An ap deployment optimization method integrating fixed and mobile terminals

Abstract

The application takes the AP deployment in the three-dimensional complex environment of the industrial wireless scene as the background, and provides an AP deployment optimization method which comprehensively considers the fixed terminal and the mobile terminal. The optimization goal of the method is to maximize the overall signal strength under the condition of using the least number of APs and meeting the signal strength constraint. Firstly, the spatial information of the three-dimensional space, the position of the fixed terminal and the trajectory of the mobile terminal are acquired, the space model and the terminal moving model are established, and the AP signal strength heat map of the fixed terminal and the mobile terminal is generated. The initial solution is generated from the AP signal strength heat map, and the mixed step optimization is performed on the initial solution. If the termination condition is reached, the historical optimal solution is taken as the final output result. The experimental results show that the application can more efficiently obtain better network performance than the traditional method by considering the demand for terminal movement in reality.

Description

Technical Field

[0001] This application relates to an AP deployment optimization method that integrates fixed terminals and mobile terminals, belonging to the fields of industrial internet and wireless networks. Background Technology

[0002] With the rapid development of the Industrial Internet, wireless local area networks (WLANs) have become a widely adopted solution in industrial communication. Wireless access points (APs) are commonly used devices for building small WLANs, offering advantages such as long wireless transmission distances, high reliability, and strong device flexibility. To better cover wireless signals, multiple wireless APs are typically deployed in Industrial Internet networks.

[0003] However, current wireless AP deployment schemes primarily consider fixed terminals, neglecting the growing demand for mobile connectivity in industry. Mobile terminals are fundamental to mobile connectivity; compared to stationary fixed terminals, mobile terminals typically have movement trajectories and speeds. Without a proper deployment optimization scheme, this can lead to problems such as degraded communication quality, equipment redundancy, and wasted resources. Therefore, in the Industrial Internet, we need to consider mobile terminals to meet the ever-increasing demand for mobile connectivity. Summary of the Invention

[0004] The main purpose of this application is to provide an AP deployment optimization method for fixed terminals and mobile terminals, aiming to achieve better network performance more efficiently while meeting the actual needs of fixed and mobile terminals.

[0005] To achieve the above objectives, this application provides an AP deployment optimization method for fixed terminals and mobile terminals, comprising the following steps:

[0006] Step 1: Obtain environmental information in three-dimensional space, location information of fixed terminals, and trajectory information of mobile terminals to establish spatial models and terminal movement models;

[0007] Step 2: Based on the spatial model, including information such as obstacle positions, path loss attenuation coefficient, and AP transmission power in the known scene, as well as the position of the fixed terminal and the path of the mobile terminal, generate AP signal strength heatmaps for the fixed terminal and the mobile terminal.

[0008] Step 3: Perform a binary search on the number of APs. In the following steps, calculate the optimal AP deployment scheme given a fixed number of APs and a fixed spatial model.

[0009] Step 4: Utilize the known AP signal intensity heatmap to quickly generate a better initial solution and reduce the number of solution iterations;

[0010] Step 5: Using a heuristic algorithm, with the initial solution obtained in the previous step and the AP signal strength heatmap, perform a mixed step-size optimization to quickly find the optimal AP deployment scheme;

[0011] Step 6: Repeat steps 3 to 6 above, adjusting the upper and lower bounds of the binary search until an AP deployment scheme that maximizes the overall signal strength while satisfying the signal strength constraint based on the minimum number of APs is found.

[0012] In step one, after obtaining the three-dimensional spatial environment information, the three-dimensional space is discretized into a grid to obtain the location information of obstacles, fixed terminals, and trajectory information of mobile terminals. The obstacle information includes the start and end boundaries and penetration loss in each direction, and the terminal position is the center coordinate of a certain spatial block after discrete segmentation.

[0013] In step two, for fixed terminals, the AP signal strength heatmap is generated directly using scene information and the fixed terminal's location; for mobile terminals, each cube block along its movement path can be considered a fixed terminal, and an AP signal strength heatmap is generated. The formula for calculating the signal strength of the signal transmitted by the i-th AP at the j-th terminal is as follows:

[0014] P i,j =PA-PL(i,j,Barriers)

[0015]

[0016] Where PA is the transmit power, PL is the signal path loss, and β k Let αi be the penetration loss of the k-th obstacle in the set of obstacles Barriers. ,j,k This indicates whether the signal transmitted by the i-th AP passes through the k-th obstacle on its path to the j-th terminal, where γ is the path loss coefficient and d is the path loss factor. ij Let be the distance between the i-th AP and the j-th terminal.

[0017] In step three, a number of access points (APs) Na0 that can effectively cover the scene needs to be determined as the upper bound of the binary search, and the lower bound is set to 1. Based on this, the binary search is performed, and the upper and lower bounds are updated according to steps four and five.

[0018] In step four, when calculating the initial solution, it is necessary to find the location of the cube with the highest heat value in the AP signal strength heat map of each cube in the path of each fixed terminal and each mobile terminal. In a two-dimensional matrix E representing the ceiling plane of the scene, the contribution of each location with the highest heat value to the initial solution is recorded. After multiplying by the corresponding weight, the contribution obtained at each location in E is sorted. The location of the ceiling of the scene corresponding to the Na (number of APs to be deployed) locations with the highest contribution can be used as the initial solution for AP deployment.

[0019] In step five, the hybrid step-size optimization refers to the process where each solution in the solution set has a probability p to perform a large-scale search, otherwise a small-scale search is performed, thus searching both the local and the entire solution space. During the small-scale search, the range of distances an individual moves in the x and y directions is respectively...

[0020]

[0021] During a large-scale search, the range of distances an individual can move in the x and y directions are respectively...

[0022]

[0023] In step five, the algorithm for determining the AP deployment location based on the AP signal strength heatmap includes the following steps:

[0024] Step 51: Generate the initial solution for AP deployment location based on the AP signal strength heatmap;

[0025] Step 52: Initialize the solution set using the initial solution;

[0026] Step 53: Perform random step size optimization, including large-scale random search and small-scale random search;

[0027] Step 54: Calculate the objective function for each solution;

[0028] Step 55: Update the solution set to the optimal solution;

[0029] Step 56: Determine if the number of iterations has reached the upper limit. If it has not reached the upper limit, copy the optimal solution to a new solution set and return to step 53. If it has reached the upper limit, end the process and output the optimal solution.

[0030] The proposed AP deployment location calculation algorithm based on AP signal strength heatmaps offers faster solution speeds compared to traditional algorithms, without experiencing exponential speed increases with network size. The algorithm proposed in step five considers optimization for both fixed and mobile terminals, addressing the limitations of traditional integer programming methods such as the cutting plane method and branch-and-bound method. In the heuristic solution process, to find better solutions locally while simultaneously searching for potentially superior solutions across the entire solution space, each solution in this step undergoes a large-scale search with probability p; otherwise, a small-scale search is performed. This effectively searches both the local and overall solution space, mitigating the problem of getting trapped in local optima under fixed-step optimization. Furthermore, the AP deployment location calculation algorithm based on AP signal strength heatmaps transforms the problem into calculating a less computationally intensive heatmap matrix, eliminating the need for repeated signal strength calculations in each iteration, thus reducing runtime and ensuring high-precision efficiency. Furthermore, the algorithm proposed in step five is an optimization algorithm that comprehensively considers signal strength and total network throughput. It can ensure the signal strength of the terminal at a certain local location, as well as the optimal overall network throughput and the lowest deployment cost.

[0031] In step six, the optimization formula for maximizing the overall signal strength while satisfying the signal strength constraint based on the minimum number of APs is as follows:

[0032]

[0033]

[0034]

[0035] P0 represents the minimum signal strength required for the Wi-Fi radio receiver in the terminal device to communicate normally. If the signal strength at the terminal is lower than P0, normal communication will not be possible. Let be the signal strength obtained by the j-th fixed terminal at the associated AP. Let N be the overall signal strength obtained by the k-th mobile terminal along its trajectory. s For a fixed number of terminals, N m N represents the number of mobile terminals. a Let ρ be the number of access points (APs). Using the deployment locations ρ of the APs and the known locations and paths of the terminals, the signal strength at each terminal can be calculated, thus determining the objective function. and

[0036] Compared with existing technologies, the advantages of this invention are as follows: This application provides an AP deployment optimization method for fixed terminals and mobile terminals. This application first obtains environmental information in three-dimensional space and the initial position and movement trajectory of each terminal, establishes a spatial model and a terminal movement model, and then, based on the spatial model, including information such as obstacle positions, path loss attenuation coefficients, AP transmission power, the position of fixed terminals and the path of mobile terminals, initializes parameters, and generates AP signal strength heatmaps for fixed terminals and mobile terminals. This transforms the problem into calculating another heatmap matrix with less computational load, eliminating the need to repeatedly solve for signal strength in each iteration, reducing running time, and effectively ensuring high-precision running efficiency, thereby quickly generating a better initial solution. Combined with hybrid step size optimization, the optimal AP deployment scheme is quickly solved, and the solution speed does not increase exponentially with the increase of network scale. This optimization method discretizes the spatial environment model and the terminal's mobility model, thereby quickly determining the optimal solution for wireless AP deployment in the case of mobile terminals based on the optimization results. Each iteration has lower computational complexity compared to traditional deployment schemes, enabling more efficient AP deployment while ensuring communication quality. Furthermore, this algorithm comprehensively considers signal strength and total network throughput, ensuring optimal signal strength for terminals at local locations while maintaining optimal overall network throughput and minimizing deployment costs. In addition, this application provides an AP deployment optimization method for both fixed and mobile terminals, addressing the limitations of traditional integer programming methods such as the cutting plane method and branch-and-bound method, and effectively resolving the problem of getting trapped in local optima under other heuristic algorithms. Experimental results show that the AP deployment optimization method for fixed and mobile terminals provided in this application achieves results close to the optimal deployment obtained through exhaustive search, significantly outperforming conventional uniform deployment methods. In most cases, it outperforms genetic algorithms with significantly shorter running times, and the algorithm's running time does not increase rapidly with the scale of the problem. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0039] Figure 1This is a flowchart illustrating the first embodiment of a wireless AP deployment optimization method for fixed and mobile terminals proposed in this invention.

[0040] Figure 2 This is a second embodiment of a wireless AP deployment optimization method for fixed terminals and mobile terminals proposed in this invention. It compares the deployment quality and computation time with the optimal deployment method obtained by exhaustive search and the uniform deployment method when the number of APs, fixed terminals and mobile terminals is determined.

[0041] Figure 3 This is a third embodiment of a wireless AP deployment optimization method for fixed terminals and mobile terminals proposed in this invention, comparing its deployment quality and computation time with those of genetic algorithm and uniform deployment method under different numbers of fixed terminals;

[0042] Figure 4 This is a fourth embodiment of a wireless AP deployment optimization method for fixed terminals and mobile terminals proposed in this invention, comparing its deployment quality and computation time with those of genetic algorithm and uniform deployment method under different numbers of mobile terminals;

[0043] Figure 5 This is the fifth embodiment of a wireless AP deployment optimization method for fixed and mobile terminals proposed in this invention, comparing its deployment quality and computation time with those of genetic algorithm and uniform deployment method under different numbers of APs;

[0044] Figure 6 This is the sixth embodiment of a wireless AP deployment optimization method for fixed terminals and mobile terminals proposed in this invention, which addresses the minimum number of APs required for coverage scenarios with different numbers of fixed terminals and mobile terminals. Detailed Implementation

[0045] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings.

[0046] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0047] Example: The first embodiment of the present invention provides an optimized method for wireless AP deployment for fixed terminals and mobile terminals, such as... Figure 1 As shown in the embodiment of the wireless AP deployment optimization method for fixed terminals and mobile terminals proposed in this invention, the wireless AP deployment optimization method for fixed terminals and mobile terminals includes the following steps:

[0048] Step S10: Obtain environmental information in three-dimensional space, location information of fixed terminals, and trajectory information of mobile terminals to establish spatial models and terminal movement models;

[0049] In this embodiment, it should be noted that the environmental information obtained in the three-dimensional space includes the x-axis, y-axis, and z-axis information, which is the deployment range of the wireless AP (e.g., if the size of the three-dimensional space is 50m × 50m × 10m, then the size in the x-axis direction is 50m, the size in the y-axis direction is 50m, and the size in the z-axis direction is 10m). After performing grid discretization processing on this three-dimensional space, the position information of obstacles, fixed terminals, and trajectory information of mobile terminals can be obtained. The obstacle information includes the start and end boundaries in each direction and the penetration loss (e.g., obstacles generally have varying heights of 2 to 5m and penetration losses of -5 to -20). The terminal position is the center coordinate of a certain spatial block after discrete segmentation. The wireless AP's transmission power and path loss coefficient need to be generated (e.g., the default transmission power of the wireless AP is 20dBm, and the path loss coefficient is 2.4).

[0050] Step S20: Based on the spatial model, including information such as obstacle positions, path loss attenuation coefficient, and AP transmission power in the known scene, as well as the position of the fixed terminal and the path of the mobile terminal, generate a heat map of AP signal strength for the fixed terminal and the mobile terminal.

[0051] In this embodiment, it should be noted that the two-dimensional AP signal heatmap in step S20 represents the coverage signal strength that the terminal can obtain when the AP is deployed at that point. Subsequently, the calculation speed can be improved by simply using these AP heatmaps to solve the AP optimization problem. Since the attenuation of radio waves propagating from the AP to the terminal is the same as that from the terminal to the AP, it can be assumed that the AP is located at the location of the terminal for which the AP signal strength heatmap is required during the calculation. The signal strength at the location of the terminal is then the signal strength at that location in the AP signal strength heatmap.

[0052] Step S30: Perform a binary search on the number of APs and calculate the optimal AP deployment scheme under the given number of APs and the given spatial model in the following steps.

[0053] In this embodiment, it should be noted that the purpose of step S30 is to reduce the number of access points (APs) used when setting up a commercial Wi-Fi network in an industrial scenario, in order to reduce network deployment costs while ensuring coverage of the industrial scenario. To quickly determine the minimum number of APs that can effectively cover the scenario, a binary search is used to find the minimum number of APs that satisfies the constraints.

[0054] Step S40: Using the known AP signal intensity heatmap, quickly generate a better initial solution and reduce the number of solution iterations;

[0055] In this embodiment, it should be noted that the initial solution is generated using the AP signal strength heatmaps of the mobile and fixed terminals obtained in step S20. In the AP signal strength heatmap of each cube in the path between each fixed terminal and each mobile terminal, the position of the cube with the highest heat value is found, and the contribution of each position with the highest heat value to the initial solution is recorded in a two-dimensional matrix E representing the ceiling plane of the scene. For fixed terminals, the contribution of 1 / Ns is calculated for the position corresponding to the cube with the highest heat value in the AP signal strength heatmap of each fixed terminal in E. For mobile terminals, the contribution of 1 / (NmQn) is calculated for the position corresponding to the cube with the highest heat value in the AP signal strength heatmap of each cube in the path of the nth mobile terminal in E.

[0056] Step S50: Using a heuristic algorithm, the initial solution obtained in the previous step and the AP signal strength heatmap are used to perform hybrid step-size optimization to quickly solve for the optimal AP deployment scheme.

[0057] In this embodiment, it should be noted that, based on the obtained AP signal strength heatmap, the original objective function and constraints can be transformed into a form specific to the AP signal strength heatmap:

[0058]

[0059]

[0060]

[0061] Among them, Q j This represents the number of cube blocks contained in the path of the j-th mobile terminal in T. This represents the heat generated by the i-th fixed terminal at its associated AP. This represents the average heat along the entire path when each cube block of the j-th mobile terminal is associated with a different AP. N represents the heat gained at the associated AP by the nth cube block on the path of the j-th mobile terminal. s For a fixed number of terminals, N m The number of mobile terminals is then used for hybrid step-size optimization.

[0062] Step S60: Repeat steps three through six above, adjusting the upper and lower bounds of the binary search until an AP deployment scheme that maximizes the overall signal strength while satisfying the signal strength constraint based on the minimum number of APs is found.

[0063] In this embodiment, it should be noted that the termination condition for the algorithm to end is that the difference between the upper and lower bounds of the binary search is 1. At this time, the lower bound is the minimum number of APs that satisfy the signal strength constraint, and the corresponding solution set is the AP deployment scheme with the largest overall signal strength among the valid solutions where all terminals are covered.

[0064] This application employs the aforementioned scheme, namely, acquiring environmental information in three-dimensional space, location information of fixed terminals, and trajectory information of mobile terminals, to establish a spatial model and a terminal movement model. Based on the spatial model, including information such as obstacle locations, path loss attenuation coefficients, and AP transmission power in the known scene, as well as the location of the fixed terminals and the paths of the mobile terminals, it generates AP signal strength heatmaps for both fixed and mobile terminals. Furthermore, it performs a binary search on the number of APs. In the following steps, it calculates the optimal AP deployment scheme given a fixed number of APs and a defined spatial model. Specifically, it uses the known AP signal strength heatmaps to quickly generate a relatively good initial solution. Then, using a heuristic algorithm, it performs a mixed-step optimization using the initial solution obtained in the previous step and the AP signal strength heatmaps to quickly find the optimal AP deployment scheme. In addition, the binary search needs to repeat steps three through six, adjusting the upper and lower bounds of the binary search, and the termination condition until an AP deployment scheme that maximizes the overall signal strength while satisfying the signal strength constraint based on the minimum number of APs is found. This algorithm has lower computational complexity compared to traditional deployment schemes, thus enabling more efficient AP deployment while ensuring communication quality.

[0065] Furthermore, in the specific second embodiment of this application, considering a three-dimensional space of 50m×50m×10m, 20 stationary terminal positions and 2 mobile terminals (one transport robot and one patrol robot respectively), with a number of APs of 4, refer to... Figure 2 After 20 iterations, the objective function reached the optimal deployment objective function value obtained through exhaustive search, and the objective function of the proposed algorithm showed a significant improvement compared to uniform deployment. This demonstrates the effectiveness of the proposed algorithm. Regarding computation time, the proposed AP deployment algorithm took a total of 280ms after 40 iterations, of which 251ms was spent on data preprocessing before iteration (generating AP signal strength heatmaps). The iterative solution process itself was relatively time-efficient.

[0066] Furthermore, in the specific third embodiment of this application, considering a three-dimensional space of 50m×50m×10m, 4 mobile terminals (2 transport robots and 2 patrol robots respectively), and an AP count of 8, multiple experiments were conducted with different numbers of fixed terminals, referring to... Figure 3 .

[0067] In the fourth specific embodiment of this application, considering a three-dimensional space of 50m×50m×10m, 80 fixed terminals, and 8 access points (APs), multiple experiments were conducted with different numbers of mobile terminals, wherein the number of transport robots and patrol robots was equal. Figure 4 .

[0068] In the fifth specific embodiment of this application, considering a three-dimensional space of 50m×50m×10m, 80 fixed terminals, and 4 mobile terminals (2 transport robots and 2 patrol robots respectively), multiple experiments were conducted with different numbers of APs, referring to... Figure 5 .

[0069] In the third, fourth, and fifth embodiments, it should be noted that, as can be seen from the experimental results, in these three embodiments, the objective function values ​​corresponding to the solutions obtained by the algorithm proposed in this invention are all much higher than the objective function values ​​of uniform deployment in the corresponding experimental conditions, and achieve almost the same results as the genetic algorithm while consuming far less time. This is because the traditional genetic algorithm needs to calculate the objective function in each iteration, while the objective function in the problem of this invention includes the calculation of non-uniform channels in complex three-dimensional industrial scenes. The repeated calculation of the objective function greatly prolongs the calculation time of the genetic algorithm, while the solution algorithm based on the terminal AP signal strength heatmap proposed in this chapter avoids the repeated calculation of the objective function. At the same time, the feasible region of the AP deployment location can also be known through the AP signal strength heatmap. This information can be used to quickly determine the constraints, avoid invalid random optimization, and improve the solution speed.

[0070] Furthermore, in the sixth specific embodiment of this application, to verify the effect of binary search on optimizing the number of deployed APs in a real industrial scenario where it is desirable to minimize the number of APs deployed, a three-dimensional space of size 50m × 50m × 10m is considered. Figure 6 In (a) of the experiment, four mobile terminals (two transport robots and two patrol robots) are considered to be involved in multiple experiments with different numbers of fixed terminals. Figure 6 In (b) of the experiment, multiple experiments were conducted with 80 fixed terminals under different numbers of mobile terminals, where the number of transport robots and patrol robots were equal. The experimental results show that, compared to the results of the fifth embodiment, binary search can effectively reduce the number of deployed access points (APs).

[0071] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. An optimized AP deployment method integrating fixed terminals and mobile terminals, characterized in that, The method includes the following steps: Step 1: Obtain environmental information in three-dimensional space, location information of fixed terminals, and trajectory information of mobile terminals to establish spatial models and terminal movement models; Step 2: Based on the spatial model, including the location of obstacles, path loss attenuation coefficient, AP transmission power information in the known scene, as well as the location of the fixed terminal and the path of the mobile terminal, generate AP signal strength heatmaps for the fixed terminal and the mobile terminal. Step 3: Perform a binary search on the number of APs. In the following steps, calculate the optimal AP deployment scheme given a fixed number of APs and a fixed spatial model. Step 4: Utilize the known AP signal intensity heatmap to quickly generate a better initial solution and reduce the number of solution iterations; Step 5: Using a heuristic algorithm, with the initial solution obtained in the previous step and the AP signal strength heatmap, perform a mixed step-size optimization to quickly find the optimal AP deployment scheme; Step Six: Repeat Steps Three through Five, adjusting the upper and lower bounds of the binary search until an AP deployment scheme that maximizes the overall signal strength while satisfying the signal strength constraint and requiring the minimum number of APs is found. In step three, it is necessary to determine the number of APs Na0 that can effectively cover the scene, as the upper bound of the binary search, and set the lower bound to 1. Based on this, the binary search is performed, and the upper and lower bounds are updated according to steps four and five. In step four, when calculating the initial solution, it is necessary to find the location of the cube with the highest heat value in the AP signal strength heat map of each cube in the path of each fixed terminal and each mobile terminal. In a two-dimensional matrix E representing the ceiling plane of the scene, the contribution of each location with the highest heat value to the initial solution is recorded. After multiplying by the corresponding weight, the contribution obtained at each location in E is sorted. The location of the scene ceiling corresponding to the Na locations with the highest contribution can be used as the initial solution for AP deployment, where Na is the number of APs to be deployed. In step five, the mixed step-size optimization refers to the fact that each solution in the solution set will have a probability p to perform a large-scale search, otherwise a small-scale search will be performed, thus searching both the local and the entire solution space. During the small-scale search, the range of distances the individual moves in the x and y directions are respectively... During a large-scale search, the range of distances an individual can move in the x and y directions are respectively... 。 2. The AP deployment optimization method integrating fixed terminals and mobile terminals as described in claim 1, characterized in that, In step one, after obtaining the three-dimensional spatial environment information, the three-dimensional space is discretized into a grid to obtain the location information of obstacles, fixed terminals, and trajectory information of mobile terminals. The obstacle information includes the start and end boundaries and penetration loss in each direction, and the terminal position is the center coordinate of a certain spatial block after discrete segmentation.

3. The AP deployment optimization method integrating fixed terminals and mobile terminals as described in claim 1, characterized in that, In step two, for fixed terminals, an AP signal strength heatmap is generated directly using scene information and the fixed terminal's location; for mobile terminals, each cube along its movement path is considered a fixed terminal, and an AP signal strength heatmap is generated, where the signal strength of the signal transmitted by the i-th AP at the j-th terminal is... The calculation formula is: ， in, For transmission power, For signal path loss, For the set of obstacles The penetration loss of the k-th obstacle. This indicates whether the signal transmitted by the i-th AP passes through the k-th obstacle on its path to the j-th terminal. This is the path loss coefficient. Let be the distance between the i-th AP and the j-th terminal.

4. The AP deployment optimization method integrating fixed terminals and mobile terminals as described in claim 1, characterized in that, Step five, the algorithm for determining AP deployment locations based on AP signal strength heatmaps, includes the following steps: Step 51: Generate the initial solution for AP deployment location based on the AP signal strength heatmap; Step 52: Initialize the solution set using the initial solution; Step 53: Perform random step size optimization, including large-scale random search and small-scale random search; Step 54: Calculate the objective function for each solution; Step 55: Update the solution set to the optimal solution; Step 56: Determine if the number of iterations has reached the upper limit. If it has not reached the upper limit, copy the optimal solution to a new solution set and return to step 53. If it has reached the upper limit, end the process and output the optimal solution.

5. The AP deployment optimization method integrating fixed terminals and mobile terminals as described in claim 1, characterized in that, In step six, the optimization formula for maximizing the overall signal strength while satisfying the signal strength constraint based on the minimum number of APs is as follows: in, This is the minimum signal strength required for the Wi-Fi radio receiver in the terminal device to communicate normally. If the signal strength at the terminal is lower than this value... After that, normal communication will be impossible. Let be the signal strength obtained by the i-th fixed terminal at the associated AP. Let be the overall signal strength obtained by the j-th mobile terminal along its motion trajectory. For a fixed number of terminals, For the number of mobile terminals, For the number of APs, use the deployment locations of the APs. Given the known terminal locations and paths, the signal strength at each terminal can be calculated, thereby enabling the calculation of the objective function. and .

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