Sensor deployment method and device, storage medium and electronic device

Abstract

The invention discloses a sensor deployment method and device, a storage medium and an electronic device, and relates to the field of computers.The sensor deployment method comprises the steps that multiple sensors are randomly arranged in a target area, and the multiple sensors are used for conducting environment monitoring on the target area; the layout positions of the plurality of sensors are adjusted through an optimization algorithm by taking maximization of a target function as a target, so that the target layout positions of the plurality of sensors are searched in the target area, the target function is used for calculating the fitness of the plurality of sensors at the target layout positions, and the fitness of the plurality of sensors at the target layout positions is used for calculating the fitness of the plurality of sensors at the target layout positions. The fitness of the plurality of sensors at the target arrangement position is maximum; and performing uncertainty analysis on the target area according to the plurality of target layout positions, and deploying the plurality of sensors in the target area according to the plurality of target layout positions under the condition of determining that the uncertainty analysis is passed.

Description

Technical Field

[0001] The present application relates to the field of computers, and more specifically, to a sensor deployment method and device, a storage medium, and an electronic device. Background Art

[0002] As a key equipment of offshore wind farms, the corrosion status of offshore substation transformers has a vital impact on the operational safety and stability of the system. Due to long-term exposure to harsh marine environments (such as high salinity, high humidity, and temperature changes), the external metal structure of the transformer is susceptible to corrosion, resulting in reduced equipment reliability and increased maintenance costs. Therefore, it is necessary to deploy an effective corrosion monitoring system to monitor the corrosion status of the transformer in real time and detect potential risks at an early stage.

[0003] In the prior art, there are mainly the following monitoring component deployment strategies:

[0004] (1) Fixed-interval deployment method: evenly arrange sensors outside the transformer and collect data regularly for analysis. This method is simple and direct, but it can easily lead to insufficient monitoring effect or sensor redundancy, and it is impossible to optimize the number and deployment location of sensors.

[0005] (2) Experience-based deployment method: Based on experience, some areas with higher corrosion risk are selected for sensor deployment. This method relies on historical data and expert knowledge, lacks scientific optimization basis, and may not cover all potential corrosion areas.

[0006] (3) Random deployment method: Sensors are randomly arranged on the surface of the transformer, hoping to achieve the best monitoring effect through random coverage. Although it can avoid the limitations of fixed-interval deployment, random deployment cannot guarantee the optimal solution and may result in high costs.

[0007] Therefore, traditional monitoring strategies often fail to fully consider the optimal layout of sensor deployment, which can easily lead to incomplete monitoring range, sensor redundancy or inaccurate data.

[0008] Regarding related technologies, traditional monitoring strategies often do not fully consider the optimal layout of sensor deployment, which can easily lead to problems such as incomplete monitoring range, sensor redundancy or inaccurate data. No effective solution has been proposed yet.

[0009] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0010] The embodiments of the present application provide a sensor deployment method and device, a storage medium and an electronic device to at least solve the problem in the related art that traditional monitoring strategies often do not fully consider the optimal layout of sensor deployment, which easily leads to incomplete monitoring range, sensor redundancy or inaccurate data.

[0011] According to one aspect of an embodiment of the present application, a sensor deployment method is provided, comprising: randomly deploying a plurality of sensors in a target area, wherein the plurality of sensors are used for performing environmental monitoring of the target area; adjusting the deployment positions of the plurality of sensors through an optimization algorithm with the goal of maximizing an objective function, so as to search for target deployment positions of the plurality of sensors in the target area, wherein the objective function is used to calculate the fitness of the plurality of sensors at the target deployment positions, and the fitness of the plurality of sensors at the target deployment positions is the largest; performing uncertainty analysis on the target area according to the plurality of target deployment positions, and deploying the plurality of sensors in the target area according to the plurality of target deployment positions when it is determined that the uncertainty analysis has passed.

[0012] In an exemplary embodiment, with the goal of maximizing the objective function, the layout positions of the multiple sensors are adjusted by an optimization algorithm to search for the target layout positions of the multiple sensors in the target area, including: iteratively updating the layout positions of the multiple sensors in the target area by a genetic algorithm, determining a first layout position of each sensor, and obtaining multiple first layout positions, wherein a first fitness corresponding to the multiple first layout positions is the largest among multiple second fitness corresponding to multiple groups of second layout positions, each group of second layout positions includes multiple second layout positions, the multiple second layout positions correspond one-to-one to the multiple sensors, and the multiple groups of second layout positions are the layout positions of the multiple sensors in multiple iterative updates; performing multiple local optimizations on the multiple first layout positions by a particle swarm optimization algorithm to obtain multiple target layout positions, wherein the multiple target layout positions correspond one-to-one to the multiple sensors, and the third fitness corresponding to the multiple target layout positions is greater than or equal to the first fitness.

[0013] In an exemplary embodiment, the layout positions of the multiple sensors are iteratively updated in the target area through a genetic algorithm to determine the first layout position of each sensor, and obtain multiple first layout positions, including: a transformation step: calculating the fourth fitness corresponding to the current layout positions of the multiple sensors through the objective function, performing a position transformation operation on the current layout positions of the multiple sensors according to the fourth fitness, to obtain multiple second layout positions, wherein the position transformation operation includes: a selection operation, a crossover operation, and a compilation operation; looping the transformation step for a preset number of times to obtain the multiple groups of second layout positions, respectively calculating the multiple second fitness corresponding to the multiple groups of second layout positions, and determining the multiple first layout positions from the multiple groups of second layout positions according to the multiple second fitness.

[0014] In an exemplary embodiment, uncertainty analysis is performed on the target area based on the multiple target layout positions, including: determining multiple environmental parameters that affect the multiple sensors; generating multiple groups of environmental samples based on the multiple target layout positions and the multiple environmental parameters, wherein the multiple groups of environmental samples are used to indicate different environmental conditions; and performing robustness testing and risk assessment on the multiple sensors based on the multiple groups of environmental samples to obtain test results and risk assessment results.

[0015] In an exemplary embodiment, after performing robustness testing and risk assessment on the multiple sensors according to the multiple groups of environmental samples and obtaining the test results and risk assessment results, the method further includes: determining whether the multiple target deployment positions pass the uncertainty analysis according to the test results and the risk assessment results; if it is determined that the multiple target deployment positions pass the uncertainty analysis, deploying the multiple sensors in the target area according to the multiple target deployment positions; if it is determined that the multiple sensors fail to pass the uncertainty analysis, adjusting the parameters of the optimization algorithm according to the test results and the risk assessment results, wherein the optimization algorithm includes: genetic algorithm, particle swarm optimization algorithm.

[0016] In an exemplary embodiment, the objective function includes: Among them, α and β are weight coefficients, (x i ,y i ) is the layout position of the ith sensor, n is the number of the multiple sensors, CR is the monitoring coverage of the target area by the sensor at the target layout position, and C is the deployment cost of the sensor at the target layout position.

[0017] According to another aspect of an embodiment of the present application, a sensor deployment device is also provided, including: a deployment module, used to randomly deploy a plurality of sensors in a target area, wherein the plurality of sensors are used to perform environmental monitoring on the target area; a search module, used to adjust the deployment positions of the plurality of sensors through an optimization algorithm with the goal of maximizing an objective function, so as to search for the target deployment positions of the plurality of sensors in the target area, wherein the objective function is used to calculate the fitness of the plurality of sensors at the target deployment positions, and the fitness of the plurality of sensors at the target deployment positions is the largest; a deployment module, used to perform uncertainty analysis on the target area according to the plurality of target deployment positions, and when it is determined that the uncertainty analysis has passed, deploy the plurality of sensors in the target area according to the plurality of target deployment positions.

[0018] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned sensor deployment method when running.

[0019] According to another aspect of an embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the sensor deployment method through the computer program.

[0020] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0021] Through the present application, firstly, a plurality of sensors are randomly arranged in a target area, and these sensors are used to perform environmental monitoring on the target area; with the goal of maximizing the objective function, the arrangement positions of these sensors are adjusted through an optimization algorithm until the target arrangement positions of these sensors are found in the target area, wherein the objective function is used to calculate the fitness of these sensors at different arrangement positions, and the fitness of these sensors at the target arrangement position is the largest; an uncertainty analysis is performed on the target area according to the plurality of target arrangement positions, and after determining that the uncertainty analysis has passed, the plurality of sensors are deployed in the target area according to the plurality of target arrangement positions; by adopting the above scheme, an optimization deployment strategy for monitoring components based on an optimization algorithm is proposed, which maximizes the monitoring effect while minimizing the deployment cost; thereby solving the problem that the traditional monitoring strategy in the related art does not fully consider the optimal layout of sensor deployment, which easily leads to incomplete monitoring range, sensor redundancy or inaccurate data. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0024] Figure 1 It is a hardware structure block diagram of a computer terminal of a sensor deployment method according to an embodiment of the present application;

[0025] Figure 2 is a flow chart of a sensor deployment method according to an embodiment of the present application;

[0026] Figure 3 It is a flow chart of an optional optimization algorithm-based optimization deployment strategy for offshore booster station transformer corrosion environment monitoring components according to an embodiment of the present application;

[0027] Figure 4 It is a structural block diagram of a sensor deployment device according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal of a sensor deployment method according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, CPU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0031] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the sensor deployment method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0032] A wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0033] In this embodiment, a sensor deployment method is provided. Figure 2is a flow chart of a sensor deployment method according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps S202-S206:

[0034] Step S202: randomly deploying a plurality of sensors in a target area, wherein the plurality of sensors are used to perform environmental monitoring on the target area;

[0035] Step S204: adjusting the layout positions of the multiple sensors by an optimization algorithm with the goal of maximizing the objective function, so as to search for the target layout positions of the multiple sensors in the target area, wherein the objective function is used to calculate the fitness of the multiple sensors at different layout positions, and the fitness of the multiple sensors at the target layout position is the largest;

[0036] Step S206: performing uncertainty analysis on the target area according to the multiple target layout positions, and if it is determined that the uncertainty analysis passes, deploying the multiple sensors in the target area according to the multiple target layout positions.

[0037] Through the above steps, firstly, a plurality of sensors are randomly arranged in the target area, and these sensors are used to perform environmental monitoring on the target area; with the goal of maximizing the objective function, the arrangement positions of these sensors are adjusted through the optimization algorithm until the target arrangement positions of these sensors are found in the target area, wherein the objective function is used to calculate the fitness of these sensors at different arrangement positions, and the fitness of these sensors at the target arrangement position is the largest; uncertainty analysis is performed on the target area according to the plurality of target arrangement positions, and after determining that the uncertainty analysis has passed, the plurality of sensors are deployed in the target area according to the plurality of target arrangement positions; by adopting the above scheme, an optimal deployment strategy for monitoring components based on the optimization algorithm is proposed, which maximizes the monitoring effect while minimizing the deployment cost; thereby solving the problem that the traditional monitoring strategy in the related technology does not fully consider the optimal layout of sensor deployment, which easily leads to incomplete monitoring range, sensor redundancy or inaccurate data.

[0038] In an exemplary embodiment, with the goal of maximizing the objective function, the layout positions of the multiple sensors are adjusted by an optimization algorithm to search for the target layout positions of the multiple sensors in the target area, including: iteratively updating the layout positions of the multiple sensors in the target area by a genetic algorithm, determining a first layout position of each sensor, and obtaining multiple first layout positions, wherein a first fitness corresponding to the multiple first layout positions is the largest among multiple second fitness corresponding to multiple groups of second layout positions, each group of second layout positions includes multiple second layout positions, the multiple second layout positions correspond one-to-one to the multiple sensors, and the multiple groups of second layout positions are the layout positions of the multiple sensors in multiple iterative updates; performing multiple local optimizations on the multiple first layout positions by a particle swarm optimization algorithm to obtain multiple target layout positions, wherein the multiple target layout positions correspond one-to-one to the multiple sensors, and the third fitness corresponding to the multiple target layout positions is greater than or equal to the first fitness.

[0039] The layout positions of multiple sensors are iteratively updated through a genetic algorithm. After each iterative update, the corresponding second fitness value is calculated through an objective function. Finally, after the iteration is completed, the first fitness value with the largest fitness value and the corresponding multiple first layout positions are determined to complete the global search of the layout positions; then, a particle swarm optimization algorithm is used to perform local optimization, determine the multiple target layout positions, and find the global optimal solution.

[0040] It should be noted that the genetic algorithm (GA) is used for discrete optimization of the number and layout of sensors. GA gradually optimizes the placement of sensors through selection, crossover and compilation operations to ensure the optimal monitoring effect under specific conditions. The particle swarm optimization algorithm (PSO) is used to optimize the location of sensors in a continuous search space. PSO simulates the foraging behavior of bird flocks and gradually adjusts the speed and position of particles (i.e., sensor locations) to find the global optimal solution, i.e., the above-mentioned multiple target placement locations.

[0041] Based on the above steps, the layout positions of the multiple sensors are iteratively updated in the target area through a genetic algorithm to determine the first layout position of each sensor, and obtain multiple first layout positions, including: a transformation step: calculating the fourth fitness corresponding to the current layout positions of the multiple sensors through the objective function, performing a position transformation operation on the current layout positions of the multiple sensors according to the fourth fitness, to obtain multiple second layout positions, wherein the position transformation operation includes: a selection operation, a crossover operation, and a compilation operation; looping the transformation step for a preset number of times to obtain the multiple groups of second layout positions, respectively calculating the multiple second fitness corresponding to the multiple groups of second layout positions, and determining the multiple first layout positions from the multiple groups of second layout positions according to the multiple second fitness.

[0042] The processing flow of the GA algorithm includes:

[0043] 1. Initialize the population: Randomly generate a set of solutions as the initial "population". Each solution is usually represented as a "chromosome", which consists of a series of "genes" (variable values).

[0044] 2. Evaluate fitness: Use the fitness function (i.e., objective function) to evaluate the quality of each chromosome in the population. The value of the fitness function usually reflects the performance of the chromosome in solving the problem.

[0045] 3. Selection: According to the fitness function value, some chromosomes are selected for genetic operation. The selection method can be roulette selection, tournament selection, etc., the purpose is to give better chromosomes a greater chance of being selected.

[0046] 4. Crossover: Pair the selected chromosomes according to certain rules, and then exchange them at certain positions of the chromosomes to produce new chromosomes. The crossover operation simulates the sexual reproduction process in biological evolution and helps to create better solutions.

[0047] 5. Mutation: Randomly change certain genes in the chromosome with a lower probability to introduce new solution space and prevent the algorithm from converging to the local optimal solution too early.

[0048] 6. Formation of a new population: After selection, crossover and mutation operations, a new generation of population is formed to replace the old population.

[0049] 7. Iterative optimization: Repeat the above process until the predetermined number of iterations (i.e. the preset number mentioned above) is reached or the fitness function value meets the termination condition. Usually, the termination condition is when a sufficiently good solution is found or the improvement of the solution is no longer obvious.

[0050] 8. Result output: Select the best solution from the final population as the solution to the problem.

[0051] An important feature of genetic algorithm is that it can handle continuous and discrete variables, and can automatically adjust the search direction during the search process, so it is very effective in solving complex multi-objective optimization problems. In the optimal deployment strategy of the corrosion environment monitoring components of the offshore booster station transformer, GA is used to deal with the discrete optimization problem of sensor layout, and a good initial solution set is obtained through global search, which provides a basis for subsequent local optimization.

[0052] Optionally, uncertainty analysis is performed on the target area based on the multiple target layout positions, including: determining multiple environmental parameters that have an impact on the multiple sensors; generating multiple groups of environmental samples based on the multiple target layout positions and the multiple environmental parameters, wherein the multiple groups of environmental samples are used to indicate different environmental conditions; and performing robustness testing and risk assessment on the multiple sensors based on the multiple groups of environmental samples to obtain test results and risk assessment results.

[0053] In the embodiment of the present application, the Monte Carlo simulation (MCS) method is used to complete the uncertainty analysis. Through this method, the various environmental parameters that may affect the sensor are first determined, and then various environmental changes and uncertainty factors (such as corrosion rate, changes in external environmental conditions) are simulated, and the robustness and risk of the optimization results are evaluated to determine whether the solution is still valid under various possible situations.

[0054] Optionally, after performing robustness testing and risk assessment on the multiple sensors according to the multiple groups of environmental samples and obtaining the test results and risk assessment results, the method further includes: determining whether the multiple target deployment positions pass the uncertainty analysis according to the test results and the risk assessment results; if it is determined that the multiple target deployment positions pass the uncertainty analysis, deploying the multiple sensors in the target area according to the multiple target deployment positions; if it is determined that the multiple sensors fail to pass the uncertainty analysis, adjusting the parameters of the optimization algorithm according to the test results and the risk assessment results, wherein the optimization algorithms include: genetic algorithm, particle swarm optimization algorithm.

[0055] According to the analysis results of MCS, if it is determined that the deployment strategy has poor robustness or high risk, the deployment strategy will not be adopted. The parameters of GA and PSO will be adjusted in combination with the analysis results to re-plan the deployment strategy and further optimize the quality of the solution until an optimal sensor deployment strategy is formed.

[0056] Through the above embodiments, the deployment strategy of the offshore booster station transformer corrosion environment monitoring components is global, can quickly converge to the optimal solution, and can effectively deal with uncertainty, thereby achieving the purpose of maximizing monitoring effects and minimizing costs.

[0057] Optionally, the objective function includes: Among them, α and β are weight coefficients, (x i ,y i ) is the layout position of the ith sensor, n is the number of the multiple sensors, CR is the monitoring coverage of the target area by the sensor at the target layout position, and C is the deployment cost of the sensor at the target layout position.

[0058] Sensor layout model: Assume that the point set of n sensors is S = {s1, s2, ..., s n}, the coordinates of each point are (xi,yi), and the goal is to find a set of S * , so that: Maximum, where α and β are weight coefficients, adjusting the importance of monitoring coverage and deployment cost, where CR represents the monitoring coverage of the sensor to the target area, C represents the deployment cost, including equipment procurement, installation, maintenance, and data transmission costs. C = C 设备 +C 安装 +C 维护 +C 数据传输 .

[0059] In an optional embodiment, the present application embodiment provides an optimization algorithm-based deployment strategy for optimizing the corrosion environment monitoring components of offshore booster station transformers, the process of which is as follows: Figure 3 As shown, the following steps are included:

[0060] Step 1, initialization: Use GA to generate the initial population, randomly set the initial sensor position and number, and form a diversified solution set through iterative selection, crossover, mutation and other operations.

[0061] Step 2: Global search: During the GA optimization process, individuals with higher fitness (i.e., better solutions) are selected to search for possible sensor layouts and quantities in a wide search space.

[0062] In the initial stage of the problem, a genetic algorithm (GA) is used for global search. GA can handle different types of variables (continuous or discrete) and generate a diverse population of initial solutions. Through selection, crossover, and mutation operations, GA can effectively explore the search space and find a good initial solution set.

[0063] Step 3: Local optimization: Based on the optimal solution found by GA, PSO is used for local optimization. The particle update rule of PSO is further refined to arrange the sensors in the optimal position.

[0064] After GA obtains the initial solution, PSO is used for local optimization. PSO can quickly converge to a better solution, further optimize near the initial solution, and find a more accurate solution. The fast convergence characteristics of PSO help speed up the optimization process and ensure that it does not fall into the local optimum.

[0065] Step 4: Uncertainty analysis: Use MCS to simulate and evaluate the results of PSO optimization under different random scenarios to determine the robustness and risk of the deployment strategy.

[0066] After obtaining the optimal solution, Monte Carlo simulation is used for uncertainty analysis. MCS can simulate a variety of environmental changes and uncertain factors (such as corrosion rate, changes in external environmental conditions), evaluate the robustness and risk of the optimization results, and ensure that the solution is still valid under various possible situations.

[0067] Step 5, iteration and improvement: According to the analysis results of MCS, the parameters of GA and PSO can be adjusted to further optimize the quality of the solution and form an optimal sensor deployment strategy.

[0068] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0069] In this embodiment, a sensor deployment device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0070] Figure 4 : is a structural block diagram of a sensor deployment device according to an embodiment of the present application, the device comprising:

[0071] A deployment module 42, configured to randomly deploy a plurality of sensors in a target area, wherein the plurality of sensors are used to perform environmental monitoring on the target area;

[0072] A search module 44 is used to adjust the layout positions of the multiple sensors by an optimization algorithm with the goal of maximizing an objective function, so as to search for the target layout positions of the multiple sensors in the target area, wherein the objective function is used to calculate the fitness of the multiple sensors at the target layout positions, and the fitness of the multiple sensors at the target layout positions is the largest;

[0073] The deployment module 46 is used to perform uncertainty analysis on the target area according to the multiple target layout positions, and deploy the multiple sensors in the target area according to the multiple target layout positions when it is determined that the uncertainty analysis passes.

[0074] Through the above device, firstly, a plurality of sensors are randomly arranged in the target area, and these sensors are used to perform environmental monitoring on the target area; with the goal of maximizing the objective function, the arrangement positions of these sensors are adjusted through the optimization algorithm until the target arrangement positions of these sensors are found in the target area, wherein the objective function is used to calculate the fitness of these sensors at different arrangement positions, and the fitness of these sensors at the target arrangement position is the largest; uncertainty analysis is performed on the target area according to the plurality of target arrangement positions, and after it is determined that the uncertainty analysis has passed, the plurality of sensors are deployed in the target area according to the plurality of target arrangement positions; by adopting the above scheme, an optimal deployment strategy for monitoring components based on the optimization algorithm is proposed, which maximizes the monitoring effect while minimizing the deployment cost; thereby solving the problem that the traditional monitoring strategy in the related technology does not fully consider the optimal layout of sensor deployment, which easily leads to incomplete monitoring range, sensor redundancy or inaccurate data.

[0075] Optionally, the above-mentioned search module 44 is also used to iteratively update the layout positions of the multiple sensors in the target area through a genetic algorithm, determine the first layout position of each sensor, and obtain multiple first layout positions, wherein the first fitness corresponding to the multiple first layout positions is the largest among the multiple second fitness corresponding to the multiple groups of second layout positions, each group of second layout positions includes multiple second layout positions, and the multiple second layout positions correspond one-to-one to the multiple sensors, and the multiple groups of second layout positions are the layout positions of the multiple sensors in multiple iterative updates; the multiple first layout positions are locally optimized multiple times through a particle swarm optimization algorithm to obtain multiple target layout positions, wherein the multiple target layout positions correspond one-to-one to the multiple sensors, and the third fitness corresponding to the multiple target layout positions is greater than or equal to the first fitness.

[0076] Optionally, the above-mentioned search module 44 is also used to loop the transformation step for a preset number of times to obtain the multiple groups of second layout positions, respectively calculate the multiple second fitnesses corresponding to the multiple groups of second layout positions, and determine the multiple first layout positions from the multiple groups of second layout positions according to the multiple second fitnesses, wherein the transformation step includes: calculating the fourth fitnesses corresponding to the current layout positions of the multiple sensors through the objective function, performing a position transformation operation on the current layout positions of the multiple sensors according to the fourth fitness to obtain multiple second layout positions, wherein the position transformation operation includes: a selection operation, a crossover operation, and a compilation operation.

[0077] Optionally, the above-mentioned deployment module 46 is also used to determine multiple environmental parameters that affect the multiple sensors; generate multiple groups of environmental samples according to the multiple target deployment positions and the multiple environmental parameters, wherein the multiple groups of environmental samples are used to indicate different environmental conditions; perform robustness testing and risk assessment on the multiple sensors according to the multiple groups of environmental samples to obtain test results and risk assessment results.

[0078] Optionally, the above-mentioned deployment module 46 is also used to determine whether the multiple target layout positions pass the uncertainty analysis based on the test results and the risk assessment results; if it is determined that the multiple target layout positions pass the uncertainty analysis, the multiple sensors are deployed in the target area according to the multiple target layout positions; if it is determined that the uncertainty analysis does not pass, the parameters of the optimization algorithm are adjusted according to the test results and the risk assessment results, wherein the optimization algorithm includes: genetic algorithm, particle swarm optimization algorithm.

[0079] Optionally, the objective function includes: Among them, α and β are weight coefficients, (x i ,y i ) is the layout position of the ith sensor, n is the number of the multiple sensors, CR is the monitoring coverage of the target area by the sensor at the target layout position, and C is the deployment cost of the sensor at the target layout position.

[0080] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0081] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0082] S1, randomly deploying a plurality of sensors in a target area, wherein the plurality of sensors are used to perform environmental monitoring on the target area;

[0083] S2, adjusting the layout positions of the multiple sensors by an optimization algorithm with the goal of maximizing an objective function, so as to search for target layout positions of the multiple sensors in the target area, wherein the objective function is used to calculate the fitness of the multiple sensors at different layout positions, and the fitness of the multiple sensors at the target layout position is the largest;

[0084] S3, performing uncertainty analysis on the target area according to the multiple target layout positions, and if it is determined that the uncertainty analysis passes, deploying the multiple sensors in the target area according to the multiple target layout positions.

[0085] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0086] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0087] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0088] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0089] S1, randomly deploying a plurality of sensors in a target area, wherein the plurality of sensors are used to perform environmental monitoring on the target area;

[0090] S2, adjusting the layout positions of the multiple sensors by an optimization algorithm with the goal of maximizing an objective function, so as to search for target layout positions of the multiple sensors in the target area, wherein the objective function is used to calculate the fitness of the multiple sensors at different layout positions, and the fitness of the multiple sensors at the target layout position is the largest;

[0091] S3, performing uncertainty analysis on the target area according to the multiple target layout positions, and if it is determined that the uncertainty analysis passes, deploying the multiple sensors in the target area according to the multiple target layout positions.

[0092] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0093] An embodiment of the present application further provides a computer program product, comprising a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program product, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0094] Optionally, in this embodiment, the above computer program may be configured to implement the following steps when executed by a processor:

[0095] S1, randomly deploying a plurality of sensors in a target area, wherein the plurality of sensors are used to perform environmental monitoring on the target area;

[0096] S2, adjusting the layout positions of the multiple sensors by an optimization algorithm with the goal of maximizing an objective function, so as to search for target layout positions of the multiple sensors in the target area, wherein the objective function is used to calculate the fitness of the multiple sensors at different layout positions, and the fitness of the multiple sensors at the target layout position is the largest;

[0097] S3, performing uncertainty analysis on the target area according to the multiple target layout positions, and if it is determined that the uncertainty analysis passes, deploying the multiple sensors in the target area according to the multiple target layout positions.

[0098] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0099] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0100] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for deploying a sensor, characterized in that: include: A plurality of sensors are randomly arranged in a target area, wherein the plurality of sensors are used to perform environmental monitoring on the target area; Taking maximizing the objective function as the goal, adjusting the layout positions of the multiple sensors through an optimization algorithm to search for the target layout positions of the multiple sensors in the target area, wherein the objective function is used to calculate the fitness of the multiple sensors at different layout positions, and the fitness of the multiple sensors at the target layout position is the largest; An uncertainty analysis is performed on the target area according to the multiple target layout positions, and if it is determined that the uncertainty analysis passes, the multiple sensors are deployed in the target area according to the multiple target layout positions.

2. The method according to claim 1, characterized in that The method of adjusting the layout positions of the multiple sensors by an optimization algorithm with the goal of maximizing the objective function, so as to search for the target layout positions of the multiple sensors in the target area, includes: Iteratively updating the layout positions of the multiple sensors in the target area by a genetic algorithm, determining a first layout position of each sensor, and obtaining multiple first layout positions, wherein a first fitness corresponding to the multiple first layout positions is the largest among multiple second fitness corresponding to multiple groups of second layout positions, each group of second layout positions includes multiple second layout positions, the multiple second layout positions correspond to the multiple sensors one-to-one, and the multiple groups of second layout positions are the layout positions of the multiple sensors in multiple iterative updates; The multiple first layout positions are locally optimized multiple times by using a particle swarm optimization algorithm to obtain multiple target layout positions, wherein the multiple target layout positions correspond one-to-one to the multiple sensors, and the third fitness corresponding to the multiple target layout positions is greater than or equal to the first fitness.

3. The method according to claim 2, characterized in that Iteratively updating the layout positions of the multiple sensors in the target area by using a genetic algorithm to determine a first layout position of each sensor, and obtaining multiple first layout positions, including: Transformation step: calculating the fourth fitness corresponding to the current layout positions of the multiple sensors by the objective function, performing a position transformation operation on the current layout positions of the multiple sensors according to the fourth fitness, to obtain a plurality of second layout positions, wherein the position transformation operation includes: a selection operation, a crossover operation, and a compilation operation; The transformation step is executed repeatedly for a preset number of times to obtain the multiple groups of second layout positions, the multiple second fitnesses corresponding to the multiple groups of second layout positions are calculated respectively, and the multiple first layout positions are determined from the multiple groups of second layout positions according to the multiple second fitnesses.

4. The method according to claim 1, characterized in that: Performing uncertainty analysis on the target area according to the plurality of target layout positions includes: determining a plurality of environmental parameters having an impact on the plurality of sensors; Generating a plurality of sets of environmental samples according to the plurality of target deployment locations and the plurality of environmental parameters, wherein the plurality of sets of environmental samples are used to indicate different environmental conditions; Robustness testing and risk assessment are performed on the multiple sensors according to the multiple groups of environmental samples to obtain test results and risk assessment results.

5. The method according to claim 4, characterized in that After performing robustness testing and risk assessment on the multiple sensors according to the multiple groups of environmental samples and obtaining test results and risk assessment results, the method further includes: Determining whether the plurality of target deployment locations pass the uncertainty analysis according to the test results and the risk assessment results; When it is determined that the uncertainty analysis is passed, deploying the plurality of sensors in the target area according to the plurality of target deployment positions; When it is determined that the uncertainty analysis fails, the parameters of the optimization algorithm are adjusted according to the test result and the risk assessment result, wherein the optimization algorithm includes: a genetic algorithm and a particle swarm optimization algorithm.

6. The method according to claim 1, characterized in that The objective function includes: Among them, α and β are weight coefficients, (x i ,y i ) is the layout position of the ith sensor, n is the number of the multiple sensors, CR is the monitoring coverage of the target area by the sensor at the target layout position, and C is the deployment cost of the sensor at the target layout position.

7. A sensor deployment device, characterized in that: include: A deployment module, used for randomly deploying a plurality of sensors in a target area, wherein the plurality of sensors are used for performing environmental monitoring on the target area; A search module, configured to adjust the layout positions of the multiple sensors by an optimization algorithm with the goal of maximizing an objective function, so as to search for the target layout positions of the multiple sensors in the target area, wherein the objective function is used to calculate the fitness of the multiple sensors at the target layout positions, and the fitness of the multiple sensors at the target layout positions is the largest; A deployment module is used to perform uncertainty analysis on the target area according to the multiple target layout positions, and if it is determined that the uncertainty analysis passes, deploy the multiple sensors in the target area according to the multiple target layout positions.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

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

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