Wireless network intelligent optimization deployment method and system

Abstract

According to the wireless network intelligent optimization deployment method and system disclosed by the invention, the network deployment strategy is issued to the edge node, so that the calculation amount of the integrated controller is reduced, and the optimization efficiency of the network deployment strategy is improved; besides, strategy chromosomes are evolved through a double-variation mechanism, and a dynamically changing gene mutation probability and an environment evolution range are introduced, so that the algorithm can quickly jump out of a local optimal solution, dynamically track and adapt to the change of a network environment, and the convergence speed of the evolutionary algorithm is remarkably improved; and finally, analyzing the strategy chromosome according to the network state data, and determining an optimized deployment strategy. According to the method, the overall overhead and delay of a network deployment strategy are remarkably reduced, and the efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication networks, and more specifically, to a method and system for intelligent optimization deployment of wireless networks. Background Art

[0002] In wireless sensor networks, nodes transmit monitoring information collected from objects within their coverage area to service terminals through collaborative sensing, thus achieving an organic integration of the cyber world and the real world. In traditional wireless network deployment, all network deployment strategies are mainly controlled by terminals, which increases the terminal's computing power and response time, resulting in problems such as delayed sensor node responses. In addition, the lack of restrictions in adjusting network deployment increases the terminal's computing power.

[0003] Therefore, the existing technology has defects and needs to be improved urgently. Summary of the Invention

[0004] In order to solve at least one of the above technical problems, the purpose of the present invention is to provide a method and system for intelligent optimization deployment of a wireless network, which can reduce the overall overhead and delay of the network deployment strategy and improve efficiency.

[0005] A first aspect of the present invention provides a wireless network intelligent optimization deployment method, comprising: Obtaining the sensor node location and filing and storing the sensor node location in the edge nodes of the corresponding area; The edge nodes report the stored sensor node locations to the centralized controller; Generate an initial strategy set containing multiple network deployment strategies based on a genetic algorithm, and encode each deployment strategy into a strategy chromosome; Based on the sampling information, the edge nodes collect the network status data of the sensor nodes; When the deployment strategy is only within the range of the corresponding edge node, the corresponding edge node evolves and optimizes the strategy chromosome according to the network status data to obtain the optimized deployment strategy; When a deployment strategy exists in multiple edge node ranges at the same time, the network status data is uploaded to the centralized controller, and the centralized controller evolves and optimizes the strategy chromosome according to the network status data to obtain the optimized deployment strategy; Send the optimized deployment strategy to the corresponding edge node for execution; The network status data includes link delay, network packet loss rate, network load, available bandwidth and node energy consumption.

[0006] In this solution, the step of generating the sampling information includes: Based on a fixed frequency, the edge node collects the original received signal strength or signal-to-noise ratio; Arrange the original received signal strength or signal-to-noise ratio in the order of acquisition time to obtain the data set ; Based on the set sliding time window, extract the adjacent data within the corresponding sliding time window; Calculate the difference between adjacent data in the sliding time window, take the absolute value, and get the difference between adjacent indicators; Traverse the entire sliding time window to obtain a set of adjacent indicator difference values, and calculate the mean of the values ​​in the adjacent indicator difference set to obtain the stability index corresponding to the current sliding time window ; Set the current signal fluctuation value to , whose formula is If the current signal fluctuation value is greater than the preset signal fluctuation threshold, sampling information is generated.

[0007] In this solution, the step of obtaining the preset signal fluctuation threshold specifically includes: Obtain the environment in which the original received signal strength or signal-to-noise ratio is collected; Determine an initial threshold value of signal fluctuation based on the corresponding environment; Extract the stability index of the previous sliding time window; The initial signal fluctuation threshold is optimized according to the stability index of the previous sliding time window to obtain the preset signal fluctuation threshold of the current sliding time window.

[0008] In this solution, the evolution steps specifically include: Extract the network load and available bandwidth from the network status data of the current time node and the previous time node; Compare and analyze the network load at the current time node with the network load at the previous time node to obtain the absolute value of the network load change; Compare and analyze the available bandwidth at the current time node with the available bandwidth at the previous time node to obtain the absolute value of the change in available bandwidth; Determine the current environment change index based on the absolute value of the network load change rate and the absolute value of the available bandwidth change rate; Based on the range of the environmental change index, the evolution range of the corresponding strategy chromosome is determined; Based on the evolution range, the values ​​of single or multiple gene bits in the strategy chromosome are randomly changed according to the preset gene mutation probability to obtain a new strategy chromosome.

[0009] This plan also includes: The preset environmental sensitivity coefficient is optimized according to the environmental change index to obtain the gene mutation probability adjustment value; The preset gene mutation probability is set to P, and its formula is ,in represents the initial probability of gene mutation, and k represents the adjusted value of gene mutation probability.

[0010] In this solution, the optimization steps specifically include: Obtaining the business to be transmitted; According to the business to be transmitted, set the corresponding optimization weight parameters; Set the strategy chromosome optimization function based on the corresponding optimization weight parameters; Divide the network status data according to the strategy chromosomes to obtain the network status data of the corresponding strategy chromosomes; Send the network status data of the corresponding strategy chromosome to the strategy chromosome optimization function to obtain the optimization index of the corresponding strategy chromosome; If the optimization index of the strategy chromosome is greater than the optimization index of the strategy chromosome before evolution, the strategy chromosome after evolution will replace the strategy chromosome before evolution; If the optimization index of the strategy chromosome is less than or equal to the optimization index of the strategy chromosome before evolution, the strategy chromosome before evolution is restored and the corresponding evolution path is recorded; After the set number of evolutions, the optimized strategy chromosome is output.

[0011] This plan also includes: Based on the set blockchain smart contract, the edge node periodically calls the roll of sensor nodes; Extract the time node when the edge node calls the sensor node and start the countdown to the set time; If the sensor node has not reported the data when the countdown of the set time reaches zero, the sensor node will be marked to obtain the marked sensor node; The preset backup sensor nodes replace the corresponding marked sensor nodes and are written into the blockchain to be broadcasted to other edge nodes and the centralized controller.

[0012] A second aspect of the present invention provides a wireless network intelligent optimization deployment system, including a memory and a processor, wherein the memory stores a wireless network intelligent optimization deployment method program, and when the wireless network intelligent optimization deployment method program is executed by the processor, the following steps are implemented: Obtaining the sensor node location and filing and storing the sensor node location in the edge nodes of the corresponding area; The edge nodes report the stored sensor node locations to the centralized controller; Generate an initial strategy set containing multiple network deployment strategies based on a genetic algorithm, and encode each deployment strategy into a strategy chromosome; Based on the sampling information, the edge nodes collect the network status data of the sensor nodes; When the deployment strategy is only within the range of the corresponding edge node, the corresponding edge node evolves and optimizes the strategy chromosome according to the network status data to obtain the optimized deployment strategy; When a deployment strategy exists in multiple edge node ranges at the same time, the network status data is uploaded to the centralized controller, and the centralized controller evolves and optimizes the strategy chromosome according to the network status data to obtain the optimized deployment strategy; Send the optimized deployment strategy to the corresponding edge node for execution; The network status data includes link delay, network packet loss rate, network load, available bandwidth and node energy consumption.

[0013] In this solution, the step of generating the sampling information includes: Based on a fixed frequency, the edge node collects the original received signal strength or signal-to-noise ratio; Arrange the original received signal strength or signal-to-noise ratio in the order of acquisition time to obtain the data set ; Based on the set sliding time window, extract the adjacent data within the corresponding sliding time window; Calculate the difference between adjacent data in the sliding time window, take the absolute value, and get the difference between adjacent indicators; Traverse the entire sliding time window to obtain a set of adjacent indicator difference values, and calculate the mean of the values ​​in the adjacent indicator difference set to obtain the stability index corresponding to the current sliding time window ; Set the current signal fluctuation value to , whose formula is If the current signal fluctuation value is greater than the preset signal fluctuation threshold, sampling information is generated.

[0014] In this solution, the step of obtaining the preset signal fluctuation threshold specifically includes: Obtain the environment in which the original received signal strength or signal-to-noise ratio is collected; Determine an initial threshold value of signal fluctuation based on the corresponding environment; Extract the stability index of the previous sliding time window; The initial signal fluctuation threshold is optimized according to the stability index of the previous sliding time window to obtain the preset signal fluctuation threshold of the current sliding time window.

[0015] The present invention discloses a wireless network intelligent optimization deployment method and system, which reduces the computational load of the centralized controller and improves the optimization efficiency of the network deployment strategy by sending the network deployment strategy to the edge nodes. In addition, the strategy chromosomes are evolved through a "double mutation mechanism", which introduces dynamically changing gene mutation probabilities and environmental evolution ranges, enabling the algorithm to quickly jump out of the local optimal solution, dynamically track and adapt to changes in the network environment, and significantly improve the convergence speed of the evolutionary algorithm. Finally, the strategy chromosomes are analyzed based on network status data to determine the optimized deployment strategy. The present invention significantly reduces the overall overhead and delay of the network deployment strategy and improves efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a wireless network intelligent optimization deployment method according to the present invention is shown; Figure 2 A block diagram of a wireless network intelligent optimization deployment system according to the present invention is shown. DETAILED DESCRIPTION

[0017] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0019] Figure 1 A flow chart of a wireless network intelligent optimization deployment method of the present invention is shown.

[0020] S101, obtaining the sensor node location, and filing and storing the sensor node location in the edge node of the corresponding area; S102, the edge node reports the stored sensor node location to the centralized controller; S103, generating an initial strategy set including multiple network deployment strategies based on a genetic algorithm, and encoding each deployment strategy into a strategy chromosome; S104, based on the sampling information, the edge node collects the network status data of the sensor node; S105, when the deployment strategy is only within the range of the corresponding edge node, the corresponding edge node evolves and optimizes the strategy chromosome according to the network status data to obtain the optimized deployment strategy; S106, when the deployment strategy exists in multiple edge node ranges at the same time, uploading the network status data to the centralized controller, and the centralized controller evolves and optimizes the strategy chromosome according to the network status data to obtain the optimized deployment strategy; S107: Send the optimized deployment strategy to the corresponding edge node for execution.

[0021] According to an embodiment of the present invention, edge nodes are configured to collect the locations of sensor nodes within a jurisdiction and report them to a centralized controller. The centralized controller then generates a diverse set of initial strategies using a built-in genetic algorithm. Each deployment strategy is encoded as a strategy chromosome, a data structure that converts complex network configuration parameters (such as power, channel, and routing) into a computer-processable and optimizable genetic string, laying the foundation for subsequent intelligent evolution. The deployment strategy is then distributed to each edge node (such as a base station or access point) for execution, with sampling information triggering data collection at the edge node. This significantly reduces unnecessary data transmission and energy consumption, and network status data is only collected when the network status changes (such as signal mutations or service switching), ensuring the real-time and validity of the data. Furthermore, when a deployment strategy involves only sensor nodes within one edge node, the corresponding edge node evolves and optimizes the deployment strategy. When a deployment strategy involves sensor nodes within two or more edge nodes, the centralized controller evolves and optimizes the deployment strategy. This effectively reduces the computational workload of the centralized controller and the data transmission between the edge node and the centralized controller through the edge nodes, further reducing channel occupancy. The network status data includes link delay, network packet loss rate, network load, available bandwidth and node energy consumption, the node energy consumption is sensor node energy consumption, and the sensor node is a wireless network sensor node.

[0022] It should be noted that in the genetic algorithm, a chromosome represents a possible solution, namely a deployment strategy. In this embodiment, the chromosome is designed as a compact binary encoding form, containing multiple gene segments, each of which corresponds to a configurable network parameter; for example, it consists of three gene segments, namely the power control segment, the channel allocation segment, and the routing selection segment. The power control segment represents the transmission power level of the device, which is divided into 16 levels, for example, so 1001 (binary) represents power level 9 (decimal); the channel allocation segment represents the selected wireless channel, The range includes channels 1-64, so 001010 represents channel 10; the routing segment represents the identifier of the next node, and the hash value of the node ID is used as the code to ensure uniqueness. For example, 01101001 represents the node with a hash value of 105. For example, the length of the policy chromosome is 4+6+8=18 (bits), the power control segment is 0101, the channel allocation segment is 001100, and the routing segment is 01101001. The corresponding policy chromosome represents power level 5, channel 12, and the hash value of the next sensor node ID is 105.

[0023] According to an embodiment of the present invention, the step of generating sampling information includes: Based on a fixed frequency, the edge node collects the original received signal strength or signal-to-noise ratio; Arrange the original received signal strength or signal-to-noise ratio in the order of acquisition time to obtain the data set ; Based on the set sliding time window, extract the adjacent data within the corresponding sliding time window; Calculate the difference between adjacent data in the sliding time window, take the absolute value, and get the difference between adjacent indicators; Traverse the entire sliding time window to obtain a set of adjacent indicator difference values, and calculate the mean of the values ​​in the adjacent indicator difference set to obtain the stability index corresponding to the current sliding time window ; Set the current signal fluctuation value to , whose formula is ,If the current signal fluctuation value is greater than the preset signal fluctuation threshold, sampling information is generated.

[0024] It should be noted that, for example, if the sliding time window is set to 10 seconds, the data within the previous 10 seconds will be extracted with the current time node as the boundary, and the data before 10 seconds will be deleted; Indicates the stability indicator of the previous sliding time window.

[0025] According to an embodiment of the present invention, the step of obtaining the preset signal fluctuation threshold specifically includes: Obtain the environment in which the original received signal strength or signal-to-noise ratio is collected; Determine an initial threshold value of signal fluctuation based on the corresponding environment; Extract the stability index of the previous sliding time window; The initial signal fluctuation threshold is optimized according to the stability index of the previous sliding time window to obtain the preset signal fluctuation threshold of the current sliding time window.

[0026] It should be noted that different environments set different initial thresholds for signal fluctuations. For example, if the environment is divided into indoor and outdoor, the initial threshold for indoor signal fluctuations is set to 3dB, and the initial threshold for outdoor signal fluctuations is set to 5dB. The stability index of the previous sliding time window is set to , the preset signal fluctuation threshold of the current sliding time window , whose formula is ,in The initial threshold of signal fluctuation in the current environment, The set smoothing coefficient, such as ; The initial threshold of signal fluctuation in the current environment is optimized through the stability index of the previous sliding time window to improve the adaptability to the current environment.

[0027] According to an embodiment of the present invention, the evolution step specifically includes: Extract the network load and available bandwidth from the network status data of the current time node and the previous time node; Compare and analyze the network load at the current time node with the network load at the previous time node to obtain the absolute value of the network load change; Compare and analyze the available bandwidth at the current time node with the available bandwidth at the previous time node to obtain the absolute value of the change in available bandwidth; Determine the current environment change index based on the absolute value of the network load change rate and the absolute value of the available bandwidth change rate; Based on the range of the environmental change index, the evolution range of the corresponding strategy chromosome is determined; Based on the evolution range, the values ​​of single or multiple gene bits in the strategy chromosome are randomly changed according to the preset gene mutation probability to obtain a new strategy chromosome.

[0028] It should be noted that the dual mutation mechanism includes gene mutation mutation and environmental adaptation mutation. The strategy chromosome is evolved through the dual mutation mechanism. The gene mutation mutation randomly changes the value of a single or multiple gene bits in the strategy chromosome with a preset gene mutation probability, thereby realizing the evolution process, such as changing the power level from 2 to 3, and changing the channel number from 4 to 6; the preset gene mutation probability prevents the algorithm from converging prematurely or over-adjusting, so that the computational complexity is within a controllable range; in addition, the network load at the current time node is subtracted from the network load at the previous time node, and the absolute value is taken to obtain the absolute value of the network load change; the available bandwidth at the current time node is subtracted from the available bandwidth at the previous time node, and the absolute value is taken to obtain the absolute value of the available bandwidth change; the environmental change index is set to R, and its formula is ,in represent the corresponding weight coefficients, and , Indicates the absolute value of the network load change. Indicates the maximum load of the network. Indicates the absolute value of the change in available bandwidth, Represents the maximum available bandwidth. When the environmental change index approaches zero, the current network environment is stable, and therefore the deployment strategy has a narrow range of adjustment. When the environmental change index approaches 1, the current environment is changing dramatically, and the deployment strategy has a wide range of adjustment. The larger the environmental change index, the larger the evolution range of the corresponding strategy chromosome. The entire evolution range can be normalized, and then the environmental change index is set to the corresponding evolution range. For example, if the environmental change index is 0.5, the evolution range of the corresponding strategy chromosome is 0.5. Its specific evolution range is the environmental change index multiplied by the actual evolution range. The environmental adaptation variation limits evolution by adjusting the evolution range.

[0029] According to an embodiment of the present invention, the further embodiment includes: The preset environmental sensitivity coefficient is optimized according to the environmental change index to obtain the gene mutation probability adjustment value; The preset gene mutation probability is set to P, and its formula is ,in represents the initial probability of gene mutation, and k represents the adjustment value of gene mutation probability.

[0030] It should be noted that the preset environmental sensitivity coefficient is set to 1.2, and the gene mutation probability adjustment value is equal to the environmental change index multiplied by the preset environmental sensitivity coefficient. When the preset gene mutation probability is greater than 1, the corresponding preset gene mutation probability is set to 1, for example, the initial gene mutation probability is set to 5%.

[0031] According to an embodiment of the present invention, the optimization step specifically includes: Obtaining the business to be transmitted; According to the business to be transmitted, set the corresponding optimization weight parameters; Set the strategy chromosome optimization function based on the corresponding optimization weight parameters; Divide the network status data according to the strategy chromosomes to obtain the network status data of the corresponding strategy chromosomes; Send the network status data of the corresponding strategy chromosome to the strategy chromosome optimization function to obtain the optimization index of the corresponding strategy chromosome; If the optimization index of the strategy chromosome is greater than the optimization index of the strategy chromosome before evolution, the strategy chromosome after evolution will replace the strategy chromosome before evolution; If the optimization index of the strategy chromosome is less than or equal to the optimization index of the strategy chromosome before evolution, the strategy chromosome before evolution is restored and the corresponding evolution path is recorded; After the set number of evolutions, the optimized strategy chromosome is output.

[0032] It should be noted that the optimization index is set to F, and its formula is: ; Where A represents link delay, B represents available bandwidth, C represents network packet loss rate, and E represents node energy consumption. 、 、 and The corresponding weight coefficients are respectively determined according to the business to be transmitted, and the corresponding optimization weight parameters are set according to the business type; dynamic configuration is performed according to the current business type. For example, if the business to be transmitted is a real-time video business, the corresponding weight coefficient can be set to , , , , to prioritize low latency and high bandwidth; if the service to be transmitted is lot data collection service, the corresponding weight coefficient can be set to , , , , to prioritize low packet loss rate and low energy consumption; for example, if the number of evolutions is set to 60, then after 60 evolutions, the strategy chromosome retained is the optimal deployment strategy; in addition, the recorded evolution path prevents the calculation of repeated paths, further reducing the amount of calculation.

[0033] Furthermore, when the evolution time is greater than a preset evolution time threshold, the evolution step is stopped, the currently retained policy chromosome is output, and the retained policy chromosome is set as the optimal deployment strategy; through the preset evolution time threshold, the calculation delay is reasonably controlled, for example, the preset evolution time threshold is set to 0.5 seconds; through the preset evolution time threshold, excessive redundant calculations in the evolution process are prevented, which leads to excessive delays, further improving the efficiency of the network deployment strategy.

[0034] According to an embodiment of the present invention, the further embodiment includes: Based on the set blockchain smart contract, the edge node periodically calls the roll of sensor nodes; Extract the time node when the edge node calls the sensor node and start the countdown to the set time; If the sensor node has not reported the data when the countdown of the set time reaches zero, the sensor node will be marked to obtain the marked sensor node; The preset backup sensor nodes replace the corresponding marked sensor nodes and are written into the blockchain to be broadcasted to other edge nodes and the centralized controller.

[0035] It should be noted that, through the blockchain smart contract, the edge node periodically calls the roll call of the sensor nodes on the blockchain. After receiving the roll call notification, the sensor node needs to reply, set the reply data, and report the reply data. If the edge node does not receive the reply data of the corresponding sensor node within the set time, the sensor node will be set to invalid, for example, the set time is 10 seconds; the present invention greatly improves the reliability and operation and maintenance efficiency of the network through the "self-healing" of the network.

[0036] According to an embodiment of the present invention, further comprising; Get the number of currently monitored failed sensor nodes; Divide the number of failed sensor nodes by the total number of nodes in the current coverage area to obtain the percentage of failed sensor nodes; If the proportion of failed sensor nodes is greater than the preset failure threshold, a warning message is generated.

[0037] It should be noted that the preset failure threshold is 0.1. When the proportion of failed sensor nodes is greater than 0.1, the corresponding coverage area may have large-scale natural disasters and other phenomena, which have destroyed a large area of ​​wireless network transmission facilities in the coverage area. Therefore, the corresponding management terminal is prompted through a warning message; in addition, the deployment strategy is replaced on a large scale based on the warning information.

[0038] Figure 2 A block diagram of a wireless network intelligent optimization deployment system according to the present invention is shown.

[0039] like Figure 2 As shown, the second aspect of the present invention provides a wireless network intelligent optimization deployment system 2, including a memory 21 and a processor 22, wherein the memory stores a wireless network intelligent optimization deployment method program, and when the wireless network intelligent optimization deployment method program is executed by the processor, the following steps are implemented: Obtaining the sensor node location and filing and storing the sensor node location in the edge nodes of the corresponding area; The edge nodes report the stored sensor node locations to the centralized controller; Generate an initial strategy set containing multiple network deployment strategies based on a genetic algorithm, and encode each deployment strategy into a strategy chromosome; Based on the sampling information, the edge nodes collect the network status data of the sensor nodes; When the deployment strategy is only within the range of the corresponding edge node, the corresponding edge node evolves and optimizes the strategy chromosome according to the network status data to obtain the optimized deployment strategy; When a deployment strategy exists in multiple edge node ranges at the same time, the network status data is uploaded to the centralized controller, and the centralized controller evolves and optimizes the strategy chromosome according to the network status data to obtain the optimized deployment strategy; Send the optimized deployment strategy to the corresponding edge node for execution; The network status data includes link delay, network packet loss rate, network load, available bandwidth and node energy consumption.

[0040] In this solution, the step of generating the sampling information includes: Based on a fixed frequency, the edge node collects the original received signal strength or signal-to-noise ratio; Arrange the original received signal strength or signal-to-noise ratio in the order of acquisition time to obtain the data set ; Based on the set sliding time window, extract the adjacent data within the corresponding sliding time window; Calculate the difference between adjacent data in the sliding time window, take the absolute value, and get the difference between adjacent indicators; Traverse the entire sliding time window to obtain a set of adjacent indicator difference values, and calculate the mean of the values ​​in the adjacent indicator difference set to obtain the stability index corresponding to the current sliding time window ; Set the current signal fluctuation value to , whose formula is ,If the current signal fluctuation value is greater than the preset signal fluctuation threshold, sampling information is generated.

[0041] In this solution, the step of obtaining the preset signal fluctuation threshold specifically includes: Obtain the environment in which the original received signal strength or signal-to-noise ratio is collected; Determine an initial threshold value of signal fluctuation based on the corresponding environment; Extract the stability index of the previous sliding time window; The initial signal fluctuation threshold is optimized according to the stability index of the previous sliding time window to obtain the preset signal fluctuation threshold of the current sliding time window.

[0042] The present invention discloses a wireless network intelligent optimization deployment method and system, which reduces the computational load of the centralized controller and improves the optimization efficiency of the network deployment strategy by sending the network deployment strategy to the edge nodes. In addition, the strategy chromosomes are evolved through a "double mutation mechanism", which introduces dynamically changing gene mutation probabilities and environmental evolution ranges, enabling the algorithm to quickly jump out of the local optimal solution, dynamically track and adapt to changes in the network environment, and significantly improve the convergence speed of the evolutionary algorithm. Finally, the strategy chromosomes are analyzed based on network status data to determine the optimized deployment strategy. The present invention significantly reduces the overall overhead and delay of the network deployment strategy and improves efficiency.

[0043] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0044] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0045] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0046] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0047] Alternatively, if the integrated units described above are implemented as software modules and sold or used as standalone products, they can also be stored on a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A wireless network intelligent optimization deployment method, characterized in that: include: Obtaining the sensor node location and filing and storing the sensor node location in the edge nodes of the corresponding area; The edge nodes report the stored sensor node locations to the centralized controller; Generate an initial strategy set containing multiple network deployment strategies based on a genetic algorithm, and encode each deployment strategy into a strategy chromosome; Based on the sampling information, the edge nodes collect the network status data of the sensor nodes; When the deployment strategy is only within the range of the corresponding edge node, the corresponding edge node evolves and optimizes the strategy chromosome according to the network status data to obtain the optimized deployment strategy; When a deployment strategy exists in multiple edge node ranges at the same time, the network status data is uploaded to the centralized controller, and the centralized controller evolves and optimizes the strategy chromosome according to the network status data to obtain the optimized deployment strategy; Send the optimized deployment strategy to the corresponding edge node for execution; The network status data includes link delay, network packet loss rate, network load, available bandwidth and node energy consumption.

2. A wireless network intelligent optimization deployment method according to claim 1, characterized in that: The step of generating the sampling information includes: Based on a fixed frequency, the edge node collects the original received signal strength or signal-to-noise ratio; Arrange the original received signal strength or signal-to-noise ratio in the order of acquisition time to obtain the data set ; Based on the set sliding time window, extract the adjacent data within the corresponding sliding time window; Calculate the difference between adjacent data in the sliding time window, take the absolute value, and get the difference between adjacent indicators; Traverse the entire sliding time window to obtain a set of adjacent indicator difference values, and calculate the mean of the values ​​in the adjacent indicator difference set to obtain the stability index corresponding to the current sliding time window ; Set the current signal fluctuation value to , whose formula is If the current signal fluctuation value is greater than the preset signal fluctuation threshold, sampling information is generated.

3. A wireless network intelligent optimization deployment method according to claim 2, characterized in that: The step of obtaining the preset signal fluctuation threshold specifically includes: Obtain the environment in which the original received signal strength or signal-to-noise ratio is collected; Determine an initial threshold value of signal fluctuation based on the corresponding environment; Extract the stability index of the previous sliding time window; The initial signal fluctuation threshold is optimized according to the stability index of the previous sliding time window to obtain the preset signal fluctuation threshold of the current sliding time window.

4. A wireless network intelligent optimization deployment method according to claim 1, characterized in that: The evolution steps specifically include: Extract the network load and available bandwidth from the network status data of the current time node and the previous time node; Compare and analyze the network load at the current time node with the network load at the previous time node to obtain the absolute value of the network load change; Compare and analyze the available bandwidth at the current time node with the available bandwidth at the previous time node to obtain the absolute value of the change in available bandwidth; Determine the current environment change index based on the absolute value of the network load change rate and the absolute value of the available bandwidth change rate; Based on the range of the environmental change index, the evolution range of the corresponding strategy chromosome is determined; Based on the evolution range, the values ​​of single or multiple gene bits in the strategy chromosome are randomly changed according to the preset gene mutation probability to obtain a new strategy chromosome.

5. A wireless network intelligent optimization deployment method according to claim 4, characterized in that: Also includes: The preset environmental sensitivity coefficient is optimized according to the environmental change index to obtain the gene mutation probability adjustment value; The preset gene mutation probability is set to P, and its formula is ,in represents the initial probability of gene mutation, and k represents the adjusted value of gene mutation probability.

6. A wireless network intelligent optimization deployment method according to claim 1, characterized in that: The optimization steps specifically include: Obtaining the business to be transmitted; According to the business to be transmitted, set the corresponding optimization weight parameters; Set the strategy chromosome optimization function based on the corresponding optimization weight parameters; Divide the network status data according to the strategy chromosomes to obtain the network status data of the corresponding strategy chromosomes; Send the network status data of the corresponding strategy chromosome to the strategy chromosome optimization function to obtain the optimization index of the corresponding strategy chromosome; If the optimization index of the strategy chromosome is greater than the optimization index of the strategy chromosome before evolution, the strategy chromosome after evolution will replace the strategy chromosome before evolution; If the optimization index of the strategy chromosome is less than or equal to the optimization index of the strategy chromosome before evolution, the strategy chromosome before evolution is restored and the corresponding evolution path is recorded; After the set number of evolutions, the optimized strategy chromosome is output.

7. A wireless network intelligent optimization deployment method according to claim 1, characterized in that: Also includes: Based on the set blockchain smart contract, the edge node periodically calls the roll of sensor nodes; Extract the time node when the edge node calls the sensor node and start the countdown to the set time; If the sensor node has not reported the data when the countdown of the set time reaches zero, the sensor node will be marked to obtain the marked sensor node; The preset backup sensor nodes replace the corresponding marked sensor nodes and are written into the blockchain to be broadcasted to other edge nodes and the centralized controller.

8. A wireless network intelligent optimization deployment system, characterized in that: The system includes a memory and a processor, wherein the memory stores a wireless network intelligent optimization deployment method program, and when the wireless network intelligent optimization deployment method program is executed by the processor, the following steps are implemented: Obtaining the sensor node location and filing and storing the sensor node location in the edge nodes of the corresponding area; The edge nodes report the stored sensor node locations to the centralized controller; Generate an initial strategy set containing multiple network deployment strategies based on a genetic algorithm, and encode each deployment strategy into a strategy chromosome; Based on the sampling information, the edge nodes collect the network status data of the sensor nodes; When the deployment strategy is only within the range of the corresponding edge node, the corresponding edge node evolves and optimizes the strategy chromosome according to the network status data to obtain the optimized deployment strategy; When a deployment strategy exists in multiple edge node ranges at the same time, the network status data is uploaded to the centralized controller, and the centralized controller evolves and optimizes the strategy chromosome according to the network status data to obtain the optimized deployment strategy; Send the optimized deployment strategy to the corresponding edge node for execution; The network status data includes link delay, network packet loss rate, network load, available bandwidth and node energy consumption.

9. A wireless network intelligent optimization deployment system according to claim 8, characterized in that: The step of generating the sampling information includes: Based on a fixed frequency, the edge node collects the original received signal strength or signal-to-noise ratio; Arrange the original received signal strength or signal-to-noise ratio in the order of acquisition time to obtain the data set ; Based on the set sliding time window, extract the adjacent data within the corresponding sliding time window; Calculate the difference between adjacent data in the sliding time window, take the absolute value, and get the difference between adjacent indicators; Traverse the entire sliding time window to obtain a set of adjacent indicator difference values, and calculate the mean of the values ​​in the adjacent indicator difference set to obtain the stability index corresponding to the current sliding time window ; Set the current signal fluctuation value to , whose formula is If the current signal fluctuation value is greater than the preset signal fluctuation threshold, sampling information is generated.

10. A wireless network intelligent optimization deployment system according to claim 9, characterized in that: The step of obtaining the preset signal fluctuation threshold specifically includes: Obtain the environment in which the original received signal strength or signal-to-noise ratio is collected; Determine an initial threshold value of signal fluctuation based on the corresponding environment; Extract the stability index of the previous sliding time window; The initial signal fluctuation threshold is optimized according to the stability index of the previous sliding time window to obtain the preset signal fluctuation threshold of the current sliding time window.

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