions in the initial solution set;

[0168] If the first fitness is not greater than the second fitness during the iterative optimization process for a preset number of times, adjust the position of the initial solution.

[0169] In one embodiment, the initial deployment result determination module 32 includes:

[0170] An initial quantity determination unit, configured to solve the deployment status corresponding to all readers according to the tag coverage target, and determine the initial quantity of the readers to be deployed;

[0171] An initial deployment result determination unit, configured to determine the initial deployment result of each reader to be deployed according to the initial quantity of the readers to be deployed.

[0172] Each module in the above RFID antenna optimized deployment device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in the form of hardware or be independent of the processor, or be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above respective modules.

[0173] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as Figure 1As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements an optimized deployment method for RFID antennas. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0174] Those skilled in the art can understand that Figure 1 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0175] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0176] Determine an optimization target according to the received power of the tag antenna and the received power of the reader antenna; the optimization target includes a tag coverage rate target and a performance target, and the performance target includes at least one of a reader interference target, a distance minimization target, and a load balancing target;

[0177] Determine the initial deployment result of each reader to be deployed according to the tag coverage rate target; the initial deployment result includes an initial position and an initial radiation power range;

[0178] Optimize the initial deployment result of each reader to be deployed according to the performance target to obtain the target deployment result of each reader to be deployed.

[0179] In one embodiment, according to the performance target, the initial deployment results of each reader / writer to be deployed are optimized to obtain the target deployment results of each reader / writer to be deployed. When the processor executes the computer program, the following steps are further implemented:

[0180] Homogenize the initial deployment results of each reader / writer to be deployed to obtain an initial solution set;

[0181] Perform local search and / or global search on the initial solutions in the initial solution set to obtain intermediate solutions;

[0182] According to the performance target and the intermediate solutions, iteratively optimize the initial solution set until the initial solutions meet the preset optimization conditions to obtain the target deployment results of each reader / writer to be deployed.

[0183] In one embodiment, the intermediate solutions include a first intermediate solution and a second intermediate solution. When performing local search and / or global search on the initial solutions in the initial solution set to obtain intermediate solutions, the processor executes the computer program and further implements the following steps:

[0184] Perform local search on the initial solutions to obtain a first intermediate solution;

[0185] According to the fitness of each initial solution in the initial solution set, screen out the target solutions whose fitness is greater than the preset fitness threshold from the initial solution set;

[0186] Perform global search on the target solutions to obtain a second intermediate solution.

[0187] In one embodiment, when performing local search on the initial solutions to obtain a first intermediate solution, the processor executes the computer program and further implements the following steps:

[0188] Screen out the optimal solution with the highest fitness from the initial solution set;

[0189] Determine the first intermediate solution according to the position of the initial solution, the positions of the neighbor solutions of the initial solution, and the optimal solution.

[0190] In one embodiment, when iteratively optimizing the initial solution set according to the performance target and the intermediate solutions, the processor executes the computer program and further implements the following steps:

[0191] According to the performance target, determine the first fitness of the intermediate solution and the second fitness of the initial solution;

[0192] If the first fitness is greater than the second fitness, use the intermediate solution as the new initial solution and return to perform local search and / or global search on the initial solutions in the initial solution set;

[0193] If the first fitness is not greater than the second fitness during the iterative optimization process for the preset number of times, adjust the position of the initial solution.

[0194] In one embodiment, according to the tag coverage target, the initial deployment results of each reader to be deployed are determined. When the processor executes the computer program, the following steps are further implemented:

[0195] According to the tag coverage target, the deployment statuses corresponding to all readers are solved to determine the initial number of readers to be deployed;

[0196] According to the initial number of readers to be deployed, the initial deployment results of each reader to be deployed are determined.

[0197] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0198] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0199] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0200] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0201] The above embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several variations and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. An optimization deployment method for an RFID antenna, characterized in that The method includes: Determining an optimization objective according to the received power of the tag antenna and the received power of the reader antenna; the optimization objective includes a tag coverage rate objective and a performance objective, and the performance objective includes at least one of a reader interference objective, a distance minimization objective, and a load balancing objective; Determining an initial deployment result of each to-be-deployed reader according to the tag coverage rate objective; the initial deployment result includes an initial position and an initial radiation power range; Optimizing the initial deployment result of each to-be-deployed reader according to the performance objective to obtain a target deployment result of each to-be-deployed reader.

2. The method according to claim 1, wherein The optimizing the initial deployment result of each to-be-deployed reader according to the performance objective to obtain a target deployment result of each to-be-deployed reader includes: Performing a homogenization process on the initial deployment result of each to-be-deployed reader to obtain an initial solution set; Performing a local search and / or a global search on the initial solutions in the initial solution set to obtain intermediate solutions; Performing iterative optimization on the initial solution set according to the performance objective and the intermediate solutions until the initial solutions meet a preset optimization condition to obtain a target deployment result of each to-be-deployed reader.

3. The method according to claim 2, wherein The intermediate solutions include a first intermediate solution and a second intermediate solution, and the performing a local search and / or a global search on the initial solutions in the initial solution set to obtain intermediate solutions includes: Performing a local search on the initial solutions to obtain the first intermediate solution; Screening out target solutions with fitness greater than a preset fitness threshold from the initial solution set according to the fitness of each initial solution in the initial solution set; Performing a global search on the target solutions to obtain the second intermediate solution.

4. The method according to claim 3, wherein The performing a local search on the initial solutions to obtain the first intermediate solution includes: Screening out the optimal solution with the highest fitness from the initial solution set; Determining the first intermediate solution according to the position of the initial solution, the positions of the neighbor solutions of the initial solution, and the optimal solution.

5. The method according to any one of claims 2-4, characterized in that, The performing iterative optimization on the initial solution set according to the performance objective and the intermediate solutions includes: Determining a first fitness of the intermediate solutions and a second fitness of the initial solutions according to the performance objective; If the first fitness is greater than the second fitness, using the intermediate solutions as new initial solutions and returning to perform the local search and / or the global search on the initial solutions in the initial solution set; If the first fitness is not greater than the second fitness during a preset number of iterative optimization processes, adjusting the positions of the initial solutions.

6. The method according to any one of claims 1-4, characterized in that, The determining an initial deployment result of each to-be-deployed reader according to the tag coverage rate objective includes: Solving the deployment status corresponding to all readers according to the tag coverage rate objective to determine the initial number of to-be-deployed readers; Determining an initial deployment result of each to-be-deployed reader according to the initial number of to-be-deployed readers.

7. An optimized deployment device for an RFID antenna, characterized in that, The device includes: An optimization objective determination module, configured to determine an optimization objective according to the received power of the tag antenna and the received power of the reader antenna; the optimization objective includes a tag coverage rate objective and a performance objective, and the performance objective includes at least one of a reader interference objective, a distance minimization objective, and a load balancing objective; An initial deployment result determination module, configured to determine an initial deployment result of each to-be-deployed reader according to the tag coverage rate objective; the initial deployment result includes an initial position and an initial radiation power range; An optimization module, configured to optimize the initial deployment result of each to-be-deployed reader according to the performance objective to obtain a target deployment result of each to-be-deployed reader.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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