Drone removal equipment layout method, device, equipment and medium

Abstract

The present invention provides a method, apparatus, device, and medium for deploying drone-repelling equipment. The method comprises: obtaining an area to be deployed and dividing the area into different area types, including core areas, sub-core areas, and ordinary areas; evaluating the area types of the area to be deployed using a biased multi-random key genetic algorithm based on a drone-repelling equipment deployment model to obtain an evaluation result; and determining a drone-repelling equipment deployment result for each area type based on the evaluation result. The present invention has the beneficial effects of improving the efficiency of drone tracking and repelling, and reducing the cost of drone repelling.

Description

Technical Field

[0001] The present invention relates to the field of computer and drone technology, and in particular to a method, device, equipment and medium for deploying drone-repelling equipment. Background Art

[0002] In recent years, with the rapid development and widespread application of drone technology, the management of drones (such as early warning and expulsion) has become increasingly urgent. Therefore, it is necessary to design a reasonable and comprehensive layout in advance for key locations.

[0003] The existing technology addresses the above technical issues by using static, large-scale and low-intelligence drones to deploy equipment, but does not take into account practical constraints and difficulties such as the difficulty in detecting drones and the short detection and countermeasure linkage time. Summary of the Invention

[0004] The main purpose of the embodiments of the present invention is to provide a method, device, equipment and medium for deploying drone-repelling equipment, which can improve the response speed and repelling speed of detecting and repelling drones in a target area.

[0005] One aspect of the present invention provides a method for deploying drone-repelling equipment, comprising:

[0006] Acquire a to-be-placed area, and divide the to-be-placed area into different area types, wherein the area types include a core area, a secondary core area, and a common area;

[0007] According to the area type of the area to be deployed, a biased multi-random key genetic algorithm is used and an evaluation is performed based on a drone-repelling equipment deployment model to obtain an evaluation result;

[0008] Based on the evaluation results, a layout result of drone-repelling equipment is determined for each area type.

[0009] According to the method for deploying drone-repelling equipment, the method further includes:

[0010] The drone repelling equipment deployment model includes an objective function and constraints. The objective function is used to characterize the fastest detection time and the fastest repelling time. The constraints include the cost of repelling drone equipment, the inability to deploy certain types of repelling drone equipment in certain areas, the minimum distance between different types of repelling drone equipment, and the requirement that the drone to be repelled is within the repelling range of the repelling drone equipment.

[0011] Among them, the objective function is

[0012]

[0013] Among them, n is the number of the drone to be driven away, N is the set of drone numbers to be driven away, Ia The area type classification of the area to be laid out, a=1, 2, 3 are different area types, y in Whether the drone n to be driven away is successfully tracked in the area numbered i, i * is the center area number of the core area, i is the area number, w a is the countermeasure weight of different area types, For the number The area and number are i * The distance between the areas;

[0014] The cost of driving away drone equipment is

[0015]

[0016] Among them, x ij is whether to deploy drone-repelling equipment type j in area numbered i, is the cost of equipment of drone-repelling equipment type j, is the total cost limit of the device j for driving away drones, j is the type number of the device to be driven away drones, and J is the set of device types to be driven away drones;

[0017] Certain areas cannot be equipped with certain types of drone repelling equipment.

[0018]

[0019] Among them, D j The set of area numbers where drone-repelling equipment type j cannot be deployed;

[0020] The shortest distances for different types of drone-repelling equipment are:

[0021]

[0022] Among them, d(i1,i2) is the distance between the regions numbered i1 and i2, The shortest distance between the j1 and j2 drones.

[0023] After detection, tracking and determination of the driving away point, the drone to be driven away is within the driving away range of the drone driving away device.

[0024]

[0025] in, Whether the drone n to be driven away is successfully driven away from the area numbered i2.

[0026] According to the drone-repelling equipment layout method, according to the area type of the area to be laid out, a biased multi-random key genetic algorithm is used and an evaluation is performed based on a drone-repelling equipment layout model, including:

[0027] Generate a population and initialize the individuals in the population;

[0028] Decoding each individual according to the objective function and the constraint conditions to obtain the fitness of each individual;

[0029] Perform biased strategy processing, including sorting all individuals according to their fitness, obtaining sorting results and dividing the sorting results into elite groups and non-elite groups in proportion, wherein the elite group includes elite individuals and the non-elite group includes non-elite individuals;

[0030] Add the elite group to the next generation, combine the elite individuals and non-elite individuals to generate new individuals to add to the next generation, and randomly generate mutant individuals to add to the next generation;

[0031] Iteratively execute individual decoding and biased strategy processing until the population iteration reaches the preset iteration requirement.

[0032] According to the method for deploying drone-repelling equipment, generating a population and initializing individuals in the population include:

[0033] When initializing individuals in the population, each individual is considered as a group of |J| [0,1] I Each random key vector includes |I| random keys, |I| represents the number of areas to be laid out, and each individual random key obeys the uniform distribution in the interval [0,1].

[0034] According to the drone-repelling device layout method, each individual is decoded according to the objective function and the constraint conditions to obtain the fitness of each individual, including:

[0035] For each random key of the individual's J random key vectors, take the maximum value of the J random key vectors and sort them in descending order;

[0036] Determine the equipment type corresponding to the area to be laid out based on the random key vector corresponding to the maximum value on the corresponding random key position, and then prioritize the areas to be laid out to obtain a priority sequence for the equipment types to be arranged in the areas to be laid out;

[0037] Determine the layout of drone repellent equipment in the area to be deployed based on the priority of drone repellent equipment types in the area to be deployed, the priority of the area to be deployed, and constraints;

[0038] According to the layout of the drone-repelling device in the area to be deployed, a constraint function is used to calculate the fitness value of the layout of the drone-repelling device.

[0039] According to the method for dispersing drones from the equipment, the multi-random key genetic algorithm further comprises:

[0040] Generate an initialized population, where the population size is n, n represents the number of individuals in the population, and the set of individuals in the population is P (0) ;

[0041] After decoding the individuals in the population, determining the end of iteration according to the preset iteration rules, wherein the preset iteration rules include the number of iterations reaching a specified number of generations, the fitness value of the best individual in the population reaching a specified value, and the fitness value of the best individual in the population not changing after the specified number of iterations;

[0042] If the conditions for ending the iteration are not met, the elite group is divided, including dividing the population P (k) Sort by fitness value and divide the current population P into (k) Divided into elite groups P e and non-elite groups P (k) -P e , among which the elite group P e Satisfy P e <P (k) -P e , where P (k) represents the kth generation population;

[0043] The elite group Completely replicated to the next generation population, that is From the elite group Randomly select π from e individuals, from the non-elite group P (k) -P e Select π t Individuals are combined to obtain the parent set. According to the fitness value of the individuals in the parent set, the probability of the individuals in the parent set passing random keys to the next generation population is determined. The crossover process of the sub-step is repeated to obtain the crossover sub-population.

[0044] By generating mutant populations For the next generation population P (k+1) Complete so that P (k+1) and P (k) Keeping consistent, the mutant population The generation method of is the same as the initialization method of the population.

[0045] According to the method for deploying drone-repelling equipment, the method further includes:

[0046] The area to be laid out is divided into sub-areas of equal size. The sub-areas are evaluated based on the drone-repelling equipment layout model according to the area type to which the sub-areas belong. The drone-repelling equipment layout processing is performed on each sub-area based on the evaluation result.

[0047] Another aspect of an embodiment of the present invention provides a device for deploying drone-repelling equipment, including:

[0048] The first module is used to obtain the area to be laid out and divide the area to be laid out into different area types, including core areas, sub-core areas and ordinary areas;

[0049] The second module is used to evaluate the area type of the area to be deployed using a biased multi-random key genetic algorithm based on a drone-repelling equipment deployment model to obtain an evaluation result;

[0050] The third module is used to determine the layout results of drone-repelling equipment for each area type based on the evaluation results.

[0051] Another aspect of an embodiment of the present invention provides an electronic device, including a processor and a memory;

[0052] The memory is used to store programs;

[0053] The processor executes the program to implement the method described above.

[0054] Embodiments of the present invention further disclose a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the method described above.

[0055] The beneficial effects of the present invention are as follows: by dividing the target area where drone-repelling devices are to be deployed into core areas, sub-core areas, and ordinary areas, and by using a biased multi-random key genetic algorithm based on a drone-repelling device layout model, the optimal drone-repelling device layout in the area to be deployed is found, thereby improving the response speed and repelling speed of discovering and repelling drones in the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0057] Figure 11 is a flow chart of a method for deploying drone-repelling equipment according to an embodiment of the present invention.

[0058] Figure 2 1 is a flow chart of a partial multi-random key genetic algorithm according to an embodiment of the present invention.

[0059] Figure 3 2 is a schematic diagram of a biased random key genetic algorithm framework according to an embodiment of the present invention.

[0060] Figure 4 1 is a flow chart of the BRKGA algorithm according to an embodiment of the present invention for generating the next generation population.

[0061] Figure 5 Schematic diagram of the division of the area to be laid out according to an embodiment of the present invention.

[0062] Figure 6 3. It is a schematic diagram comparing the optimal values ​​of the optimization results of BMRKGA in an embodiment of the present invention and the classic genetic algorithm.

[0063] Figure 7 3. It is a schematic diagram comparing the mean values ​​of the optimization results of the BMRKGA of the embodiment of the present invention and the classic genetic algorithm.

[0064] Figure 8 1 is a schematic diagram of the layout scheme corresponding to the BMRKGA optimal solution and case simulation results according to an embodiment of the present invention.

[0065] Figure 9 It is a schematic diagram of the layout scheme corresponding to the optimal solution of the classic genetic algorithm and the case simulation results of an embodiment of the present invention.

[0066] Figure 10 Schematic diagram of the layout of the drone-repelling equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. In the subsequent description, suffixes such as "module," "component," or "unit" used to represent elements are used solely to facilitate the description of the present invention and have no specific meaning in themselves. Therefore, "module," "component," or "unit" may be used interchangeably. "First," "second," and the like are used solely to distinguish technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features. In this subsequent description, the consecutive numbering of method steps is for ease of review and understanding. In conjunction with the overall technical solution of the present invention and the logical relationship between the various steps, adjusting the order of implementation of the steps does not affect the technical effects achieved by the technical solution of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and should not be construed as limiting the present invention.

[0068] Explanation of some parameters in the embodiment of the present invention:

[0069] I is the set of area numbers to be laid out;

[0070] I1 is the core area number set;

[0071] I2 is the secondary core area number set;

[0072] I3 is a set of common area numbers;

[0073] i * Number the central area of ​​the core area;

[0074] w1 is the counter-attack weight of the core area;

[0075] w2 is the countermeasure weight of the secondary core area;

[0076] w3 is the counter-attack weight of the common area;

[0077] w tracking point action weight;

[0078] J. A collection of equipment types for driving away drones;

[0079] N is the numbered set of drones to be driven away;

[0080] x ij Whether drone repelling equipment type j is deployed in area numbered i;

[0081] y in Whether the drone n to be driven away is successfully tracked in the area numbered i;

[0082] z inWhether the drone n to be driven away is successfully driven away from the area numbered i;

[0083] The total cost limit of the device that drives away the drone is j;

[0084] D j The set of area numbers where drone device type j cannot be deployed or repelled;

[0085] The shortest distance between the j1 and j2 drones.

[0086] The maximum effective distance for driving away a device of type j from a drone;

[0087] d(i1,i2) is the distance between the regions numbered i1 and i2;

[0088] c j The cost of driving away a device of type j from a drone.

[0089] Explanation of some terms in the embodiments of the present invention:

[0090] BMRKGA, biased random key genetic algorithm.

[0091] refer to Figure 1 , Figure 1 1 is a flow chart of a method for deploying drone-repelling equipment according to an embodiment of the present invention, which includes but is not limited to steps S100 to S300:

[0092] S100: Acquire the area to be laid out, and divide the area to be laid out into different area types, where the area types include core areas, sub-core areas, and common areas.

[0093] In some embodiments, the area to be laid out is divided according to the actual situation of the area to be laid out, such as the peripheral area of ​​the airport, the airport runway area, and the airport building area.

[0094] S200 , based on the area type of the area to be deployed, a biased multi-random key genetic algorithm is used to perform an evaluation based on a drone-repelling equipment deployment model to obtain an evaluation result.

[0095] The drone repelling equipment deployment model includes an objective function and constraints. The objective function is used to characterize the fastest detection time and the fastest repelling time. The constraints include the cost of repelling drone equipment, the inability to deploy certain types of repelling drone equipment in certain areas, the minimum distance between different types of repelling drone equipment, and the requirement that the drone to be repelled is within the repelling range of the repelling drone equipment.

[0096] Among them, the objective function is

[0097]

[0098] Among them, n is the number of the drone to be driven away, N is the set of drone numbers to be driven away, I a The area type classification of the area to be laid out, a=1, 2, 3 are different area types, y in Whether the drone n to be driven away is successfully tracked in the area numbered i, i * is the center area number of the core area, i is the area number, w a is the countermeasure weight of different area types, For the number The area and number are i * The distance between the areas;

[0099] The cost of driving away drone equipment is

[0100]

[0101] Among them, x ij is whether to deploy drone-repelling equipment type j in area numbered i, is the cost of equipment of drone-repelling equipment type j, is the total cost limit of the device j for driving away drones, j is the type number of the device to be driven away drones, and J is the set of device types to be driven away drones;

[0102] Certain areas cannot be equipped with certain types of drone repelling equipment.

[0103]

[0104] Among them, D j The set of area numbers where drone-repelling equipment type j cannot be deployed;

[0105] The shortest distances for different types of drone-repelling equipment are:

[0106]

[0107] Among them, d(i1,i2) is the distance between the regions numbered i1 and i2, The shortest distance between the j1 and j2 drones.

[0108] After detection, tracking and determination of the driving away point, the drone to be driven away is within the driving away range of the drone driving away device.

[0109]

[0110] in, Whether the drone n to be driven away is successfully driven away from the area numbered i2.

[0111] In some embodiments, reference Figure 2 Schematic diagram of the process flow of the partial multi-random key genetic algorithm. It includes but is not limited to steps S210 to S250:

[0112] S210, generating a population and initializing individuals in the population.

[0113] In some embodiments, initializing the individuals in the population includes initializing the individuals in the population by treating each individual as a group of |J| [0,1] I Each random key vector includes |I| random keys, |I| represents the number of areas to be laid out, and each individual random key obeys the uniform distribution in the interval [0,1].

[0114] S220 , decoding each individual according to the objective function and the constraint conditions to obtain the fitness of each individual.

[0115] In some embodiments, decoding each individual according to the objective function and the constraint condition to obtain the fitness of each individual includes: sorting each random key of the J random key vectors of the individual by taking the maximum value on the J random key vectors and performing descending sorting;

[0116] Determine the equipment type corresponding to the area to be laid out based on the random key vector corresponding to the maximum value on the corresponding random key position, and then prioritize the areas to be laid out to obtain a priority sequence for the equipment types to be arranged in the areas to be laid out;

[0117] Determine the layout of drone repellent equipment in the area to be deployed based on the priority of drone repellent equipment types in the area to be deployed, the priority of the area to be deployed, and constraints;

[0118] According to the layout of the drone-repelling device in the area to be deployed, a constraint function is used to calculate the fitness value of the layout of the drone-repelling device.

[0119] For example, individuals [0.1, 0.4, 0.3, 0.6], [0.15, 0.05, 0.55, 0.45], corresponding to the priority sequence of area number selection [4, 3, 2, 1], considering the random key vector corresponding to the maximum value on the corresponding random key position, the priority sequence of selecting areas and corresponding deployment drone repellent equipment models is [4(1), 3(2), 2(1), 1(2)], indicating that priority is given to deploying drone repellent equipment of type 1 in area number 4, then considering deploying drone repellent equipment of type 2 in area number 3, then considering deploying drone repellent equipment of type 1 in area number 3, and finally considering deploying drone repellent equipment of type 2 in area number 1. The priority vector represents the priority order of areas and drone repellent equipment types. Areas and drone repellent equipment types with higher rankings will be given priority, but areas and drone repellent equipment types that cannot be arranged due to constraints will be postponed. After all arrangements are completed, the fitness value is calculated according to the objective function.

[0120] S230, executing biased strategy processing, including sorting all individuals according to their fitness, obtaining sorting results, and dividing the sorting results into elite groups and non-elite groups in proportion, wherein the elite group includes elite individuals, and the non-elite group includes non-elite individuals.

[0121] S240, adding the elite group to the next generation, combining the elite individuals and the non-elite individuals to generate new individuals to add to the next generation, and randomly generating mutant individuals to add to the next generation.

[0122] S250, iteratively executing individual decoding and biased strategy processing until the iteration of the population reaches a preset iteration requirement.

[0123] In some embodiments, reference Figure 3 , is a schematic diagram of the biased random key genetic algorithm framework. Figure 3 As shown in the figure, the BRKGA framework is divided into two parts: problem-independent and problem-dependent. These two parts exhibit distinct differences and connections. The problem-independent part contains no information about the problem itself and is a black-box optimization process. The problem-dependent part, on the other hand, incorporates all connections to the problem being solved. The decoder decodes chromosomes into solutions and calculates the fitness of these solutions. This part embodies the use of random key theory in BRKGA. Therefore, to define a BRKGA heuristic, one only needs to define the corresponding decoder. The upper left and lower right corners outside the box represent the typical simple genetic algorithm process, including initialization and mutation. The lower left corner, within the box, represents the selection and crossover processes of the genetic algorithm. Unlike common genetic algorithms, BRKGA employs a biased strategy in these two processes, favoring elite groups during the iterative process of the population.

[0124] In some embodiments, reference Figure 4 The BRKGA algorithm shown in the figure generates the next generation population. The process is as follows:

[0125] Generate an initialized population, where the population size is n, n represents the number of individuals in the population, and the set of individuals in the population is P (0) ;

[0126] After decoding the individuals in the population, the iteration end is determined according to the preset iteration rules, where the preset iteration rules include the number of iterations reaching a specified number of generations, the fitness value of the best individual in the population reaching a specified value, and the fitness value of the best individual in the population not changing after the specified number of iterations;

[0127] If the conditions for ending the iteration are not met, the elite group is divided, including dividing the population P (k) Sort by fitness value and divide the current population P into (k) Divided into elite groups P e and non-elite groups P (k) -P e , among which the elite group P e Satisfy P e <P (k) -P e , where P (k) represents the kth generation population;

[0128] The elite group P e (k) Completely replicated to the next generation population, that is From the elite group Randomly select π from e individuals, from the non-elite group P (k) -P e Select π t Individuals are combined to obtain the parent set. According to the fitness value of the individuals in the parent set, the probability of the individuals in the parent set passing random keys to the next generation population is determined. The crossover process of the sub-step is repeated to obtain the crossover sub-population. It is understandable that the crossover strategy used in the embodiment of the present invention is biased towards the elite group. In traditional genetic algorithms, the two parent individuals that cross over to produce offspring are selected from the entire population, while BMRKGA is achieved by controlling the parameter π e and π t Select from elite and non-elite groups respectively. The scale is much smaller than P (k) -P eThe crossover strategy of BMRKGA makes the elite group have a larger proportion in the generation of offspring, which can better retain excellent solutions in iterations.

[0129] By generating mutant populations For the next generation population P (k+1) Complete so that P (k+1) and P (k) Keeping consistent, the mutant population The generation method of is consistent with the population initialization method. It can be understood that after this step is completed, in order to make the population meet the requirements, it is necessary to jump back to the decoding step.

[0130] S300: Determine the layout result of the drone-repelling equipment for each area type based on the evaluation result.

[0131] It is understandable that when the optimal drone removal

[0132] In some embodiments, reference Figure 5 Schematic diagram of the division of the area to be deployed. In order to verify the effectiveness of the algorithm, this paper uses simulation cases to test the performance of the algorithm. Consider the size of the area to be deployed, the location of the drones to be driven away, cost constraints, geographical restrictions and equipment parameters and other conditions for the optimization problem of the layout selection scheme of the anti-UAV equipment in the area to be deployed. In the embodiment of the present invention, the scope of the area to be deployed is set as a square of 1.6km×1.6km, and it is divided into 0.1km0.1km×0.1km squares. Each square is an area where equipment can be deployed. From left to right, from bottom to top, numbering starts from 1, and the square range of 0.4km×0.4km in the center of the square (that is, 16 areas numbered 97~99, 112~115, 127~130, 142~145) is set as the core area, and the range of 1.0km×1.0km in the center of the square, excluding the core area, is set as the sub-core area, and the other areas are ordinary areas. Figure 5 As shown, red is the core area, orange is the secondary core area, and green is the ordinary area.

[0133] In some embodiments, the drone cluster area to be repelled is located in the southwest corner and due south of the area to be deployed. The drones to be repelled fly from the area to the center of the area to be deployed, in a straight line, at a speed of 100 km / h. The repelling drone equipment deployed in the area to be deployed includes two types: detection equipment (low-altitude sensing devices) and repelling equipment (microwave devices). The parameters of the low-altitude sensing devices and microwave devices are shown in Table 2.

[0134] Table 2 Parameters of low-altitude induction device and microwave device

[0135] Parameter name Low-altitude sensing device microwave device Effective radius 3-6km 3km Success rate 90％ 95％ Reaction time 6s 0.05s Secondary action time 18s / cost 1 million yuan 1 million yuan

[0136] The present invention was tested in an experimental environment with an octa-core, 3.20GHz Corei5-8250U CPU, 8GB of RAM, and Windows 11. A classical genetic algorithm was selected as a control group to verify the performance of the BMRKGA algorithm of the present invention in finding a drone repelling layout. Each algorithm was run 100 times to obtain the optimal solution. The parameter settings of the two algorithms are shown in Table 3.

[0137] Table 3 Parameters of classic genetic algorithm and BMRKGA algorithm

[0138]

[0139] The embodiment of the present invention is tested on a case by using a classic genetic algorithm and an improved BMRKGA algorithm. After running each algorithm 100 times, the average value and the optimal value of each algorithm are taken for comparison.

[0140] Figure 6 This is a comparison of the optimization results of the classic genetic algorithm and BMRKGA. Figure 6 The red line (BMRKGA) and the blue line (classic genetic algorithm) show the time-varying trends of the best results for each generation of the 100 runs. As can be seen, the optimal global fitness of BMRKGA is 881.87, while that of the classic genetic algorithm is 856.42, indicating that BMRKGA outperforms the classic genetic algorithm. In terms of time, BMRKGA can generate an initial population that meets the constraints in a very short time and has a high fitness, resulting in a faster convergence speed, reaching the optimal solution in approximately 20 seconds. The classic genetic algorithm, on the other hand, requires nearly 10 seconds to generate an initial solution that meets the constraints. The search speed is slow in the early stages but accelerates in the later stages. Both algorithms can optimize the initial population within a relatively small number of iterations.

[0141] Figure 7 This is a schematic diagram comparing the mean optimization results of the BMRKGA of the present invention and the classical genetic algorithm. Figure 7 Comparing the mean values ​​of the two algorithms, the red line (BMRKGA) reflects the trend of the mean value of the BMRKGA fitness function at each iteration of the 100,100 runs, while the blue line (classical genetic algorithm) reflects the trend of the mean value of the classic genetic algorithm fitness function at each iteration. It can be seen that both algorithms show a stable upward trend, but the BMRKGA has smaller fluctuations and a larger mean fitness value, indicating that the BMRKGA is more stable and optimal than the classic genetic algorithm. This results in more efficient layout of drone repellent devices and faster tracking and repelling speeds.

[0142] In some embodiments, the present invention has compared the trend of change of the optimal solutions of the two algorithms during the iteration process in the results of running BMRKGA and the classic genetic algorithm 100 times respectively. The present invention visualizes the layout solutions and simulation results corresponding to the optimal solutions of the two algorithms and compares them. As shown in the figure, Figure 8 and Figure 9 The layout schemes and simulation results corresponding to the optimal solutions of BMRKGA and classic genetic algorithm are shown respectively.

[0143] As can be seen, the solution generated by the BMRKGA utilizes fewer drone repelling devices, conserving resources. However, due to its more optimal selection of equipment deployment areas, it can detect, track, and repel targets earlier. The solution generated by the classical genetic algorithm, on the other hand, relies more heavily on piling up equipment, using increased numbers to compensate for network gaps in the area to be deployed, which is less scientific and rational. This clearly demonstrates that the proposed BMRKGA outperforms the classical genetic algorithm in optimizing the placement of drone repelling and anti-drone equipment in a specific area to be deployed, finding a high-quality, optimal drone deployment solution within a reasonable timeframe.

[0144] Figure 10 FIG. 1 is a diagram of a UAV equipment layout analysis device according to an embodiment of the present invention. The device includes a first module 1010 , a second module 1020 , and a third module 1030 .

[0145] Among them, the first module is used to obtain the area to be laid out and divide the area to be laid out into different area types, including core areas, sub-core areas and ordinary areas; the second module is used to use a biased multi-random key genetic algorithm and a drone-repelling equipment layout model to evaluate the area type of the area to be laid out to obtain an evaluation result; the third module is used to determine the drone-repelling equipment layout result for each area type based on the evaluation result, and layout the drone-repelling equipment for the area to be laid out based on the drone-repelling equipment layout result.

[0146] Exemplarily, with the cooperation of the first module, the second module, and the third module in the device, the embodiment device can implement any of the aforementioned drone-repelling equipment layout methods, namely, obtaining the area to be laid out, dividing the area to be laid out into different area types, including core areas, sub-core areas, and ordinary areas; according to the area type of the area to be laid out, using a biased multi-random key genetic algorithm and based on the drone-repelling equipment layout model to perform an evaluation to obtain an evaluation result; based on the evaluation result, determining the drone-repelling equipment layout result for each area type. The beneficial effects of the present invention are: by dividing the target area where the drone-repelling equipment is to be laid out into core areas, sub-core areas, and ordinary areas, and using a biased multi-random key genetic algorithm and based on the drone-repelling equipment layout model to find the optimal drone-repelling equipment layout in the area to be laid out, the response speed and the speed of drone discovery and expulsion in the target area can be improved.

[0147] An embodiment of the present invention further provides an electronic device, the electronic device including a processor and a memory;

[0148] The memory stores a program;

[0149] The processor executes a program to perform the aforementioned drone-repelling device layout method; the electronic device has the function of carrying and running the software system for drone device layout provided by an embodiment of the present invention, such as a personal computer, a minicomputer, a main frame, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or communicates with a charged particle tool or other imaging device, etc.

[0150] An embodiment of the present invention further provides a computer-readable storage medium storing a program, wherein the program is executed by a processor to implement the aforementioned method for dispersing drones from the device layout.

[0151] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0152] Embodiments of the present invention also disclose a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method for deploying drone-repelling devices.

[0153] Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise indicated, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art using ordinary skill will be able to implement the present invention set forth in the claims without undue experimentation. It will also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0154] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0155] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0156] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0157] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0158] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0159] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0160] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for deploying drone-repelling equipment, characterized in that: include: Acquire a to-be-placed area, and divide the to-be-placed area into different area types, wherein the area types include a core area, a secondary core area, and a common area; According to the area type of the area to be deployed, a biased multi-random key genetic algorithm is used and an evaluation is performed based on a drone-repelling equipment deployment model to obtain an evaluation result; Determine the layout of drone-repelling equipment for each area type based on the assessment results; The method further comprises: The drone repelling equipment deployment model includes an objective function and constraints. The objective function is used to characterize the fastest detection time and the fastest repelling time. The constraints include the cost of repelling drone equipment, the inability to deploy certain types of repelling drone equipment in certain areas, the minimum distance between different types of repelling drone equipment, and the requirement that the drone to be repelled is within the repelling range of the repelling drone equipment. Among them, the objective function is Among them, n is the number of the drone to be driven away, Number and gather the drones to be driven away. The area type classification for the area to be laid out, For different regional types, Whether the drone n to be driven away is successfully tracked in the area numbered i, The center area number of the core area, is the area number, is the countermeasure weight of different area types, For the number The area and number are The distance between the areas; The cost of driving away drone equipment is in, Whether to deploy drone-repelling equipment type j in the area numbered i, is the cost of driving away the device of type j of the drone, The total cost limit of the device j for driving away the drone, j is the type number of the device to drive away the drone, A collection of equipment types to drive away drones; Certain areas cannot be equipped with certain types of drone repelling equipment. in, The set of area numbers where drone-repelling equipment type j cannot be deployed; The shortest distances for different types of drone-repelling equipment are: in, is the distance between the regions numbered i1 and i2, The shortest distance between the j1 and j2 drones. After detection, tracking and determination of the driving away point, the drone to be driven away is within the driving away range of the drone driving away device. in, For the number Whether the drone n to be driven away is successfully driven away from the area.

2. The method for deploying drone-repelling equipment according to claim 1, characterized in that: The method of using a biased multi-random key genetic algorithm and a drone-repelling equipment deployment model to perform evaluation according to the area type of the area to be deployed includes: Generate a population and initialize the individuals in the population; Decoding each individual according to the objective function and the constraint conditions to obtain the fitness of each individual; Perform biased strategy processing, including sorting all individuals according to their fitness, obtaining sorting results and dividing the sorting results into elite groups and non-elite groups in proportion, wherein the elite group includes elite individuals and the non-elite group includes non-elite individuals; Add the elite group to the next generation, combine the elite individuals and non-elite individuals to generate new individuals to add to the next generation, and randomly generate mutant individuals to add to the next generation; Iteratively execute individual decoding and biased strategy processing until the population iteration reaches the preset iteration requirement.

3. The method for deploying drone-repelling equipment according to claim 2, characterized in that: The generating of the population and initializing the individuals in the population include: When initializing individuals in the population, each individual is considered as a group indivual A random key vector, each random key includes Random keys, Indicates the number of areas to be laid out, and the random key of each individual obeys the interval uniform distribution.

4. The method for deploying drone-repelling equipment according to claim 2, wherein: Decoding each individual according to the objective function and the constraint condition to obtain the fitness of each individual includes: For individuals Each random key of the random key vector is taken Sort the maximum values ​​on the random key vectors in descending order; Determine the equipment type corresponding to the area to be laid out based on the random key vector corresponding to the maximum value on the corresponding random key position, and then prioritize the areas to be laid out to obtain a priority sequence for the equipment types to be arranged in the areas to be laid out; Determine the layout of drone repellent equipment in the area to be deployed based on the priority of drone repellent equipment types in the area to be deployed, the priority of the area to be deployed, and constraints; According to the layout of the drone-repelling device in the area to be deployed, a constraint function is used to calculate the fitness value of the layout of the drone-repelling device.

5. The method for deploying drone-repelling equipment according to claim 2, wherein: The biased multi-random key genetic algorithm also includes: Generate an initialized population, where the population size is n, n represents the number of individuals in the population, and the set of individuals in the population is ; After decoding the individuals in the population, determining the end of iteration according to the preset iteration rules, wherein the preset iteration rules include the number of iterations reaching a specified number of generations, the fitness value of the best individual in the population reaching a specified value, and the fitness value of the best individual in the population not changing after the specified number of iterations; If the conditions for ending the iteration are not met, the elite group will be divided, including dividing the population Sort by fitness value and divide the current population into Divided into elite groups and non-elite groups , among which the elite group satisfy ,in represents the kth generation population; The elite group Completely replicated to the next generation population, that is , from the elite group Randomly select Individuals, from and from non-elite groups Select Individuals are combined to obtain the parent set. According to the fitness value of the individuals in the parent set, the probability of the individuals in the parent set passing random keys to the next generation population is determined. The crossover process of the sub-step is repeated to obtain the crossover sub-population. ; By generating mutant populations To the next generation of population Complete so that and Keeping consistent, the mutant population The generation method of is the same as the initialization method of the population.

6. The method for deploying drone-repelling equipment according to claim 4, characterized in that: The method further comprises: The area to be laid out is divided into sub-areas of equal size. The sub-areas are evaluated based on the drone-repelling equipment layout model according to the area type to which the sub-areas belong. The drone-repelling equipment layout processing is performed on each sub-area based on the evaluation result.

7. A drone-repelling equipment layout device, characterized in that: include: The first module is used to obtain the area to be laid out and divide the area to be laid out into different area types, including core areas, sub-core areas and ordinary areas; The second module is used to evaluate the area type of the area to be deployed using a biased multi-random key genetic algorithm based on a drone-repelling equipment deployment model to obtain an evaluation result; The third module is used to determine the layout results of drone repelling equipment for each area type based on the evaluation results; The device further comprises: The drone repelling equipment deployment model includes an objective function and constraints. The objective function is used to characterize the fastest detection time and the fastest repelling time. The constraints include the cost of repelling drone equipment, the inability to deploy certain types of repelling drone equipment in certain areas, the minimum distance between different types of repelling drone equipment, and the requirement that the drone to be repelled is within the repelling range of the repelling drone equipment. Among them, the objective function is Among them, n is the number of the drone to be driven away, Number and gather the drones to be driven away. The area type classification for the area to be laid out, For different regional types, Whether the drone n to be driven away is successfully tracked in the area numbered i, The center area number of the core area, is the area number, is the countermeasure weight of different area types, For the number The area and number are The distance between the areas; The cost of driving away drone equipment is in, Whether to deploy drone-repelling equipment type j in the area numbered i, is the cost of driving away the device of type j of the drone, The total cost limit of the device j for driving away the drone, j is the type number of the device to drive away the drone, A collection of equipment types to drive away drones; Certain areas cannot be equipped with certain types of drone repelling equipment. in, The set of area numbers where drone-repelling equipment type j cannot be deployed; The shortest distances for different types of drone-repelling equipment are: in, is the distance between the regions numbered i1 and i2, The shortest distance between the j1 and j2 drones. After detection, tracking and determination of the driving away point, the drone to be driven away is within the driving away range of the drone driving away device. in, For the number Whether the drone to be driven away is successfully driven away from the area n.

8. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the drone-repelling device layout method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The storage medium stores a program, and the program is executed by a processor to implement the drone-repelling device layout method according to any one of claims 1 to 6.

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