Method, system and device for cooperative position deployment of electromagnetic suppression equipment based on potential game and medium

Abstract

The application discloses a method, system, equipment and medium for cooperative position deployment of electromagnetic suppression equipment based on potential game, and relates to the field of electromagnetic suppression equipment deployment. The method comprises the following steps: analyzing a suppression task of an electromagnetic suppression area, establishing a suppression scene model, and initializing parameters of the model; establishing an optimal cooperative suppression deployment model according to the obtained parameter-initialized suppression scene model; solving the optimal cooperative suppression deployment model based on potential game; and the solving process comprises the following steps: game initialization, game iteration and game stop, and optimal scheme cooperative suppression effect evaluation and feedback. The system, equipment and medium are used for realizing the method for cooperative position deployment of electromagnetic suppression equipment based on potential game. The application realizes precise suppression under the condition that the whole area has high suppression effect, is stable and robust, and the number of protected equipment misjudgments is the least, improves the timeliness of deployment, and has the characteristics of reducing the influence of serious electromagnetic interference on social production and life.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of communication countermeasures, and particularly relates to an electromagnetic suppression equipment cooperative position deployment method, system, device and medium based on potential game. BACKGROUND

[0002] Illegal frequency target management and control is a major event security, and the research on electromagnetic target suppression or containment technology has always been mainly driven by military electronic countermeasure applications. In order to meet the demand of electromagnetic containment in a local area, a high-power omnidirectional radio frequency signal is radiated continuously to block communication, which can effectively prevent and control various radio explosive devices, but there is still an urgent need to develop electromagnetic target precise cooperative containment technology compatible with electromagnetic wave propagation calculation.

[0003] The existing electromagnetic equipment cooperative management and control technology in the security activity area only considers the suppression effect, adopts the maximum equipment output power, the maximum number of deployable devices and the widest frequency range scheme to achieve the best effect, but in a complex electromagnetic environment, it will cause serious electromagnetic interference to other frequency bands that need to work normally, and excessive power will harm the human body and have an additional serious impact on daily social production and life in non-suppression areas.

[0004] After preliminary research, there is no public research on key containment area suppression equipment position deployment algorithm in the domestic related field, and extensive suppression equipment deployment is often used, which has equipment redundancy and requires higher activity security cost. If the traversal method is used as a comparison, the traversal method can calculate the optimal solution under a given mathematical model, but its calculation complexity is high and the simulation time is long, and when the suppression task changes, it cannot meet the timeliness requirement of the task, and it cannot give the equipment deployment position scheme within the maximum timeliness. SUMMARY

[0005] In order to overcome the above-mentioned shortcomings of the prior art, the purpose of the present application is to provide an electromagnetic suppression equipment cooperative position deployment method, system, device and medium based on potential game, which uses an electromagnetic propagation model based on UMi_Street_Canyon model to simulate the actual electromagnetic environment, reduces the uncertain factors in rough calculation, solves the optimal cooperative suppression deployment model through potential game, realizes precise suppression under the condition of high suppression effect in the whole area, stable robustness and minimum number of protected equipment errors, improves the timeliness of deployment, and reduces the influence of serious electromagnetic interference on social production and life.

[0006] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is:

[0007] The electromagnetic suppression equipment cooperative position deployment method based on potential game comprises:

[0008] Step 1: Analyze the suppression task of the electromagnetic suppression area, establish a suppression scene model, and initialize the suppression scene model parameters;

[0009] Step 2: According to the parameter initialized suppression scene model obtained in step 1, the optimal cooperative suppression deployment model is established in cooperation with the suppression deployment;

[0010] Step 3: Based on potential game, the optimal cooperative suppression deployment model obtained in step 2 is solved to obtain the optimal scheme, which includes game initialization, game iteration and game stop;

[0011] Step 4: According to the optimal scheme obtained in step 3, the suppression effect evaluation and feedback are carried out.

[0012] The specific process of step 1 is as follows:

[0013] Step 1.1: Analyze the suppression task of the electromagnetic suppression area, and obtain the suppression edge area, suppression frequency band, suppression threshold power, protected device frequency band and location, i.e. white list device frequency band and suppression device, maximum deployable suppression device quantity, suppression device parameters, suppression effect evaluation point, i.e. typical point location information, from the suppression task demand;

[0014] Step 1.2: According to the information obtained in step 1.1, the suppression scene model is established, and the suppression scene model parameters are initialized, and the path loss is calculated, and the specific process is as follows:

[0015] The Umi-Street Canyon path loss model suitable for dense urban areas is adopted, and the line of sight propagation (LOS) and non-line of sight propagation (NLOS) are considered. When initializing the typical point parameters, the highest frequency band and the maximum bandwidth of the suppression frequency band are selected. The frequency suppression factor at the typical point is calculated by using the spectrum template formula. The interference power at the typical point is calculated by considering the transmission power and antenna gain of the suppression device. The path loss from all deployable device points to all typical points is calculated by using the UMi wave propagation formula combined with the key area mathematical model parameters;

[0016] For the white list device, the working frequency band of the white list is judged. If the white list and the highest frequency band of the suppression frequency band are the same frequency, the calculated value at the typical point is taken. If the white list and the highest frequency band of the suppression frequency band are different frequencies, the closest suppression frequency band to the white list is selected. According to the suppression frequency band, the suppression threshold power and the protected device frequency band, the frequency suppression factor at each white list is calculated by using the spectrum template formula. According to the suppression edge area, the suppression threshold power, the protected device location and the frequency suppression factor, the path loss from all deployable device points to all white lists is calculated by using the UMi wave propagation formula. Finally, the better interference power at the white list is obtained by considering the transmission power and antenna gain parameters of the suppression device;

[0017] After calculating the above parameters, the initialization of the suppression scene model parameters is completed.

[0018] In step 2, the optimal collaborative suppression deployment model is established. The specific process is as follows: the objective function is set as the weighted sum of the root mean square error between the received power and the average received power at each typical point and the number of false positives in the whitelist. The constraints of the optimization problem include the maximum number of deployable suppression devices, the selectable locations of the suppression devices, the suppression frequency band, and the frequency band and location used by the whitelist devices.

[0019] In step 3, the specific process of game initialization is as follows:

[0020] Step 3.1: Construct loss matrices from the pressing equipment to typical points, whitelisted equipment, and control stations. Construct three location parameter matrices with the pressing equipment as the row and the typical points, whitelisted equipment, and control stations as the column.

[0021] Step 3.2: Calculate the electromagnetic propagation loss from the suppression equipment to the typical point, whitelisted equipment and control station using the UMi radio wave propagation formula, and calculate the FDR loss between each suppression equipment and the typical point equipment, whitelisted equipment and control station;

[0022] Step 3.3: Calculate the mean square error of the average received power U1 and the number of false positives in the whitelist from the three position parameter matrices obtained in Step 3.1 and their corresponding electromagnetic propagation loss and FDR loss obtained in Step 3.2. Set the utility function U as a weighted sum of U1 and U2, assigning weights w to each, i.e., U = w * U1 + (1 - w) * U2. Obtain the minimum utility function through game theory. Set the convergence threshold ε, the initial minimum utility function U_min = Inf, the typical point suppression threshold threshold_mW, and the whitelist false positive threshold threshold_mW_White.

[0023] The specific process of the game iteration is as follows:

[0024] Step 3.4: Randomly generate a set of equipment placement schemes. Given the available placement locations and the number of equipment to be deployed, generate the placement location schemes.

[0025] Step 3.5: Calculate the utility value under this point layout scheme:

[0026] Calculate the received power Prx of a single device at all typical points under this deployment scheme, and calculate the received power Prx_sum of all devices at each typical point. If the Prx_sum at all typical points is greater than the threshold_mW, the deployment scheme is considered to achieve the suppression effect; otherwise, return to step 3.4. Calculate the root mean square error of the average received power at all typical points to measure whether the suppression intensity is uniformly distributed, and use it as the utility value U1.

[0027] Calculate the received power Prx_White of a single device at all whitelisted devices under this deployment scheme, and calculate the received power Prx_White_sum of all devices superimposed at each whitelisted device. If the received power Prx_White_sum at a certain whitelist is less than the threshold threshold_mW_White, it is considered that the deployment scheme has not caused false damage to the whitelisted device; otherwise, it is recorded as false damage. Calculate the number of whitelists that have been falsely damaged, measure the degree of suppression of false damage, and use it as the utility value U2.

[0028] The specific process of stopping the game is as follows:

[0029] Step 3.6: Compare the current utility function U with the minimum utility function U_min. If the absolute value of the difference between the two is less than the convergence threshold ε, then convergence is considered to have been achieved and the iteration stops. If the current utility function U is less than U_min, then the corresponding device placement position is the optimal solution in the game. Otherwise, the U_min solution is the optimal solution for the corresponding device placement position.

[0030] Step 3.7: If convergence is not achieved, record the minimum utility value and its corresponding location, and return to step 3.4.

[0031] The specific process of step 4 is as follows:

[0032] Determine whether the suppression power at each typical point meets the requirements. If the suppression power at a typical point does not meet the requirements, remove the current deployment scheme from the set of available deployment schemes and return to step 3.4 to regenerate the collaborative suppression deployment scheme.

[0033] If the suppression power of all typical points meets the requirements, calculate the standard deviation. If the standard deviation is less than 0.2, the scheme is considered to have met the expected requirements, and the collaborative deployment device location scheme is output. Otherwise, remove the current deployment scheme from the set of optional deployment schemes and return to step 3.4 to regenerate the collaborative suppression deployment scheme.

[0034] A potential game-based electromagnetic suppression equipment cooperative deployment system includes:

[0035] Suppression Scenario Module: Analyzes the suppression task in the electromagnetic suppression area, establishes a suppression scenario model, and initializes the parameters of the suppression scenario model;

[0036] Collaborative Deployment Module: Based on the suppression scenario model initialized with parameters, collaboratively suppress and deploy, and establish the optimal collaborative suppression and deployment model;

[0037] Model Solving Module: Solving the optimal cooperative suppression deployment model based on potential game theory;

[0038] Effect evaluation module: Based on the optimal solution obtained from the model solving module, evaluate and provide feedback on the collaborative suppression effect.

[0039] Electromagnetic suppression equipment based on potential game theory collaborative positioning deployment devices include:

[0040] Memory: Used to store the computer program for the cooperative deployment method of electromagnetic suppression equipment based on potential game theory;

[0041] Processor: Used to implement a method for coordinated deployment of electromagnetic suppression equipment based on potential game theory when executing the computer program.

[0042] A computer-readable storage medium storing a computer program that, when executed by a processor, enables a method for the coordinated deployment of electromagnetic suppression equipment based on potential game theory.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] 1. This invention uses the UMi_Street_Canyon electromagnetic propagation model and performs propagation loss simulation for each communication link. Combined with the characteristics of actual terrain and features, signal loss attenuation is calculated, which can more accurately realize the modeling of suppression scenarios.

[0045] 2. This invention fully considers the problem of accidental damage to whitelisted devices during precise pressing, and minimizes the number of accidental damages to whitelisted devices, thereby achieving protection of key objects during pressing and better meeting the needs of real-world applications.

[0046] 3. This invention considers the cooperative relationship between different devices and proposes a device location deployment scheme based on potential game theory, which considers the root mean square error between the received power and the average received power at typical joint receiving points and the number of false positives in the whitelist. This scheme generates a greater power gain through device cooperative suppression, while ensuring the suppression effect and solution efficiency, reducing computation time, and obtaining the deployment scheme faster.

[0047] 4. Under the premise of fulfilling the pressing task, the present invention completes the equipment deployment through the collaborative work between pressing devices, achieves precise pressing effect, and avoids problems such as equipment damage and energy waste caused by rough pressing.

[0048] In summary, this invention utilizes an electromagnetic propagation model based on the UMi_Street_Canyon model to simulate the actual electromagnetic environment, reducing uncertainties in coarse calculations. By solving the optimal collaborative suppression deployment model through potential game theory, it achieves precise suppression with high stability and robustness of the full-area suppression effect and minimal damage to protected equipment. Attached Figure Description

[0049] Figure 1This is a flowchart of the method of the present invention.

[0050] Figure 2 This is a graph showing the number of iterations in the potential game under different numbers of devices.

[0051] Figure 3 This is a comparison chart of the average values ​​of potential game and traversal schemes under different numbers of devices.

[0052] Figure 4 This is a comparison chart of the variances of potential game and traversal schemes under different numbers of devices.

[0053] Figure 5 This is a graph showing the trend of utility function changes under different weights. Detailed Implementation

[0054] The present invention will now be described in further detail with reference to the accompanying drawings.

[0055] like Figure 1 As shown, the method for coordinated deployment of electromagnetic suppression equipment based on potential game theory includes the following steps:

[0056] Step 1: Analyze the suppression task in the electromagnetic suppression area, establish a suppression scenario model, and initialize the model parameters;

[0057] Step 1.1: Analyze the suppression task in the electromagnetic suppression area and obtain the suppression edge area, suppression frequency band, suppression threshold power, protection device frequency band and location (i.e., whitelist device frequency band and suppression device), maximum number of deployable suppression devices, suppression device parameters, and location information of suppression effect evaluation points (i.e., typical points) from the suppression task requirements.

[0058] Step 1.2: Based on the information obtained in Step 1.1, establish a suppression scenario model, initialize the parameters of the suppression scenario model, and calculate the path loss. The specific process is as follows:

[0059] The Umi-Street Canyon path loss model, suitable for dense urban areas, is adopted. It considers both line-of-sight (LOS) and non-line-of-sight (NLOS) propagation. When initializing the parameters of typical points, the highest frequency band and the maximum bandwidth of the frequency band to be suppressed are selected. The frequency suppression factor at the typical points is calculated using the spectrum template formula. The interference power at the typical points is calculated considering the transmit power of the suppression equipment and the antenna gain. Combined with the mathematical model parameters of key areas, the path loss from all deployable equipment locations to all typical points is calculated using the UMi radio wave propagation formula.

[0060] For whitelisted devices, determine the operating frequency band of the whitelist. If the highest frequency band of the whitelist is the same as the highest frequency band of the suppression band, take the value calculated at a typical point. If the highest frequency band of the whitelist is different from the highest frequency band of the suppression band, select the suppression band closest to the whitelist. Based on the suppression band, suppression threshold power, and protection device frequency band, calculate the frequency suppression factor at each whitelist location using the spectrum template formula. Based on the suppression edge region, suppression threshold power, protection device location, and frequency suppression factor, calculate the path loss from all deployable device points to all whitelists using the UMi radio wave propagation formula. Finally, considering the transmit power and antenna gain parameters of the suppression device, obtain the better interference power at the whitelist location.

[0061] After calculating the above parameters, the initialization of the suppression scenario parameters is completed, and the refined loss parameters are obtained.

[0062] Step 2: Based on the suppression scenario model initialized with parameters obtained in Step 1, coordinate suppression deployment and establish the optimal coordinated suppression deployment model:

[0063] The objective function is set as the weighted sum of the root mean square error between the received power and the average received power at each typical point and the number of false positives in the whitelist. The constraints of the optimization problem include the maximum number of deployable suppression devices, the selectable locations of the suppression devices, the suppression frequency band, and the frequency band and location used by the whitelisted devices.

[0064] Step 3: Solve the optimal cooperative suppression deployment model obtained in Step 2 based on the potential game theory.

[0065] Step 3.1: Game Initialization

[0066] Step 3.1.1: Construct loss matrices from the pressing equipment to typical points, whitelisted equipment, and control stations. Construct three location parameter matrices with the pressing equipment as the row and the typical points, whitelisted equipment, and control stations as the column.

[0067] Step 3.1.2: Calculate the electromagnetic propagation loss from the suppression equipment to the typical point, whitelisted equipment and control station using the UMi_Street_Canyon model, and calculate the FDR loss between each suppression equipment and the typical point equipment, whitelisted equipment and control station;

[0068] Step 3.1.3: Calculate the mean square error of average received power U1 and the number of false positives in the whitelist from the three position parameter matrices obtained in Step 3.1.1 and their corresponding electromagnetic propagation loss and FDR loss obtained in Step 3.1.2. Set the utility function U as a weighted sum of U1 and U2, and assign weights w to them respectively, i.e., U = w * U1 + (1 - w) * U2. Obtain the minimum utility function through game theory.

[0069] Set a convergence threshold ε, and set the initial minimum utility function U_min = Inf.

[0070] Set the typical point suppression threshold threshold_mW, and set the whitelist false alarm threshold threshold_mW_White;

[0071] Step 3.2: Game Iteration

[0072] Step 3.2.1: Randomly generate a set of equipment placement schemes. Given the available placement locations and the number of equipment to be deployed, generate the placement location scheme.

[0073] Step 3.2.2: Calculate the utility value under this point layout scheme:

[0074] Calculate the received power Prx of a single device at all typical points under this deployment scheme, and calculate the received power Prx_sum of all devices at each typical point. If the Prx_sum at all typical points is greater than the threshold_mW, then the deployment scheme is considered to achieve the suppression effect; otherwise, return to step 3.2.1. Calculate the root mean square error of the average received power at all typical points to measure whether the suppression intensity is uniformly distributed, and use it as the utility value U1.

[0075] Calculate the received power Prx_White of a single device at all whitelisted devices under this deployment scheme. Calculate the sum of the received power Prx_White_sum of all devices at each whitelisted device. If the received power Prx_White_sum at a certain whitelisted device is less than the threshold threshold_mW_White, then the deployment scheme is considered to have not caused false positives for that whitelisted device; otherwise, it is considered to have caused false positives. Calculate the number of whitelisted devices that have been falsely affected, measure the degree of suppression of false positives, and use this as the utility value U2.

[0076] Step 3.3: Game Stops:

[0077] Step 3.3.1: Compare the current utility function U with the minimum utility function U_min. If the absolute value of the difference between the two is less than the convergence threshold ε, then convergence is considered to have been achieved and the iteration stops. If the current utility function U is less than U_min, then the corresponding device placement position is the optimal solution in the game. Otherwise, the U_min solution is the optimal solution for the corresponding device placement position.

[0078] Step 3.3.2: If convergence is not achieved, record the minimum utility value and its corresponding location, and return to step 3.2.

[0079] Step 4: Evaluation and feedback of the synergistic suppression effect;

[0080] Determine whether the suppression power at each typical point meets the requirements. If the suppression power at any typical point does not meet the requirements, remove the current deployment scheme from the set of optional deployment schemes and regenerate the collaborative suppression deployment scheme. If the suppression power at all typical points meets the requirements, calculate the standard deviation. If the standard deviation is less than 0.2, the scheme is considered to have met the expected requirements, and the collaborative deployment device location scheme is output. Otherwise, remove the current deployment scheme from the set of optional deployment schemes and return to step 3.2.1 to regenerate the collaborative suppression deployment scheme.

[0081] A potential game-based electromagnetic suppression equipment cooperative deployment system includes:

[0082] Suppression Scenario Module: Analyzes the suppression task in the electromagnetic suppression area, establishes a suppression scenario model, and initializes the parameters of the suppression scenario model;

[0083] Collaborative Deployment Module: Based on the suppression scenario model initialized with parameters, collaboratively suppress and deploy, and establish the optimal collaborative suppression and deployment model;

[0084] Model Solving Module: Solving the optimal cooperative suppression deployment model based on potential game theory;

[0085] Effect evaluation module: Based on the optimal solution obtained from the model solving module, evaluate and provide feedback on the collaborative suppression effect.

[0086] Electromagnetic suppression equipment based on potential game theory collaborative positioning deployment devices include:

[0087] Memory: Used to store the computer program for the cooperative deployment method of electromagnetic suppression equipment based on potential game theory;

[0088] Processor: Used to implement a method for coordinated deployment of electromagnetic suppression equipment based on potential game theory when executing the computer program.

[0089] A computer-readable storage medium storing a computer program that, when executed by a processor, enables a method for the coordinated deployment of electromagnetic suppression equipment based on potential game theory.

[0090] The invention will be explained in detail below with reference to simulation results:

[0091] Simulation conditions: In this communication scenario, it is assumed that there are 20 deployable device points, 40 typical points for receiving power calculation, 10 whitelisted points, and 5 suppression devices in the entire area. The point information includes latitude and longitude, altitude, power, gain, spectrum template, and operating frequency information. It is also assumed that there are four frequency bands to be suppressed: f1, f2, f3, and f4.

[0092] Simulation content:

[0093] like Figure 2As shown, the simulation results demonstrate the number of iterations required for convergence of the potential game under different device conditions. It can be observed that, under different numbers of devices, the objective function value decreases rapidly within a small number of iterations, reaching convergence. This reflects the timeliness of the algorithm, which can quickly calculate the device deployment scheme that meets the suppression requirements.

[0094] like Figure 3 As shown, the average values ​​of the device deployment schemes obtained by the game theory algorithm and the traversal algorithm are compared under different numbers of devices. The larger the average value, the greater the average received power of the deployed devices at each typical point, and the better the suppression effect. In the worst case, the performance of the game theory algorithm is about 10 dBm worse than that of the traversal algorithm, and in the best case, it is about 6 dBm worse. The game theory algorithm significantly reduces the computation time at the expense of optimization performance, and while achieving the suppression effect, it reduces the complexity of the algorithm and improves the timeliness of deployment.

[0095] like Figure 4 As shown, the mean squared error (MSE) of the device deployment schemes obtained by the game theory algorithm and the traversal algorithm is compared under different numbers of devices. The smaller the MSE, the smaller the average received power fluctuation of the deployed devices at each typical point, and the more uniform the coverage effect of the suppression power. In the worst case, the performance of the game theory algorithm is about 5 dBm worse than that of the traversal algorithm, and in the best case, it is about 3 dBm worse. The game theory algorithm significantly reduces the computation time at the expense of optimization performance, reducing algorithm complexity and improving deployment timeliness while achieving the suppression effect.

[0096] like Figure 5 As shown, the utility function changes when different weights are assigned to the two influencing factors. The utility function value initially increases with the increase of the weight of the U1 component, and then decreases with further increases in weight. This demonstrates that both the root mean square error of the average received power at typical points and the number of false positives in the whitelist have a certain impact on the total utility function, and that the impact varies under different weighting factors.

[0097] The electromagnetic suppression equipment collaborative deployment method based on potential game theory of this invention can quickly output a suppression plan while meeting the timeliness requirements of the suppression task. Moreover, through simulation comparison, the suppression effect of the scheme output by this invention is small compared with the traversal output scheme. It can achieve a good balance between the task achievement effect and the timeliness of the scheme, and achieve the goal of high sealing effect accuracy and low interference in unrelated areas.

Claims

1. A method for coordinated deployment of electromagnetic suppression equipment based on potential game theory, characterized in that, include: Step 1: Analyze the suppression task in the electromagnetic suppression area, establish a suppression scenario model, and initialize the parameters of the suppression scenario model; The specific process of step 1 is as follows: Step 1.1: Analyze the suppression task in the electromagnetic suppression area and obtain the suppression edge area, suppression frequency band, suppression threshold power, protection device frequency band and location (i.e., whitelist device frequency band and suppression device), maximum number of deployable suppression devices, suppression device parameters, and location information of suppression effect evaluation points (i.e., typical points) from the suppression task requirements. Step 1.2: Based on the information obtained in Step 1.1, establish a suppression scenario model, initialize the parameters of the suppression scenario model, and calculate the path loss. The specific process is as follows: The Umi-Street Canyon path loss model, suitable for dense urban areas, is adopted. It considers both line-of-sight (LOS) and non-line-of-sight (NLOS) propagation. When initializing the parameters of typical points, the highest frequency band and the maximum bandwidth of the frequency band to be suppressed are selected. The frequency suppression factor at the typical points is calculated using the spectrum template formula. The interference power at the typical points is calculated considering the transmit power of the suppression equipment and the antenna gain. Combined with the mathematical model parameters of key areas, the path loss from all deployable equipment locations to all typical points is calculated using the UMi radio wave propagation formula. For whitelisted devices, determine the operating frequency band of the whitelist. If the highest frequency band of the whitelist is the same as the highest frequency band of the suppression band, take the value calculated at a typical point. If the highest frequency band of the whitelist is different from the highest frequency band of the suppression band, select the suppression band closest to the whitelist. Based on the suppression band, suppression threshold power, and protection device frequency band, calculate the frequency suppression factor at each whitelist location using the spectrum template formula. Based on the suppression edge region, suppression threshold power, protection device location, and frequency suppression factor, calculate the path loss from all deployable device points to all whitelists using the UMi radio wave propagation formula. Finally, considering the transmit power and antenna gain parameters of the suppression device, obtain the better interference power at the whitelist location. After calculating the above parameters, the initialization of the suppression scene model parameters is completed; Step 2: Based on the suppression scenario model initialized with parameters obtained in Step 1, coordinate suppression deployment and establish the optimal coordinated suppression deployment model; The process of establishing the optimal collaborative suppression deployment model is as follows: the objective function is set as the weighted sum of the root mean square error between the received power and the average received power at each typical point and the number of false alarms in the whitelist. The constraints of the optimization problem include the maximum number of deployable suppression devices, the selectable locations of the suppression devices, the suppression frequency band, and the frequency band and location used by the whitelist devices. Step 3: Based on the potential game, solve the optimal cooperative suppression deployment model obtained in Step 2 to obtain the optimal solution, specifically: game initialization, game iteration and game termination; The specific process of game initialization is as follows: Step 3.1: Construct loss matrices from the pressing equipment to typical points, whitelisted equipment, and control stations. Construct three location parameter matrices with the pressing equipment as the row and the typical points, whitelisted equipment, and control stations as the column. Step 3.2: Calculate the electromagnetic propagation loss from the suppression equipment to the typical point, whitelisted equipment and control station using the UMi radio wave propagation formula, and calculate the FDR loss between each suppression equipment and the typical point equipment, whitelisted equipment and control station; Step 3.3: Calculate the mean square error of average received power U1 and the number of false positives in the whitelist using the three position parameter matrices obtained in Step 3.1 and the corresponding electromagnetic propagation loss and FDR loss obtained in Step 3.

2. Set the utility function U as a weighted sum of U1 and U2, assigning weights w to each, i.e., U = w * U1 + (1 - w) * U2. Obtain the minimum utility function through game theory. Set the convergence threshold ε, the initial minimum utility function U_min = Inf, the typical point suppression threshold threshold_mW, and the whitelist false positive threshold threshold_mW_White. Step 4: Based on the optimal solution obtained in Step 3, evaluate and provide feedback on the collaborative suppression effect.

2. The method for coordinated deployment of electromagnetic suppression equipment based on potential game theory according to claim 1, characterized in that, In step 3, the specific process of the game iteration is as follows: Step 3.4: Randomly generate a set of equipment placement schemes. Given the available placement locations and the number of equipment to be deployed, generate the placement location schemes. Step 3.5: Calculate the utility value under this point layout scheme: Calculate the received power Prx of a single device at all typical points under this deployment scheme, and calculate the received power Prx_sum of all devices at each typical point. If the Prx_sum at all typical points is greater than the threshold_mW, the deployment scheme is considered to achieve the suppression effect; otherwise, return to step 3.

4. Calculate the root mean square error of the average received power at all typical points to measure whether the suppression intensity is uniformly distributed, and use it as the utility value U1. Calculate the received power Prx_White of a single device at all whitelisted devices under this deployment scheme, and calculate the received power Prx_White_sum of all devices superimposed at each whitelisted device. If the received power Prx_White_sum at a certain whitelist is less than the threshold threshold_mW_White, it is considered that the deployment scheme has not caused false damage to the whitelisted device; otherwise, it is recorded as false damage. Calculate the number of whitelists that have been falsely damaged, measure the degree of suppression of false damage, and use it as the utility value U2. The specific process of stopping the game is as follows: Step 3.6: Compare the current utility function U with the minimum utility function U_min. If the absolute value of the difference between the two is less than the convergence threshold ε, then convergence is considered to have been achieved and the iteration stops. If the current utility function U is less than U_min, then the corresponding device placement position is the optimal solution in the game. Otherwise, the U_min solution is the optimal solution for the corresponding device placement position. Step 3.7: If convergence is not achieved, record the minimum utility value and its corresponding location, and return to step 3.

4.

3. The method for coordinated deployment of electromagnetic suppression equipment based on potential game theory according to claim 1, characterized in that, The specific process of step 4 is as follows: Determine whether the suppression power at each typical point meets the requirements. If the suppression power at a typical point does not meet the requirements, remove the current deployment scheme from the set of available deployment schemes and return to step 3.4 to regenerate the collaborative suppression deployment scheme. If the suppression power of all typical points meets the requirements, calculate the standard deviation. If the standard deviation is less than 0.2, the scheme is considered to have met the expected requirements, and the collaborative deployment device location scheme is output. Otherwise, remove the current deployment scheme from the set of optional deployment schemes and return to step 3.4 to regenerate the collaborative suppression deployment scheme.

4. A potential game-based electromagnetic suppression equipment cooperative positioning deployment system based on the method of claim 1, characterized in that, include: Suppression Scenario Module: Analyzes the suppression task in the electromagnetic suppression area, establishes a suppression scenario model, and initializes the parameters of the suppression scenario model; Collaborative Deployment Module: Based on the suppression scenario model initialized with parameters, collaboratively suppress and deploy, and establish the optimal collaborative suppression and deployment model; Model Solving Module: Solving the optimal cooperative suppression deployment model based on potential game theory; Effect evaluation module: Based on the optimal solution obtained from the model solving module, evaluate and provide feedback on the collaborative suppression effect.

5. A collaborative positioning deployment device for electromagnetic suppression equipment based on potential game theory, characterized in that, include: Memory: for storing the computer program of the electromagnetic suppression equipment cooperative positioning deployment method based on potential game theory as described in any one of claims 1-3; Processor: Used to implement a method for coordinated deployment of electromagnetic suppression equipment based on potential game theory when executing the computer program according to any one of claims 1-3.

6. A computer-readable storage medium storing a computer program that, when executed by a processor, enables the implementation of the electromagnetic suppression equipment cooperative positioning deployment method based on potential game theory as described in any one of claims 1-3.

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