Wireless sensor network routing method and device, electronic equipment and storage medium

Abstract

The invention provides a wireless sensor network routing method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the information transmission from any node of a wireless sensor network, and carrying out the routing search through employing an elite Levy flight Harris eagle optimization algorithm in the information transmission process, and determining an optimal communication route; and transmitting power grid fault information to a terminal node based on the optimal communication route, the power grid fault information being obtained based on node detection in the wireless sensor network. According to the method, the device, the electronic equipment and the storage medium provided by the invention, the safety performance of the wireless sensor network can be improved, the network energy consumption is reduced, the network service life is prolonged, and a safe and reliable routing strategy is provided for detection information transmission of the source network load storage collaborative optimization power distribution network.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a wireless sensor network routing method, device, electronic equipment and storage medium. Background Art

[0002] Source-grid-load-storage is a new type of power operation mode with "power source, grid, load, and energy storage" as the overall planning. At present, the key to realizing the coordinated optimization of distribution network by source-grid-load-storage is to establish a safe, reliable, and real-time communication network. Without an advanced network, the coordinated optimization of distribution network by source-grid-load-storage cannot realize its own advantages. As the perceptual nerve endings of the power system, wireless sensor networks have the advantages of low power consumption, flexible self-organizing networks, and scalability. They can provide the necessary communication mechanism for the transmission of communication data of the coordinated optimization of distribution network by source-grid-load-storage.

[0003] When a fault occurs in the source-grid-load-storage coordinated optimization distribution network, the fault information needs to be transmitted to the base station in a timely manner, and then the faulty node needs to be repaired. Otherwise, a large number of equipment in the source-grid-load-storage distribution network will be worn out or scrapped, which will greatly affect the safe operation of the entire source-grid-load-storage distribution network system. Therefore, the energy consumption, lifespan and safety assurance of wireless sensor networks have become key factors for the power system to improve the efficiency of the power grid and ensure the quality of power supply.

[0004] Wireless sensor networks are composed of a large number of small and low-cost single sensor nodes that are fixedly deployed or randomly scattered in the detection area. Sensor nodes are usually small in size, with built-in battery energy and non-grid power supply. The node energy is greatly limited. In addition, the power area environment where the wireless sensor network for source-grid-load-storage collaborative optimization of the distribution network is usually deployed is very complex and difficult for ordinary workers to reach. It is relatively difficult to replenish the energy of sensor nodes. At the same time, each node is exposed to the external environment for a long time and is vulnerable to malicious attacks from external factors, resulting in node paralysis and data forwarding failure. How to use energy efficiently to extend the network life cycle and ensure routing forwarding security is a key issue that needs to be solved in wireless sensor networks. Most of the existing wireless sensor network routing methods solve the single-target energy consumption problem, such as Flooding algorithm, SPIN algorithm and DD algorithm. Due to the characteristics of the method itself, it cannot resist network black hole attacks and can no longer fully meet the existing routing needs. Summary of the invention

[0005] The present invention provides a wireless sensor network routing method, device, electronic device and storage medium, which are used to solve the defects of low accuracy, slow convergence speed, high energy consumption and poor safety performance in the routing method of the prior art in the related art.

[0006] The present invention provides a wireless sensor network routing method, comprising: Information transmission is started from any node of the wireless sensor network. During the information transmission process, an elite Levy Flying Harris Hawk optimization algorithm is applied to perform route search to determine an optimal communication route. The elite Levy Flying Harris Hawk optimization algorithm is used to construct a communication route set based on an elected cluster head node, and through an iterative optimization process, each communication route in the communication route set is evaluated to determine the optimal communication route from the communication route set according to the evaluation result. The cluster head node is elected from each node of the wireless sensor network. Based on the optimal communication route, power grid fault information is transmitted to a terminal node, wherein the power grid fault information is obtained based on node detection in the wireless sensor network.

[0007] According to a wireless sensor network routing method provided by the present invention, the elite Levy Flying Harris Hawk optimization algorithm is applied to perform routing search to determine the optimal communication route, including: Selecting a cluster head node from each node of the wireless sensor network, and establishing clusters based on the cluster head node; Based on the cluster head node, construct a communication route set, wherein the communication route set includes multiple communication routes; Calculating the fitness value of each communication route in the communication route set, and determining a target communication route from the communication routes based on the fitness value of each communication route; Based on the position of the target communication route in the search space, updating the position of other communication routes in the communication route set except the target communication route; When the location update is completed, recalculate the fitness value of each communication route, and construct a new communication route set based on the fitness value of each communication route; Iterative optimization is performed based on the new communication route set until the iterative process ends to obtain the optimal communication route.

[0008] According to a wireless sensor network routing method provided by the present invention, the step of selecting a cluster head node from each node of the wireless sensor network includes: Determining a fitness function value of each node based on the attributes of each node in the wireless sensor network, wherein the attributes of each node include coverage, residual energy, proximity, and communication cost; Based on the fitness function value of each node, a cluster head node is selected from each node.

[0009] According to a wireless sensor network routing method provided by the present invention, the communication routing set is constructed based on the cluster head node, including: Based on the cluster head node, all communication routes are determined, and the number of the cluster head nodes is applied to encode the position of each communication route; Based on the encoding result, the communication route set is constructed.

[0010] According to a wireless sensor network routing method provided by the present invention, the location of other communication routes in the communication route set except the target communication route is updated, and then the method further includes: Check whether the updated communication routes are out of bounds; If the position of any communication route is out of bounds, the position of any communication route is adjusted.

[0011] According to a wireless sensor network routing method provided by the present invention, the construction of a new communication route set based on the fitness value of each communication route includes: Based on the elite selection strategy, the communication route with the highest fitness value among the communication routes is saved in the elite pool; The communication routes in the elite pool are sorted, and a preset number of communication routes are screened out from the elite pool based on the sorting result to construct a new communication route set.

[0012] The present invention also provides a wireless sensor network routing device, comprising: A route determination unit, used to start information transmission from any node of the wireless sensor network, and in the process of information transmission, apply the elite Levy Flying Harris Hawk optimization algorithm to perform route search and determine the optimal communication route, wherein the elite Levy Flying Harris Hawk optimization algorithm is used to construct a communication route set based on the elected cluster head node, and evaluate each communication route in the communication route set through an iterative optimization process, so as to determine the optimal communication route from the communication route set according to the evaluation result, wherein the cluster head node is elected from each node of the wireless sensor network; An information transmission unit is used to transmit power grid fault information to a terminal node based on the optimal communication route, wherein the power grid fault information is obtained based on node detection in the wireless sensor network.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any of the wireless sensor network routing methods described above is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the wireless sensor network routing method described in any one of the above is implemented.

[0015] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the wireless sensor network routing method described above is implemented.

[0016] The wireless sensor network routing method, device, electronic device and storage medium provided by the present invention select an efficient clustering routing scheme in the wireless sensor network of the source-grid-load-storage collaborative optimization distribution network, and apply the elite Levy Flying Harris Hawk optimization algorithm to perform routing search. The algorithm has a simple and clear process for solving problems, relatively few algorithm parameters, and fast optimization speed, which effectively improves the data transmission performance and security of the wireless sensor network. In particular, compared with the existing LEACH-C and LEACH, the present invention can obtain the optimal communication route in fewer iterations, and has a strong global search capability, and will not fall into the local optimum and cause the evolutionary calculation to stagnate. In addition, by introducing the elite Levy Flying Harris Hawk optimization algorithm, the present invention can greatly improve the routing search efficiency and quickly determine the optimal communication route, thereby reducing the energy consumption of the wireless sensor network, effectively extending the network life, and having the advantages of safety, energy saving, strong robustness, etc., thereby improving the comprehensive performance of the source-grid-load-storage distribution network, and helping to build an economical, efficient and practical source-grid-load-storage collaborative optimization distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of the wireless sensor network routing method provided by the present invention; Figure 2 It is a flow chart of step S100 in the wireless sensor network routing method provided by the present invention; Figure 3 It is a comparison chart of the number of packet transmissions of the algorithm of the present invention, LEACH-C and LEACH in a 100m×100m area; Figure 4 It is a comparison chart of node death between the algorithm of the present invention and LEACH-C and LEACH in the 100m×100m area; Figure 5 It is a structural schematic diagram of the wireless sensor network routing device provided by the present invention; Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Wireless sensor networks are composed of a large number of small and low-cost single sensor nodes that are fixedly deployed or randomly scattered in the detection area. Sensor nodes are usually small in size, with built-in battery energy and non-grid power supply. The node energy is greatly limited. In addition, the power area environment where the wireless sensor network for source-grid-load-storage coordinated optimization of distribution network is usually deployed is very complex and difficult for general workers to reach. It is relatively difficult to replenish the energy of sensor nodes. At the same time, each node is exposed to the external environment for a long time and is vulnerable to malicious attacks from external factors, resulting in node paralysis and data forwarding failure. How to use energy efficiently to extend the network life cycle and ensure routing forwarding security is a key issue that needs to be solved in wireless sensor networks. Most of the existing wireless sensor network routing methods solve the single-target energy consumption problem, such as the Flooding algorithm, the SPIN (Self Play fine-tuning) algorithm and the DD (Directed Diffusion) algorithm. Due to the characteristics of the method itself, it cannot resist network black hole attacks and can no longer fully meet the existing routing needs.

[0021] Based on the above reasons, in order to further ensure the efficient routing of the current source-grid-load-storage collaborative optimization distribution network wireless sensor network, a large number of experts and scholars have optimized and improved their methods in the hope of better results. For example, the existing patent with announcement number CN105430706A and name "A wireless sensor network routing optimization method based on improved particle swarm algorithm" uses a relationship matrix containing the entire network topology information as the encoding method of the particle swarm algorithm to deal with routing optimization problems, and applies the crossover and mutation mechanism of the genetic algorithm to achieve global convergence search. However, due to the limitations of the algorithm mechanism of the particle swarm optimization algorithm (Particle Swarm Optimization, PSO) and the genetic algorithm (Genetic Algorithm, GA), it falls into the local optimal solution too early in the evolution process, resulting in a decrease in algorithm performance and a slow convergence speed. It is impossible to obtain better results in short-term iterations, and the routing solution QoS (Quality of Service) and security performance are not high. Especially when the sensor network node scale is large, even if the two algorithms are combined, the optimization speed is relatively slow, which cannot meet the actual needs of the power system.

[0022] Harris Hawks Optimization (HHO) is an optimization algorithm proposed by researchers in the scientific community in 2019 based on the process of Harris Hawks preying on rabbits. It has the characteristics of strong optimization ability and fast convergence speed. By improving the Harris Hawk Optimization Algorithm, this paper proposes an Elite Levy Flight Harris Hawks Optimization (ELHHO) algorithm, which is a variant of the Harris Hawk algorithm that combines elite selection and Levy flight mechanisms. ELHHO starts from the routing source node randomly, searches for routes in sequence, finds the optimal routing solution through population evolution of each generation, and judges the quality of the solution by evaluating the route of each Harris Hawk individual. ELHHO simulates the behavioral changes of Harris Hawks during hunting. During the search process, multiple Harris Hawk individuals simultaneously perform optimization, which greatly improves the running speed of the algorithm. It uses the population iteration mechanism to make the search process converge continuously and eventually approach the optimal solution. The elite Levy Flying Harris Hawk optimization algorithm has high solution accuracy and fast algorithm optimization, and has shown good performance in many practical complex problems. Since the efficient routing problem of the wireless sensor network of the source-grid-load-storage collaborative optimization distribution network is exactly the NP (Non-deterministic Polynomial, non-deterministic polynomial complexity) difficult problem that the elite Levy Flying Harris Hawk optimization algorithm is good at solving, how to apply the elite Levy Flying Harris Hawk optimization algorithm to select efficient routing paths to reduce network energy consumption and extend network life is one of the key issues studied in this invention.

[0023] It should be noted that the wireless sensor network routing method provided by the present invention runs on the wireless sensor network side of the source-grid-load-storage collaboratively optimized distribution network. The method selects the optimal routing scheme for the wireless sensor network of the source-grid-load-storage collaboratively optimized distribution network based on the elite Levy Flying Harris Hawk optimization algorithm, wherein the elite Levy Flying Harris Hawk optimization algorithm runs on the wireless sensor network of the source-grid-load-storage collaboratively optimized distribution network.

[0024] Figure 1 FIG. 1 is a flow chart of the wireless sensor network routing method provided by the present invention, such as Figure 1 As shown, the method includes: Step S100, starting information transmission from any node of the wireless sensor network, during the information transmission process, applying the elite Levy Flying Harris Hawk optimization algorithm to perform route search and determine the optimal communication route, the elite Levy Flying Harris Hawk optimization algorithm is used to build a communication route set based on the elected cluster head node, and through an iterative optimization process, each communication route in the communication route set is evaluated to determine the optimal communication route from the communication route set according to the evaluation result, and the cluster head node is elected from each node of the wireless sensor network; Step S200: transmitting power grid fault information to a terminal node based on the optimal communication route, wherein the power grid fault information is obtained based on node detection in the wireless sensor network.

[0025] It should be noted that the wireless sensor network is a self-organizing network structure formed by N wireless sensor nodes in an autonomous manner. These sensor nodes are interconnected and coordinated through wireless communication, and each sensor node can communicate with its surrounding neighboring nodes. Through this wireless sensor network, when a fault is detected in the source-grid-load-storage collaborative optimization distribution network, the fault can be quickly responded to, and the grid fault information can be collected and transmitted through mutual communication between nodes.

[0026] Specifically, the ELHHO algorithm can randomly start from any node in the wireless sensor network and search for routes in sequence. Therefore, information transmission can be started from any sensor node, and the ELHHO algorithm can be used to select the best communication routing path. The purpose of the above step S100 is to find the optimal data transmission path through an intelligent algorithm, reduce communication costs, and improve data transmission efficiency. It should be understood that when determining the optimal communication route, although the route search is started from any node in the wireless sensor network, the optimal communication route determined by the ELHHO algorithm is based on the structure of the entire network, the location of the nodes, energy consumption, communication costs and other factors. Comprehensive consideration is obtained, therefore, the optimal communication route is applicable to communication between any nodes in the network.

[0027] The nodes in the wireless sensor network work in a collaborative way. They can communicate with each other and share information. When a node detects a power grid fault, it will generate corresponding fault information and transmit the power grid fault information to the terminal node through the optimal communication route. Here, the power grid fault information is detected by the nodes in the wireless sensor network. The nodes here refer to the sensor devices in the wireless sensor network that are responsible for collecting environmental data (such as power grid status, fault information, etc.). These nodes usually have sensing, data processing and wireless communication capabilities, can monitor the status of the power grid in real time, and generate corresponding fault information when a fault is detected. The above-mentioned terminal nodes refer to a part of the nodes in the wireless sensor network, which are usually located at the edge or specific location of the network, and are used to receive data from other nodes and perform further processing or forwarding.

[0028] It can be understood that when the ELHHO algorithm is applied to determine the optimal communication route, the algorithm will consider factors such as the coverage, residual energy, proximity, and communication cost of each node in the wireless sensor network to elect the cluster head node and cluster the nodes in the network. After the clustering is completed, the algorithm will initialize a Harris Hawk population based on the cluster head node (i.e., build an initial communication route set). Each Harris Hawk individual in the population (equivalent to a communication route in the communication route set) represents a possible routing scheme. For each Harris Hawk individual in the population (i.e., each communication route scheme), the algorithm will calculate its fitness value and select the best individual from it as the prey position based on the fitness value. According to the hunting behavior simulation of the Harris Hawk, the algorithm will update the position of each Harris Hawk individual to search for a better solution. This iterative process will be repeated until a certain termination condition is met. During the iteration process, the algorithm will also adopt an elite strategy to retain the excellent individuals in the crowd. These excellent individuals (i.e., the best performing routing scheme in the current iteration round) will be saved in the elite pool and used to initialize a new population at the beginning of each iteration to ensure the convergence of the algorithm and the quality of the solution. When the iterative process ends, the algorithm selects the Harris Hawk individual with the highest fitness value from the elite pool as the final solution, that is, the optimal routing scheme, which will guide the nodes in the wireless sensor network on how to efficiently transmit power grid fault information to the terminal nodes.

[0029] The method provided by the embodiment of the present invention selects an efficient cluster routing scheme in the wireless sensor network of the source-grid-load-storage collaborative optimization distribution network, and applies the elite Levy Flying Harris Hawk optimization algorithm to perform route search. The algorithm has a simple and clear process for solving the problem, relatively few algorithm parameters, and a fast optimization speed, which effectively improves the data transmission performance and security of the wireless sensor network. In particular, compared with the existing LEACH-C and LEACH, the present invention can obtain the optimal communication route in fewer iterations, and has a strong global search capability, and will not fall into the local optimum and cause the evolutionary calculation to stagnate. In addition, by introducing the elite Levy Flying Harris Hawk optimization algorithm, the present invention can greatly improve the efficiency of route search and quickly determine the optimal communication route, thereby reducing the energy consumption of the wireless sensor network, effectively extending the network life, and having the advantages of safety, energy saving, strong robustness, etc., thereby improving the comprehensive performance of the source-grid-load-storage distribution network, and helping to build an economical, efficient, and practical source-grid-load-storage collaborative optimization distribution network.

[0030] Based on any of the above embodiments, Figure 2 is a flow chart of step S100 in the wireless sensor network routing method provided by the present invention, such as Figure 2 As shown, step S100 specifically includes: Step S110, selecting a cluster head node: selecting a cluster head node from each node of the wireless sensor network; Furthermore, step S110 specifically includes: Determining a fitness function value of each node based on the attributes of each node in the wireless sensor network, wherein the attributes of each node include coverage, residual energy, proximity, and communication cost; Based on the fitness function value of each node, a cluster head node is selected from each node.

[0031] It should be noted that the algorithm of the present invention introduces factors such as coverage, residual energy, proximity and communication cost on the basis of LEACH-C as important conditions for selecting the best node as the cluster head node, which can not only ensure the rationality of the election, but also ensure the uniform distribution among the cluster head nodes. Here, the LEACH-C algorithm, whose full name is Centralized Low-EnergyAdaptive Clustering Hierarchy, is an improved version of the LEACH (Low-EnergyAdaptive Clustering Hierarchy) algorithm. The LEACH algorithm itself is an energy management protocol widely used in wireless sensor networks, which aims to extend the life cycle of the network and reduce energy consumption by clustering. The LEACH-C algorithm introduces a centralized control mechanism based on LEACH to further optimize energy consumption and network performance.

[0032] The steps of implementing the LEACH-C algorithm include: ① Information collection: Each node in the network sends its location and energy information to the central controller (such as a base station); ② Cluster head election: The central controller calculates the best cluster head distribution through an optimization algorithm based on the collected information, and assigns the cluster head identity to the selected node. In the embodiment of the present invention, this process will take into account factors such as node coverage, residual energy, proximity, and communication cost; ③ Cluster member selection: Non-cluster head nodes select the cluster to which they belong based on the distance from the cluster head node; ④ Data transmission: The cluster head node is responsible for collecting data from cluster member nodes and transmitting it to the central controller (such as a base station). During this process, the cluster head node will perform a certain degree of data aggregation to reduce the amount of data transmission and save energy.

[0033] Specifically, when electing a cluster head node, the election can be determined based on the fitness function value of each node in the wireless sensor network. When designing the fitness function, the following four points are important to consider: (1) Coverage: The coverage is calculated according to the coverage radius, as shown in the following formula:

[0034] In the above formula, Representation Node The coverage, Representation Node The coverage radius, from which we can get the first target equation:

[0035] In the above formula, It represents the first target in the fitness function, which is the average coverage of all nodes in the network. This target is used to evaluate the coverage performance of the cluster head node in the network. Represents the total number of relay nodes in the network, Indicates the total number of nodes.

[0036] (2) Node residual energy: After the cluster head node completes the data receiving and transmission tasks, the residual energy is usually used to fuse the data. The calculation formula is as follows:

[0037] In the above formula, represents the total energy of a relay node, represents the energy required for data collection, represents the energy required for data transmission, Indicates the energy required for data reception, represents the energy required for data fusion, thus obtaining the second objective equation:

[0038] It represents the second objective in the fitness function, which is the average value of the residual energy of all nodes in the network.

[0039] (3) Proximity: The proximity between the current node and its neighboring nodes is calculated based on the distance between the adjacent nodes, as shown in the following formula:

[0040] in, represents the proximity of the nodes, Represents the distance between two adjacent nodes, thus obtaining the third objective equation:

[0041] It is understood that proximity is an indicator used to evaluate the degree of proximity between nodes in a network. Proximity usually refers to the measure of the distance between a node and its neighboring nodes. Each node calculates the distance between itself and all its neighboring nodes to obtain its own proximity.

[0042] It represents the third objective in the fitness function, which is the average of the proximity of all nodes in the network. This objective is used to evaluate the distribution of nodes in the network, especially their mutual proximity.

[0043] (4) Communication cost: The cost calculation formula for inter-node communication is as follows:

[0044] In the above formula, represents the average distance between adjacent nodes, represents the cluster radius, from which the fourth objective equation can be obtained:

[0045] It represents the fourth objective in the fitness function, which is the average value of the communication cost of all nodes in the network.

[0046] The fitness function is established based on multiple objectives. First, the weights Multiply by the target value, and then add the above target quantities to obtain a fitness function. The fitness function designed by the present invention can be obtained by the above method as follows:

[0047] In the above formula, the weight The total value of is 1, , , when setting the weights, it is necessary to maximize the node coverage and residual energy, minimize the proximity between nodes and the communication cost, and the purpose of designing the cluster head election function (i.e., fitness function) is to maximize this value. When this value requirement is reached, it can become a qualified cluster head node and send data packets to the base station. Here, the purpose of designing the fitness function is to evaluate the fitness of each node in the network to become a cluster head, so as to elect a suitable cluster head node according to the fitness function value of each node. It should be understood that in the parameter setting of the fitness function, the coverage weight =0.287, node residual energy weight =0.369, proximity weight =0.251, communication cost weight =0.093.

[0048] Step S120, clustering: using Haversine distance to search for adjacent nodes to divide clusters; It should be noted that by clustering the nodes in the wireless sensor network, a large-scale network can be divided into multiple small-scale networks called clusters, thereby reducing the difficulty of topology management. Each cluster is managed by a cluster head node. The member nodes in the cluster only need to communicate with the cluster head node, and the cluster head node is responsible for communicating with other cluster head nodes, which greatly simplifies the complexity of network management. The cluster head node will fuse the data sent by the member nodes of the cluster before forwarding it, which can reduce data redundancy and reduce the amount of information communication, thereby saving network energy and reducing network energy consumption. In addition, clustered routing can more effectively control the flow of data in the network. Through reasonable cluster head election, routing selection and data transmission strategies, network energy consumption can be balanced to avoid some nodes from quickly running out of energy due to excessive use, thereby extending the life cycle of the entire network.

[0049] Specifically, in the cluster establishment phase, nodes often need to identify the surrounding cluster head nodes first. Regarding the termination / startup of sensor nodes, relay nodes or cluster heads, trigger signals and node location information are required. At this time, the Haversine distance formula is used to calculate the node distance, as shown in the following formula:

[0050] In the above formula, Representation Node and nodes The distance between and Represent the positions of two nodes respectively.

[0051] The distance from the node to the base station is as follows:

[0052] The location of the base station is determined by Represents. The distance between adjacent nodes is determined by the Haversine distance formula, as follows:

[0053] In the above formula, Represents the distance between two nodes calculated according to the Haversine distance formula, which is used to determine the great circle distance between two geographic locations. Haversine distance is a method used to calculate the shortest distance between two points on the Earth, which takes into account the spherical shape of the Earth.

[0054] Use the formula to calculate the distance between each node and its neighbor nodes This distance will be used for subsequent cluster member decisions. Non-cluster head nodes will use the distance between themselves and the cluster head node to determine the cluster membership. The value is used to decide which cluster to join. As non-cluster head nodes join the selected cluster head node, clusters begin to form. It should be understood that if a node fails or dies, the cluster formation process will restart. Here, node failure or death refers to the state where a sensor node in the network cannot continue to work normally or participate in data collection and transmission due to various reasons.

[0055] Step S130, population initialization: constructing a communication route set based on the cluster head node, wherein the communication route set includes multiple communication routes; Specifically, step S130 includes: Based on the cluster head node, all communication routes are determined, and the number of cluster head nodes is applied to encode the position of each communication route; based on the encoding result, a communication route set is constructed.

[0056] It should be noted that, according to the number of cluster head nodes, the position of each Harris Hawk individual (i.e., each communication routing scheme) of the ELHHO algorithm can be encoded to form the position of the Harris Hawk in the search space. Here, after the cluster head node is elected according to the fitness function value, the ELHHO algorithm can be used to find the optimal data transmission route from the cluster head node to the base station. In this process, each Harris Hawk individual represents a potential routing scheme.

[0057] It is understandable that the position of the above Harris Hawk individual is an abstract concept in the ELHHO algorithm. It represents a point in the algorithm search space, which corresponds to a specific solution (i.e., a specific communication routing scheme). Each Harris Hawk individual has a corresponding position in the search space, which is represented by a code that contains all the necessary information that constitutes the routing scheme (such as the connection relationship between nodes, routing path, etc.). The algorithm evaluates the pros and cons of each individual in the search space by calculating its fitness value, where the individual with the highest fitness value is regarded as the "prey", that is, the optimal solution currently found.

[0058] Specifically, the Harris Hawk population is randomly initialized within the search boundary, and the parameters of the elite Levy flight Harris Hawk optimization algorithm are initialized. This can be achieved through the following steps: ① Determine the cluster head node information: First, it is necessary to clarify the number, identifier (ID), location and other basic information of the cluster head node. This information usually comes from the previous cluster head election process. ② Define the solution space and encoding method: According to the characteristics of the problem (such as routing optimization of wireless sensor networks), define the structure of the solution space (i.e., search space). The solution space should contain all possible routing schemes, which are composed of connections between cluster head nodes. Select a suitable encoding method to represent the location of the Harris Hawk individual (i.e., routing scheme). The encoding method should be able to uniquely represent a routing scheme and facilitate the algorithm to search and update. For example, binary encoding, integer encoding, or real number encoding can be used. ③ Initialize the population size: Set the Harris Hawk population size to M, where M is a natural number, indicating that the population will contain M Harris Hawk individuals. ④ Construct the initial population: For each Harris Hawk individual in the population, perform the following steps: Generate an initial encoding for the Harris Hawk individual within the search boundary , the encoding should conform to the encoding method defined previously and represent a potential routing solution. The search boundary can be determined based on the number and location of cluster head nodes and the constraints of the routing problem. ⑤ Initialize algorithm parameters: In addition to the population size M, other algorithm parameters need to be initialized, such as the upper limit of the number of iterations G (G is a natural number). At this point, the initial Harris Hawk population has been constructed, and each individual represents a potential routing solution. Next, the algorithm will enter the iterative search process to find the optimal routing solution by evaluating fitness, updating position, and other steps.

[0059] Step S140, calculating the fitness value of each communication route in the communication route set, and determining a target communication route from the communication routes based on the fitness value of each communication route; Specifically, after constructing the initialized Harris Hawk population (i.e., the communication route set), the fitness value of each Harris Hawk individual (i.e., each communication route) can be calculated, sorted according to the size of the fitness value, and the position of the individual with the best fitness value in the current population is recorded as the prey position. Here, the fitness value of each Harris Hawk individual can be calculated according to the above fitness function. The design of the fitness function includes comprehensive consideration of many factors, which is applicable to both the election of cluster head nodes and the data transmission stage.

[0060] It can be understood that the algorithm first evaluates the fitness value of each Harris Hawk individual, which reflects the performance of the individual in solving the current problem (i.e., finding the best communication routing path). Then, the algorithm sorts these individuals according to their fitness values ​​to determine their relative merits. Through sorting, the algorithm is able to identify the individual with the highest fitness value in the current population, that is, the solution that performs best in the current iteration round. This optimal solution is regarded as the "prey", that is, the target communication route, because it represents the best discovery of the algorithm in the current search space. Finally, the algorithm records the position of this optimal solution (prey) in the search space. This position information is crucial for the subsequent search process because it provides other individuals (predators) with a direction and target to move. In subsequent iterations, other individuals will update their positions based on the position of the prey in order to approach or surpass the prey.

[0061] Step S150, based on the position of the target communication route in the search space, updating the positions of other communication routes in the communication route set except the target communication route; It should be noted that after determining the target communication route (i.e., prey), the algorithm will update the position of each Harris hawk individual by simulating the hunting behavior of Harris hawks to search for a better solution.

[0062] Specifically, each Harris hawk individual can update its position according to the behavior of its prey through a global search process and a local development process: (1) Global search process

[0063] In this stage, Harris's hawk searches for prey through two search strategies. The model formula is as follows:

[0064] in, and are the positions of individuals in the current and next iterations, respectively. is the number of iterations, is a randomly selected individual position, is the prey position, that is, the position of the individual with the best fitness, , , , , They are all random numbers between [0,1]. q Used to randomly select the strategy to be adopted, is the individual average position, and the expression is:

[0065] in, For the population The location of each Harris Hawk individual, M For population size.

[0066] (2) Search and development conversion process

[0067] In this stage, Harris Hawks use the energy of prey escape Switching between searching and hunting behaviors, i.e. escape energy It is used to determine whether the algorithm should transition from the global search phase to the local development process. The prey escape energy formula is as follows:

[0068] in, is the prey escape energy, and the maximum number of iterations is given by express, is the initial energy, and .like , the prey is active, and the eagles remain in the search phase; if , the prey's ability to move decreases, and the eagles begin to hunt it.

[0069] (3) Local development process

[0070] Global search refers to the process of finding the optimal solution in the entire search space. Its goal is to explore the solution space of the entire problem and find the global optimal solution. Local development refers to the process of conducting a deeper search near the solution that has been found and optimizing the current solution. It is usually performed on the basis of global search and aims to improve the quality of the current solution.

[0071] a) Soft siege

[0072] when The hunting model is as follows:

[0073] Harris hawk's relative position to prey is determined by express, represents the random jump factor during the prey's escape process, is a random number. In addition, the above is also a random number. In the local search phase of the Harris Hawk algorithm, Used to decide which siege strategy to adopt. The introduction of can increase the randomness and diversity of the algorithm. In this way, the algorithm can explore more widely in the solution space and find better solutions.

[0074] b) Hard Siege

[0075] when The hunting model is as follows:

[0076] c) Accumulative Dive Soft Siege

[0077] when The hunting model is as follows:

[0078] The rules when using Levy flight mode are as follows:

[0079] The above formula defines the Levy flight step length The calculation method of and is the parameter that controls the shape of the Levy flight distribution, , is the gamma function, which is used to calculate the distribution of the Levy step size, is the scale parameter of the step size, which determines the standard deviation of the step size distribution. This formula is used to generate random step sizes with a heavy-tailed distribution, which helps the algorithm jump out of the local optimum and explore a wider search space. Z represents the updated position of the current Harris Hawk individual. Y represents an intermediate position calculated under the soft siege strategy. S is a scaling factor used to adjust the size of the Levy flight step, thereby affecting the exploration range of the search process. LF(D) represents the Levy flight vector, which is a randomly generated step size used to explore a large range based on the current position. Levy flight is a random walk model with a heavy-tailed distribution, which allows the algorithm to occasionally make long jumps to jump out of the local optimal solution area.

[0080] From this, we can design a strategy for soft encirclement to update the position of the eagle group:

[0081] in, represents the fitness function, Indicates at location The fitness function value of Indicates at location The fitness function value of Indicates at location The fitness function value of .

[0082] d) Accelerated Dive Hard Siege

[0083] when The hunting model is as follows:

[0084] Step S160, checking whether the updated Harris Hawk individual position is out of bounds, if so, it is necessary to make corresponding adjustments, and then recalculate the fitness value of the Harris Hawk individual.

[0085] Specifically, out-of-bounds means that the position of an individual exceeds the predefined boundary of the search space. In optimization problems, the search space usually has clear boundaries, and the update of the individual position needs to comply with these boundary constraints. By checking whether the position of the updated Harris Hawk individual (i.e., the communication route) is out of bounds, if the position of the Harris Hawk individual exceeds the boundary of the search space, the position of the Harris Hawk individual needs to be adjusted accordingly.

[0086] Step S170, using the elite strategy, save the individuals with the best fitness value in the current Harris Hawk population into the elite pool, and select the best top Z Harris Hawk individuals from the elite pool and put them into the initial population of the next iteration process.

[0087] Specifically, after updating the position of each Harris Hawk individual, the fitness value of each Harris Hawk individual is recalculated so that the individual with the best fitness value can be selected from the current Harris Hawk population and saved in the elite pool. All Harris Hawk individuals in the elite pool (equivalent to each communication route) are sorted so that the best first Z Harris Hawk individuals can be selected from the elite pool and put into the initial population of the next iteration process.

[0088] Step S180, performing iterative optimization based on the new communication route set (ie, the new Harris Hawk population obtained in step S170), until the iterative process is completed to obtain the optimal communication route.

[0089] Specifically, determine whether the current number of iterations meets the loop termination condition. If so, output the sequence carried by the globally optimal Harris Hawk individual as the optimal routing solution for the source-grid-load-storage collaborative optimization of the distribution network wireless sensor network; if not, continue to return to step S140.

[0090] Based on any of the above embodiments, a software simulation experiment is performed according to the method in the above embodiments. The parameter settings and simulation results of the simulation experiment are introduced below: In the embodiment of the present invention, there are 100 sensor nodes in total, and the network coverage is 100×100 (m 2 ), base station coordinates (50, 50), the initial node energy is 1 J, the node communication radius is 50 meters, and the optimal number of clusters is given in advance as 8. The source-grid-load-storage collaborative optimization distribution network wireless sensor network can divide 100 sensor nodes into 8 clusters. The source-grid-load-storage collaborative optimization distribution network wireless sensor network can arbitrarily select one or several relay nodes from the 8 cluster head nodes to form a forwarding route. The number of Harris Hawk individuals in the elite Levy flying Harris Hawk optimization algorithm is M =70, the upper limit of the number of iterations G =8000.

[0091] Figure 3 This is a comparison chart of the number of packet transmissions of the algorithm of the present invention, LEACH-C and LEACH in a 100m×100m area. Figure 3 As shown in the figure, it shows the number of data packets transmitted, which is one of the performance indicators of the network. After the number of iterations reaches 5000, the number of data packets received by the base station from all networks is stable. The number of packets transmitted by the algorithm of the present invention is 83.3% higher than that of LEACH-C, and is significantly higher than that of the LEACH algorithm.

[0092] Figure 4 This is a comparison chart of the node death of the algorithm of the present invention, LEACH-C and LEACH in the 100m×100m area. Figure 4 As shown in the figure, the higher the number of iterations corresponding to the death of nodes in each stage, the longer the corresponding network life cycle. When the first node dies, the number of iterations of the algorithm of the present invention is 25.7% and 175% higher than that of the LEACH-C and LEACH algorithms, respectively. When all nodes die, the life of the algorithm of the present invention is 41.2% and 125% higher than that of the LEACH-C and LEACH algorithms, respectively.

[0093] The wireless sensor network routing device provided by the present invention is described below. The wireless sensor network routing device described below and the wireless sensor network routing method described above can be referred to each other.

[0094] Based on any of the above embodiments, Figure 5 Schematic diagram of the structure of the wireless sensor network routing device provided by the present invention. Figure 5 As shown, the device comprises: A route determination unit 510 is used to start information transmission from any node of the wireless sensor network. During the information transmission process, an elite Levy Flying Harris Hawk optimization algorithm is applied to perform route search to determine an optimal communication route. The elite Levy Flying Harris Hawk optimization algorithm is used to construct a communication route set based on the elected cluster head node, and evaluate each communication route in the communication route set through an iterative optimization process to determine an optimal communication route from the communication route set according to the evaluation result. The cluster head node is elected from each node of the wireless sensor network. The information transmission unit 520 is used to transmit the power grid fault information to the terminal node based on the optimal communication route, and the power grid fault information is obtained based on the node detection in the wireless sensor network.

[0095] The device provided by the embodiment of the present invention selects an efficient clustering routing scheme in the wireless sensor network of the source-grid-load-storage collaborative optimization distribution network, and applies the elite Levy Flying Harris Hawk optimization algorithm to perform route search. The process of solving the problem by the algorithm is concise and clear, the algorithm parameters are relatively few, the optimization speed is fast, and the data transmission performance and security of the wireless sensor network are effectively improved. In particular, compared with the existing LEACH-C and LEACH, the present invention can obtain the optimal communication route in fewer iterations, and has a strong global search capability, and will not fall into the local optimum and cause the evolutionary calculation to stagnate. In addition, by introducing the elite Levy Flying Harris Hawk optimization algorithm, the present invention can greatly improve the efficiency of route search and quickly determine the optimal communication route, thereby reducing the energy consumption of the wireless sensor network, effectively extending the network life, and having the advantages of safety, energy saving, strong robustness, etc., thereby improving the comprehensive performance of the source-grid-load-storage distribution network, and helping to build an economical, efficient and practical source-grid-load-storage collaborative optimization distribution network.

[0096] Based on any of the above embodiments, the route determination unit 510 includes: A cluster head node election subunit, used to elect a cluster head node from each node of the wireless sensor network and establish a sub-cluster based on the cluster head node; A route set construction subunit, used to construct a communication route set based on the cluster head node, wherein the communication route set includes multiple communication routes; a target route determination subunit, configured to calculate the fitness value of each communication route in the communication route set, and determine a target communication route from the communication routes based on the fitness value of each communication route; A location updating subunit, configured to update the locations of other communication routes in the communication route set except the target communication route based on the location of the target communication route in the search space; An elite selection subunit, used for recalculating the fitness value of each communication route when the location update is completed, and constructing a new communication route set based on the fitness value of each communication route; The iterative optimization subunit is used to perform iterative optimization based on the new communication route set until the iterative process ends and the optimal communication route is obtained.

[0097] Based on any of the above embodiments, the cluster head node election subunit is specifically used for: Determining a fitness function value of each node based on the attributes of each node in the wireless sensor network, wherein the attributes of each node include coverage, residual energy, proximity, and communication cost; Based on the fitness function value of each node, a cluster head node is selected from each node.

[0098] Based on any of the above embodiments, the routing set construction subunit is specifically used for: Based on the cluster head node, all communication routes are determined, and the number of the cluster head nodes is applied to encode the position of each communication route; Based on the encoding result, the communication route set is constructed.

[0099] Based on any of the above embodiments, the route determination unit 510 further includes a location checking subunit, which is used to: Check whether the updated communication routes are out of bounds; If the position of any communication route is out of bounds, the position of any communication route is adjusted.

[0100] Based on any of the above embodiments, the elite selection subunit is specifically used for: Based on the elite selection strategy, the communication route with the highest fitness value among the communication routes is saved in the elite pool; The communication routes in the elite pool are sorted, and a preset number of communication routes are screened out from the elite pool based on the sorting result to construct a new communication route set.

[0101] Figure 6 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 complete mutual communication through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the wireless sensor network routing method, the method comprising: starting information transmission from any node of the wireless sensor network, in the process of information transmission, applying the elite Levy Flying Harris Hawk optimization algorithm to perform route search and determine the optimal communication route, the elite Levy Flying Harris Hawk optimization algorithm is used to construct a communication route set based on the elected cluster head node, and through an iterative optimization process, evaluate each communication route in the communication route set to determine the optimal communication route from the communication route set according to the evaluation result, the cluster head node is elected from each node of the wireless sensor network; based on the optimal communication route, the power grid fault information is transmitted to the terminal node, the power grid fault information is obtained based on the node detection in the wireless sensor network.

[0102] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the relevant technology or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0103] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the wireless sensor network routing method provided by the above-mentioned methods, the method including: starting information transmission from any node of the wireless sensor network, and during the information transmission process, applying the elite Levy Flying Harris Hawk optimization algorithm to perform route search and determine the optimal communication route, the elite Levy Flying Harris Hawk optimization algorithm is used to construct a communication route set based on the elected cluster head node, and through an iterative optimization process, each communication route in the communication route set is evaluated to determine the optimal communication route from the communication route set according to the evaluation result, the cluster head node is elected from each node of the wireless sensor network; based on the optimal communication route, the power grid fault information is transmitted to the terminal node, and the power grid fault information is obtained based on the node detection in the wireless sensor network.

[0104] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the wireless sensor network routing method provided by the above-mentioned methods, the method comprising: starting information transmission from any node of the wireless sensor network, and during the information transmission process, applying the elite Levy Flying Harris Hawk optimization algorithm to perform route search to determine the optimal communication route, the elite Levy Flying Harris Hawk optimization algorithm is used to construct a communication route set based on the elected cluster head node, and through an iterative optimization process, evaluate each communication route in the communication route set to determine the optimal communication route from the communication route set according to the evaluation result, the cluster head node is elected from each node of the wireless sensor network; based on the optimal communication route, transmitting power grid fault information to the terminal node, the power grid fault information is obtained based on node detection in the wireless sensor network.

[0105] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0106] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wireless sensor network routing method, characterized in that: include: Information transmission is started from any node of the wireless sensor network. During the information transmission process, an elite Levy Flying Harris Hawk optimization algorithm is applied to perform route search to determine an optimal communication route. The elite Levy Flying Harris Hawk optimization algorithm is used to construct a communication route set based on an elected cluster head node, and through an iterative optimization process, each communication route in the communication route set is evaluated to determine the optimal communication route from the communication route set according to the evaluation result. The cluster head node is elected from each node of the wireless sensor network. Based on the optimal communication route, power grid fault information is transmitted to a terminal node, wherein the power grid fault information is obtained based on node detection in the wireless sensor network.

2. The wireless sensor network routing method according to claim 1, characterized in that: The application of the elite Levy Flight Harris Hawk optimization algorithm to perform route search and determine the optimal communication route includes: Selecting a cluster head node from each node of the wireless sensor network, and establishing clusters based on the cluster head node; Based on the cluster head node, construct a communication route set, wherein the communication route set includes multiple communication routes; Calculating the fitness value of each communication route in the communication route set, and determining a target communication route from the communication routes based on the fitness value of each communication route; Based on the position of the target communication route in the search space, updating the position of other communication routes in the communication route set except the target communication route; When the location update is completed, recalculate the fitness value of each communication route, and construct a new communication route set based on the fitness value of each communication route; Iterative optimization is performed based on the new communication route set until the iterative process ends to obtain the optimal communication route.

3. The wireless sensor network routing method according to claim 2, characterized in that: The step of selecting a cluster head node from each node of the wireless sensor network includes: Determining a fitness function value of each node based on the attributes of each node in the wireless sensor network, wherein the attributes of each node include coverage, residual energy, proximity, and communication cost; Based on the fitness function value of each node, a cluster head node is selected from each node.

4. The wireless sensor network routing method according to claim 2, characterized in that: The step of constructing a communication route set based on the cluster head node includes: Based on the cluster head node, all communication routes are determined, and the number of the cluster head nodes is applied to encode the position of each communication route; Based on the encoding result, the communication route set is constructed.

5. The wireless sensor network routing method according to claim 2, characterized in that: The step of updating the locations of other communication routes in the communication route set except the target communication route further comprises: Check whether the updated communication routes are out of bounds; If the position of any communication route is out of bounds, the position of any communication route is adjusted.

6. The wireless sensor network routing method according to any one of claims 2 to 5, characterized in that: The constructing a new communication route set based on the fitness value of each communication route comprises: Based on the elite selection strategy, the communication route with the highest fitness value among the communication routes is saved in the elite pool; The communication routes in the elite pool are sorted, and a preset number of communication routes are screened out from the elite pool based on the sorting result to construct a new communication route set.

7. A wireless sensor network routing device, characterized in that: include: A route determination unit, used to start information transmission from any node of the wireless sensor network, and in the process of information transmission, apply the elite Levy Flying Harris Hawk optimization algorithm to perform route search and determine the optimal communication route, wherein the elite Levy Flying Harris Hawk optimization algorithm is used to construct a communication route set based on the elected cluster head node, and evaluate each communication route in the communication route set through an iterative optimization process, so as to determine the optimal communication route from the communication route set according to the evaluation result, wherein the cluster head node is elected from each node of the wireless sensor network; An information transmission unit is used to transmit power grid fault information to a terminal node based on the optimal communication route, wherein the power grid fault information is obtained based on node detection in the wireless sensor network.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the wireless sensor network routing method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the wireless sensor network routing method according to any one of claims 1 to 6 is implemented.

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

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