Heterogeneous wireless sensor network coverage method for mountain new energy construction site

Abstract

The invention relates to the technical field of wireless sensor networks, in particular to a heterogeneous wireless sensor network coverage method for a mountain new energy construction site, which comprises the following steps of: constructing a three-dimensional mountain monitoring area, inputting related parameters and establishing a multi-target optimization model taking the non-coverage rate, the reciprocal of the connectivity rate and the minimum total energy consumption as targets; initializing a dung beetle population, dividing the dung beetle population into four classes, obtaining an initial coverage scheme, and screening a non-dominated coverage scheme solution set for storage; iteratively updating the positions of various types of dung beetles based on a multi-target dung beetle optimization algorithm; the current coverage scheme and the archive set are combined, the archive set is updated after non-dominated sorting, and dense solutions are discarded to guarantee diversity when the scale is over-scale; and outputting a solution set after the maximum number of iterations is reached, and selecting a target scheme to complete node deployment. The method is adaptive to complex mountain scenes, network coverage and communication quality are improved, energy consumption is reduced, local optimum is avoided, and mountain monitoring effects of high coverage, low energy consumption and strong adaptation are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless sensor networks, and particularly relates to a heterogeneous wireless sensor network coverage method for a mountain new energy construction site. BACKGROUND

[0002] With the development of the new energy industry, mountainous areas have become a hot region for the development of new energy resources such as wind and solar energy due to their unique geographical environment and climate conditions. However, the mountainous environment has characteristics such as large terrain undulations, variable climate, and inconvenient transportation, which puts high requirements on real-time monitoring of energy facilities and safety management of construction personnel. Deploying a wireless sensor network (WSN) as a flexible and low-cost solution can achieve real-time monitoring of structural health, environmental parameters, and safety hazards in a new energy mountain construction monitoring environment. However, a single wireless sensor network cannot meet the comprehensive perception and regulation of the construction site. Deploying a heterogeneous wireless sensor network (HWSN) for monitoring of a new energy construction site in the mountains can obtain rich multi-source heterogeneous data, and the current heterogeneous technology has important application prospects. A heterogeneous wireless sensor network is a wireless network composed of heterogeneous wireless sensor nodes in a self-organizing and multi-hop manner, which monitors real-time data including environmental temperature and humidity, wind speed, solar radiation, etc. The coverage rate, connectivity rate, and energy consumption of the network are key indicators of the heterogeneous wireless sensor network. Adequate coverage and connectivity can effectively improve the communication quality of the sensor network, and lower energy consumption can prolong the network operation life. Current technology usually uses homogeneous wireless sensor networks, and rarely considers the terrain when deploying nodes. How to efficiently deploy a heterogeneous wireless sensor network to achieve accurate monitoring is the focus of current research.

[0003] In the prior art, a Chinese patent application for invention with application publication number CN118785544A discloses a heterogeneous wireless sensor network coverage method for three-dimensional space monitoring, which proposes a heterogeneous wireless sensor network coverage optimization scheme for three-dimensional obstacle space. The scheme uses an improved moth-flame optimizer (MOSWO) to solve the multi-objective problem of non-coverage rate, non-connectivity rate, and energy consumption. However, this scheme still has the following shortcomings: (1) it focuses on the shielding interference of "discrete obstacles (such as factory equipment)", and does not consider the signal attenuation caused by "continuous terrain undulations" in three-dimensional terrain (such as mountains), which cannot be applied to mountainous areas; (2) the moth-flame algorithm is prone to local optimization in a large search space in the mountains; (3) it cannot adapt to the "perception probability uncertainty" caused by the influence of slope and height on the signal in the mountains, and the optimization result may have a large deviation from the actual deployment.

[0004] Therefore, there is an urgent need for a heterogeneous wireless sensor network coverage method for mountain new energy construction sites to solve the above problems of the prior art and achieve high coverage, low energy consumption and strong adaptation of monitoring effect for mountain new energy construction sites. SUMMARY

[0005] In view of the above problems of the prior art, the present application provides a heterogeneous wireless sensor network coverage method for mountain new energy construction sites, which can achieve high coverage, low energy consumption and strong adaptation of monitoring effect for mountain new energy construction sites.

[0006] The basic scheme provided by the present application is a heterogeneous wireless sensor network coverage method for mountain new energy construction sites, which comprises the following steps: S1, a three-dimensional mountain monitoring area is constructed, the three-dimensional space size of the mountain monitoring area, the heterogeneous wireless sensor parameters and the multi-objective melolontha optimization algorithm parameters are input, a multi-objective optimization model with non-coverage rate, reciprocal of connectivity rate and minimum total energy consumption as optimization objectives is established, and the function definition of the multi-objective optimization model is as follows:

[0007] wherein, is the non-coverage rate, is the reciprocal of the connectivity rate, is the total energy consumption; S2, the melolontha population is initialized and divided into four types of rolling melolontha, breeding melolontha, foraging melolontha and thief melolontha, all initial coverage schemes are obtained, the non-coverage rate, the reciprocal of the connectivity rate and the energy consumption are calculated based on the melolontha positions in all initial coverage schemes, the initial non-dominated coverage scheme solution set of the multi-objective optimization model is obtained by fast non-dominated sorting and stored in the external Pareto archive set; S3, the positions of each type of melolontha in the melolontha population are iteratively updated based on the multi-objective melolontha optimization algorithm, and the optimization strategy of the multi-objective melolontha optimization algorithm comprises: the position updating strategy of the rolling melolontha adopts dynamic step adjustment, the position updating strategy of the breeding melolontha and the foraging melolontha adopts adjustment of the breeding area and the foraging area based on a dynamic convergence factor, and the position updating strategy of the thief melolontha adopts the golden section method; S4, the non-coverage rate, the reciprocal of the connectivity rate and the energy consumption of all current coverage schemes are calculated based on the updated melolontha positions, the non-dominated coverage scheme solution set in the external Pareto archive set is screened by non-dominated sorting for all current coverage schemes, the current non-dominated coverage scheme solution set is obtained and updated and stored in the external Pareto archive set, and if the number of solutions in the current non-dominated coverage scheme solution set exceeds the preset maximum size of the external Pareto archive set, the crowding distance is calculated and the solutions exceeding the preset maximum size of the external Pareto archive set are removed in order from low to high according to the crowding distance; S5, judge whether the current iteration number reaches the maximum iteration number, if not, return to step S3 to continue iteration update, if yes, output the final non-dominated coverage scheme solution set, and select the target coverage scheme based on the final non-dominated coverage scheme solution set to complete the deployment of the heterogeneous wireless sensor node.

[0008] The principle of the application is: 1. Firstly, a three-dimensional mountain monitoring area is constructed according to the monitoring requirements of the mountain new energy construction site, and the core parameters (such as sensing radius, communication radius, etc.) and algorithm running parameters of the heterogeneous sensor are determined; a multi-objective optimization model is established, the non-coverage rate (reflecting the coverage integrity), the reciprocal of the connectivity rate (reflecting the communication stability, the smaller the value, the better the connectivity), and the total energy consumption (reflecting the operation cost) are taken as the core optimization objectives, and a three-dimensional optimization function of minimizing the non-coverage rate, the reciprocal of the connectivity rate, and the total energy consumption is formed.

[0009] 2. Then, the individuals in the melon worm population are mapped to the deployment scheme of the heterogeneous wireless sensor node, the population size of the rolling ball melon worm, the breeding melon worm, the foraging melon worm, and the thief melon worm (P1, P2, P3, and P4 respectively) is reasonably divided to form an initial deployment scheme, then the function value of the multi-objective optimization model of the scheme is calculated, and then the initial non-dominated coverage scheme solution set is screened out by fast non-dominated sorting and stored in the external Pareto archive set to provide a benchmark for subsequent iteration optimization.

[0010] 3. Then, the differentiated position update strategy is designed according to the functional positioning of the four types of melon worms: the rolling ball melon worm is responsible for global exploration of the potential optimal deployment area, the breeding melon worm and the foraging melon worm focus on local fine search, and the thief melon worm improves the population diversity by stealing high-quality solutions, and through the cooperative update of various melon worms, the dynamic search of the population to the optimal deployment scheme is realized.

[0011] 4. After each iteration, the non-coverage rate, the reciprocal of the connectivity rate, and the energy consumption of all current coverage schemes are calculated based on the updated melon worm positions, and the non-dominated scheme solution set in the external Pareto archive set is screened out by non-dominated sorting, to obtain the non-dominated coverage scheme solution set under the current iteration round, and the non-dominated coverage scheme solution set is updated and stored in the external Pareto archive set, if the number of solutions in the non-dominated coverage scheme solution set under the current iteration round exceeds the preset maximum size of the external Pareto archive set, the crowding distance is calculated and the solutions exceeding the preset maximum size of the external Pareto archive set are removed in order from low to high according to the crowding distance, under the premise of ensuring the diversity of the non-dominated coverage scheme in the solution set, the local optimum and the excessive aggregation of solutions in the solution set are avoided, and the flexibility of subsequent deployment selection is ensured.

[0012] 5. Finally, the algorithm terminates by setting a maximum number of iterations. When the number of iterations reaches the threshold of the maximum number of iterations, the final non-dominated coverage solution set is output. Users can select the appropriate target solution from the final non-dominated coverage solution set to complete the deployment of sensor nodes according to the site monitoring priority (e.g., high coverage for key areas and low energy consumption for remote areas).

[0013] The beneficial effects of this invention are as follows: 1. Enhanced Scene Adaptability: Compared to existing technologies that focus on discrete obstacle occlusion, this solution constructs a three-dimensional mountain monitoring area and combines it with multi-target optimization to fully adapt to the signal attenuation problem caused by continuous terrain undulations in mountains. This solves the pain point that traditional methods cannot be applied to mountain scenes and ensures that the deployment solution is highly matched with the actual mountain environment.

[0014] 2. Performance Improvement: The multi-target dung beetle optimization algorithm is adopted. By improving the algorithm through a differentiated position update strategy of cooperative search of four types of dung beetles, the problem of existing algorithms getting trapped in local optima in the large search space of mountainous areas is effectively avoided, and the optimization quality of the coverage scheme is significantly improved.

[0015] Furthermore, the formula for constructing the three-dimensional mountain monitoring area in S1 is as follows:

[0016] In the formula, Representing coordinates of Axis coordinates This indicates the total number of hills and depressions. Indicates the first The coordinates of the plane center of a hill or depression. For height parameters, Indicates the first Along a hill or depression Attenuation in the axial direction, Indicates the first Along a hill or depression Attenuation in the axial direction, This represents the natural exponential function.

[0017] By quantifying the spatial distribution, height, and attenuation characteristics of hills and depressions, the topographic undulations of three-dimensional mountains can be reconstructed, providing a spatial model that fits the actual mountain environment for subsequent sensor deployment.

[0018] Furthermore, the position update strategy for dynamic step size adjustment in S3 is implemented through the following formula:

[0019] in, Step size weight, For the search step size, express and Multiply corresponding components This is the current position of the dung beetle. This shows the updated position of the dung beetle after Levi's flight. The step size, representing a Lévy distribution, is calculated using the following formula:

[0020]

[0021]

[0022] In the formula, The step size distribution index, For gamma function, and It follows a standard normal distribution. , ; Search Step Size The following formula is used to calculate:

[0023] in, This represents the current iteration number. This represents the maximum number of iterations.

[0024] The rolling dung beetle, as a core force in global population search, uses a dynamic step-size strategy to adjust the search step size. With the number of iterations Correlation enables adaptive adjustment of the optimization process. In the early stages of iteration, the current iteration number... Smaller, search step size With a larger value, combined with the long step size characteristic of the Lévy distribution, the search range can be expanded to efficiently explore potential optimal deployment areas within the large space of mountainous terrain; in the later stages of iteration, the number of iterations... Approaching the maximum number of iterations, search step size By reducing the value, the algorithm focuses on refined local searches, improving the accuracy of the optimal solution. The global-then-local search pattern effectively balances the algorithm's exploration and development capabilities, avoiding the problems of traditional fixed-step-size algorithms easily getting trapped in local optima or experiencing low search efficiency. This improves the efficiency and solution set quality of the dung beetle search for optimal deployment schemes in complex mountainous environments.

[0025] Furthermore, in S3, the position update strategy for adjusting the breeding and foraging areas of breeding and foraging dung beetles based on a dynamic convergence factor is implemented through the following formula:

[0026]

[0027] in, This indicates the current local optimum. and For two sizes Independent random vectors, This refers to the coordinate parameters of all randomly deployed heterogeneous wireless sensor nodes. To determine the current location of the breeding dung beetle, The new location for breeding dung beetles. To find the dung beetle's current location, The updated location of the dung beetles in search of food; and These represent the lower and upper bounds of the simulated spawning range, respectively. and These represent the lower and upper bounds of the optimal foraging region, respectively. , , and Calculate using the following formulas respectively:

[0028]

[0029]

[0030]

[0031] in, and Let these represent the lower and upper bounds of the optimization problem, respectively. This is the current globally optimal position; The dynamic convergence factor is calculated using the following formula:

[0032] in, This represents the current iteration number. This represents the maximum number of iterations.

[0033] Dynamic convergence factor The range of the breeding and foraging areas changes sinusoidally with the iteration process, achieving adaptive adjustment. In the early stages of iteration... The larger value indicates a wider breeding and foraging area, which is beneficial for breeding and foraging dung beetles to search for high-quality solutions within a larger space; as iterations progress, The value dynamically decreases, the search area shrinks, and the focus is on refining the search around the local optimum. This dynamic adjustment mechanism makes the position updates of the two types of dung beetles more closely aligned with the optimization process, ensuring both the diversity of the search in the early stages and improving the accuracy of the search in the later stages. This effectively optimizes the network coverage and energy consumption control, and improves the search efficiency of both breeding and foraging dung beetles for the optimum.

[0034] Furthermore, the position update strategy for the dung beetle in S3, based on the golden ratio, is implemented using the following formula:

[0035] in, This shows the current location of the thief dung beetle. The updated location of the dung beetle thief. , , and In each iteration, a value is randomly selected from its range. and These are the golden ratios, calculated using the following formulas:

[0036]

[0037]

[0038] in, and The initial value of the golden ratio. , , Represents the golden ratio. This is the current globally optimal position.

[0039] Golden Ratio The introduction of (approximately 0.618) makes the dung beetle's position update strategy more regular and accurate. This is achieved through the golden ratio system. and Through dynamic calculation, the thief dung beetle can efficiently steal high-quality solutions around the globally optimal position, while also combining it with... and The random selection of values ​​avoids the mechanical nature of position updates, enhances population diversity, effectively prevents convergence of solution sets, makes the distribution of non-dominated schemes more uniform and diverse, and improves the flexibility and practicality of the schemes.

[0040] Furthermore, the non-coverage rate in S2 Calculated using the following formula:

[0041]

[0042] in, The spatial dimensions of the mountain monitoring area. This indicates the number of target monitoring points in the mountainous monitoring area. This represents all heterogeneous wireless sensors monitoring the i-th target point. The joint perception probability, , Describes the set of heterogeneous wireless sensor nodes and , This indicates the number of heterogeneous wireless sensor nodes randomly deployed within the area. Calculated using the following formula:

[0043] in, The perception probability of the heterogeneous wireless sensor node perception model is calculated using the following formula:

[0044] in, This represents the i-th heterogeneous wireless sensor node. , , The sensing radius of the heterogeneous wireless sensor node. This represents the fluctuation value of the uncertain detection capability radius of heterogeneous wireless sensor nodes. and These are preset control parameters that reflect the characteristics of the terrain and environment. For heterogeneous wireless sensor nodes With target monitoring points The Euclidean distance between them is calculated using the following formula:

[0045] in, Heterogeneous wireless sensor nodes coordinates It is the target monitoring point The coordinates.

[0046] This scheme provides accurate quantitative basis for coverage optimization, ensures the scientific and objective nature of coverage effect evaluation, and provides clear guidance for subsequent optimization directions.

[0047] Furthermore, the probability of perception was analyzed using Monte Carlo simulation. Updates will be made, including: conduct The second judgment satisfies... The number of times is recorded as The number of times the condition is not met is recorded as If satisfied Then the probability of perception will be Update to 1, otherwise perceive probability. Updated to 0; where, To generate a random number function that randomly generates a real number between 0 and 1, , The preset number of judgments.

[0048] In mountainous environments, sensor signals are easily affected by factors such as slope, altitude, and climate, leading to uncertainty in the sensing probability. Monte Carlo simulations... Secondary random judgment (such as) This method utilizes probabilistic statistical laws to correct the perceived probability value, transforming continuous perceived probabilities into binary results of 0 or 1, which better aligns with the needs of actual monitoring for determining coverage and non-coverage. This approach effectively reduces subjective bias in perceived probability calculations, improves the reliability of perceived probabilities, makes coverage effect assessments closer to reality, reduces the deviation between optimization results and actual deployment, and enhances the engineering practicality of the solution.

[0049] Furthermore, the reciprocal of the connectivity in S2 Calculated using the following formula:

[0050]

[0051]

[0052] in, This represents the connectivity of the entire heterogeneous wireless sensor network. The minimum communication radius between the two sensors. Represents heterogeneous wireless sensor nodes heterogeneous wireless sensor nodes The connectivity between them For heterogeneous wireless sensor nodes heterogeneous wireless sensor nodes The Euclidean distance between them.

[0053] This calculation method provides a clear quantitative indicator for optimizing network connectivity quality.

[0054] Furthermore, the total energy consumption in S2 Calculated using the following formula:

[0055] in, For the first The energy consumption of each heterogeneous wireless sensor node is calculated as follows:

[0056] in, For the first The power consumption of a heterogeneous wireless sensor node in receiving data from other heterogeneous wireless sensor nodes. For the first The transmission power consumption of each heterogeneous wireless sensor node in sending data to other heterogeneous wireless sensor nodes is calculated using the following formulas:

[0057]

[0058]

[0059] in, It is the first Each sensor node can send and receive bytes of data per round. Electronic circuit power consumption and These represent the unit energy coefficients of the transmitter amplifier under the free-space loss model and the multipath ground reflection model, respectively.

[0060] By distinguishing between receiving energy consumption and transmitting energy consumption, and combining the connectivity status and distance between nodes, the energy consumption value of each node is accurately calculated, thereby obtaining the total network energy consumption. The transmitting energy consumption part dynamically selects either the free space loss model or the multipath ground reflection model based on the distance between nodes, fully adapting to the signal transmission loss characteristics in mountainous environments; the receiving energy consumption is calculated based on electronic circuit energy consumption and data volume, ensuring the comprehensiveness of energy consumption assessment.

[0061] Furthermore, the selection of a target coverage scheme to complete the deployment of heterogeneous wireless sensor nodes in S5 specifically includes: selecting a coverage scheme with the lowest non-coverage rate, lowest non-connectivity rate, lowest total energy consumption, or closest to the preset non-coverage rate, non-connectivity rate, and total energy consumption requirements, based on the monitoring priority of the mountainous new energy construction site.

[0062] The selection of solutions is based on the differentiated needs of various monitoring scenarios at mountainous new energy construction sites, enhancing the practicality and flexibility of the technical solutions. For example, in core areas of the site (such as equipment rooms and key construction points), the solution with the lowest coverage rate can be prioritized to ensure comprehensive monitoring; in remote areas (such as monitoring points on the edge of mountains), the solution with the lowest total energy consumption can be selected to reduce operating costs; in scenarios with high requirements for communication stability (such as real-time data transmission links), the solution with the lowest connectivity rate (the reciprocal of the connectivity rate) can be selected; if multiple objectives need to be considered, the coverage solution closest to the preset coverage rate, connectivity rate, and total energy consumption requirements can be selected based on actual needs. This flexible selection method allows the technical solutions to adapt to different engineering needs, enhancing their engineering application value. Attached Figure Description

[0063] Figure 1 This is a schematic diagram of an embodiment of the heterogeneous wireless sensor network coverage method for mountainous new energy construction sites according to the present invention.

[0064] Figure 2 An initial deployment diagram of heterogeneous wireless sensor nodes according to an embodiment of the present invention.

[0065] Figure 3 This is the initial connectivity graph of heterogeneous wireless sensor nodes in an embodiment of the present invention.

[0066] Figure 4 This is a comparison chart of the final non-dominated coverage solution sets of the multi-objective optimization models used in this invention, along with those of MOGWO, MOPSO, NSDBO, and NSGA-II algorithms.

[0067] Figure 5 This is a MODBO optimized deployment diagram for heterogeneous wireless sensor nodes according to an embodiment of the present invention.

[0068] Figure 6 The MODBO optimized connectivity graph for heterogeneous wireless sensor nodes in this embodiment of the invention.

[0069] Figure 7 A top-down view of the MODBO optimized deployment of heterogeneous wireless sensor nodes according to an embodiment of the present invention.

[0070] Figure 8 A top view of MODBO optimized connectivity for heterogeneous wireless sensor nodes in an embodiment of the present invention. Detailed Implementation

[0071] The following detailed description illustrates the specific implementation method: The basic implementation examples are as follows: Figure 1 The method for heterogeneous wireless sensor network coverage at mountainous new energy construction sites includes the following steps: S1. Construct a 3D mountain monitoring area. Input the 3D spatial dimensions of the mountain monitoring area, the parameters of the heterogeneous wireless sensors, and the parameters of the multi-objective dung beetle optimization algorithm. Establish a multi-objective optimization model with the optimization objectives of minimizing non-coverage rate, the inverse of connectivity rate, and total energy consumption. The function definition of the multi-objective optimization model is as follows:

[0072] in, Non-coverage rate, It is the reciprocal of the connectivity. Total energy consumption; S2. Initialize the dung beetle population and divide it into four categories: rolling dung beetles, reproductive dung beetles, foraging dung beetles, and thieving dung beetles. Obtain all initial coverage schemes. Calculate the non-coverage rate, the inverse of the connectivity rate, and the energy consumption based on the dung beetle positions in all initial coverage schemes. Obtain the initial non-dominated coverage scheme solution set of the multi-objective optimization model through fast non-dominated sorting and store it in an external Pareto archive set. S3. The positions of various dung beetles in the dung beetle population are iteratively updated based on a multi-objective dung beetle optimization algorithm. The optimization strategies of the multi-objective dung beetle optimization algorithm include: a position update strategy with dynamic step size adjustment for the rolling ball dung beetle, a position update strategy with dynamic convergence factor adjustment for the breeding and foraging areas for the breeding and foraging dung beetles, and a position update strategy with golden ratio for the thief dung beetle. S4. Based on the updated dung beetle positions, calculate the non-coverage rate, reciprocal of connectivity, and energy consumption of all current coverage schemes. Sort and filter all current coverage schemes and non-dominated coverage scheme solution sets in the external Pareto archive to obtain the current non-dominated coverage scheme solution set and update and store it in the external Pareto archive. If the number of solutions in the current non-dominated coverage scheme solution set exceeds the preset maximum size of the external Pareto archive, calculate the congestion distance and discard solutions that exceed the preset maximum size of the external Pareto archive in order of congestion distance from low to high. S5. Determine whether the current iteration count has reached the maximum iteration count. If not, return to step S3 to continue iterating and updating. If it has, output the final non-dominated coverage scheme solution set. Based on the final non-dominated coverage scheme solution set, select the target coverage scheme to complete the deployment of heterogeneous wireless sensor nodes.

[0073] In this embodiment, a mountainous new energy construction site is selected. The region is used as the monitoring object to construct a three-dimensional mountain monitoring area.

[0074] The formula for constructing a three-dimensional mountain monitoring area is:

[0075] In the formula, Representing coordinates of Axis coordinates This indicates the total number of hills and depressions. Indicates the first The coordinates of the plane center of a hill or depression. For height parameters, Indicates the first Along a hill or depression Attenuation in the axial direction, Indicates the first Along a hill or depression Attenuation in the axial direction, Representing a natural exponential function, this determines the total number of hills and depressions based on regional topographic survey data. Arbitrary coordinates can be calculated using a three-dimensional mountain monitoring area construction formula. corresponding Axis coordinates Terrain modeling was completed; two types of heterogeneous wireless sensors were selected: 18 sensors 1 (with attachments) Figure 2 Sensor 1, sensing radius Uncertainty about the radius of fluctuation of detection capability , communication radius ) and 12 sensors 2 (attached) Figure 2 Sensor 2, sensing radius Uncertainty about the radius of fluctuation of detection capability , communication radius ).

[0076] Set parameters for the multi-objective dung beetle optimization algorithm: maximum number of iterations. The total dung beetle population size is P=100, with the following sizes: *P1* = 25, *P2* = 25, *P3* = 25, and *P4* = 25. The largest external Pareto archive set is... Setting the value too high results in a longer algorithm computation time, while setting it too low results in a smaller solution set. In this embodiment, we take... .

[0077] A multi-objective optimization model is established with the objectives of minimizing non-coverage rate, the reciprocal of connectivity rate, and total energy consumption, where: S2 Non-coverage Calculated using the following formula:

[0078]

[0079] in, The spatial dimensions of the mountain monitoring area. This indicates the number of target monitoring points in the mountainous monitoring area; in this embodiment, it is taken as 40,000. This represents all heterogeneous wireless sensors monitoring the i-th target point. The joint perception probability, , Describes the set of heterogeneous wireless sensor nodes and , This represents the number of heterogeneous wireless sensor nodes randomly deployed within the area; in this embodiment, it is taken as... , Calculated using the following formula:

[0080] in, The perception probability of the heterogeneous wireless sensor node perception model is calculated using the following formula:

[0081] in, This represents the i-th heterogeneous wireless sensor node. , , The sensing radius of the heterogeneous wireless sensor node. This represents the fluctuation value of the uncertain detection capability radius of heterogeneous wireless sensor nodes. and These are preset control parameters that reflect the characteristics of the terrain environment. The attenuation shape is controlled; in this embodiment, it is set to 1.01 to make the attenuation shape closer to linear. Determined based on terrain and environment; when the terrain is complex and the signal attenuates rapidly. Choose a relatively large value, such as When the terrain is flat and the signal is relatively stable, Choose a relatively small value, such as In this embodiment, take , For heterogeneous wireless sensor nodes With target monitoring points The Euclidean distance between them is calculated using the following formula:

[0082] in, Heterogeneous wireless sensor nodes coordinates It is the target monitoring point The coordinates.

[0083] Perceptual probability was studied using Monte Carlo simulation. Updates will be made, including: conduct The second judgment satisfies... The number of times is recorded as The number of times the condition is not met is recorded as If satisfied Then the probability of perception will be Update to 1, otherwise perceive probability. Updated to 0; where, To generate a random number function that randomly generates a real number between 0 and 1, , The preset number of judgments. The larger the value, the longer the computation time. To avoid excessive waste of computing power, this embodiment selects... After 50 random trials, the statistical results basically conformed to a normal distribution, and its statistical significance showed good stability.

[0084] reciprocal of connectivity Calculated using the following formula:

[0085]

[0086]

[0087] in, This represents the connectivity of the entire heterogeneous wireless sensor network. The minimum communication radius between the two sensors. Represents heterogeneous wireless sensor nodes heterogeneous wireless sensor nodes The connectivity between them For heterogeneous wireless sensor nodes heterogeneous wireless sensor nodes The Euclidean distance between them.

[0088] This calculation method provides a clear quantitative indicator for optimizing network connectivity quality.

[0089] Furthermore, the total energy consumption in S2 Calculated using the following formula:

[0090] in, For the first The energy consumption of each heterogeneous wireless sensor node is calculated as follows:

[0091] in, For the first The power consumption of a heterogeneous wireless sensor node in receiving data from other heterogeneous wireless sensor nodes. For the first The transmission power consumption of each heterogeneous wireless sensor node in sending data to other heterogeneous wireless sensor nodes is calculated using the following formulas:

[0092]

[0093]

[0094] in, It is the first Each sensor node can send and receive bytes of data per round. Indicates the energy consumption of electronic circuits. and These represent the unit energy coefficients of the transmitter amplifier under the free-space loss model and the multipath ground reflection model, respectively.

[0095] The specific steps for selecting a target coverage scheme to complete the deployment of heterogeneous wireless sensor nodes include: based on the monitoring priority of the mountainous new energy construction site, selecting a coverage scheme with the lowest non-coverage rate, lowest non-connectivity rate, lowest total energy consumption, or closest to the preset non-coverage rate, non-connectivity rate, and total energy consumption requirements. In this embodiment, the scheme with the lowest non-coverage rate is selected as the final deployment scheme.

[0096] The population was divided into four categories: rolling dung beetles (P1), reproductive dung beetles (P2), foraging dung beetles (P3), and thieving dung beetles (P4), resulting in an initial coverage scheme (see attached). Figure 2 and Figure 3 (As shown). The initial coverage rate, the reciprocal of the connectivity rate, and the total energy consumption were calculated. The calculated initial coverage rate was 61.767%, which means the initial coverage rate was 38.233%.

[0097] The initial solutions are filtered using a fast non-dominated sorting algorithm to obtain an initial set of non-dominated covered solutions, which are then stored in an external Pareto archive. In the non-dominated sorting, for all objective functions, if a solution to A is no worse than a solution to B, and at least one objective function has a solution better than B, then the solution to B is said to be dominated by the solution to A. Its mathematical expression is as follows: satisfy and satisfy

[0098] In the formula, This represents the number of objective functions, i.e., the number of optimization objectives in a multi-objective optimization model. In this embodiment, the optimization objectives are to minimize the non-coverage rate, minimize the inverse of the connectivity rate, and minimize the total energy consumption. .

[0099] The position update strategy for dynamic step size adjustment in S3 is implemented through the following formula:

[0100] in, For step size weights, when At times, a step size that is too large can easily lead to frequent long jumps and low search accuracy. When the step size is too small, nearest neighbor search dominates, making it easy to get trapped in local optima. In this embodiment, we select... , For the search step size, express and Multiply corresponding components This is the current position of the dung beetle. This shows the updated position of the dung beetle after Levi's flight. The step size, representing a Lévy distribution, is calculated using the following formula:

[0101]

[0102]

[0103] In the formula, The step size distribution index is the step size distribution index. The value of is usually between (0, 2). When When the value is large, the Levy flight search mainly focuses on the vicinity of the current solution, and is prone to getting trapped in local optima; when When the value is small, the search may be far from the current solution region, resulting in low search efficiency or even failure to converge; in this embodiment Choosing a value of 1.5 ensures that the algorithm has a certain degree of global search capability while avoiding excessive randomness. For gamma function, and It follows a standard normal distribution. , ; Search Step Size The following formula is used to calculate:

[0104] in, This represents the current iteration number. This represents the maximum number of iterations.

[0105] In S3, the strategy for updating the positions of breeding and foraging dung beetles based on a dynamic convergence factor is implemented using the following formula:

[0106]

[0107] in, This indicates the current local optimum. and For two sizes Independent random vectors, The coordinate parameters of all randomly deployed heterogeneous wireless sensor nodes, i.e. , The number of heterogeneous wireless sensor nodes randomly deployed in this area is defined in this embodiment. Each heterogeneous wireless sensor node's coordinates have three parameters. , To determine the current location of the breeding dung beetle, The new location for breeding dung beetles. To find the dung beetle's current location, The updated location of the dung beetles in search of food; and These represent the lower and upper bounds of the simulated spawning range, respectively. and These represent the lower and upper bounds of the optimal foraging region, respectively. , , and Calculate using the following formulas respectively:

[0108]

[0109]

[0110]

[0111] in, and Let these represent the lower and upper bounds of the optimization problem, respectively. This is the current globally optimal position; The dynamic convergence factor is calculated using the following formula:

[0112] in, This represents the current iteration number. This represents the maximum number of iterations.

[0113] The position update strategy for the dung beetle in S3, based on the golden ratio, is implemented using the following formula:

[0114] in, This shows the current location of the thief dung beetle. The updated location of the dung beetle thief. , , and In each iteration, a value is randomly selected from its range. and These are the golden ratios, calculated using the following formulas:

[0115]

[0116]

[0117] in, and The initial value of the golden ratio. , , Represents the golden ratio. This is the current globally optimal position.

[0118] In step S4, all current coverage schemes after position updates are sorted and filtered using a non-dominated sorting method compared with the coverage scheme solution set in the external Pareto archive to obtain the current non-dominated coverage scheme solution set. The obtained non-dominated coverage scheme solution set is then updated and stored in the external Pareto archive set. The specific operations are as follows:

[0119] In the formula, This represents the non-dominated operator, which performs a non-dominated sort on a given set and finds the solution set of the Pareto non-dominated cover scheme for the given set. This represents the Pareto archive set obtained after non-dominated sorting. Indicates the current external Pareto archive set. Let represent all coverage schemes obtained by the algorithm in the current iteration. The size is ,if Greater than , This indicates the maximum size of the preset Pareto archive set. Discarding items in order of increasing crowding distance. - A Pareto optimal solution is obtained, thereby ensuring the diversity of non-dominated covering schemes in the solution set.

[0120] As attached Figure 1 As shown, the number of iterations The process is repeated iteratively, incrementing by 1 each time, and each iteration checks the number of dung beetles. Determine which of the following dung beetles it belongs to: *P1* (rolling dung beetle), *P2* (reproducing dung beetle), *P3* (foraging dung beetle), or *P4* (stealing dung beetle), and execute the corresponding position update strategy. Then, determine the current iteration number. Has the maximum number of iterations been reached? If the target is not reached, continue iterating; if the target is reached, terminate the iteration and output the final non-dominated cover solution set.

[0121] The solution sets of the final non-dominated coverage schemes of the multi-objective optimization models using the Multi-Objective Dung Beetle Optimization Algorithm (MODBO), Multi-Objective Gray Wolf Optimization Algorithm (MOGWO), Multi-Objective Particle Swarm Optimization Algorithm (MOPSO), and Non-Dominated Multi-Objective Genetic Algorithm (NSGA-II) used in this embodiment are attached. Figure 4 As shown.

[0122] In this embodiment, high coverage must be prioritized in the core construction area. Therefore, the solution with the lowest non-coverage rate from the final non-dominant coverage solution set is selected for deployment, as shown in the attached figure. Figure 5 , Figure 6 , Figure 7 and Figure 8 As shown, the optimized solution has a non-coverage rate of 8.156%, which is equivalent to a coverage rate of 91.844%. Compared with the initial solution, the optimized solution improves the coverage rate by 30.077%, achieving the goal of high coverage deployment of heterogeneous wireless sensor networks at mountainous new energy construction sites.

[0123] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A heterogeneous wireless sensor network coverage method for mountain new energy construction sites, characterized in that, The method comprises the following steps: S1, constructing a three-dimensional mountain monitoring area, inputting three-dimensional space size of the mountain monitoring area, heterogeneous wireless sensor parameters and multi-objective melolontha optimization algorithm parameters, establishing a multi-objective optimization model with non-coverage rate, reciprocal of connectivity rate and minimum total energy consumption as optimization objectives, and defining functions of the multi-objective optimization model as follows: wherein, is the non-coverage, is the connectedness inverse, is the total energy consumption; S2, initializing a melolontha population and dividing it into four types of rolling melolontha, breeding melolontha, foraging melolontha and thief melolontha to obtain all initial coverage schemes, calculating non-coverage rate, reciprocal of connectivity rate and energy consumption based on melolontha positions in all initial coverage schemes, obtaining initial non-dominated coverage scheme solution set of the multi-objective optimization model through fast non-dominated sorting and storing it in an external Pareto archive set; S3, iteratively updating positions of melolonthas of different types in the melolontha population based on the multi-objective melolontha optimization algorithm, wherein the optimization strategy of the multi-objective melolontha optimization algorithm comprises: a position updating strategy for rolling melolontha with dynamic step adjustment, a position updating strategy for breeding melolontha and foraging melolontha with breeding area and foraging area adjusted based on a dynamic convergence factor, and a position updating strategy for thief melolontha based on golden section; S4, calculating non-coverage rate, reciprocal of connectivity rate and energy consumption of all current coverage schemes based on updated melolontha positions, performing non-dominated sorting on all current coverage schemes and non-dominated coverage scheme solution set in the external Pareto archive set to obtain a current non-dominated coverage scheme solution set and update and store it in the external Pareto archive set, and if the number of solutions in the current non-dominated coverage scheme solution set exceeds the preset maximum size of the external Pareto archive set, calculating a crowding distance and sequentially removing solutions with low to high crowding distance from the external Pareto archive set until the number of solutions removed reaches the preset maximum size of the external Pareto archive set; S5, determining whether the current iteration number reaches a maximum iteration number, and if not, returning to step S3 for iterative updating; if so, outputting a final non-dominated coverage scheme solution set and selecting a target coverage scheme based on the final non-dominated coverage scheme solution set to complete deployment of the heterogeneous wireless sensor nodes.

2. The heterogeneous wireless sensor network coverage method for mountain new energy construction site according to claim 1, characterized in that, The formula for constructing the three-dimensional mountain monitoring area in S1 is as follows: wherein denotes the coordinates of the axis coordinate, denotes the total number of hills and pits, denotes the plane center coordinate of the hills or pits, is a height parameter, denotes the decay amount of the hills or pits in the axis direction, denotes the decay amount of the hills or pits in the axis direction, represents a natural exponential function.

3. The heterogeneous wireless sensor network coverage method for mountain new energy construction site according to claim 2, characterized in that, The position updating strategy with dynamic step adjustment in S3 is implemented through the following formula: wherein, is the step weight, is the search step, denotes with multiplying the corresponding components, is the current position of the tumbleweed, is the position of the tumbleweed after the Levy flight update, denotes a step that obeys a Levy distribution and is calculated by the following equation: wherein is the step length distribution index, is the gamma function, and satisfies the standard normal distribution, , ; Search step This is calculated as follows: wherein, is the current iteration number, is the maximum iteration number.

4. The heterogeneous wireless sensor network coverage method for mountain new energy construction site according to claim 2, characterized in that, The position updating strategy for breeding melolontha and foraging melolontha with breeding area and foraging area adjusted based on a dynamic convergence factor in S3 is implemented through the following formula: wherein, denotes the current local optimal position, and are two independent random vectors of size , is the coordinate parameter quantity of all randomly deployed heterogeneous wireless sensor nodes, is the current position of the breeding scarab, is the updated position of the breeding scarab, is the current position of the foraging scarab, is the updated position of the foraging scarab; and denote the lower and upper bounds of the simulated oviposition range, respectively, and denote the lower and upper bounds of the optimal foraging area, respectively, , , and are calculated by the following equations, respectively: where, and respectively represent lower and upper bounds of the optimization problem, is the current global optimal position; is a dynamic convergence factor, calculated by the following formula: wherein, is the current iteration number, is the maximum iteration number.

5. The heterogeneous wireless sensor network coverage method for mountain new energy construction site according to claim 2, characterized in that, The position updating strategy for thief melolontha based on golden section in S3 is implemented through the following formula: wherein is the current position of the thief beetle, is the updated position of the thief beetle, , , and is randomly taken within its range of values at each iteration, and is the golden section coefficient, calculated by the following equations, respectively: wherein, and is the initial value of the golden section coefficient, , , denotes the golden section ratio, is the current global optimum position.

6. The heterogeneous wireless sensor network coverage method for mountain new energy construction site according to claim 2, characterized in that, The non-coverage in S2 Calculated by the following equation: wherein, is the spatial dimension of the mountain monitoring region, denotes the number of target monitoring points of the mountain monitoring region, denotes the joint sensing probability of all heterogeneous wireless sensors for the i-th target monitoring point, , , denotes the set of heterogeneous wireless sensor nodes and , denotes the number of randomly deployed heterogeneous wireless sensor nodes within the region, is calculated by the following formula: wherein, is the perception probability of the heterogeneous wireless sensor node perception model, and the calculation formula is: wherein, denotes the Ith heterogeneous wireless sensor node, , , is the sensing radius of the heterogeneous wireless sensor node, is the uncertain detection capability radius fluctuation value of the heterogeneous wireless sensor node, and is a preset control parameter reflecting the characteristics of the terrain environment, is the Euclidean distance between the Ith heterogeneous wireless sensor node and the target monitoring point , and the calculation formula is: wherein is a coordinate of an isomorphic wireless sensor node , is a coordinate of a target monitoring point .

7. The heterogeneous wireless sensor network coverage method for mountain new energy construction site according to claim 6, characterized in that, Perceptual probability was studied using Monte Carlo simulation. Updates will be made, including: conduct The second judgment satisfies... The number of times is recorded as The number of times the condition is not met is recorded as If satisfied Then the probability of perception will be Update to 1, otherwise perceive probability. Updated to 0; where, To generate a random number function that randomly generates a real number between 0 and 1, , The preset number of judgments. 8.The method for heterogeneous wireless sensor network coverage of mountain new energy construction site according to claim 7, characterized in that, the S2 communication rate inverse is calculated by the following equation: wherein, denotes the connectivity rate of the whole heterogeneous wireless sensor network, is the minimum communication radius of two sensors, denotes a heterogeneous wireless sensor node and a heterogeneous wireless sensor node between them, is the Euclidean distance between a heterogeneous wireless sensor node and a heterogeneous wireless sensor node between them.

9. The heterogeneous wireless sensor network coverage method for mountain new energy construction site according to claim 8, characterized in that, The total energy consumption in S2 is calculated by the following equation: in, For the first The energy consumption of each heterogeneous wireless sensor node is calculated as follows: wherein, is the transmission energy consumption of the i-th heterogeneous wireless sensor node for transmitting data to other heterogeneous wireless sensor nodes, is the reception energy consumption of the i-th heterogeneous wireless sensor node for receiving data from other heterogeneous wireless sensor nodes, is the transmission energy consumption of the i-th heterogeneous wireless sensor node for transmitting data to other heterogeneous wireless sensor nodes, is the reception energy consumption of the i-th heterogeneous wireless sensor node for receiving data from other heterogeneous wireless sensor nodes, respectively calculated by the following equations: wherein, is the number of sensor nodes per round that can transceive byte data, electronic circuit energy consumption, and and represent the unit energy coefficient of the transmitting amplifier under the free space loss model and under the multipath ground reflection model, respectively.​ 10. The heterogeneous wireless sensor network coverage method for mountain new energy construction site according to claim 1, characterized in that, The selection of the target coverage scheme to complete deployment of the heterogeneous wireless sensor nodes in S5 specifically comprises: selecting a coverage scheme with minimum non-coverage rate, minimum non-connectivity rate, minimum total energy consumption or closest to preset non-coverage rate, non-connectivity rate and total energy consumption demand according to the monitoring priority of the mountain new energy construction site.

Images