Microgrid wireless communication network optimization method, device, equipment, medium and product

Abstract

The invention discloses a micro-grid wireless communication network optimization method, device and equipment, a medium and a product, and relates to the technical field of network optimization. The method comprises the following steps: firstly, establishing a mapping model of an adjustment strategy and operation information and index parameters according to a dynamic network adjustment strategy oriented to historical execution of a micro-grid wireless communication network, node operation information before the execution of the corresponding strategy and multi-dimensional network state index parameters after the execution of the corresponding strategy; then, a dynamic network adjustment strategy which is oriented to the communication network and currently to be executed is optimized based on a self-adaptive hummingbird algorithm, the strategy and an optimal search result used for minimizing a fitness function are obtained, and finally, the communication network is dynamically adjusted according to the optimal search result. Therefore, the network fluctuation and load change of the micro-grid wireless communication network can be self-adapted, the instability of network communication and data acquisition is prevented from being influenced, and the requirements of real-time control and data interaction of the micro-grid are met.

Description

Technical Field

[0001] The present invention belongs to the technical field of network optimization, and specifically relates to a microgrid wireless communication network optimization method, device, equipment, medium and product. Background Art

[0002] A microgrid is a small-scale, autonomous power system typically composed of renewable energy sources (such as photovoltaic and / or wind power) and energy storage devices, capable of operating independently, either connected to or disconnected from the main grid. Microgrids can improve energy reliability and security, and are particularly suitable for remote areas or places where the grid is not available. However, microgrids also face a series of limitations, including difficulties in coordination between node devices within the microgrid, suboptimal energy scheduling, complex system management, and instability in network communication and data collection. These problems can lead to inefficient and unstable operation of microgrids, affecting their effectiveness in practical applications.

[0003] As the coordination, dispatching and system management center between node devices in a microgrid, edge computer devices usually use a star-shaped wireless communication network (for example, based on WiFi wireless communication technology or long-distance radio communication technology) to wirelessly connect to each node device in the microgrid (such as photovoltaic power generation equipment, wind turbines and energy storage equipment, etc.), so as to achieve coordination between node devices in the microgrid, energy dispatch of node devices in the microgrid and management of the entire microgrid system. However, the aforementioned star-shaped wireless communication network has network fluctuations and load changes in actual applications, which will affect the instability of network communication and data collection, such as low signal-to-noise ratio, low transmit and receive signal strength, and high packet loss rate, making it difficult to meet the needs of real-time control and data interaction of the microgrid. Summary of the Invention

[0004] The purpose of the present invention is to provide a microgrid wireless communication network optimization method, device, computer equipment, computer-readable storage medium and computer program product to solve the problem that the existing microgrid star-type wireless communication network is unstable due to network fluctuations and load changes in actual applications, which affects network communication and data acquisition, and thus makes it difficult to meet the needs of microgrid real-time control and data interaction.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, a microgrid wireless communication network optimization method is provided, which is performed by an edge computer device for wirelessly connecting node devices in the microgrid, comprising:

[0007] Real-time collection of node operation information of node devices within the microgrid and multi-dimensional network status indicator parameters of the microgrid wireless communication network, where the microgrid wireless communication network refers to a star-shaped wireless communication network with local devices as central nodes and node devices within the microgrid as peripheral nodes;

[0008] Based on the dynamic network adjustment strategy historically executed for the microgrid wireless communication network, the node operation information in the most recent unit period before the corresponding strategy execution, and the multi-dimensional network status indicator parameters in the next unit period after the corresponding strategy execution, a mapping model between the dynamic network adjustment strategy and the node operation information and the multi-dimensional network status indicator parameters is established;

[0009] Optimizing a currently pending dynamic network adjustment strategy for a microgrid wireless communication network based on an adaptive hummingbird algorithm to obtain an optimal search result for the dynamic network adjustment strategy and for minimizing a fitness function, wherein the independent variables of the fitness function include a multidimensional network status indicator parameter obtained by importing the currently pending dynamic network adjustment strategy for the microgrid wireless communication network and node operation information for a recent unit time period into a mapping model;

[0010] The microgrid wireless communication network is dynamically adjusted based on the best search results.

[0011] Based on the above invention content, a new solution for optimizing and adjusting the microgrid wireless communication network based on the adaptive hummingbird algorithm is provided, namely, first, according to the dynamic network adjustment strategy historically executed for the microgrid wireless communication network, the node operation information before the execution of the corresponding strategy, and the multi-dimensional network status indicator parameters after the execution of the corresponding strategy, a mapping model of the adjustment strategy and the operation information and the indicator parameters is established, and then based on the adaptive hummingbird algorithm, the dynamic network adjustment strategy currently to be executed for the communication network is optimized to obtain the best search result for the strategy and for minimizing the fitness function, and finally, the communication network is dynamically adjusted according to the best search result. In this way, the network fluctuations and load changes of the microgrid wireless communication network can be adaptively adjusted, and the instability affecting network communication and data collection can be avoided, thereby ensuring that the network performance is always in a better state, which is conducive to reducing network delay and reducing bandwidth consumption and packet loss rate, thereby meeting the needs of real-time control and data interaction of the microgrid, and facilitating practical application and promotion.

[0012] In one possible design, the node operation information includes the computing resource usage rate of the corresponding node device, the node load, the task queue length, the power generation and / or storage capacity;

[0013] And / or, the multi-dimensional network status indicator parameters include signal-to-noise ratio, transmit-receive signal strength and / or packet loss rate.

[0014] In a possible design, when the multi-dimensional network status indicator parameters include signal-to-noise ratio, transmit and receive signal strength, and packet loss rate, the calculation formula of the fitness function F is expressed as follows:

[0015] F=a×D+β×B+γ×L

[0016] Where D represents the signal-to-noise ratio in the multidimensional network status indicator parameter obtained by importing the dynamic network adjustment strategy to be selected for the microgrid wireless communication network and the node operation information of the most recent unit time period into the mapping model, B represents the transmit and receive signal strength in the multidimensional network status indicator parameter, L represents the packet loss rate in the multidimensional network status indicator parameter, and a, β, and γ represent the preset weight coefficients, respectively.

[0017] In one possible design, a dynamic network adjustment strategy currently to be executed for a microgrid wireless communication network is optimized based on an adaptive hummingbird algorithm to obtain the optimal search result for the dynamic network adjustment strategy and for minimizing the fitness function, including but not limited to the following steps S301 to S307:

[0018] S301. Initialize the optimization algorithm parameters of the adaptive hummingbird algorithm, including the population size N and the maximum number of iterations T, and randomly generate a set of parameters to be optimized and a corresponding initial search value array for each hummingbird individual in the hummingbird population containing N hummingbird individuals. Then, execute step S302, wherein the set of parameters to be optimized includes multi-dimensional parameters obtained by encoding and having a mapping relationship with the dynamic network adjustment strategy currently to be executed for the microgrid wireless communication network. The initial search value array x 0 It is expressed as follows:

[0019]

[0020] In the formula, k represents a positive integer less than or equal to K, K represents the total number of parameters in the parameter set to be optimized, represents the initial search value corresponding to the kth parameter in the parameter set to be optimized, u c,k represents the upper limit of the parameter search space corresponding to the kth parameter, l c,k represents the lower limit of the parameter search space corresponding to the kth parameter, and rand(0,1) represents a pure decimal random generator function;

[0021] S302. For each hummingbird individual, the corresponding initial search value array is mapped to a dynamic network adjustment strategy to be selected for the microgrid wireless communication network. The dynamic network adjustment strategy and the node operation information for the most recent unit period are then imported into the mapping model to obtain a first multidimensional network status indicator parameter. The first multidimensional network status indicator parameter is then imported as an independent variable into the fitness function to obtain a corresponding first fitness. Finally, step S303 is executed.

[0022] S303. The initial search value array corresponding to the minimum first fitness is used as the current optimal search value array, and the current iteration number t=1 is initialized and set, and then step S304 is executed;

[0023] S304. For each hummingbird individual, update the corresponding current search value array according to the current number of iterations, the current optimal search value array and the corresponding pre-update search value array, and then execute step S305, where the current search value array x t It is expressed as follows:

[0024]

[0025] Where, Indicates that the current search value array x t The search value corresponding to the kth parameter in Represents the search value corresponding to the kth parameter in the search value array before updating. represents the search value corresponding to the kth parameter in the current optimal search value array, AF() represents the adaptive adjustment factor calculation function, sig() represents the adaptive Sigmoid function, Ca(0,1) represents a random variable that satisfies the standard Cauchy distribution, and Ga(0,1) represents a random variable that satisfies the standard Gaussian distribution;

[0026] S305. For each hummingbird individual, the corresponding current search value array is mapped into a dynamic network adjustment strategy to be selected for the microgrid wireless communication network. The dynamic network adjustment strategy and the node operation information for the most recent unit period are then imported into the mapping model to obtain a second multidimensional network status indicator parameter. The second multidimensional network status indicator parameter is then imported as an independent variable into the fitness function to obtain a corresponding second fitness. Finally, step S306 is executed.

[0027] S306. Determine whether the minimum second fitness is lower than the fitness corresponding to the current optimal search value array. If so, update the current optimal search value array to the current search value array corresponding to the minimum second fitness, and then execute step S307. Otherwise, execute step S307 directly.

[0028] S307. Determine whether the current number of iterations t reaches the maximum number of iterations T. If so, use the current optimal search value array as the best search result obtained by optimizing the dynamic network adjustment strategy for the microgrid wireless communication network and currently to be executed and used to minimize the fitness function. Otherwise, increment the current number of iterations t by 1, and then return to step S304.

[0029] In one possible design, the dynamic network adjustment strategy includes increasing / decreasing the number of wireless communication connections of peripheral nodes, adjusting the transmission power for wireless communication, reselecting the channel for wireless communication, adjusting the posture of the transmitting and receiving antennas for wireless communication, adjusting the position of mobile peripheral nodes, reselecting the anti-interference processing algorithm for wireless communication and / or adjusting the number of retransmissions for wireless communication.

[0030] In one possible design, the method further includes: predicting, based on a time series model, the probability of a failure of a node device in the microgrid in the next unit time period according to node operation information of the node device in the microgrid in the most recent consecutive unit time periods;

[0031] Based on the adaptive hummingbird algorithm, the dynamic network adjustment strategy for the microgrid wireless communication network and currently to be executed is optimized to obtain the dynamic network adjustment strategy and the best search result for minimizing the fitness function, including: based on the adaptive hummingbird algorithm, the dynamic network adjustment strategy for the microgrid wireless communication network and currently to be executed is optimized to obtain the best search result for the dynamic network adjustment strategy, which is used to minimize the fitness function and can meet the fault equipment avoidance activation condition, wherein the independent variable of the fitness function includes the multi-dimensional network status indicator parameters obtained by importing the dynamic network adjustment strategy for the microgrid wireless communication network and to be selected and the node operation information of the current most recent unit time period into the mapping model, and the fault equipment avoidance activation condition means that if the probability of failure of the node device in the microgrid in the current next unit time period exceeds the preset probability threshold, then the node device in the microgrid is prohibited from being activated in the current next unit time period.

[0032] In a second aspect, a microgrid wireless communication network optimization device is provided, which is arranged in an edge computer device used for wireless communication to connect node devices in the microgrid, and includes a real-time data collection unit, a mapping model establishment unit, an adjustment strategy optimization unit, and a network dynamic adjustment unit;

[0033] The real-time data collection unit is used to collect in real time node operation information of node devices in the microgrid and multi-dimensional network status indicator parameters of the microgrid wireless communication network, wherein the microgrid wireless communication network refers to a star-shaped wireless communication network with local devices as central nodes and node devices in the microgrid as peripheral nodes;

[0034] The mapping model establishment unit is communicatively connected to the real-time data collection unit and is used to establish a mapping model between the dynamic network adjustment strategy and the node operation information and the multi-dimensional network status indicator parameters based on the dynamic network adjustment strategy historically executed for the microgrid wireless communication network, the node operation information in the most recent unit time period before the execution of the corresponding strategy, and the multi-dimensional network status indicator parameters in the next unit time period after the execution of the corresponding strategy;

[0035] The adjustment strategy optimization unit is communicatively connected to the real-time data collection unit and the mapping model establishment unit, respectively, and is used to optimize the dynamic network adjustment strategy currently to be executed for the microgrid wireless communication network based on the adaptive hummingbird algorithm to obtain the best search result for the dynamic network adjustment strategy and for minimizing the fitness function, wherein the independent variables of the fitness function include a multi-dimensional network status indicator parameter obtained by importing the dynamic network adjustment strategy to be selected for the microgrid wireless communication network and the node operation information of the most recent unit time period into the mapping model;

[0036] The network dynamic adjustment unit is communicatively connected to the adjustment strategy optimization unit and is used to dynamically adjust the microgrid wireless communication network according to the best search result.

[0037] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a transceiver that are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the microgrid wireless communication network optimization method as described in the first aspect or any possible design of the first aspect.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium having instructions stored thereon. When the instructions are executed on a computer, the microgrid wireless communication network optimization method as described in the first aspect or any possible design of the first aspect is executed.

[0039] In a fifth aspect, the present invention provides a computer program product, comprising a computer program or instructions, which, when executed by a computer, implements the microgrid wireless communication network optimization method as described in the first aspect or any possible design of the first aspect.

[0040] Beneficial effects of the above scheme:

[0041] (1) The present invention creatively provides a new solution for optimizing and adjusting a microgrid wireless communication network based on an adaptive hummingbird algorithm, namely, first, according to the dynamic network adjustment strategy historically executed for the microgrid wireless communication network, the node operation information before the execution of the corresponding strategy, and the multi-dimensional network status index parameters after the execution of the corresponding strategy, a mapping model of the adjustment strategy and the operation information and the index parameters is established, and then based on the adaptive hummingbird algorithm, the dynamic network adjustment strategy currently to be executed for the communication network is optimized to obtain the best search result for the strategy and for minimizing the fitness function, and finally, the communication network is dynamically adjusted according to the best search result, so as to adapt to the network fluctuation and load change of the microgrid wireless communication network, avoid affecting the instability of network communication and data acquisition, ensure that the network performance is always in a better state, and help reduce network delay and reduce bandwidth consumption and packet loss rate, thereby meeting the needs of real-time control and data interaction of the microgrid;

[0042] (2) When optimizing the wireless communication network of the microgrid, the fault prediction results of the node equipment in the microgrid can also be taken into account, so that the final optimized dynamic network adjustment strategy can meet the conditions for avoiding and enabling faulty equipment, enhance the intelligent control function, and further ensure that the network performance is always in a better state;

[0043] (3) It can adapt to changes in the network structure and load demand of the microgrid and has strong flexibility and scalability. That is, when the scale of the microgrid is expanded or the equipment is updated, the system can quickly integrate new equipment through adaptive adjustment and optimization, reconfigure the network topology and control strategy, and ensure that the entire system always maintains good performance and operating status, meet the needs of the continuous development of the microgrid, and facilitate practical application and promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 A flow chart of a microgrid wireless communication network optimization method provided in an embodiment of the present application.

[0046] Figure 2 A schematic diagram of the structure of a star-shaped wireless communication network based on edge computer devices and node devices within a microgrid provided in an embodiment of the present application.

[0047] Figure 3 A schematic diagram of the structure of a microgrid wireless communication network optimization device provided in an embodiment of the present application.

[0048] Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly introduced below in conjunction with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structures of the drawings is only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these embodiments without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0050] It should be understood that although the terms first, second, etc. may be used herein to describe various objects, these objects should not be limited by these terms. These terms are merely used to distinguish one object from another. For example, a first object can be referred to as a second object, and similarly, a second object can be referred to as a first object without departing from the scope of the exemplary embodiments of the present invention.

[0051] It should be understood that the term "and / or" that may appear in this document is merely a description of the association relationship between associated objects, indicating that there may be three relationships. For example, A and / or B can indicate three situations: A exists alone, B exists alone, or A and B exist at the same time. For another example, A, B and / or C can indicate the existence of any one of A, B and C or any combination of them. The term " / and" that may appear in this document describes another type of association object relationship, indicating that there may be two relationships. For example, A / and B can indicate two situations: A exists alone or A and B exist at the same time. In addition, the character " / " that may appear in this document generally indicates that the previous and next associated objects are in an "or" relationship.

[0052] Example

[0053] like Figure 1 As shown, the microgrid wireless communication network optimization method provided in the first aspect of this embodiment can be, but is not limited to, executed by an edge computer device having certain computing resources and used for wireless communication to connect to node devices in the microgrid. Figure 1 As shown, the microgrid wireless communication network optimization method may include, but is not limited to, the following steps S1 to S4.

[0054] S1. Real-time collection of node operation information of node devices within a microgrid and multi-dimensional network status indicator parameters of a microgrid wireless communication network, wherein the microgrid wireless communication network refers to a star-shaped wireless communication network with a local device (i.e., the edge computer device) as a central node and the node devices within the microgrid as peripheral nodes.

[0055] In step S1, the node devices in the microgrid may include but are not limited to photovoltaic power generation equipment, wind power generators and / or energy storage equipment; the microgrid wireless communication network is used to achieve collaboration between node devices in the microgrid and energy scheduling of the node devices in the microgrid by the edge computer device and management of the entire microgrid system. The network structure is as follows: Figure 2 Specifically, the node operation information includes, but is not limited to, the computing resource utilization rate of the corresponding node device, the node load (i.e., the maximum load or access volume that the corresponding node device can withstand), the task queue length, the power generation and / or storage capacity, etc., which can be collected by the node devices in the microgrid and conventionally transmitted wirelessly. The multi-dimensional network status indicator parameters include but are not limited to signal-to-noise ratio, receiving and transmitting signal strength and / or packet loss rate, etc., which can be collected by the edge computer device itself; for example, the edge computer device can obtain a real-time SNR value by reading a register based on a wireless communication chip equipped with an SNR (Signal to Noise Ratio, signal-to-noise power ratio) measurement function; the edge computer device can obtain a real-time RSSI value by reading a register based on a wireless communication chip equipped with an RSSI (Received Signal Strength Indicator, an indicator for measuring the signal strength received by a wireless communication device) measurement function; the edge computer device can obtain a real-time packet loss rate by conventionally counting node packet loss based on a wireless communication chip equipped with a packet loss rate statistics function. The specific formula can be: packet loss rate = (number of data packets sent - number of successful receptions) ÷ number of data packets sent × 100%. Based on the aforementioned step S1, the edge computer device can have a wireless network status monitoring function.

[0056] S2. Based on the dynamic network adjustment strategy historically executed for the microgrid wireless communication network, the node operation information in the most recent unit time period before the execution of the corresponding strategy, and the multidimensional network status indicator parameters in the next unit time period after the execution of the corresponding strategy, a mapping model of the dynamic network adjustment strategy and the node operation information and the multidimensional network status indicator parameters is established.

[0057] In the step S2, specifically, the dynamic network adjustment strategy includes but is not limited to increasing / decreasing the number of wireless communication connections of the peripheral nodes, adjusting the transmission power for wireless communication (which is one of the optimization directions of the data transmission path parameters, used to improve the signal-to-noise ratio), reselecting the channel for wireless communication (which is one of the optimization directions of the data transmission path parameters, used to improve the signal-to-noise ratio), adjusting the posture of the transceiver antenna for wireless communication (which is one of the optimization directions of the data transmission path parameters, used to improve RSSI), adjusting the position of the mobile peripheral node (which is one of the optimization directions of the data transmission path parameters, used to improve RSSI), reselecting the anti-interference processing algorithm for wireless communication (which is one of the optimization directions of the data transmission path parameters, used to reduce the packet loss rate) and / or adjusting the number of retransmissions for wireless communication (which is one of the optimization directions of the data transmission path parameters, used to reduce the packet loss rate), etc. The specific construction process of the mapping model can be based on, but not limited to, artificial intelligence algorithms such as support vector machines, K-nearest neighbor method, stochastic gradient descent method, multi-layer perceptron, decision tree, back propagation neural network or radial basis function network (which is a core artificial intelligence algorithm that specializes in studying how computers simulate or implement human learning behavior to acquire new knowledge or skills, reorganize existing knowledge structures to continuously improve their own performance, and is the fundamental way to make computers intelligent), applying multiple groups of dynamic network adjustment strategies for the historical execution of the microgrid wireless communication network and the node operation information in the most recent unit time period before the execution of the corresponding strategy and the multi-dimensional network status indicator parameters in the next unit time period after the execution of the corresponding strategy (i.e., each group of dynamic network adjustment strategies for the historical execution of the microgrid wireless communication network and the node operation information in the most recent unit time period before the execution of the corresponding strategy and the multi-dimensional network status indicator parameters in the next unit time period after the execution of the corresponding strategy). The data is taken as a sample data, in which the dynamic network adjustment strategy historically executed for the microgrid wireless communication network and the node operation information of the most recent unit time period before the execution of the corresponding strategy are used as model input items, and the multi-dimensional network status indicator parameters of the next unit time period after the execution of the corresponding strategy are used as model output items), and are trained through a conventional calibration modeling method (the specific process includes a calibration process and a verification process of the model, that is, first comparing the model simulation results with the measured data, and then adjusting the model parameters according to the comparison results so that the simulation results are consistent with the actual process); in the calibration verification modeling process of the mapping model, the model parameters can be specifically tuned by a Bayesian optimization algorithm based on a tree structure. In addition, the aforementioned unit time period can be at the minute level, for example, 1 minute or 5 minutes.

[0058] S3. Based on the adaptive hummingbird algorithm, the dynamic network adjustment strategy currently to be executed for the microgrid wireless communication network is optimized to obtain the dynamic network adjustment strategy and the best search result for minimizing the fitness function, wherein the independent variables of the fitness function include the multi-dimensional network status indicator parameters obtained by importing the dynamic network adjustment strategy to be selected for the microgrid wireless communication network and the node operation information of the most recent unit time period into the mapping model.

[0059] In step S3, the adaptive hummingbird algorithm is a bio-inspired optimization algorithm based on the intelligent behavior of hummingbirds, which aims to solve optimization problems; it is inspired by the foraging behavior and group characteristics of hummingbirds in nature, and gradually optimizes the solution by simulating the random flight, food source tracking and detection, and information exchange of hummingbirds. Therefore, it can be modified and applied in this embodiment to achieve the purpose of optimizing the dynamic network adjustment strategy currently to be executed for the microgrid wireless communication network. The fitness function is used to integrate multiple key indicators of network performance into a comprehensive evaluation index by weighted summation or other combination methods after comprehensively considering them, so as to measure the pros and cons of the dynamic network adjustment strategy represented by each hummingbird individual (that is, the lower the comprehensive evaluation index value, the better the dynamic network adjustment strategy, and vice versa). Specifically, when the multi-dimensional network status indicator parameters include but are not limited to signal-to-noise ratio, transmit and receive signal strength, and packet loss rate, the calculation formula of the fitness function F is expressed as follows:

[0060] F=a×D+β×B+γ×L

[0061] In the formula, D represents the signal-to-noise ratio in the multidimensional network status indicator parameter obtained by importing the dynamic network adjustment strategy to be selected for the microgrid wireless communication network and the node operation information of the most recent unit time period into the mapping model, B represents the transmit and receive signal strength in the multidimensional network status indicator parameter, L represents the packet loss rate in the multidimensional network status indicator parameter, and a, β, and γ respectively represent preset weight coefficients. The aforementioned weight coefficients a, β, and γ are used to balance the importance of different objectives. They first need to be normalized according to the dimension. Parameters that meet the requirements can be obtained by testing different weight combinations in simulation. After deployment, they are dynamically adjusted in real time based on on-site conditions. For example, when the packet loss rate increases, the weight coefficient γ is temporarily increased. The sum of the aforementioned weight coefficients a, β, and γ does not need to be 1. Given the positive correlation between the signal-to-noise ratio and the optimal performance of the network status / dynamic network adjustment strategy, the weight coefficient a needs to be negative so that the calculated fitness is negatively correlated with the optimal performance of the network status / dynamic network adjustment strategy, thereby facilitating the optimal search result for minimizing the fitness function. Furthermore, given the positive correlation between the transmit and receive signal strength and the optimal performance of the network status / dynamic network adjustment strategy, the weight coefficient β needs to be negative so that the calculated fitness is negatively correlated with the optimal performance of the network status / dynamic network adjustment strategy, thereby facilitating the optimal search result for minimizing the fitness function. (Since the packet loss rate has a negative correlation with the optimal performance of the network status / dynamic network adjustment strategy, the weight coefficient γ is typically positive.) Based on the aforementioned fitness function F, complex network status assessment results can be converted into a single value, facilitating comparison and decision-making in subsequent optimization steps. Furthermore, to ensure consistent dimensionality among the independent variables in the fitness function F and thereby improve the accuracy of the fitness calculation results, the aforementioned signal-to-noise ratio D, transmit / receive signal strength B, and packet loss rate L are preferably normalized.

[0062] In step S3, during the operation of the adaptive hummingbird algorithm, it is necessary to adjust and control the Cauchy and Gaussian fusion mutation operators to perturb individual hummingbirds, thereby iteratively updating the population position. In order to balance global search and local development capabilities during the algorithm's operation and prevent the algorithm from prematurely falling into a local optimum, preferably, the dynamic network adjustment strategy currently being executed for the microgrid wireless communication network is optimized based on the adaptive hummingbird algorithm to obtain the optimal search result for the dynamic network adjustment strategy that minimizes the fitness function, including but not limited to the following steps S301 to S307.

[0063] S301. Initialize the optimization algorithm parameters of the adaptive hummingbird algorithm including the population size N and the maximum number of iterations T, and randomly generate a set of parameters to be optimized and a corresponding initial search value array for each hummingbird individual in the hummingbird population including N hummingbird individuals, and then execute step S302, wherein the set of parameters to be optimized includes but is not limited to multidimensional parameters obtained by encoding and having a mapping relationship with the dynamic network adjustment strategy currently to be executed for the microgrid wireless communication network, and the initial search value array x 0 It is expressed as follows:

[0064]

[0065] Wherein, k represents a positive integer less than or equal to K, K represents the total number of parameters in the parameter set to be optimized, represents the initial search value corresponding to the kth parameter in the parameter set to be optimized, u c,k represents the upper limit of the parameter search space corresponding to the kth parameter, l c,k represents the lower limit of the parameter search space corresponding to the kth parameter, and rand(0,1) represents a pure decimal random generation function.

[0066] In step S301, the initial search value array maps the dynamic network adjustment initial strategy to be selected for the microgrid wireless communication network. The upper / lower limit of the parameter search space corresponding to the kth parameter can be conventionally determined based on the coding range corresponding to the kth parameter. For example, for the parameter of the number of retransmissions, the corresponding coding range is 1 to 9 and is an integer, then the upper limit of the parameter search space corresponding to the parameter is 9, and the lower limit is 1.

[0067] S302. For each hummingbird individual, the corresponding initial search value array is mapped to the dynamic network adjustment strategy to be selected for the microgrid wireless communication network, and then the dynamic network adjustment strategy and the node operation information of the current most recent unit time period are imported into the mapping model, and the first multidimensional network status indicator parameter is output, and the first multidimensional network status indicator parameter is imported into the fitness function as an independent variable to obtain the corresponding first fitness, and finally step S303 is executed.

[0068] In step S302, when mapping the initial search value array to the dynamic network adjustment strategy to be selected for the microgrid wireless communication network, it is necessary to consider the need for rounding of certain parameters. For example, the number of retransmissions parameter requires rounding during the mapping process. In addition, the first multi-dimensional network status indicator parameter is the estimated network status result for the current next unit time period. Specifically, the signal-to-noise ratio, transmit and receive signal strength, and packet loss rate can be used to calculate the fitness.

[0069] S303. The initial search value array corresponding to the minimum first fitness is used as the current optimal search value array, and the current iteration number t=1 is initialized and set, and then step S304 is executed.

[0070] S304. For each hummingbird individual, update the corresponding current search value array according to the current number of iterations, the current optimal search value array and the corresponding pre-update search value array, and then execute step S305, wherein the current search value array x t It is expressed as follows:

[0071]

[0072] Where, Represents the current search value in the array x t and the search value corresponding to the k-th parameter, represents the search value in the pre-update search value array and corresponding to the kth parameter, represents the search value corresponding to the k-th parameter in the current optimal search value array, AF() represents the adaptive adjustment factor calculation function, sig() represents the adaptive Sigmoid function, Ca(0,1) represents a random variable that satisfies the standard Cauchy distribution, and Ga(0,1) represents a random variable that satisfies the standard Gaussian distribution.

[0073] In step S304, the adaptive adjustment factor calculation function AF() is used to adjust and control the Cauchy and Gaussian fusion mutation operators to perturb the hummingbird individuals; since the standard sigmoid function may have the following deficiencies when balancing exploration and development: (1) insufficient attenuation speed in the initial stage, resulting in low global search efficiency; (2) insufficient local development capability in the later stage, which is prone to falling into suboptimal solutions, the adaptive Sigmoid function is used to adjust the parameters, which can better meet the dynamic needs of microgrid wireless communication network optimization, and then the adaptive adjustment factor can be dynamically adjusted according to the current number of iterations, so as to achieve the purpose of performing global search in the early stage of the algorithm and focusing on local development in the later stage.

[0074] S305. For each hummingbird individual, the corresponding current search value array is mapped to the dynamic network adjustment strategy to be selected for the microgrid wireless communication network, and then the dynamic network adjustment strategy and the node operation information of the current most recent unit time period are imported into the mapping model, and the second multidimensional network status indicator parameter is output, and the second multidimensional network status indicator parameter is imported into the fitness function as an independent variable to obtain the corresponding second fitness, and finally step S306 is executed.

[0075] In step S305, the specific details are derived from the aforementioned step S302 and will not be repeated here.

[0076] S306. Determine whether the minimum second fitness is lower than the fitness corresponding to the current optimal search value array. If so, update the current optimal search value array to the current search value array corresponding to the minimum second fitness, and then execute step S307; otherwise, directly execute step S307.

[0077] S307. Determine whether the current number of iterations t reaches the maximum number of iterations T. If so, use the current optimal search value array as the best search result obtained by optimizing the dynamic network adjustment strategy currently to be executed for the microgrid wireless communication network and used to minimize the fitness function. Otherwise, increment the current number of iterations t by 1, and then return to execute step S304.

[0078] S4. Dynamically adjust the microgrid wireless communication network according to the best search result.

[0079] In the step S4, the best search result is specifically mapped to the dynamic network adjustment strategy to be executed for the microgrid wireless communication network (for specific details, refer to the derivation of the aforementioned step S302, which will not be repeated here), and then the microgrid wireless communication network is dynamically adjusted according to the dynamic network adjustment strategy. In this way, the network fluctuations and load changes of the microgrid wireless communication network can be adaptive, avoiding the instability affecting network communication and data acquisition, ensuring that the network performance is always in a better state, which is conducive to reducing network delays and reducing bandwidth consumption and packet loss rate, thereby meeting the needs of real-time control and data interaction of the microgrid, and facilitating practical application and promotion. In addition, in the specific adjustment process, the feasibility of implementation and the interference with existing network services must also be considered to achieve a smooth transition as much as possible.

[0080] Therefore, based on the microgrid wireless communication network optimization method described in the aforementioned steps S1 to S4, a new solution for optimizing and adjusting the microgrid wireless communication network based on the adaptive hummingbird algorithm is provided. That is, first, based on the dynamic network adjustment strategy historically executed for the microgrid wireless communication network, the node operation information before the execution of the corresponding strategy, and the multi-dimensional network status indicator parameters after the execution of the corresponding strategy, a mapping model of the adjustment strategy and the operation information and the indicator parameters is established. Then, based on the adaptive hummingbird algorithm, the dynamic network adjustment strategy currently to be executed for the communication network is optimized to obtain the best search result for the strategy and for minimizing the fitness function. Finally, the communication network is dynamically adjusted based on the best search result. In this way, the network fluctuations and load changes of the microgrid wireless communication network can be adaptively adjusted, avoiding the instability that affects network communication and data acquisition, ensuring that the network performance is always in a relatively good state, and helping to reduce network delay and reduce bandwidth consumption and packet loss rate, thereby meeting the needs of real-time control and data interaction of the microgrid, and facilitating practical application and promotion.

[0081] Based on the technical solution of the first aspect mentioned above, this embodiment also provides a possible design for how to perform intelligent control enhancement, that is, the method further includes: according to the node operation information of the node equipment in the microgrid in the current multiple consecutive unit time periods, based on the time series model, predicting the failure probability of the node equipment in the microgrid in the current next unit time period; based on the adaptive hummingbird algorithm, optimizing the dynamic network adjustment strategy facing the microgrid wireless communication network and currently to be executed, and obtaining the best search result for the dynamic network adjustment strategy and minimizing the fitness function, including: based on the adaptive hummingbird algorithm, optimizing the dynamic network adjustment strategy facing the microgrid wireless communication network and currently to be executed The dynamic network adjustment strategy executed is optimized to obtain the best search result of the dynamic network adjustment strategy, which is used to minimize the fitness function and can meet the fault equipment avoidance activation condition, wherein the independent variable of the fitness function includes the multi-dimensional network status indicator parameters obtained by importing the dynamic network adjustment strategy to be selected for the microgrid wireless communication network and the node operation information of the current most recent unit time period into the mapping model, and the fault equipment avoidance activation condition means that if the probability of failure of the node device in the microgrid in the current next unit time period exceeds a preset probability threshold, it is prohibited to enable the node device in the microgrid in the current next unit time period. The specific prediction process of the aforementioned fault probability may include, but is not limited to, firstly based on a certain amount of positive sample data (i.e., the model input item is the node operation information of the node device in the microgrid in the most recent multiple consecutive unit time periods before the historical fault event occurred, and the model input item is the value "1") and negative sample data (i.e., the model input item is the node operation information of the node device in the microgrid in the most recent multiple consecutive unit time periods before the historical fault event did not occur, and the model input item is the value "0"), based on the time series model (which is the theory and method of establishing a mathematical model through curve fitting and parameter estimation based on the time series data obtained by system observation, such as but not limited to the use of a long short-term memory network model) first conventional training to obtain a prediction model for estimating the probability of failure of the node device in the microgrid in the next unit time period, and then importing the node operation information of the node device in the microgrid in the current multiple consecutive unit time periods into the prediction model to obtain the failure probability of the node device in the microgrid in the current next unit time period (specifically, the confidence of the output number "1" can be used as the failure probability).In addition, the specific detailed steps for making the best search result meet the fault device avoidance activation condition can be to first map the generated / updated search value array to the dynamic network adjustment strategy to be selected for the microgrid wireless communication network in steps S301 and S304, and then routinely determine whether the dynamic network adjustment strategy meets the fault device avoidance activation condition. If not, it is necessary to regenerate the search value array until the fault device avoidance activation condition is met.

[0082] Based on the aforementioned possible design, the fault prediction results of node devices within the microgrid can be taken into account when optimizing the microgrid's wireless communication network. This allows the final optimized dynamic network adjustment strategy to meet the conditions for avoiding the activation of faulty devices, enhance intelligent control functions, and further ensure that network performance is always optimal. Furthermore, to further enhance intelligent control functions, it is possible to accurately predict microgrid load changes based on real-time data and combine artificial intelligence technology to make reasonable scheduling decisions in advance. For example, the output power of power generation equipment can be reasonably adjusted, and the charging and discharging strategies of energy storage equipment can be optimized to achieve stable operation of the microgrid and efficient energy utilization. At the same time, the health status of equipment can be evaluated in real time and predictive maintenance can be carried out to reduce equipment failure rate and maintenance costs, thereby extending equipment service life. Furthermore, to improve system reliability, the collaborative work and load balancing mechanism between multiple edge computing terminals (i.e., the edge computing devices) can effectively avoid single point failures and resource overload problems. In the event of a local network failure or partial node failure, the system can automatically reconfigure the network and rationally distribute tasks to other normal nodes, ensuring the continued stable operation of the entire microgrid system and improving the system's fault tolerance and reliability. In addition, in order to optimize energy management, it is also possible to achieve reasonable distribution and efficient utilization of energy through intelligent control and optimized scheduling of various energy equipment in the microgrid (such as solar panels, wind turbines and energy storage batteries, etc.). On the premise of meeting the electricity needs of users, it can reduce the operating costs of the microgrid, improve energy utilization efficiency, and reduce dependence on traditional energy, which has good economic and environmental benefits.

[0083] like Figure 3 As shown, the second aspect of this embodiment provides a virtual device for implementing the microgrid wireless communication network optimization method described in the first aspect or possible design one, which is arranged in an edge computer device for wirelessly connecting node devices in the microgrid, and includes a real-time data collection unit, a mapping model establishment unit, an adjustment strategy optimization unit, and a network dynamic adjustment unit;

[0084] The real-time data collection unit is used to collect in real time node operation information of node devices in the microgrid and multi-dimensional network status indicator parameters of the microgrid wireless communication network, wherein the microgrid wireless communication network refers to a star-shaped wireless communication network with local devices as central nodes and node devices in the microgrid as peripheral nodes;

[0085] The mapping model establishment unit is communicatively connected to the real-time data collection unit and is used to establish a mapping model between the dynamic network adjustment strategy and the node operation information and the multi-dimensional network status indicator parameters based on the dynamic network adjustment strategy historically executed for the microgrid wireless communication network, the node operation information in the most recent unit time period before the execution of the corresponding strategy, and the multi-dimensional network status indicator parameters in the next unit time period after the execution of the corresponding strategy;

[0086] The adjustment strategy optimization unit is communicatively connected to the real-time data collection unit and the mapping model establishment unit, respectively, and is used to optimize the dynamic network adjustment strategy currently to be executed for the microgrid wireless communication network based on the adaptive hummingbird algorithm to obtain the best search result for the dynamic network adjustment strategy and for minimizing the fitness function, wherein the independent variables of the fitness function include a multi-dimensional network status indicator parameter obtained by importing the dynamic network adjustment strategy to be selected for the microgrid wireless communication network and the node operation information of the most recent unit time period into the mapping model;

[0087] The network dynamic adjustment unit is communicatively connected to the adjustment strategy optimization unit and is used to dynamically adjust the microgrid wireless communication network according to the best search result.

[0088] The working process, working details and technical effects of the aforementioned device provided in the second aspect of this embodiment can be referred to the microgrid wireless communication network optimization method described in the first aspect or possible design one, and will not be repeated here.

[0089] like Figure 4As shown, the third aspect of this embodiment provides a computer device for executing the microgrid wireless communication network optimization method as described in the first aspect or possible design one, comprising a memory, a processor, and a transceiver that are sequentially connected in communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the microgrid wireless communication network optimization method as described in the first aspect or possible design one. For example, the memory may include, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a flash memory, a first-input first-output memory (FIFO), and / or a first-input last-output memory (FILO), etc.; the processor may include, but is not limited to, a microprocessor of the STM32F105 series. In addition, the computer device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0090] The working process, working details and technical effects of the aforementioned computer equipment provided in the third aspect of this embodiment can be referred to the microgrid wireless communication network optimization method described in the first aspect or possible design one, and will not be repeated here.

[0091] A fourth aspect of this embodiment provides a computer-readable storage medium storing instructions including the microgrid wireless communication network optimization method as described in the first aspect or possible design 1. Specifically, the computer-readable storage medium stores instructions that, when executed on a computer, execute the microgrid wireless communication network optimization method as described in the first aspect or possible design 1. The computer-readable storage medium refers to a data storage medium and may include, but is not limited to, computer-readable storage media such as a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive, and / or a memory stick. The computer may be a general-purpose computer, a dedicated computer, a computer network, or other programmable device.

[0092] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in the fourth aspect of this embodiment can be referred to the microgrid wireless communication network optimization method described in the first aspect or possible design one, and will not be repeated here.

[0093] A fifth aspect of this embodiment provides a computer program product, including a computer program or instructions, which, when executed by a computer, implements the microgrid wireless communication network optimization method described in the first aspect or possible design 1. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0094] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A microgrid wireless communication network optimization method, characterized in that: The method is executed by an edge computer device for wirelessly connecting node devices in a microgrid, including: Real-time collection of node operation information of node devices within the microgrid and multi-dimensional network status indicator parameters of the microgrid wireless communication network, where the microgrid wireless communication network refers to a star-shaped wireless communication network with local devices as central nodes and node devices within the microgrid as peripheral nodes; Based on the dynamic network adjustment strategy historically executed for the microgrid wireless communication network, the node operation information in the most recent unit period before the corresponding strategy execution, and the multi-dimensional network status indicator parameters in the next unit period after the corresponding strategy execution, a mapping model between the dynamic network adjustment strategy and the node operation information and the multi-dimensional network status indicator parameters is established; Optimizing a currently pending dynamic network adjustment strategy for a microgrid wireless communication network based on an adaptive hummingbird algorithm to obtain an optimal search result for the dynamic network adjustment strategy and for minimizing a fitness function, wherein the independent variables of the fitness function include a multidimensional network status indicator parameter obtained by importing the currently pending dynamic network adjustment strategy for the microgrid wireless communication network and node operation information for a recent unit time period into a mapping model; The microgrid wireless communication network is dynamically adjusted based on the best search results.

2. The microgrid wireless communication network optimization method according to claim 1, characterized in that: Node operation information includes computing resource utilization, node load, task queue length, power generation and / or storage capacity of the corresponding node device; And / or, the multi-dimensional network status indicator parameters include signal-to-noise ratio, transmit-receive signal strength and / or packet loss rate.

3. The microgrid wireless communication network optimization method according to claim 1, characterized in that: When the multi-dimensional network status indicator parameters include signal-to-noise ratio, transmit and receive signal strength, and packet loss rate, the calculation formula of the fitness function F is expressed as follows: F=a×D+β×B+γ×L Where D represents the signal-to-noise ratio in the multidimensional network status indicator parameter obtained by importing the dynamic network adjustment strategy to be selected for the microgrid wireless communication network and the node operation information of the most recent unit time period into the mapping model, B represents the transmit and receive signal strength in the multidimensional network status indicator parameter, L represents the packet loss rate in the multidimensional network status indicator parameter, and a, β, and γ represent the preset weight coefficients, respectively.

4. The microgrid wireless communication network optimization method according to claim 1, characterized in that: Optimizing a dynamic network adjustment strategy currently to be executed for a microgrid wireless communication network based on an adaptive hummingbird algorithm to obtain the optimal search result for the dynamic network adjustment strategy and minimizing the fitness function includes but is not limited to the following steps S301 to S307: S301. Initialize the optimization algorithm parameters of the adaptive hummingbird algorithm, including the population size N and the maximum number of iterations T, and randomly generate a set of parameters to be optimized and a corresponding initial search value array for each hummingbird individual in the hummingbird population containing N hummingbird individuals. Then, execute step S302, wherein the set of parameters to be optimized includes multi-dimensional parameters obtained by encoding and having a mapping relationship with the dynamic network adjustment strategy currently to be executed for the microgrid wireless communication network. The initial search value array x 0 It is expressed as follows: In the formula, k represents a positive integer less than or equal to K, K represents the total number of parameters in the parameter set to be optimized, represents the initial search value corresponding to the kth parameter in the parameter set to be optimized, u c,k represents the upper limit of the parameter search space corresponding to the kth parameter, l c,k represents the lower limit of the parameter search space corresponding to the kth parameter, and rand(0,1) represents a pure decimal random generator function; S302. For each hummingbird individual, the corresponding initial search value array is mapped to a dynamic network adjustment strategy to be selected for the microgrid wireless communication network. The dynamic network adjustment strategy and the node operation information for the most recent unit period are then imported into the mapping model to obtain a first multidimensional network status indicator parameter. The first multidimensional network status indicator parameter is then imported as an independent variable into the fitness function to obtain a corresponding first fitness. Finally, step S303 is executed. S303. The initial search value array corresponding to the minimum first fitness is used as the current optimal search value array, and the current iteration number t=1 is initialized and set, and then step S304 is executed; S304. For each hummingbird individual, update the corresponding current search value array according to the current number of iterations, the current optimal search value array and the corresponding pre-update search value array, and then execute step S305, where the current search value array x t It is expressed as follows: Where, Indicates that the current search value array x t The search value corresponding to the kth parameter in Represents the search value corresponding to the kth parameter in the search value array before updating. represents the search value corresponding to the kth parameter in the current optimal search value array, AF() represents the adaptive adjustment factor calculation function, sig() represents the adaptive Sigmoid function, Ca(0,1) represents a random variable that satisfies the standard Cauchy distribution, and Ga(0,1) represents a random variable that satisfies the standard Gaussian distribution; S305. For each hummingbird individual, the corresponding current search value array is mapped into a dynamic network adjustment strategy to be selected for the microgrid wireless communication network. The dynamic network adjustment strategy and the node operation information for the most recent unit period are then imported into the mapping model to obtain a second multidimensional network status indicator parameter. The second multidimensional network status indicator parameter is then imported as an independent variable into the fitness function to obtain a corresponding second fitness. Finally, step S306 is executed. S306. Determine whether the minimum second fitness is lower than the fitness corresponding to the current optimal search value array. If so, update the current optimal search value array to the current search value array corresponding to the minimum second fitness, and then execute step S307. Otherwise, execute step S307 directly. S307. Determine whether the current number of iterations t reaches the maximum number of iterations T. If so, use the current optimal search value array as the best search result obtained by optimizing the dynamic network adjustment strategy for the microgrid wireless communication network and currently to be executed and used to minimize the fitness function. Otherwise, increment the current number of iterations t by 1, and then return to step S304.

5. The microgrid wireless communication network optimization method according to claim 1, characterized in that: Dynamic network adjustment strategies include increasing / decreasing the number of wireless communication connections of peripheral nodes, adjusting the transmission power for wireless communication, reselecting the channel for wireless communication, adjusting the posture of the transmitting and receiving antennas for wireless communication, adjusting the position of mobile peripheral nodes, reselecting the anti-interference processing algorithm for wireless communication and / or adjusting the number of retransmissions for wireless communication.

6. The microgrid wireless communication network optimization method according to claim 1, characterized in that: The method further includes: predicting, based on a time series model, the probability of a failure of a node device in the microgrid in the next unit period according to node operation information of the node device in the microgrid in the most recent consecutive unit periods; Based on the adaptive hummingbird algorithm, the dynamic network adjustment strategy for the microgrid wireless communication network and currently to be executed is optimized to obtain the dynamic network adjustment strategy and the best search result for minimizing the fitness function, including: based on the adaptive hummingbird algorithm, the dynamic network adjustment strategy for the microgrid wireless communication network and currently to be executed is optimized to obtain the best search result for the dynamic network adjustment strategy, which is used to minimize the fitness function and can meet the fault equipment avoidance activation condition, wherein the independent variable of the fitness function includes the multi-dimensional network status indicator parameters obtained by importing the dynamic network adjustment strategy for the microgrid wireless communication network and to be selected and the node operation information of the current most recent unit time period into the mapping model, and the fault equipment avoidance activation condition means that if the probability of failure of the node device in the microgrid in the current next unit time period exceeds the preset probability threshold, then the node device in the microgrid is prohibited from being activated in the current next unit time period.

7. A microgrid wireless communication network optimization device, characterized in that: It is arranged in an edge computer device used for wireless communication to connect node devices in a microgrid, and includes a real-time data collection unit, a mapping model establishment unit, an adjustment strategy optimization unit, and a network dynamic adjustment unit; The real-time data collection unit is used to collect in real time node operation information of node devices in the microgrid and multi-dimensional network status indicator parameters of the microgrid wireless communication network, wherein the microgrid wireless communication network refers to a star-shaped wireless communication network with local devices as central nodes and node devices in the microgrid as peripheral nodes; The mapping model establishment unit is communicatively connected to the real-time data collection unit and is used to establish a mapping model between the dynamic network adjustment strategy and the node operation information and the multi-dimensional network status indicator parameters based on the dynamic network adjustment strategy historically executed for the microgrid wireless communication network, the node operation information in the most recent unit time period before the execution of the corresponding strategy, and the multi-dimensional network status indicator parameters in the next unit time period after the execution of the corresponding strategy; The adjustment strategy optimization unit is communicatively connected to the real-time data collection unit and the mapping model establishment unit, respectively, and is used to optimize the dynamic network adjustment strategy currently to be executed for the microgrid wireless communication network based on the adaptive hummingbird algorithm to obtain the best search result for the dynamic network adjustment strategy and for minimizing the fitness function, wherein the independent variables of the fitness function include a multi-dimensional network status indicator parameter obtained by importing the dynamic network adjustment strategy to be selected for the microgrid wireless communication network and the node operation information of the most recent unit time period into the mapping model; The network dynamic adjustment unit is communicatively connected to the adjustment strategy optimization unit and is used to dynamically adjust the microgrid wireless communication network according to the best search result.

8. A computer device, characterized in that: The invention comprises a memory, a processor and a transceiver which are communicatively connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the microgrid wireless communication network optimization method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed on the computer, the microgrid wireless communication network optimization method according to any one of claims 1 to 6 is executed.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or the instruction is executed by a computer, the microgrid wireless communication network optimization method according to any one of claims 1 to 6 is implemented.

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