Method, device and equipment for evaluating comprehensive performance of wireless sensor network

Abstract

The invention relates to a wireless sensor network comprehensive performance evaluation method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring network coverage data of a wireless sensor network of a target three-dimensional deployable geographic area in an electromagnetic environment; acquiring network data packet receiving success data of the wireless sensor network; acquiring network service data of the wireless sensor network in the target three-dimensional deployable geographic area; establishing a comprehensive performance evaluation model of the wireless sensor network in the electromagnetic environment according to the network coverage data, the network data packet receiving success data and the network service data, and solving the comprehensive performance evaluation model of the wireless sensor network in the electromagnetic environment by combining a particle swarm with a grey wolf algorithm. And outputting a comprehensive performance evaluation result of the wireless sensor network. By adopting the method, the performance indexes such as the network coverage rate, the data packet receiving success rate and the service cost can be comprehensively considered, and the optimal deployment of the wireless sensor network in the power environment is realized.

Description

Technical Field

[0001] The present application relates to the technical field of network performance evaluation, and in particular to a method, device, equipment, storage medium and computer program product for comprehensive performance evaluation of a wireless sensor network. Background Art

[0002] There are electromagnetic waves generated by power equipment such as switch cabinets and transformers in the power system, forming a complex electromagnetic field. In this environment, electronic information equipment, especially equipment with communication, control and information processing functions, will be unstable or fail due to electromagnetic interference, which will have a serious impact on the stable operation of the power environment monitoring system, and will cause the stability and reliability of the system's perception and communication to fluctuate, or even fail, and fail to achieve effective monitoring of the power system. In order to achieve all-round monitoring of the electromagnetic environment of the substation, the wireless sensor network should meet two requirements: the perception coverage requirement of the target monitoring area and the communication requirement between wireless sensor nodes. In addition, the cost of wireless sensor networks is an important factor restricting their large-scale and widespread application, specifically referring to the cost required for wireless sensor networks to provide monitoring services. Therefore, when deploying wireless sensor networks in the power environment, it is necessary to analyze the impact of electromagnetic interference generated by power equipment on the wireless sensor network and achieve the optimal deployment of wireless sensor networks in the power environment.

[0003] At present, the traditional comprehensive performance evaluation method of wireless sensor networks is based on a two-dimensional deployment area and the performance evaluation index is relatively single. It does not fully consider the electromagnetic interference of the electromagnetic environment, perception coverage requirements, communication requirements, and network services. Therefore, how to solve the above problems and realize a comprehensive performance evaluation method of wireless sensor networks that considers the electromagnetic environment, perception coverage requirements, communication requirements, and wireless sensor networks is a technical problem that needs to be solved urgently. Summary of the invention

[0004] Based on this, it is necessary to provide a wireless sensor network comprehensive performance evaluation method, device, equipment, storage medium and computer program product to address the above technical problems, which can comprehensively consider performance indicators such as network coverage data, data packet reception success data and network service data, and achieve the optimal deployment of wireless sensor networks in power environments.

[0005] In a first aspect, the present application provides a method for evaluating the comprehensive performance of a wireless sensor network, the method comprising:

[0006] Acquire network coverage data of wireless sensor networks in a target three-dimensional deployable geographic area under an electromagnetic environment;

[0007] Acquire network data packet reception success data of the wireless sensor network;

[0008] Obtain the network service data of the wireless sensor network within the target three-dimensional deployable geographical area;

[0009] According to the network coverage data, the network data packet reception success data, and the network service data, establish a comprehensive performance evaluation model for the wireless sensor network in the electromagnetic environment, and solve the comprehensive performance evaluation model for the wireless sensor network in the electromagnetic environment through the particle swarm combined with the grey wolf algorithm, and output the comprehensive performance evaluation result of the wireless sensor network;

[0010] Among them, the comprehensive performance evaluation model for the wireless sensor network in the electromagnetic environment includes an objective function with the optimization objective of maximizing the network coverage data, maximizing the network data packet reception success data, and minimizing the network service data.

[0011] In one embodiment, the obtaining of the network coverage data of the wireless sensor network in the target three-dimensional deployable geographical area in the electromagnetic environment includes:

[0012] Discretize the target three-dimensional deployable geographical area into multiple grid points;

[0013] Calculate the coverage probability of each grid point by the nodes of the wireless sensor network;

[0014] Obtain the environmental electromagnetic interference factor at each node position, and calculate the coverage probability distribution matrix of each layer of grid points in the target three-dimensional deployable geographical area according to the environmental electromagnetic interference factor at each node position and the coverage probability of each grid point by the nodes of the wireless sensor network;

[0015] Obtain the probability distribution matrix of all grid points being covered in the target three-dimensional deployable geographical area according to the coverage probability distribution matrix of each layer of grid points in the target three-dimensional deployable geographical area, and calculate the network coverage rate of the wireless sensor network in the target three-dimensional deployable geographical area in the electromagnetic environment according to the probability distribution matrix of all grid points being covered in the target three-dimensional deployable geographical area.

[0016] In one embodiment, the obtaining of the environmental electromagnetic interference factor at each node position includes:

[0017] Calculate the magnetic induction intensity at each node position according to the Biot-Savart law;

[0018] Normalize the magnetic induction intensity at all node positions to obtain the environmental electromagnetic interference factor at each node position.

[0019] In one embodiment, the obtaining of the network data packet reception success data of the wireless sensor network includes:

[0020] Calculate the path loss exponent at the location of each node in the wireless sensor network;

[0021] Calculate the signal-to-noise ratio (SNR) of the communication path between each node and at least one neighbor node according to the SNR model of path loss and shadow fading and the path loss exponent at the location of each node;

[0022] Calculate the network packet reception success rate of the wireless sensor network according to the SNR of the communication path between each node and at least one neighbor node and the signal modulation mode of the wireless sensor network.

[0023] In one embodiment, the obtaining the network service data of the wireless sensor network in the target three-dimensional deployable geographical area includes:

[0024] Calculate the deployment cost and monitoring cost of the wireless sensor network in the target three-dimensional deployable geographical area;

[0025] Calculate the network service cost of the wireless sensor network in the target three-dimensional deployable geographical area according to the deployment cost and the monitoring cost.

[0026] In one embodiment, the solving the comprehensive performance evaluation model of the wireless sensor network in the electromagnetic environment by using the particle swarm optimization combined with the grey wolf algorithm includes:

[0027] Optimize the grey wolf algorithm by using the particle swarm optimization algorithm to obtain the particle swarm optimization combined with the grey wolf algorithm;

[0028] Mix the search strategies of the particle swarm optimization algorithm and the grey wolf algorithm by using the particle swarm optimization combined with the grey wolf algorithm to update the positions and velocities of each particle and grey wolf, and simultaneously search for the optimal solution of the comprehensive performance evaluation function of the wireless sensor network in the electromagnetic environment by using global search and local search.

[0029] In a second aspect, the present application further provides a device for evaluating the comprehensive performance of a wireless sensor network. The device includes:

[0030] A first calculation module, configured to obtain the network coverage data of the wireless sensor network in the target three-dimensional deployable geographical area in the electromagnetic environment;

[0031] A second calculation module, configured to obtain the network packet reception success data of the wireless sensor network;

[0032] A third calculation module, configured to obtain the network service data of the wireless sensor network in the target three-dimensional deployable geographical area;

[0033] A comprehensive performance evaluation module, which is used to establish a comprehensive performance evaluation model of a wireless sensor network in an electromagnetic environment according to the network coverage data, the network data packet reception success data, and the network service data, solve the comprehensive performance evaluation model of the wireless sensor network in the electromagnetic environment through a particle swarm combined with a gray wolf algorithm, and output a comprehensive performance evaluation result of the wireless sensor network;

[0034] Among them, the comprehensive performance evaluation model of the wireless sensor network in the electromagnetic environment includes an objective function with the optimization objectives of maximizing the network coverage data, maximizing the network data packet reception success data, and minimizing the network service data.

[0035] In a third aspect, the present application also provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the following steps are implemented:

[0036] Obtain the network coverage data of a wireless sensor network in a target three-dimensional deployable geographical area in an electromagnetic environment;

[0037] Obtain the network data packet reception success data of the wireless sensor network;

[0038] Obtain the network service data of the wireless sensor network in the target three-dimensional deployable geographical area;

[0039] According to the network coverage data, the network data packet reception success data, and the network service data, establish a comprehensive performance evaluation model of a wireless sensor network in an electromagnetic environment, solve the comprehensive performance evaluation model of the wireless sensor network in the electromagnetic environment through a particle swarm combined with a gray wolf algorithm, and output a comprehensive performance evaluation result of the wireless sensor network;

[0040] Among them, the comprehensive performance evaluation model of the wireless sensor network in the electromagnetic environment includes an objective function with the optimization objectives of maximizing the network coverage data, maximizing the network data packet reception success data, and minimizing the network service data.

[0041] In a fourth aspect, the present application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0042] Obtain the network coverage data of a wireless sensor network in a target three-dimensional deployable geographical area in an electromagnetic environment;

[0043] Obtain the network data packet reception success data of the wireless sensor network;

[0044] Obtain the network service data of the wireless sensor network within the target three-dimensional deployable geographical area;

[0045] According to the network coverage data, the network data packet reception success data, and the network service data, establish a comprehensive performance evaluation model for the wireless sensor network in the electromagnetic environment, solve the comprehensive performance evaluation model for the wireless sensor network in the electromagnetic environment through the particle swarm combined with the grey wolf algorithm, and output the comprehensive performance evaluation result of the wireless sensor network;

[0046] Among them, the comprehensive performance evaluation model for the wireless sensor network in the electromagnetic environment includes an objective function with the optimization goal of maximizing the network coverage data, maximizing the network data packet reception success data, and minimizing the network service data.

[0047] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0048] Obtain the network coverage data of the wireless sensor network in the target three-dimensional deployable geographical area in the electromagnetic environment;

[0049] Obtain the network data packet reception success data of the wireless sensor network;

[0050] Obtain the network service data of the wireless sensor network within the target three-dimensional deployable geographical area;

[0051] According to the network coverage data, the network data packet reception success data, and the network service data, establish a comprehensive performance evaluation model for the wireless sensor network in the electromagnetic environment, solve the comprehensive performance evaluation model for the wireless sensor network in the electromagnetic environment through the particle swarm combined with the grey wolf algorithm, and output the comprehensive performance evaluation result of the wireless sensor network;

[0052] Among them, the comprehensive performance evaluation model for the wireless sensor network in the electromagnetic environment includes an objective function with the optimization goal of maximizing the network coverage data, maximizing the network data packet reception success data, and minimizing the network service data.

[0053] The embodiments of the present application have the following beneficial effects:

[0054] The wireless sensor network comprehensive performance evaluation method, device, computer device, storage medium and computer program product provided by the embodiments of the present application can, based on three network performance evaluation indicators, namely network coverage data, network data packet reception success data and network service data, construct a comprehensive performance evaluation function for the wireless sensor network under the electromagnetic environment and use it as the objective function model while considering the electromagnetic interference in the electromagnetic environment, so as to convert the deployment problem of the wireless sensor network under the electromagnetic environment into an optimization problem. Moreover, by combining the particle swarm algorithm with the grey wolf algorithm to optimize and solve the optimal deployment positions of all nodes in the wireless sensor network, it has a fast convergence speed and strong ability to find the global extreme value, realizing the accurate and rapid comprehensive performance evaluation of the wireless sensor network. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a schematic flowchart of the wireless sensor network comprehensive performance evaluation method in one embodiment;

[0056] Figure 2 is a schematic plan view of the node probability perception coverage model in a complex electromagnetic environment in one embodiment;

[0057] Figure 3 is a schematic diagram of grid point division in one embodiment;

[0058] Figure 4 is a schematic diagram of the deployment of the wireless sensor network in a complex electromagnetic environment in one embodiment;

[0059] Figure 5 is a structural block diagram of the wireless sensor network comprehensive performance evaluation device in one embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] The wireless sensor network comprehensive performance evaluation method provided by the embodiments of the present application can be applied to a terminal or a server. The data storage system can store the data that the server needs to process. The data storage system can be integrated on the server, or placed in the cloud or other network servers. Among them, the terminal can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0062] Embodiment 1

[0063] In one embodiment, as Figure 1 shown, a method for comprehensively evaluating the performance of a wireless sensor network is provided, and the method includes:

[0064] S1. Obtain the network coverage data of the wireless sensor network in the target three-dimensional deployable geographical area under the electromagnetic environment;

[0065] S2. Obtain the network data packet reception success data of the wireless sensor network;

[0066] S3. Obtain the network service data of the wireless sensor network within the target three-dimensional deployable geographical area;

[0067] S4. Establish a comprehensive performance evaluation model for the wireless sensor network under the electromagnetic environment, solve the comprehensive performance evaluation model for the wireless sensor network under the electromagnetic environment through the particle swarm combined with the grey wolf algorithm, and output the comprehensive performance evaluation result of the wireless sensor network.

[0068] Among them, the comprehensive performance evaluation model for the wireless sensor network under the electromagnetic environment includes an objective function with the optimization objectives of maximizing the network coverage data, maximizing the network data packet reception success data, and minimizing the network service cost.

[0069] Specifically, since electromagnetic interference in the electromagnetic environment will affect the sensing range of sensor nodes, considering the electromagnetic interference of equipment in the power environment, first set the deployment parameters of the wireless sensor network and perform initialization, obtain the network coverage data of the wireless sensor network in the target three-dimensional deployable geographical area under the electromagnetic environment, then obtain the network packet reception success data of the wireless sensor network according to the signal modulation mode of the wireless sensor network, then obtain the network service data of the wireless sensor network in the target three-dimensional deployable geographical area, and finally establish a comprehensive performance evaluation model of the wireless sensor network under the electromagnetic environment by combining the network coverage data, network packet reception success data and network service data, and solve the comprehensive performance evaluation model of the wireless sensor network under the electromagnetic environment through the Particle Swarm Optimization-Grey Wolf Optimizer (PSO-GWO). By adopting such a technical solution, it is possible to construct a comprehensive performance evaluation model of the wireless sensor network under the electromagnetic environment based on three network performance evaluation indicators, namely network coverage data, network packet reception success data and network service data, and use it as the objective function model under the condition of considering electromagnetic interference in the electromagnetic environment, so as to convert the deployment problem of the wireless sensor network under the electromagnetic environment into an optimization problem, and then optimize and solve the optimal deployment positions of all nodes in the wireless sensor network through the particle swarm combined with the grey wolf algorithm, which has a fast convergence speed and strong ability to find the global extreme value, and realizes the accurate and rapid comprehensive performance evaluation of the wireless sensor network.

[0070] In one embodiment, S1 includes:

[0071] S11. Discretize the target three-dimensional deployable geographical area into multiple grid points;

[0072] S12. Calculate the coverage probability of each grid point by the nodes of the wireless sensor network;

[0073] S13. Obtain the environmental electromagnetic interference factor at each node position, and calculate the coverage probability distribution matrix of each layer of grid points in the target three-dimensional deployable geographical area according to the environmental electromagnetic interference factor at each node position and the coverage probability of each grid point by the nodes of the wireless sensor network;

[0074] S14. Obtain the probability distribution matrix of all grid points being covered in the target three-dimensional deployable geographical area according to the coverage probability distribution matrix of each layer of grid points in the target three-dimensional deployable geographical area, and calculate the network coverage rate of the wireless sensor network in the target three-dimensional deployable geographical area under the electromagnetic environment according to the probability distribution matrix of all grid points being covered in the target three-dimensional deployable geographical area.

[0075] Specifically, referring to Figure 2, assume that the number of nodes in the wireless sensor network deployed in the target three-dimensional deployable geographical area Area in the power distribution room environment is N, the position coordinates of each node are assumed to have been initialized and assigned, and the sensing radius of the node is R s , and its communication radius is R c , then the node set of the wireless sensor can be expressed as Equation (1):

[0076] S = {s1, s2,..., s N} (1)

[0077] In Equation (1), s i = s i {x i , y i , z i}(i = 1, 2,..., N) represents that the sensing range of the node s i is a sphere with (x i , y i , z i , z i ) as the center of the sphere and R s as the radius in the plane at the height of z

[0078] Exemplarily, referring to Figure 3 , the target three-dimensional deployable geographical area Area can be discretized into h×w×l grid points, and the distance between the grid point t(x t , y t , z t ) and the sensor node s i (x i , y i , z i ) is Equation (2):

[0079]

[0080] Since the electromagnetic interference in the complex electromagnetic environment will affect the sensing range of the sensor node, considering the equipment electromagnetic interference existing in the power environment, the node probability sensing coverage model P r (s, t) in the complex electromagnetic environment can be expressed as Equation (3):

[0081]

[0082] In Equation (3), P r (s, t) represents the probability that the grid point t(x t , y t , z t ) in the target three-dimensional deployable geographical area Area is covered by the wireless sensor node s i , and the parameter r e (0 < re <R s ) is the sensing distance error of the wireless sensor node.

[0083] To improve the measurement probability accuracy, multiple sensor nodes are used to measure the target simultaneously. Whether each grid point is covered is represented by the joint measurement probability of the node set as Equation (4):

[0084]

[0085] In Equation (4), t qmn represents the grid point at the q-th layer, m-th row, and n-th column, where q = 1, 2, ……, h; m = 1, 2, ……, w; n = 1, 2, ……, l. S C represents the set of wireless sensor nodes that measure the grid point t qmn .

[0086] The grid point matrix of the q-th layer of the target three-dimensional deployable geographical area Area is Equation (5):

[0087]

[0088] Its deployment priority matrix of the grid points in the q-th layer is represented as Equation (6):

[0089]

[0090] Substitute the environmental electromagnetic interference factor λ at all node positions into the calculation to obtain the probability distribution matrix of the grid points in the q-th layer of the target three-dimensional deployable geographical area Area being covered as Equation (7):

[0091]

[0092] Calculate the probability distribution matrix of the grid points in each layer being covered in turn, so as to obtain the probability distribution matrix P r (S C , T), substitute the elements in P r (S C , T) to obtain the coverage rate Cov(S, λ) of the network receiving end for the target three-dimensional deployable geographical area in the substation environment. Among them, the coverage rate Cov of the wireless sensor network for the target three-dimensional deployable geographical area is defined as the ratio of the coverage ranges of all wireless sensor nodes to the volume of the target three-dimensional deployable geographical area Area. T represents the set of divided grid points, and is represented by the joint measurement probability of the node set as Equation (8):

[0093]

[0094] Specifically, the network coverage data includes the network coverage rate. The target three-dimensional deployable geographical area can be discretized into multiple grid points, and the probability of each grid point being covered by the nodes of the wireless sensor network is calculated by dividing it into grid points. Multiple wireless sensor nodes can be used to measure the target grid point simultaneously to improve the measurement accuracy of the probability of each grid point being covered by the nodes of the wireless sensor network. Since the electromagnetic interference in a complex electromagnetic environment will affect the sensing range of the sensor nodes, considering the equipment electromagnetic interference in the power environment, the environmental electromagnetic interference factor at each node location can also be obtained, and the coverage probability distribution matrix of each layer of grid points in the target three-dimensional deployable geographical area is calculated according to the environmental electromagnetic interference factor at each node location and the probability of each grid point being covered by the nodes of the wireless sensor network. According to the coverage probability distribution matrix of each layer of grid points in the target three-dimensional deployable geographical area, the probability distribution matrix of all grid points being covered in the target three-dimensional deployable geographical area can be obtained, so that the network coverage rate of the wireless sensor network in the target three-dimensional deployable geographical area in a complex electromagnetic environment can be calculated. By adopting such a technical solution, the target three-dimensional deployable geographical area can be discretized into multiple grid points, and the coverage rate of each grid point in a complex electromagnetic environment is considered, so as to accurately calculate the network coverage rate of the wireless sensor network in the target three-dimensional deployable geographical area in a complex electromagnetic environment.

[0095] In one embodiment, S13 includes:

[0096] S131. Calculate the magnetic induction intensity at each node location according to the Biot-Savart law;

[0097] S132. Perform normalization processing on the magnetic induction intensities at all node locations to obtain the environmental electromagnetic interference factor at each node location.

[0098] Specifically, the magnetic induction intensity at each node location can be calculated by the Biot-Savart law, that is, B = {B1, B2, …, B N}. Where B i = B p (x i , y i , zi) represents the magnetic induction intensity at the location of the i-th node. Normalization processing of the magnetic induction intensities at all node locations can obtain the environmental electromagnetic interference factors λ = {λ1, λ2, …, λ N} at the locations of all wireless sensor nodes. Where λ i (i = 1, 2, ……, N) represents node S iThe environmental electromagnetic interference factor at the location. By adopting such a technical solution, the environmental electromagnetic interference factor at each node location can be calculated, so as to be conveniently substituted into the calculation to obtain the network coverage rate of the wireless sensor network for the target three-dimensional deployable geographical area considering the electromagnetic interference in a complex electromagnetic environment.

[0099] In one embodiment, S2 includes:

[0100] S21. Calculate the path loss exponent at the location of each node in the wireless sensor network;

[0101] S22. Calculate the signal-to-noise ratio of the communication path between each node and at least one neighbor node according to the signal-to-noise ratio model of path loss and shadow fading and the path loss exponent at the location of each node;

[0102] S23. Calculate the network data packet reception success rate of the wireless sensor network according to the signal-to-noise ratio of the communication path between each node and at least one neighbor node and the signal modulation mode of the wireless sensor network.

[0103] Specifically, the network data packet reception success data includes the network data packet reception success rate. Assume that the sensor node s deployed inside the target three-dimensional deployable geographical area Area i (x i , t i , z i ) has ε neighbor nodes, then the path loss exponent of the communication between the sensor node and its k-th neighbor node is Equation (9):

[0104]

[0105] In Equation (9), η i , η k are respectively the path loss exponents of the path between the node s i (x i , y i , z i ) and its k-th neighbor node location, and after rounding their coordinates as the index, they can be obtained from the matrix η, that is, Equation (10):

[0106]

[0107] In Equation (10), is the ceiling function, and the calculation method of η k is the same by analogy.

[0108] Exemplarily, in the signal modulation mode of the wireless sensor network, the Offset-Quadrature Phase Shift Keying (O-QPSK) mode is adopted, and the relationship between the Signal to Interference plus Noise Ratio (SNR) and the Packet success rate (PSR) of wireless communication is expressed by Equation (11):

[0109]

[0110] In Equation (11), B W is the noise bandwidth of the transceiver of the wireless sensor node, R b is the transmission rate of the wireless communication data packet, τ is the specification size of the wireless communication transmission data packet, and its unit is Byte. Q(x) is the right-tail function of the standard normal distribution with a mean of 0 and a standard deviation of 1, that is, Equation (12):

[0111]

[0112] The signal-to-noise ratio is defined as the ratio of the signal power to the noise power, which is Equation (13):

[0113]

[0114] When the signal-to-noise ratio is expressed in decibels, its form is as follows in Equation (14):

[0115]

[0116] Finally, the signal-to-noise ratio model considering path loss and shadow fading is obtained, as shown in the following Equation (15):

[0117]

[0118] Then, the signal-to-noise ratio of the communication path between the wireless sensor node s i (x i ,y i ,z i ) and its k-th neighbor node is expressed by Equation (16):

[0119]

[0120] In Equation (16), d ik represents the Euclidean distance between the wireless sensor node s i (x i ,y i ,z i ) and its k-th neighbor node, which is calculated by the following Equation (17):

[0121]

[0122] Wireless sensor node s i (x i , y i , z i ) can be expressed as Equation (18):

[0123]

[0124] The packet reception success rate of the wireless sensor network within the target three-dimensional deployable geographical area Area can be expressed as Equation (19):

[0125]

[0126] Specifically, the signal modulation mode of the wireless sensor network usually adopts the O-QPSK mode. According to the relationship between the signal-to-noise ratio SNR of wireless communication and the packet reception success rate, the packet reception success rate can be obtained from the signal-to-noise ratio. The received signal strength calculation is in the wireless sensor network deployment environment. The path loss, shadow fading, and multipath effects are superimposed together to reflect the received signal strength with respect to the path loss caused by distance, multipath effect, and shadow fading. The path loss describes the average decibel path loss, and then a log-normal random variable is added to reflect the random attenuation caused by shadow fading. Combining the expression of the ratio of signal power to noise power and the path loss, a signal-to-noise ratio model considering path loss and shadow fading is obtained. Finally, the signal-to-noise ratio is obtained, and thus the packet reception success rate of the wireless sensor network is obtained. By adopting such a technical solution, path loss can be considered, and the calculation accuracy of the packet reception success rate can be improved.

[0127] In one embodiment, S3 includes:

[0128] S31. Calculate the deployment cost and monitoring cost of the wireless sensor network within the target three-dimensional deployable geographical area;

[0129] S32. Calculate the network service cost of the wireless sensor network within the target three-dimensional deployable geographical area based on the deployment cost and monitoring cost.

[0130] Specifically, the network service data includes the network service cost. The network service cost mainly includes the deployment cost and the monitoring cost. Exemplarily, to obtain the deployment cost D(S) of the network service, it is assumed that the relationship between the installation cost of the wireless sensor node at different positions within the target three-dimensional deployable geographical area Area and the position of the wireless sensor node satisfies a Gaussian function, as shown in Equation (20):

[0131]

[0132] Equation (21):

[0133]

[0134] where (x i , y i , z i ) is the position coordinate of the wireless sensor node s i in the target three-dimensional deployable geographical area Area, and they are not correlated with each other; then the total deployment cost of the entire wireless sensor network in the target three-dimensional deployable geographical area Area is given by Equation (22):

[0135]

[0136] where C j represents the unit price of the j-th type of sensor node, and n j represents the number of the j-th type of sensor node.

[0137] To find the monitoring cost H(S) of the network service, assume that the set formed by the sensing areas of all sensor nodes is G = {g1, g2,..., g N}, that is, the i-th sensor node s i is responsible for providing monitoring services for all grid points t in its sensing area g i ; then the monitoring cost of the entire wireless sensor network in the target three-dimensional deployable geographical area Area can be expressed as Equation (23):

[0138]

[0139] where f i (t) represents the cost required for the i-th sensor node to monitor the events occurring at the grid point t in its sensing area g i , and is expressed as Equation (24):

[0140]

[0141] Also assume that the monitoring costs of a group of points at the same distance from the wireless sensor node s i are the same, that is, f i (t1) = f i (t2), where t1, t2 ∈ Area and ||s i - t1|| = ||s i - t2||.

[0142] Then the average service cost of the entire wireless sensor network in the target three-dimensional deployable geographical area Area is given by Equation (25):

[0143]

[0144] By adopting such a technical solution, the deployment cost and monitoring cost of the wireless sensor network in the target three-dimensional deployable geographical area can be calculated respectively, so as to obtain the network service cost of the wireless sensor network in the target three-dimensional deployable geographical area, which is convenient for subsequent comprehensive network performance evaluation in combination with the network service cost.

[0145] In one embodiment, S4 includes:

[0146] S41. Optimize the gray wolf algorithm through the particle swarm algorithm to obtain the particle swarm combined with the gray wolf algorithm;

[0147] S42. Update the positions and velocities of each particle and gray wolf by mixing the search strategies of the particle swarm algorithm combined with the gray wolf algorithm, the particle swarm algorithm, and the gray wolf algorithm, and at the same time search for the optimal solution of the comprehensive performance evaluation function of the wireless sensor network in the electromagnetic environment by using global search and local search.

[0148] Specifically, the established comprehensive performance evaluation function model of the wireless sensor network in the electromagnetic environment is as shown in Equation (26):

[0149]

[0150] And Equation (27):

[0151]

[0152] Among them, ω1, ω2, and ω3 respectively represent the weights corresponding to the network coverage rate, the packet reception success rate, and the network service cost, and different weights can be set according to the requirements of the actual application scenario. Con = 1 means that the network connectivity rate is 1, that is, the network is fully connected, and this is used as a constraint condition to ensure the connectivity of the deployed wireless sensor network. Refer to Figure 4 , after finally calculating the optimal solution of the comprehensive performance evaluation function of the wireless sensor network through the particle swarm combined with the gray wolf algorithm, the final deployment of the wireless sensor network in the complex electromagnetic environment can be obtained.

[0153] Specifically, the particle swarm combined with the grey wolf algorithm can combine the particle swarm algorithm and the grey wolf algorithm, utilize the search ability of the grey wolf algorithm to improve the development ability of the particle swarm algorithm, achieve a balance between local development and global exploration, and update the particle velocity using the moving direction vector of the grey wolf swarm. The algorithm process is as follows: First, randomly generate the particle swarm (position and velocity), and at the same time initialize the grey wolf population to determine the α, β, and δ wolves. Second, evaluate the fitness value corresponding to the particle, and update the historical best position of the particle swarm algorithm, the best position in the population, and the α, β, and δ wolves in the grey wolf algorithm according to the fitness value. The particles are updated according to the improved particle swarm algorithm velocity formula, integrating the leader position of the grey wolf algorithm. Or, execute the steps of the particle swarm algorithm and the grey wolf algorithm in stages, alternately optimize, and stop after reaching the maximum number of iterations or accuracy requirements to obtain the global optimal solution of the comprehensive performance evaluation function model of the wireless sensor network in the electromagnetic environment. By adopting such a technical solution, it is possible to optimize and solve the optimal deployment positions of all nodes in the wireless sensor network through the particle swarm combined with the grey wolf algorithm, with a relatively fast convergence speed, strong ability to find the global extreme value, and the ability to ensure the solution accuracy while improving the calculation speed.

[0154] In this embodiment, based on three network performance evaluation indicators, namely network coverage data, network data packet reception success data, and network service data, considering the electromagnetic interference in the complex electromagnetic environment, a comprehensive performance evaluation model of the wireless sensor network in the electromagnetic environment is constructed and used as the objective function model, thereby converting the deployment problem of the wireless sensor network in the electromagnetic environment into an optimization problem. Moreover, through the particle swarm combined with the grey wolf algorithm, the optimal deployment positions of all nodes in the wireless sensor network are optimized and solved, with a relatively fast convergence speed and strong ability to find the global extreme value, realizing the accurate and rapid comprehensive performance evaluation of the wireless sensor network.

[0155] It should be understood that although the steps in the flowcharts involved in the above-mentioned embodiments are sequentially shown according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same moment, but can be executed at different moments. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0156] Embodiment 2

[0157] Based on the same inventive concept, an embodiment of the present application further provides a comprehensive performance evaluation device for a wireless sensor network for implementing the above-mentioned comprehensive performance evaluation method for a wireless sensor network. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the comprehensive performance evaluation device for a wireless sensor network provided below can refer to the limitations on the comprehensive performance evaluation method for a wireless sensor network in the above text, and will not be repeated here.

[0158] In one embodiment, as Figure 5 shown, a comprehensive performance evaluation device for a wireless sensor network is provided, including:

[0159] A first acquisition module, configured to acquire the network coverage rate of a wireless sensor network in a target three-dimensional deployable geographical area in an electromagnetic environment;

[0160] A second acquisition module, configured to acquire the success rate of receiving network data packets of the wireless sensor network;

[0161] A third acquisition module, configured to acquire network service data of the wireless sensor network in the target three-dimensional deployable geographical area;

[0162] A comprehensive performance evaluation module, configured to establish a comprehensive performance evaluation model for a wireless sensor network in an electromagnetic environment according to the network coverage data, the network data packet reception success data, and the network service data, solve the comprehensive performance evaluation model for a wireless sensor network in the electromagnetic environment through a particle swarm combined with a grey wolf algorithm, and output a comprehensive performance evaluation result of the wireless sensor network;

[0163] Wherein, the comprehensive performance evaluation model for a wireless sensor network in the electromagnetic environment includes an objective function with the optimization objective of maximizing the network coverage rate, maximizing the success rate of receiving network data packets, and minimizing network service data.

[0164] Further, the first calculation module is used to discretize the target three-dimensional deployable geographical area into multiple grid points; and to calculate the coverage probability of each of the grid points by the nodes of the wireless sensor network; and is further used to obtain the environmental electromagnetic interference factor at each of the node positions, and calculate the coverage probability distribution matrix of the grid points in each layer of the target three-dimensional deployable geographical area according to the environmental electromagnetic interference factor at each of the node positions and the coverage probability of each of the grid points by the nodes of the wireless sensor network; and is further used to obtain the probability distribution matrix of all the grid points in the target three-dimensional deployable geographical area being covered according to the coverage probability distribution matrix of the grid points in each layer of the target three-dimensional deployable geographical area, and calculate the network coverage rate of the wireless sensor network in the target three-dimensional deployable geographical area in the electromagnetic environment according to the probability distribution matrix of all the grid points in the target three-dimensional deployable geographical area being covered.

[0165] Further, the first calculation module is also used to calculate the magnetic induction intensity at each of the node positions according to the Biot-Savart law; and to normalize the magnetic induction intensity at all the node positions to obtain the environmental electromagnetic interference factor at each of the node positions.

[0166] Further, the second calculation module is also used to calculate the path loss exponent at the position of each node of the wireless sensor network; and to calculate the signal-to-noise ratio of the communication path between each of the nodes and at least one neighbor node according to the signal-to-noise ratio model of path loss and shadow fading and the path loss exponent at the position of each of the nodes; and is further used to calculate the network packet reception success rate of the wireless sensor network according to the signal-to-noise ratio of the communication path between each of the nodes and at least one neighbor node and the signal modulation mode of the wireless sensor network.

[0167] Further, the third calculation module is also used to calculate the deployment cost and the monitoring cost of the wireless sensor network in the target three-dimensional deployable geographical area; and to calculate the network service cost of the wireless sensor network in the target three-dimensional deployable geographical area according to the deployment cost and the monitoring cost.

[0168] Further, the comprehensive performance evaluation module is also used to optimize the grey wolf algorithm through the particle swarm algorithm to obtain the particle swarm combined with the grey wolf algorithm; and to mix the search strategies of the particle swarm algorithm and the grey wolf algorithm through the particle swarm combined with the grey wolf algorithm, update the positions and velocities of each particle and grey wolf, and simultaneously search for the optimal solution of the comprehensive performance evaluation function of the wireless sensor network in the electromagnetic environment by using global search and local search.

[0169] Each module in the above-mentioned comprehensive performance evaluation device for wireless sensor networks can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in the processor of a computer device in hardware form or independent of the processor, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0170] Embodiment III

[0171] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0172] Obtain the network coverage data of the wireless sensor network in the target three-dimensional deployable geographical area under the electromagnetic environment;

[0173] Obtain the network data packet reception success data of the wireless sensor network;

[0174] Obtain the network service data of the wireless sensor network in the target three-dimensional deployable geographical area;

[0175] According to the network coverage data, the network data packet reception success data, and the network service data, establish a comprehensive performance evaluation model for the wireless sensor network under the electromagnetic environment, solve the comprehensive performance evaluation model for the wireless sensor network under the electromagnetic environment by combining the particle swarm optimization algorithm with the grey wolf algorithm, and output the comprehensive performance evaluation result of the wireless sensor network;

[0176] Among them, the comprehensive performance evaluation model for the wireless sensor network under the electromagnetic environment includes an objective function with the optimization goal of maximizing the network coverage data, maximizing the network data packet reception success data, and minimizing the network service data.

[0177] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0178] Discretize the target three-dimensional deployable geographical area into a plurality of grid points;

[0179] Calculate the coverage probability of each grid point by the nodes of the wireless sensor network;

[0180] Obtain the environmental electromagnetic interference factor at each node position, and calculate the coverage probability distribution matrix of each layer of grid points in the target three-dimensional deployable geographical area according to the environmental electromagnetic interference factor at each node position and the coverage probability of each grid point by the nodes of the wireless sensor network;

[0181] Obtain the probability distribution matrix of all grid points being covered in the target three-dimensional deployable geographical area according to the coverage probability distribution matrix of each grid point layer in the target three-dimensional deployable geographical area, and calculate the network coverage rate of the wireless sensor network in the target three-dimensional deployable geographical area under the electromagnetic environment according to the probability distribution matrix of all grid points being covered in the target three-dimensional deployable geographical area.

[0182] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0183] Calculate the magnetic induction intensity at each node position according to the Biot-Savart law;

[0184] Normalize the magnetic induction intensity at all node positions to obtain the environmental electromagnetic interference factor at each node position.

[0185] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0186] Calculate the path loss exponent at the location of each node in the wireless sensor network;

[0187] Calculate the signal-to-noise ratio of the communication path between each node and at least one neighbor node according to the signal-to-noise ratio model of path loss and shadow fading and the path loss exponent at the location of each node;

[0188] Calculate the network data packet reception success rate of the wireless sensor network according to the signal-to-noise ratio of the communication path between each node and at least one neighbor node and the signal modulation mode of the wireless sensor network.

[0189] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0190] Calculate the deployment cost and monitoring cost of the wireless sensor network in the target three-dimensional deployable geographical area;

[0191] Calculate the network service cost of the wireless sensor network in the target three-dimensional deployable geographical area according to the deployment cost and the monitoring cost.

[0192] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0193] Optimize the grey wolf algorithm through the particle swarm algorithm to obtain the particle swarm combined with the grey wolf algorithm;

[0194] By combining the particle swarm algorithm and the search strategy of the grey wolf algorithm, update the positions and velocities of each particle and grey wolf, and at the same time use global search and local search to search for the optimal solution of the comprehensive performance evaluation function of the wireless sensor network under the electromagnetic environment.

[0195] Example 4

[0196] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0197] Obtain the network coverage data of the wireless sensor network in the target three-dimensional deployable geographical area under the electromagnetic environment;

[0198] Obtain the network data packet reception success data of the wireless sensor network;

[0199] Obtain the network service data of the wireless sensor network in the target three-dimensional deployable geographical area;

[0200] According to the network coverage data, the network data packet reception success data, and the network service data, establish a comprehensive performance evaluation model of the wireless sensor network under the electromagnetic environment, and solve the comprehensive performance evaluation model of the wireless sensor network under the electromagnetic environment by combining the particle swarm algorithm and the grey wolf algorithm, and output the comprehensive performance evaluation result of the wireless sensor network;

[0201] Among them, the comprehensive performance evaluation model of the wireless sensor network under the electromagnetic environment includes an objective function with the optimization goal of maximizing the network coverage data, maximizing the network data packet reception success data, and minimizing the network service data.

[0202] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented:

[0203] Discretize the target three-dimensional deployable geographical area into multiple grid points;

[0204] Calculate the coverage probability of each grid point by the nodes of the wireless sensor network;

[0205] Obtain the environmental electromagnetic interference factor at each node position, and calculate the coverage probability distribution matrix of each layer of grid points in the target three-dimensional deployable geographical area according to the environmental electromagnetic interference factor at each node position and the coverage probability of each grid point by the nodes of the wireless sensor network;

[0206] Based on the coverage probability distribution matrix of each grid point layer in the target three-dimensional deployable geographical area, obtain the probability distribution matrix of all grid points being covered in the target three-dimensional deployable geographical area, and calculate the network coverage rate of the wireless sensor network in the target three-dimensional deployable geographical area under the electromagnetic environment according to the probability distribution matrix of all grid points being covered in the target three-dimensional deployable geographical area.

[0207] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0208] Calculate the magnetic induction intensity at each node position according to the Biot-Savart law;

[0209] Normalize the magnetic induction intensity at all node positions to obtain the environmental electromagnetic interference factor at each node position.

[0210] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0211] Calculate the path loss exponent at the location of each node in the wireless sensor network;

[0212] Calculate the signal-to-noise ratio of the communication path between each node and at least one neighbor node according to the signal-to-noise ratio model of path loss and shadow fading and the path loss exponent at the location of each node;

[0213] Calculate the network packet reception success rate of the wireless sensor network according to the signal-to-noise ratio of the communication path between each node and at least one neighbor node and the signal modulation mode of the wireless sensor network.

[0214] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0215] Calculate the deployment cost and monitoring cost of the wireless sensor network in the target three-dimensional deployable geographical area;

[0216] Calculate the network service cost of the wireless sensor network in the target three-dimensional deployable geographical area according to the deployment cost and the monitoring cost.

[0217] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0218] Optimize the gray wolf algorithm through the particle swarm algorithm to obtain the particle swarm combined with the gray wolf algorithm;

[0219] By combining the particle swarm algorithm and the search strategy of the grey wolf algorithm, the positions and velocities of each particle and grey wolf are updated, and the global search and local search are used to search for the optimal solution of the comprehensive performance evaluation function of the wireless sensor network under the electromagnetic environment.

[0220] Embodiment 5

[0221] In one embodiment, a computer program product is provided, including a computer program which, when executed by a processor, implements the following steps:

[0222] Obtain the network coverage data of the wireless sensor network in the target three-dimensional deployable geographical area under the electromagnetic environment;

[0223] Obtain the network data packet reception success data of the wireless sensor network;

[0224] Obtain the network service data of the wireless sensor network within the target three-dimensional deployable geographical area;

[0225] According to the network coverage data, the network data packet reception success data, and the network service data, establish a comprehensive performance evaluation model for the wireless sensor network under the electromagnetic environment, and solve the comprehensive performance evaluation model for the wireless sensor network under the electromagnetic environment by combining the particle swarm algorithm and the grey wolf algorithm, and output the comprehensive performance evaluation result of the wireless sensor network;

[0226] Wherein, the comprehensive performance evaluation model of the wireless sensor network under the electromagnetic environment includes an objective function with the optimization objectives of maximizing the network coverage data, maximizing the network data packet reception success data, and minimizing the network service data.

[0227] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented:

[0228] Discretize the target three-dimensional deployable geographical area into multiple grid points;

[0229] Calculate the coverage probability of each grid point by the nodes of the wireless sensor network;

[0230] Obtain the environmental electromagnetic interference factor at each node position, and calculate the coverage probability distribution matrix of each layer of grid points in the target three-dimensional deployable geographical area according to the environmental electromagnetic interference factor at each node position and the coverage probability of each grid point by the nodes of the wireless sensor network;

[0231] Obtain the probability distribution matrix of all grid points being covered in the target three-dimensional deployable geographical area according to the coverage probability distribution matrix of each layer of grid points in the target three-dimensional deployable geographical area, and calculate the network coverage rate of the wireless sensor network in the target three-dimensional deployable geographical area under the electromagnetic environment according to the probability distribution matrix of all grid points being covered in the target three-dimensional deployable geographical area.

[0232] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0233] Calculate the magnetic induction intensity at each node position according to the Biot-Savart law;

[0234] Normalize the magnetic induction intensity at all node positions to obtain the environmental electromagnetic interference factor at each node position.

[0235] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0236] Calculate the path loss exponent at the position where each node of the wireless sensor network is located;

[0237] Calculate the signal-to-noise ratio of the communication path between each node and at least one neighbor node according to the signal-to-noise ratio model of path loss and shadow fading and the path loss exponent at the position where each node is located;

[0238] Calculate the network packet reception success rate of the wireless sensor network according to the signal-to-noise ratio of the communication path between each node and at least one neighbor node and the signal modulation mode of the wireless sensor network.

[0239] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0240] Calculate the deployment cost and monitoring cost of the wireless sensor network in the target three-dimensional deployable geographical area;

[0241] Calculate the network service cost of the wireless sensor network in the target three-dimensional deployable geographical area according to the deployment cost and the monitoring cost.

[0242] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0243] Optimize the grey wolf algorithm through the particle swarm algorithm to obtain the particle swarm combined with the grey wolf algorithm;

[0244] By combining the particle swarm algorithm and the search strategy of the grey wolf algorithm, the positions and velocities of each particle and grey wolf are updated, and the optimal solution of the comprehensive performance evaluation function of the wireless sensor network in the complex electromagnetic environment is searched by using global search and local search.

[0245] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0246] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0247] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0248] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A comprehensive performance evaluation method for wireless sensor networks, characterized in that, The method includes: Obtaining network coverage data of a wireless sensor network in a target three-dimensional deployable geographical area under an electromagnetic environment; Obtaining network data packet reception success data of the wireless sensor network; Obtaining network service data of the wireless sensor network within the target three-dimensional deployable geographical area; According to the network coverage data, the network data packet reception success data, and the network service data, establishing a comprehensive performance evaluation model for the wireless sensor network under the electromagnetic environment, solving the comprehensive performance evaluation model for the wireless sensor network under the electromagnetic environment through a particle swarm combined with a grey wolf algorithm, and outputting a comprehensive performance evaluation result of the wireless sensor network; Wherein, the comprehensive performance evaluation model for the wireless sensor network under the electromagnetic environment includes an objective function with the optimization objectives of maximizing the network coverage data, maximizing the network data packet reception success data, and minimizing the network service data.

2. The method according to claim 1, characterized in that, The obtaining of the network coverage data of the wireless sensor network in the target three-dimensional deployable geographical area under the electromagnetic environment includes: Discretizing the target three-dimensional deployable geographical area into a plurality of grid points; Calculating the coverage probability of each grid point by the nodes of the wireless sensor network; Obtaining the environmental electromagnetic interference factor at each node position, and calculating the coverage probability distribution matrix of each layer of grid points in the target three-dimensional deployable geographical area according to the environmental electromagnetic interference factor at each node position and the coverage probability of each grid point by the nodes of the wireless sensor network; Obtaining the probability distribution matrix of all grid points being covered in the target three-dimensional deployable geographical area according to the coverage probability distribution matrix of each layer of grid points in the target three-dimensional deployable geographical area, and calculating the network coverage rate of the wireless sensor network in the target three-dimensional deployable geographical area under the electromagnetic environment according to the probability distribution matrix of all grid points being covered in the target three-dimensional deployable geographical area.

3. The method according to claim 2, wherein The obtaining of the environmental electromagnetic interference factor at each node position includes: Calculating the magnetic induction intensity at each node position according to the Biot-Savart law; Performing normalization processing on the magnetic induction intensities at all node positions to obtain the environmental electromagnetic interference factor at each node position.

4. The method according to claim 1, wherein The obtaining of the network data packet reception success data of the wireless sensor network includes: Calculating the path loss exponent at the location of each node of the wireless sensor network; Calculating the signal-to-noise ratio of the communication path between each node and at least one neighbor node according to the signal-to-noise ratio model of path loss and shadow fading and the path loss exponent at the location of each node; Calculating the network data packet reception success rate of the wireless sensor network according to the signal-to-noise ratio of the communication path between each node and at least one neighbor node and the signal modulation mode of the wireless sensor network.

5. The method according to claim 1, wherein The obtaining of the network service data of the wireless sensor network within the target three-dimensional deployable geographical area includes: Calculating the deployment cost and monitoring cost of the wireless sensor network within the target three-dimensional deployable geographical area; Calculating the network service cost of the wireless sensor network within the target three-dimensional deployable geographical area based on the deployment cost and the monitoring cost.

6. The method according to claim 1, wherein Solving the comprehensive performance evaluation model of the wireless sensor network in the electromagnetic environment by combining the particle swarm optimization algorithm with the grey wolf algorithm, including: Optimizing the grey wolf algorithm through the particle swarm optimization algorithm to obtain the particle swarm optimization algorithm combined with the grey wolf algorithm; Mixing the search strategies of the particle swarm optimization algorithm and the grey wolf algorithm by the particle swarm optimization algorithm combined with the grey wolf algorithm to update the positions and velocities of each particle and the grey wolf, and simultaneously searching for the optimal solution of the comprehensive performance evaluation function of the wireless sensor network in the electromagnetic environment by using global search and local search.

7. An integrated performance evaluation device for a wireless sensor network, characterized in that, The device includes: A first calculation module, configured to obtain the network coverage data of the wireless sensor network in the target three-dimensional deployable geographical area in the electromagnetic environment; A second calculation module, configured to obtain the network data packet reception success data of the wireless sensor network; A third calculation module, configured to obtain the network service data of the wireless sensor network within the target three-dimensional deployable geographical area; A comprehensive performance evaluation module, configured to establish a comprehensive performance evaluation model of the wireless sensor network in the electromagnetic environment according to the network coverage data, the network data packet reception success data, and the network service data, solve the comprehensive performance evaluation model of the wireless sensor network in the electromagnetic environment by combining the particle swarm optimization algorithm with the grey wolf algorithm, and output the comprehensive performance evaluation result of the wireless sensor network; Wherein, the comprehensive performance evaluation model of the wireless sensor network in the electromagnetic environment includes an objective function with the optimization objective of maximizing the network coverage data, maximizing the network data packet reception success data, and minimizing the network service data.

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

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

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

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