Methods, devices, equipment, media and products for optimizing wireless communication networks in microgrids

Abstract

This invention discloses a method, apparatus, device, medium, and product for optimizing a microgrid wireless communication network, relating to the field of network optimization technology. The method first establishes a mapping model between the adjustment strategy, operational information, and indicator parameters based on historically executed dynamic network adjustment strategies for the microgrid wireless communication network, node operating information before the strategy's execution, and multi-dimensional network state index parameters after the strategy's execution. Then, it optimizes the current dynamic network adjustment strategy for the communication network using an adaptive hummingbird algorithm, obtaining the optimal search result that minimizes the fitness function. Finally, it dynamically adjusts the communication network based on the optimal search result. This approach adapts to network fluctuations and load changes in the microgrid wireless communication network, avoiding instability that could affect network communication and data acquisition, and meeting the needs of real-time control and data interaction in the microgrid.

Description

Technical Field

[0001] This invention belongs to the field of network optimization technology, specifically relating to a method, apparatus, equipment, medium, and product for optimizing a microgrid wireless communication network. Background Technology

[0002] A microgrid is a small-scale, autonomous power system typically composed of renewable energy sources (such as solar and / or wind power) and energy storage devices, capable of operating independently, either connected to or disconnected from the main power grid. Microgrids can improve energy reliability and security, making them particularly suitable for remote areas or locations not covered by the main grid. However, microgrids also face a number of limitations, including difficulties in coordination between nodes, suboptimal energy dispatching, complex system management, and instability in network communication and data acquisition. These issues can lead to inefficiencies and operational instability in microgrids, affecting their effectiveness in practical applications.

[0003] As the coordination, scheduling, and system management center among nodes within a microgrid, edge computing devices typically employ a star-shaped wireless communication network (e.g., built using WiFi or long-range radio communication technologies) to wirelessly connect to various nodes (such as photovoltaic power generation equipment, wind turbines, and energy storage devices) within the microgrid. This enables coordination among nodes, energy scheduling of these nodes, and management of the entire microgrid system. However, in practical applications, the aforementioned star-shaped wireless communication network suffers from network fluctuations and load variations, which can affect the instability of network communication and data acquisition. This can lead to problems such as low signal-to-noise ratio, low transmit and receive signal strength, and high packet loss rate, making it difficult to meet the real-time control and data interaction requirements of the microgrid. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for optimizing microgrid wireless communication networks. This is to solve the problem that existing microgrid star-shaped wireless communication networks are unstable due to network fluctuations and load changes in practical applications, which affect network communication and data acquisition, and thus make it difficult to meet the needs of real-time control and data interaction in microgrids.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, a method for optimizing a microgrid wireless communication network is provided, executed by an edge computer device for wireless communication connection of node devices within the microgrid, comprising:

[0007] Real-time collection of node operation information of node devices in microgrid and multi-dimensional network status index parameters of microgrid wireless communication network. Among them, microgrid wireless communication network refers to star wireless communication network with local devices as central nodes and microgrid node devices as peripheral nodes.

[0008] Based on the dynamic network adjustment strategies historically executed for microgrid wireless communication networks, the node operation information of the most recent unit time period before the corresponding strategy was executed, and the multi-dimensional network status index parameters of the next unit time period after the corresponding strategy was executed, a mapping model between dynamic network adjustment strategies, node operation information, and multi-dimensional network status index parameters is established.

[0009] The adaptive hummingbird algorithm is used to optimize the dynamic network adjustment strategy for microgrid wireless communication network and the current dynamic network adjustment strategy to be executed. The optimal search result for minimizing the fitness function is obtained. The independent variable of the fitness function includes multi-dimensional network state index parameters obtained by importing the dynamic network adjustment strategy for microgrid wireless communication network and the node operation information of the most recent unit time period into the mapping model.

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

[0011] Based on the above-mentioned invention, a novel scheme for optimizing and adjusting a microgrid wireless communication network using an adaptive hummingbird algorithm is provided. First, a mapping model between the adjustment strategy, operational information, and indicator parameters is established based on the historical dynamic network adjustment strategies executed for the microgrid wireless communication network, node operating information before the corresponding strategy execution, and multi-dimensional network state indicator parameters after the strategy execution. Then, the adaptive hummingbird algorithm is used to optimize the dynamic network adjustment strategy currently to be executed for the communication network, obtaining the optimal search result that minimizes the fitness function. Finally, the communication network is dynamically adjusted based on the optimal search result. This approach can adapt to network fluctuations and load changes in the microgrid wireless communication network, avoiding instability in network communication and data acquisition, ensuring that network performance remains at an optimal level, reducing network latency, bandwidth consumption, and packet loss rate. This meets the needs of real-time control and data interaction in microgrids, facilitating practical application and promotion.

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

[0013] And / or, multidimensional network state metrics include signal-to-noise ratio, transmit and receive signal strength, and / or packet loss rate.

[0014] In a possible design, when the multidimensional network state index parameters include signal-to-noise ratio, transmit and receive signal strength, and packet loss rate, the fitness function F is calculated as follows:

[0015] F = a × D + β × B + γ × L

[0016] In the formula, D represents the signal-to-noise ratio in the multidimensional network state index 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; B represents the transmit and receive signal strength in the multidimensional network state index parameters; L represents the packet loss rate in the multidimensional network state index parameters; and a, β and γ represent the preset weight coefficients, respectively.

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

[0018] S301. Initialize the adaptive hummingbird algorithm with optimization parameters including the population size N and the maximum number of iterations T. For each hummingbird in the population of N individuals, randomly generate a set of parameters to be optimized and a corresponding initial search value array. Then execute step S302, where the set of parameters to be optimized contains multi-dimensional parameters obtained by encoding and mapped to the dynamic network adjustment strategy 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, and K represents the total number of parameters in the parameter set to be optimized. u represents the initial search value corresponding to the k-th parameter in the set of parameters to be optimized. c,k l represents the upper limit of the parameter search space corresponding to the k-th parameter. c,k This represents the lower bound of the parameter search space corresponding to the k-th parameter, and rand(0,1) represents a function for generating pure decimal random numbers.

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

[0022] S303. Take the initial search value array corresponding to the smallest first fitness as the current optimal search value array, initialize the current iteration number t=1, and then execute step S304;

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

[0024]

[0025] In the formula, This indicates the current search value array x t And the search value corresponding to the k-th parameter, This represents the search value in the array of search values ​​before the update that corresponds to the k-th parameter. This represents the search value in the current optimal search value array that corresponds to the k-th parameter. 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 individual hummingbird, map the corresponding current search value array to a dynamic network adjustment strategy to be selected for the microgrid wireless communication network. Then, import the dynamic network adjustment strategy and the node operation information of the most recent unit time period into the mapping model, output the second multidimensional network state index parameter, and import the second multidimensional network state index parameter as the independent variable into the fitness function to obtain the corresponding second fitness. Finally, execute step S306.

[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, directly execute step S307.

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

[0029] In one possible design, dynamic network adjustment strategies include increasing / decreasing the number of wireless communication connections for peripheral nodes, adjusting the transmit power for wireless communication, reselecting the channel for wireless communication, adjusting the orientation of the transceiver antennas for wireless communication, adjusting the location 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 the probability of failure of the node device in the next unit time period based on the node operation information of the node device in the microgrid in the most recent consecutive unit time periods using a time series model;

[0031] The adaptive hummingbird algorithm is used to optimize the dynamic network adjustment strategy for the microgrid wireless communication network to be executed. The optimal search result for minimizing the fitness function of the dynamic network adjustment strategy is obtained. This includes: optimizing the dynamic network adjustment strategy for the microgrid wireless communication network to be executed based on the adaptive hummingbird algorithm, obtaining the optimal search result for the dynamic network adjustment strategy that minimizes the fitness function and satisfies the fault device avoidance activation condition. The independent variable of the fitness function includes multi-dimensional network state index 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. The fault device avoidance activation condition means that if the probability of fault occurrence of a node device in the microgrid in the next unit time period exceeds a preset probability threshold, then the activation of the node device in the microgrid in the next unit time period is prohibited.

[0032] In a second aspect, a microgrid wireless communication network optimization device is provided, which is arranged in an edge computer device for wireless communication connection of 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 node operation information of node devices in the microgrid and multi-dimensional network status index parameters of the microgrid wireless communication network in real time. 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. It is used to establish a mapping model between the dynamic network adjustment strategy and node operation information and the multi-dimensional network status index parameters based on the dynamic network adjustment strategy historically executed for the microgrid wireless communication network, the node operation information of the most recent unit time period before the execution of the corresponding strategy, and the multi-dimensional network status index parameters of 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. It is used to optimize the dynamic network adjustment strategy for the microgrid wireless communication network and the dynamic network adjustment strategy to be executed based on the adaptive hummingbird algorithm, and to obtain the best search result for the dynamic network adjustment strategy to minimize the fitness function. The independent variable of the fitness function includes multi-dimensional network state index parameters obtained by importing the dynamic network adjustment strategy 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 based on the best search results.

[0037] Thirdly, the present invention provides a computer device comprising a memory, a processor, and a transceiver connected in sequence for 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 any possible design in the first aspect.

[0038] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the microgrid wireless communication network optimization method as described in the first aspect or any possible design within the first aspect.

[0039] Fifthly, the present invention provides a computer program product, including a computer program or instructions, wherein the computer program or instructions, when executed by a computer, implement the microgrid wireless communication network optimization method as described in the first aspect or any possible design in the first aspect.

[0040] The beneficial effects of the above scheme are:

[0041] (1) This invention creatively provides a new scheme for optimizing and adjusting the wireless communication network of a microgrid based on the adaptive hummingbird algorithm. First, based on the dynamic network adjustment strategy executed in the history of the wireless communication network of the microgrid, the node operation information before the corresponding strategy is executed, and the multi-dimensional network status index parameters after the corresponding strategy is executed, a mapping model between the adjustment strategy and the operation information and the index parameters is established. Then, the dynamic network adjustment strategy for the communication network and the current dynamic network adjustment strategy to be executed is optimized based on the adaptive hummingbird algorithm to obtain the best search result for the strategy and to minimize 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 wireless communication network of the microgrid can be adapted to avoid affecting the instability of network communication and data acquisition, ensuring that the network performance is always in a better state, which is conducive to reducing network latency and reducing bandwidth consumption and packet loss rate, thereby meeting the needs of real-time control and data interaction of the microgrid.

[0042] (2) It can also take into account the fault prediction results of node equipment in the microgrid when optimizing the wireless communication network of the microgrid, so that the final optimized dynamic network adjustment strategy can meet the fault avoidance and activation conditions of the fault 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 microgrids, and has strong flexibility and scalability. That is, when the scale of microgrids expands or equipment is updated, the system can quickly integrate new equipment, reconfigure network topology and control strategy through adaptive adjustment and optimization, and ensure that the whole system always maintains good performance and operating status, meets the needs of the continuous development of microgrids, and is convenient for practical application and promotion. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating the microgrid wireless communication network optimization method provided in an embodiment of this application.

[0046] Figure 2 This is a schematic diagram of the structure of a star-shaped wireless communication network based on edge computing devices and node devices within a microgrid, provided in an embodiment of this application.

[0047] Figure 3 This is a schematic diagram of the structure of the microgrid wireless communication network optimization device provided in the embodiments of this application.

[0048] Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying 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 effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

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

[0051] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, or A and B exist simultaneously. Another example is A, B and / or C, which can mean that any one of A, B, and C or any combination thereof exists. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone or A and B exist simultaneously. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it 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 executed, but is not limited to, by an edge computer device with certain computing resources and used for wireless communication connection of node devices within 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. Collect node operation information of node devices in the microgrid and multi-dimensional network status index parameters of the microgrid wireless communication network in real time. The microgrid wireless communication network refers to a star-shaped wireless communication network with the local device (i.e., the edge computer device) as the central node and the node devices in the microgrid as the peripheral nodes.

[0055] In step S1, the node devices within the microgrid may include, but are not limited to, photovoltaic power generation equipment, wind turbines, and / or energy storage devices; the microgrid wireless communication network is used to realize the coordination between node devices within the microgrid and the energy dispatching of the node devices and the management of the entire microgrid system by the edge computing device. Its network structure is exemplified by... Figure 2 As shown. Specifically, the node operation information includes, but is not limited to, the computing resource utilization rate of the corresponding node device, the node load capacity (i.e., the maximum load or access capacity that the corresponding node device can withstand), the task queue length, power generation and / or energy storage, etc., which can be collected by the node devices in the microgrid and routinely transmitted wirelessly. The multi-dimensional network status indicators include, but are not limited to, signal-to-noise ratio (SNR), transmit / receive signal strength, and / or packet loss rate, which can be collected by the edge computing device itself. For example, the edge computing device can obtain the real-time SNR value by reading registers based on a wireless communication chip configured with SNR (Signal to Noise Ratio) measurement function; the edge computing device can obtain the real-time RSSI value by reading registers based on a wireless communication chip configured with RSSI (Received Signal Strength Indicator) measurement function; the edge computing device can obtain the real-time packet loss rate by routinely statistically analyzing node packet loss using a wireless communication chip configured with packet loss rate statistics function, specifically using the formula: Packet Loss Rate = (Number of Data Packets Sent - Number of Successfully Received Data Packets) ÷ Number of Data Packets Sent × 100%. Based on the aforementioned step S1, the edge computing 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 corresponding strategy was executed, and the multidimensional network status index parameters in the next unit time period after the corresponding strategy was executed, establish a mapping model between the dynamic network adjustment strategy, the node operation information, and the multidimensional network status index parameters.

[0057] In step S2, specifically, the dynamic network adjustment strategy includes, but is not limited to, increasing / decreasing the number of wireless communication connections of peripheral nodes, adjusting the transmit power used for wireless communication (which is one of the directions for optimizing data transmission path parameters to improve the signal-to-noise ratio), reselecting the channel used for wireless communication (which is one of the directions for optimizing data transmission path parameters to improve the signal-to-noise ratio), adjusting the attitude of the transceiver antennas used for wireless communication (which is one of the directions for optimizing data transmission path parameters to improve the RSSI), adjusting the position of mobile peripheral nodes (which is one of the directions for optimizing data transmission path parameters to improve the RSSI), reselecting the anti-interference processing algorithm used for wireless communication (which is one of the directions for optimizing data transmission path parameters to reduce the packet loss rate), and / or adjusting the number of retransmissions used for wireless communication (which is one of the directions for optimizing data transmission path parameters to reduce the packet loss rate), etc. The specific construction process of the mapping model can be, but is not limited to, based on artificial intelligence algorithms such as support vector machines, K-nearest neighbor method, stochastic gradient descent method, multilayer perceptron, decision tree, backpropagation neural network, or radial basis function network (which is a core algorithm of artificial intelligence that specifically studies how computers can simulate or realize human learning behavior to acquire new knowledge or skills, reorganize existing knowledge structures to continuously improve their performance, and is the fundamental way to make computers intelligent). It applies multiple sets of dynamic network adjustment strategies historically executed by the microgrid wireless communication network, as well as the node operation information of the most recent unit time period before the execution of the corresponding strategy and the multidimensional network state index parameters of the next unit time period after the execution of the corresponding strategy (i.e., each set...). The data serves as sample data. In this sample data, the dynamic network adjustment strategies historically executed by the microgrid wireless communication network and the node operation information in the most recent unit time period before the execution of the corresponding strategies are used as model inputs. The multi-dimensional network state index parameters in the next unit time period after the execution of the corresponding strategies are used as model outputs. The model is trained using a conventional calibration modeling method (the specific process includes model calibration and verification, i.e., first comparing the model simulation results with measured data, and then adjusting the model parameters based on the comparison results to ensure the simulation results match the actual results). During the calibration and verification modeling process of the mapping model, a tree-based Bayesian optimization algorithm can be used to fine-tune the model parameters. Furthermore, the aforementioned unit time period can be at the minute level, for example, 1 minute or 5 minutes.

[0058] S3. Optimize the dynamic network adjustment strategy for the microgrid wireless communication network to be executed based on the adaptive hummingbird algorithm, and obtain the best search result for the dynamic network adjustment strategy to minimize the fitness function, wherein the independent variable of the fitness function includes the multidimensional network state index 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.

[0059] In step S3, the adaptive hummingbird algorithm is a bio-inspired optimization algorithm based on the intelligent behavior of hummingbirds, designed to solve optimization problems. Inspired by the foraging behavior and group characteristics of hummingbirds in nature, it 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 to this embodiment to optimize 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 through weighted summation or other combinations, after comprehensively considering them, so as to measure the merits of the dynamic network adjustment strategy represented by each hummingbird individual (i.e., the lower the comprehensive evaluation index value, the better the dynamic network adjustment strategy, and vice versa). Specifically, when the multi-dimensional network state index parameters include, but are not limited to, signal-to-noise ratio, transmit / receive signal strength, and packet loss rate, the calculation formula of the fitness function F is 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 state index 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; B represents the transmit and receive signal strength in the multidimensional network state index parameters; L represents the packet loss rate in the multidimensional network state index parameters; and a, β, and γ represent preset weight coefficients, respectively. The aforementioned weighting coefficients a, β, and γ are used to balance the importance of different objectives. They first need to be parameterized according to their dimensions, and the required parameters can be obtained by testing different weight combinations in simulations. After deployment, they are dynamically adjusted in real time according to the field conditions; for example, when the packet loss rate increases, the weighting coefficient γ is temporarily increased. The sum of the aforementioned weighting coefficients a, β, and γ does not need to be 1. Given the positive correlation between signal-to-noise ratio and the optimal state of the network / dynamic network adjustment strategy, the weighting coefficient a needs to be negative so that the calculated fitness is negatively correlated with the optimal state of the network / dynamic network adjustment strategy, thus facilitating the acquisition of the optimal search result that minimizes the fitness function. Similarly, given the positive correlation between transmit and receive signal strength and the optimal state of the network / dynamic network adjustment strategy, the weighting coefficient β needs to be negative so that the calculated fitness is negatively correlated with the optimal state of the network / dynamic network adjustment strategy, thus facilitating the acquisition of the optimal search result that minimizes the fitness function (since the packet loss rate is negatively correlated with the optimal state of the network / dynamic network adjustment strategy, the weighting coefficient γ is usually positive). Therefore, based on the aforementioned fitness function F, the complex network state evaluation results can be transformed into a single numerical value, facilitating comparison and decision-making in subsequent optimization steps. Furthermore, to ensure consistency in the dimensions of the independent variables in the fitness function F and 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 values.

[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 operator to perturb individual hummingbirds, thereby iteratively updating the population position. To balance global search and local exploitation capabilities during algorithm operation, and to avoid the algorithm prematurely falling into local optima, preferably, the adaptive hummingbird algorithm is used to optimize the dynamic network adjustment strategy currently to be executed for the microgrid wireless communication network, obtaining the optimal search result for minimizing the fitness function, including but not limited to the following steps S301 to S307.

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

[0064]

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

[0066] In step S301, the initial search value array maps to the dynamic network adjustment initial strategy to be selected for the microgrid wireless communication network. The upper / lower limits of the parameter search space corresponding to the k-th parameter can be conventionally determined based on the encoding range corresponding to the k-th parameter. For example, for the parameter of retransmission count, the corresponding encoding range is 1 to 9 and takes integer values. Then the upper limit of the parameter search space corresponding to this parameter is 9 and the lower limit is 1.

[0067] S302. For each individual hummingbird, the corresponding initial search value array is mapped to the dynamic network adjustment strategy to be selected for the microgrid wireless communication network. Then, the dynamic network adjustment strategy and the node operation information of the most recent unit time period are imported into the mapping model to output the first multidimensional network state index parameter. The first multidimensional network state index parameter is then used as the independent variable to import the fitness function to obtain the corresponding first fitness. 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 rounding requirements of certain parameters. For example, the retransmission count parameter needs to be rounded during the mapping process. Furthermore, the first multi-dimensional network state index parameter is the network state estimation result for the next unit time period, specifically the signal-to-noise ratio, transmit / receive signal strength, and packet loss rate, which can be used in the fitness calculation.

[0069] S303. Take the initial search value array corresponding to the smallest first fitness as the current optimal search value array, initialize the current iteration number t=1, and then execute step S304.

[0070] S304. For each individual hummingbird, update the corresponding current search value array based on the current iteration number, 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] In the formula, Indicates the current search value array x t And the search value corresponding to the k-th parameter, This represents the search value in the array of search values ​​before the update that corresponds to the k-th parameter. represents the search value in the array of current optimal search values ​​and corresponding to the k-th parameter, 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 the Cauchy and Gaussian fusion mutation operator to perturb the hummingbird individual. Since the standard sigmoid function may have the following shortcomings when balancing exploration and development: (1) insufficient decay rate in the initial stage, resulting in low global search efficiency; (2) insufficient local development capability in the later stage, which is easy to fall into suboptimal solution, by using the adaptive sigmoid function to adjust the parameters, it can better meet the dynamic needs of microgrid wireless communication network optimization, and thus can dynamically adjust the adaptive adjustment factor according to the current iteration number, so as to achieve the purpose of global search in the early stage of the algorithm and focus on local development in the later stage.

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

[0075] The specific details of step S305 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 iteration number t has reached the maximum iteration number T. If so, use the current optimal search value array as the best search result obtained by optimizing the dynamic network adjustment strategy to be executed for the microgrid wireless communication network and minimizing the fitness function. Otherwise, increment the current iteration number t by 1 and return to step S304.

[0078] S4. Dynamically adjust the microgrid wireless communication network based on the best search result.

[0079] In step S4, the optimal search result is specifically mapped to the dynamic network adjustment strategy to be executed for the microgrid wireless communication network (derived in step S302 above for details, and will not be repeated here). Then, the microgrid wireless communication network is dynamically adjusted according to the dynamic network adjustment strategy. This can adapt to network fluctuations and load changes in the microgrid wireless communication network, avoid affecting the instability of network communication and data acquisition, ensure that network performance is always in a better state, reduce network latency, bandwidth consumption and packet loss rate, and thus meet the needs of real-time control and data interaction of the microgrid, which is convenient for practical application and promotion. In addition, in the specific adjustment process, the feasibility of implementation and interference with existing network services should 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 steps S1 to S4 above, a new scheme for optimizing and adjusting the microgrid wireless communication network based on the adaptive hummingbird algorithm is provided. First, a mapping model between the adjustment strategy, operational information, and indicator parameters is established based on the historical dynamic network adjustment strategies executed for the microgrid wireless communication network, node operation information before the corresponding strategy is executed, and multi-dimensional network state index parameters after the corresponding strategy is executed. Then, the adaptive hummingbird algorithm is used to optimize the dynamic network adjustment strategy currently to be executed for the communication network, obtaining the optimal search result that minimizes the fitness function. Finally, the communication network is dynamically adjusted based on the optimal search result. This allows for adaptation to network fluctuations and load changes in the microgrid wireless communication network, avoiding instability affecting network communication and data acquisition, ensuring that network performance is always in a superior state, reducing network latency, bandwidth consumption, and packet loss rate, thereby meeting the needs of real-time control and data interaction in the microgrid, facilitating practical application and promotion.

[0081] Based on the aforementioned first aspect of the technical solution, this embodiment also provides a possible design for enhancing intelligent control, namely, the method further includes: predicting the probability of fault occurrence of the node device in the next unit time period based on the node operation information of the node device in the microgrid in the most recent consecutive unit time periods using a time series model; optimizing the dynamic network adjustment strategy for the microgrid wireless communication network to be executed based on an adaptive hummingbird algorithm to obtain the optimal search result for minimizing the fitness function of the dynamic network adjustment strategy, including: optimizing the dynamic network adjustment strategy for the microgrid wireless communication network to be executed based on an adaptive hummingbird algorithm. The executed dynamic network adjustment strategy is optimized to obtain the optimal search result that minimizes the fitness function and satisfies the fault device avoidance activation condition. The independent variable of the fitness function includes the multi-dimensional network state index 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 fault device avoidance activation condition means that if the probability of fault occurrence of the node device in the microgrid in the next unit time period exceeds a preset probability threshold, then the activation of the node device in the microgrid in the next unit time period is prohibited. The aforementioned specific prediction process for the probability of failure may include, but is not limited to, the following: First, based on a certain amount of positive sample data (i.e., the model input is the node operation information of the most recent consecutive unit time period before the historical failure event of the node equipment in the microgrid, and the model input is the value "1") and negative sample data (i.e., the model input is the node operation information of the most recent consecutive unit time period before the historical failure event of the node equipment in the microgrid, and the model input is the value "0"), a prediction model for estimating the probability of failure of the node equipment in the microgrid in the next unit time period is obtained through conventional training based on a time series model (which is the theory and method of establishing a mathematical model based on the time series data obtained from system observations, through curve fitting and parameter estimation, such as, but not limited to, using a long short-term memory network model). Then, the node operation information of the node equipment in the current most recent consecutive unit time period is imported into the prediction model to obtain the probability of failure of the node equipment in the current next unit time period (specifically, the confidence level of the output number "1" can be used as the probability of failure).Furthermore, the specific steps for ensuring that the optimal search result meets the fault device avoidance activation conditions can be as follows: In steps S301 and S304, the generated / updated search value array is first mapped to the dynamic network adjustment strategy to be selected for the microgrid wireless communication network. Then, it is routinely determined whether the dynamic network adjustment strategy meets the fault device avoidance activation conditions. If not, the search value array needs to be regenerated until the fault device avoidance activation conditions are met.

[0082] Based on the aforementioned possible design, the fault prediction results of node devices within the microgrid can also be considered during the optimization of the microgrid wireless communication network. This ensures that the final optimized dynamic network adjustment strategy meets the conditions for avoiding and activating faulty devices, enhancing intelligent control functions and further ensuring that network performance remains at an optimal level. Furthermore, to further enhance intelligent control functions, real-time data combined with artificial intelligence technology can be used to accurately predict microgrid load changes, enabling timely and reasonable scheduling decisions. For example, the output power of generators can be adjusted appropriately, and the charging and discharging strategies of energy storage devices can be optimized to achieve stable operation of the microgrid and efficient energy utilization. Simultaneously, real-time assessment and predictive maintenance of equipment health can be performed, reducing equipment failure rates and maintenance costs, and extending equipment lifespan. To improve system reliability, collaborative work and load balancing mechanisms among multiple edge computing terminals (i.e., the edge computer devices) can effectively avoid single points of failure and resource overload. In the event of a partial network failure or the failure of some nodes, the system can automatically reconfigure the network, rationally distributing tasks to other normal nodes, ensuring the continuous and stable operation of the entire microgrid system, and improving the system's fault tolerance and reliability. In addition, to optimize energy management, intelligent control and optimized scheduling of various energy devices in the microgrid (such as solar panels, wind turbines, and energy storage batteries) can be used to achieve rational allocation and efficient utilization of energy. Under the premise of meeting users' electricity demand, the operating cost of the microgrid can be reduced, energy utilization efficiency can be improved, and dependence on traditional energy sources can be reduced, 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, which is arranged in an edge computer device for wireless communication connection of 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 node operation information of node devices in the microgrid and multi-dimensional network status index parameters of the microgrid wireless communication network in real time. 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. It is used to establish a mapping model between the dynamic network adjustment strategy and node operation information and the multi-dimensional network status index parameters based on the dynamic network adjustment strategy historically executed for the microgrid wireless communication network, the node operation information of the most recent unit time period before the execution of the corresponding strategy, and the multi-dimensional network status index parameters of 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. It is used to optimize the dynamic network adjustment strategy for the microgrid wireless communication network and the dynamic network adjustment strategy to be executed based on the adaptive hummingbird algorithm, and to obtain the best search result for the dynamic network adjustment strategy to minimize the fitness function. The independent variable of the fitness function includes multi-dimensional network state index parameters obtained by importing the dynamic network adjustment strategy 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 based on the best search results.

[0088] The working process, working details and technical effects of the aforementioned device provided in the second aspect of this embodiment can be found in the microgrid wireless communication network optimization method described in the first aspect or possible design, 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. The device includes a memory, a processor, and a transceiver connected in sequence. The memory stores a computer program, the transceiver sends and receives messages, and the processor reads the computer program and executes the microgrid wireless communication network optimization method as described in the first aspect or possible design one. Specifically, the memory may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), flash memory, first-input first-output (FIFO), and / or first-input last-output (FILO), etc.; the processor may include, but is not limited to, a microprocessor of the STM32F105 series. Furthermore, 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 found in the microgrid wireless communication network optimization method described in the first aspect or possible design, and will not be repeated here.

[0091] This fourth aspect of the embodiment provides a computer-readable storage medium storing instructions comprising a microgrid wireless communication network optimization method as described in the first aspect or possible design one. Specifically, the computer-readable storage medium stores instructions that, when executed on a computer, perform the microgrid wireless communication network optimization method as described in the first aspect or possible design one. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[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 found in the microgrid wireless communication network optimization method as described in the first aspect or possible design, and will not be repeated here.

[0093] This fifth aspect of the embodiment provides a computer program product, including a computer program or instructions, which, when executed by a computer, implement the microgrid wireless communication network optimization method as described in the first aspect or possible design. 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 merely 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 should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing a microgrid wireless communication network, characterized in that, The edge computer device for connecting node devices in a micro-grid by wireless communication is executed, comprising: Real-time collection of node running information of node devices in a micro-grid and multi-dimensional network state index parameters of a micro-grid wireless communication network, wherein the micro-grid wireless communication network refers to a star-type wireless communication network with the edge computer device as a central node and the node devices in the micro-grid as peripheral nodes; According to the dynamic network adjustment strategy for the micro-grid wireless communication network and the historical execution, and the node running information of the last unit period before the execution of the corresponding strategy and the multi-dimensional network state index parameters of the next unit period after the execution of the corresponding strategy, a mapping model of the dynamic network adjustment strategy, the node running information and the multi-dimensional network state index parameters is established; Optimization of the dynamic network adjustment strategy for the micro-grid wireless communication network and the current to-be-executed by the adaptive hummingbird algorithm, to obtain the best search result of the dynamic network adjustment strategy for minimizing the fitness function, wherein the set of to-be-optimized parameters in the optimization process includes multi-dimensional parameters obtained by using an encoding method and having a mapping relationship with the dynamic network adjustment strategy for the micro-grid wireless communication network and the current to-be-executed, and the independent variables of the fitness function include multi-dimensional network state index parameters obtained by importing the dynamic network adjustment strategy for the micro-grid wireless communication network and the to-be-selected and the node running information of the last unit period into the mapping model; Dynamic adjustment of the micro-grid wireless communication network according to the best search result.

2. The microgrid wireless communication network optimization method of claim 1, wherein, The node running information includes the computing resource utilization rate, node load capacity, task queue length, power generation and / or power storage of the corresponding node device; And / or, the multi-dimensional network state index parameters include signal-to-noise ratio, transceiver signal strength and / or packet loss rate.

3. The method of claim 1, wherein, When the multi-dimensional network state index parameters include signal-to-noise ratio, transceiving signal strength and packet loss rate, the fitness function is calculated according to the following formula: In the formula, represents a signal-to-noise ratio in a multi-dimensional network state index parameter obtained by importing a dynamic network adjustment strategy facing a micro-grid wireless communication network and a node operation information of a current nearest unit period into a mapping model, represents a signal strength in the multi-dimensional network state index parameter, represents a packet loss rate in the multi-dimensional network state index parameter, , and respectively represent preset weight coefficients.

4. The method of claim 1, wherein, The optimization of the dynamic network adjustment strategy for the micro-grid wireless communication network and the current to-be-executed by the adaptive hummingbird algorithm to obtain the best search result of the dynamic network adjustment strategy for minimizing the fitness function includes but is not limited to the following steps S301-S307: S301. Initialize the adaptive hummingbird algorithm, including population size. and maximum number of iterations The optimized algorithm parameters are determined, and are designed for applications containing... For each individual hummingbird in a population, a set of parameters to be optimized and a corresponding initial search value array are randomly generated. Then, step S302 is executed. The set of parameters to be optimized includes multi-dimensional parameters obtained through encoding and mapped to the dynamic network adjustment strategy to be executed in the microgrid wireless communication network. The initial search value array... It is expressed as follows: In the formula, represents less than or equal to is a positive integer, represents the total number of parameters in the parameter set to be optimized, represents the initial search value corresponding to the th parameter in the parameter set to be optimized, represents the upper limit of the parameter search space corresponding to the th parameter, represents the lower limit of the parameter search space corresponding to the th parameter, represents a pure decimal random generation function; S302. For each hummingbird individual, map the corresponding initial search value array to the dynamic network adjustment strategy for the micro-grid wireless communication network and the to-be-selected, then import the dynamic network adjustment strategy and the node running information of the last unit period into the mapping model, output to obtain the first multi-dimensional network state index parameters, and import the first multi-dimensional network state index parameters as independent variables into the fitness function to obtain the corresponding first fitness, and finally execute step S303; S303. Set the initial search value array corresponding to the smallest first fitness as the current optimal search value array, and initialize the current iteration number Step S304 is then executed. S304. For each hummingbird individual, a corresponding current search value array is updated according to the current iteration number, the current optimal search value array and the corresponding pre-update search value array, and then step S305 is executed, wherein the current search value array is represented as follows: wherein denotes the search value in the current search value array and corresponding to the i-th parameter, denotes the search value in the current search value array and corresponding to the i-th parameter, denotes the search value in the current search value array and corresponding to the i-th parameter, denotes the search value in the current search value array denotes the adaptive adjustment factor calculation function, denotes the adaptive Sigmoid function, denotes a random variable satisfying a standard Cauchy distribution, denotes a random variable satisfying a standard Gaussian distribution. S305. For each hummingbird individual, map the corresponding current search value array to the dynamic network adjustment strategy for the micro-grid wireless communication network and the to-be-selected, then import the dynamic network adjustment strategy and the node running information of the last unit period into the mapping model, output to obtain the second multi-dimensional network state index parameters, and import the second multi-dimensional network state index parameters as independent variables into the fitness function to obtain the corresponding second fitness, and finally execute step S306; S306. Determine whether the minimum second fitness is lower than the fitness corresponding to the current optimal search value array. If yes, 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. S307. Determine whether the current iteration number reaches the maximum iteration number , if yes, the current optimal search value array is taken as the best search result obtained by optimizing the dynamic network adjustment strategy for the micro-grid oriented wireless communication network and the current to-be-executed dynamic network adjustment strategy and used to minimize the fitness function, otherwise, the current iteration number is increased by 1, and then the step S304 is executed.

5. The method of claim 1, wherein, The dynamic network adjustment strategy includes increasing / decreasing the number of wireless communication connections of the peripheral node, adjusting the transmission power for wireless communication, reselecting the channel for wireless communication, adjusting the posture of the transceiver antenna for wireless communication, adjusting the position of the movable peripheral node, reselecting the anti-interference processing algorithm for wireless communication, and / or adjusting the number of retransmissions for wireless communication.

6. The method of Claim 1, wherein, The method further comprises: based on the time series model, predicting the failure occurrence probability of the node device in the micro-grid in the current next unit time period according to the node operation information of the node device in the micro-grid in the current nearest consecutive multiple unit time periods. The adaptive hummingbird algorithm is used to optimize the dynamic network adjustment strategy currently to be executed for the micro-grid wireless communication network, and the optimal search result of the dynamic network adjustment strategy for minimizing the fitness function and satisfying the failure device avoidance enabling condition is obtained, wherein the independent variable of the fitness function includes a multi-dimensional network state index parameter obtained by importing the dynamic network adjustment strategy to be selected for the micro-grid wireless communication network and the node operation information in the current nearest unit time period into the mapping model, and the failure device avoidance enabling condition means that if the failure occurrence probability of the node device in the micro-grid in the current next unit time period exceeds the preset probability threshold, the node device in the micro-grid is prohibited from being enabled in the current next unit time period.

7. A microgrid wireless communication network optimization apparatus, comprising: The edge computer device for wirelessly connecting the node devices in the micro-grid is arranged, 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 configured to collect the node operation information of the node devices in the micro-grid and the multi-dimensional network state index parameter of the micro-grid wireless communication network in real time, wherein the micro-grid wireless communication network refers to a star-type wireless communication network with the edge computer device as a central node and the node devices in the micro-grid as peripheral nodes. The mapping model establishment unit is in communication connection with the real-time data collection unit and is configured to establish a mapping model of the dynamic network adjustment strategy and the node operation information and the multi-dimensional network state index parameter according to the historical dynamic network adjustment strategies for the micro-grid wireless communication network and the node operation information in the nearest unit time period before the execution of the corresponding strategy and the multi-dimensional network state index parameter in the next unit time period after the execution of the corresponding strategy. The mapping model establishment unit is in communication connection with the real-time data collection unit and is configured to establish a mapping model of the dynamic network adjustment strategy and the node operation information and the multi-dimensional network state index parameter according to the historical dynamic network adjustment strategies for the micro-grid wireless communication network and the node operation information in the nearest unit time period before the execution of the corresponding strategy and the multi-dimensional network state index parameter in the next unit time period after the execution of the corresponding strategy. The adjustment strategy optimization unit is respectively communicatively connected with the real-time data collection unit and the mapping model establishment unit, is used for optimizing the dynamic network adjustment strategy facing the micro-grid wireless communication network and currently to be executed based on the adaptive hummingbird algorithm, obtains the dynamic network adjustment strategy and is used for minimizing the best search result of fitness function, wherein the parameter set to be optimized in the optimization process contains the multi-dimensional parameters obtained by using the coding mode and having the mapping relationship with the dynamic network adjustment strategy facing the micro-grid wireless communication network and currently to be executed, and the independent variable of the fitness function contains the multi-dimensional network state index parameters obtained by importing the dynamic network adjustment strategy facing the micro-grid wireless communication network and to be selected and the node operation information of the current nearest unit period into the mapping model. The network dynamic adjustment unit is communicatively connected with the adjustment strategy optimization unit, and is used for dynamically adjusting the micro-grid wireless communication network according to the best search result.

8. A computer device, comprising: The micro-grid wireless communication network optimization method comprises the steps of: collecting real-time data of the micro-grid wireless communication network; establishing a mapping model of the micro-grid wireless communication network; optimizing a dynamic network adjustment strategy facing the micro-grid wireless communication network and currently to be executed based on the adaptive hummingbird algorithm; and dynamically adjusting the micro-grid wireless communication network according to the best search result.

9. A computer-readable storage medium, characterized in that The computer readable storage medium stores instructions, and when the instructions run on the computer, the micro-grid wireless communication network optimization method is executed.

10. A computer program product comprising computer programs or instructions, characterized in that, The computer program or the instructions realize the micro-grid wireless communication network optimization method when executed by the computer. The computer program or the instructions realize the micro-grid wireless communication network optimization method when executed by the computer.

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