WAPI network optimization and simulation system based on digital twinning

Abstract

The invention discloses a wireless authentication and privacy infrastructure (WAPI) network optimization and simulation system based on digital twinning, which belongs to the technical field of wireless network optimization and specifically comprises the following steps of: acquiring physical entity data of a WAPI network by utilizing a sensor and monitoring equipment and transmitting the physical entity data to a data processing center; the data processing center constructs a digital twinborn model in sequence and compares the digital twinborn model with actual data; based on the digital twinborn model, evaluating and analyzing the network by adopting an optimization algorithm, finding out problems and formulating a network optimization strategy; simulating a network optimization strategy on the digital twin model, and adjusting the network optimization strategy according to a simulation result; applying the adjusted network optimization strategy to an actual network, continuously monitoring through the model, comparing data before and after optimization, and further optimizing the model and the network optimization strategy based on feedback information; according to the method, the problems of low optimization efficiency and poor accuracy of the traditional WAPI network are solved, efficient and accurate optimization is realized through digital twinning, and the network performance and reliability are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless network optimization, and specifically to a WAPI network optimization and simulation system based on digital twins. Background Art

[0002] Wireless LAN Authentication and Privacy Infrastructure (WAPI) networks have unique advantages in terms of security and stability. However, traditional network optimization methods often rely on field testing and empirical judgment, which are inefficient, costly, and difficult to fully and accurately grasp the network operation status. Digital twin technology can achieve real-time monitoring, analysis, and optimization of the network by building a virtual digital model corresponding to the physical network, but the digital twin optimization and simulation system for WAPI networks is not yet perfect.

[0003] For example, a Chinese patent application with publication number CN116801429A discloses an optimal networking method for a photovoltaic power plant supporting WAPI, including: dividing the photovoltaic power plant into N groups, N>1, each photovoltaic power plant group including n photovoltaic panels, n>1; configuring an optical fiber network in each photovoltaic power plant group, and installing an Internet of Things substation device on each photovoltaic panel; using a self-organizing network node adjacent search algorithm to establish a wireless network communication, and connecting the Internet of Things substation devices through antennas to form an automatic networking link for photovoltaic panels; using the Dijkstra algorithm to achieve the optimal path for the network transmission distance between photovoltaic panels, and then using the WAPI encryption technology and protocol to perform security authentication on the technical solution through the equipment of the Internet of Things substation installed on the photovoltaic panels of the photovoltaic power plant, and through the mutual connection between the Internet of Things substation devices, the optimal networking method for photovoltaic power plants supporting WAPI is achieved to form an automatic network.

[0004] The above existing technologies all have the following problems: lack of accurate network model construction and calibration; lack of systematic network performance optimization and evaluation mechanism; only disclose the use of self-organizing network node neighbor search algorithm to establish wireless network communication and the use of Dijkstra algorithm to optimize the transmission distance, but there is no detailed description of error handling and retransmission mechanism during data transmission. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a WAPI network optimization and simulation system based on digital twins, which uses sensors and monitoring equipment to collect physical entity data of the WAPI network and transmits it to the data processing center; the data processing center constructs a digital twin model based on this and compares it with the actual data; based on the digital twin model, an optimization algorithm is used to evaluate and analyze the network, identify problems and formulate a network optimization strategy; the network optimization strategy is simulated on the digital twin model, and the network optimization strategy is adjusted according to the simulation results; the adjusted network optimization strategy is applied to the actual network, and the model is continuously monitored, the data before and after optimization are compared, and the model and network optimization strategy are further optimized based on the feedback information; this application solves the problems of low efficiency and poor accuracy of traditional WAPI network optimization, and realizes efficient and accurate optimization through digital twins, thereby improving network performance and reliability.

[0006] To achieve the above object, the present invention provides the following technical solutions: The WAPI network optimization and simulation system based on digital twins includes: data acquisition module, digital twin model construction module, model calibration and verification module, network optimization strategy formulation module, simulation and effect evaluation module, implementation and feedback module; The data acquisition module includes a sensor unit and a data transmission unit. The sensor unit is configured with an intelligent perception strategy, which is used to perceive the physical entity data of the network; the data transmission unit is configured with an automatic retransmission request hybrid dynamic adjustment retransmission number strategy, which is used to transmit the physical entity data collected by the sensor unit to the data processing center; The digital twin model construction module includes a topology construction unit, in which an improved depth-first search algorithm is configured. The improved depth-first search algorithm, combined with the network layering idea, identifies the connection relationship in the WAPI network and constructs a network topology structure; The model calibration and verification module includes a data comparison unit, in which a difference detection algorithm is configured. The difference detection algorithm is used to compare the network performance indicators output by the digital twin model with the actual network operation data at each time point, and calculate the difference value and trend change between the two; The network optimization strategy formulation module includes a problem diagnosis unit and a strategy generation unit. The strategy generation unit is used to integrate multiple performance indicators to generate a network optimization strategy.

[0007] Specifically, the sensor unit adopts an intelligent sensing strategy, and the specific steps of the intelligent sensing strategy include: A1: Start the sensor and record the current network traffic value as the initial traffic value ; A2: Set the initial sampling interval , according to the initial sampling interval Carry out data collection, and obtain the current network traffic value after each collection is completed , and calculate the network traffic change rate based on the current network traffic value ,in, i Indicates the number of collections index. Indicates i The network traffic change rate when collecting data, Indicates i -1 Network traffic value recorded during collection.

[0008] Specifically, the specific steps of the intelligent perception strategy also include: A3: Based on the calculated network traffic change rate , calculate the sampling interval for the next data collection ,in, Indicates i The sampling interval when collecting data, k represents a constant; A4: Set the threshold fluctuation range of the sampling interval to ; like , then set the sampling interval to ; like , then set the sampling interval to ; like , then set the sampling interval to ; A5: The sensor waits for the next data collection time according to the updated sampling interval, and repeats A2 to A4 to complete data collection when the time arrives.

[0009] Specifically, the data transmission unit adopts an automatic repeat request hybrid dynamic adjustment retransmission number strategy, and the specific steps include: B1: When the data transmission unit is started, the channel quality monitoring function is turned on to obtain the initial channel bit error rate through communication protocol interaction with the data processing center. , at the same time, set a timer , used to periodically update the measured value of the channel bit error rate; B2: Obtain the physical entity data collected by the sensor unit, classify the physical entity data using a predefined method, and assign an initial data importance coefficient to each type of data ; B3: After the sensor unit collects the physical entity data, the data transmission unit packages the collected physical entity data into a transmission data packet format and adds a data type identifier as header information.

[0010] Specifically, the specific steps of the automatic repeat request hybrid dynamic adjustment of the retransmission number strategy also include: B4: Based on the current channel bit error rate And the corresponding data importance coefficient , calculate the number of retransmissions for each transmission , and sends the data packet to the data processing center, and at the same time, starts a timer to wait for the confirmation feedback signal from the data processing center; wherein, Indicates i The number of retransmissions of the physical entity data transmission collected, Express Round up. Indicates i The importance coefficient of the physical entity data transmission collected, Indicates i The channel bit error rate of the physical entity data transmission collected, represents the logarithmic function; B5: Get the feedback signal confirmation time from the data processing center ; like , it means that the data transmission is successful, the timer is stopped, and the transmission time and number of retransmissions of this transmission are recorded; like , then enter the retransmission operation.

[0011] Specifically, the specific steps of the automatic repeat request hybrid dynamic adjustment of the retransmission number strategy also include: B6: The number of retransmissions calculated , retransmit the data packet, restart the timer each time it is retransmitted, and wait for the confirmation feedback signal; If a confirmation feedback signal is received, retransmission is stopped; If the upper limit of retransmission times is reached and no confirmation feedback signal is received, the transmission failure is recorded; B7: According to the set timer , re-measure the channel bit error rate and update it to , and dynamically adjust the data importance coefficient.

[0012] Specifically, the topology construction unit adopts an improved depth-first search algorithm, and the specific steps of the improved depth-first search algorithm include: C1: Mark all access points and terminal device network nodes as unvisited, and create an empty data structure to store network topology information, where the created empty data structure is an adjacency list; C2: The access point is regarded as the first layer, and the terminal devices directly connected to the access point are regarded as the second layer. An initial layer tag is set for each node; C3: Select an access point as the starting node of the depth-first search, visit the starting access point, mark it as visited, and record the starting access point hierarchy information; C4: Find neighbor nodes connected to the starting access point and Determine the connection weights of neighbor nodes w , where b and c represent weight coefficients, s represents signal strength, Indicates bandwidth.

[0013] Specifically, the specific steps of the improved depth-first search algorithm also include: C5: Determine the level of neighbor nodes according to their types; If the neighbor node is a terminal device, the level of the neighbor node is the current access point level plus 1; If the neighbor node is an access point other than the access point currently being visited, the level of the neighbor node is the level of the current access point; C6: For each unvisited neighbor node, in the recursive process, the access status, hierarchy information and connection relationship of the node are continuously updated and recorded in the topology data structure; C7: After completing the depth-first search of all nodes, the adjacency list data structure is converted into a topology graph, where nodes represent access points and terminal devices, edges represent connection relationships, and edge weights represent connection weights, to obtain a complete network topology structure.

[0014] Specifically, the data comparison unit adopts a difference detection algorithm, and the specific steps of the difference detection algorithm include: D1: Collect network performance indicator data and actual network operation data in the digital twin model in the same time interval, and assign a unique performance indicator identifier to each indicator; D2: Starting from the first time point, the network performance indicator data output by the digital twin model is compared with each performance indicator in the actual network operation data; D3: For each performance indicator, use the formula Calculate the difference value, where Indicates at a point in time t Performance indicators j The difference value of Represents the digital twin model at a point in time t Output performance indicators j The value of Indicates the actual network at a point in time t Performance indicators in real-time operation dataj The value of D4: Store the difference value, corresponding timestamp and performance indicator identifier of each time point into a pre-built difference data list; D5: Use the difference method to calculate the trend change, and determine whether the trend is rising, falling or stable based on the trend calculation results; D6: Organize the analysis results of difference values ​​and trend changes into a report format.

[0015] Specifically, the network optimization strategy includes: adjusting the location of access points, optimizing channel allocation, enhancing network security, performing load balancing, and adjusting transmission power.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention proposes a WAPI network optimization and simulation system based on digital twins, and optimizes and improves the architecture, operation steps and processes. The system has the advantages of simple process, low investment and operation costs, and low production work costs.

[0017] 2. The present invention proposes a WAPI network optimization and simulation system based on digital twins. In the data collection link, the intelligent perception strategy of the sensor unit can flexibly perceive the physical entity data of the network, and can automatically adjust the perception frequency and accuracy according to the network environment, while ensuring the collection of critical and accurate data, avoiding energy waste and data redundancy; and the automatic retransmission request hybrid dynamic adjustment retransmission number strategy configured by the data transmission unit can reasonably determine the retransmission number with reference to the channel quality and data importance, thereby improving the reliability and efficiency of data transmission.

[0018] 3. The present invention proposes a WAPI network optimization and simulation system based on digital twins. The digital twin model construction module in the system uses an improved depth-first search algorithm combined with the network layering idea to accurately identify network connection relationships and construct a topological structure, providing a practical network model for subsequent analysis; the model calibration and verification module uses a difference detection algorithm to achieve a point-by-point comparison of the network performance indicator data output by the digital twin model and the actual network operation data, accurately grasp the difference value and trend changes between the two, and ensure the accuracy of the model; the network optimization strategy formulation module generates optimization strategies by integrating multiple performance indicators, which can effectively diagnose network problems and provide targeted solutions, realize all-round optimization of the WAPI network, and improve the overall performance and operation quality of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is the architecture diagram of the WAPI network optimization and simulation system based on digital twins of the present invention; Figure 2 This is a flow chart for realizing the sensor unit of the WAPI network optimization and simulation system based on digital twins of the present invention; Figure 3 This is a flowchart for implementing the data transmission unit of the WAPI network optimization and simulation system based on digital twins in the present invention. DETAILED DESCRIPTION

[0020] Example 1

[0021] See also Figure 1 , an embodiment provided by the present invention: a WAPI network optimization and simulation system based on digital twins, comprising: Data acquisition module, digital twin model construction module, model calibration and verification module, network optimization strategy formulation module, simulation and effect evaluation module, implementation and feedback module; Data collection module, multiple sensors and network monitoring devices distributed in the WAPI network, used to collect physical entity data of the network in real time and transmit the data to the data processing center through wired or wireless communication; The digital twin model construction module is located in the data processing center and is responsible for building the digital twin model of the WAPI network based on the collected data. It uses advanced modeling software and algorithms to achieve accurate construction and real-time updating of the model. The model calibration and verification module is used to calibrate and verify the digital twin model by comparing it with the actual network operation data to ensure the accuracy and reliability of the model. It uses automated verification tools and algorithms to improve verification efficiency. The network optimization strategy formulation module is used to generate network optimization strategies based on the analysis results of the digital twin model using optimization algorithms, taking into account multiple factors and constraints to ensure the feasibility and effectiveness of the network optimization strategies; The simulation and effect evaluation module is used to conduct simulation experiments on network optimization strategies on the digital twin model, evaluate the performance indicators of the optimized network, and display the simulation results through a visual interface to facilitate user analysis and decision-making; The implementation and feedback module is used to apply network optimization strategies to the actual network and feed back the actual network operation data to the digital twin model, so as to achieve continuous monitoring and improvement of network optimization effects and form a closed-loop optimization process.

[0022] The data acquisition module includes: a sensor unit and a data transmission unit; A sensor unit, used to sense the physical entity data of the network using an intelligent sensing strategy; The data transmission unit is used to accurately and timely transmit various network physical entity data collected by the sensor unit to the data processing center through the wireless communication protocol, using the automatic retransmission request mixed with the dynamic adjustment of the retransmission number strategy, to ensure that the data can smoothly enter the subsequent processing flow and avoid the impact of data loss or transmission delay on the system.

[0023] The digital twin model construction module includes: topology construction unit, parameter configuration unit, and model update unit; A topology construction unit is used to identify access points, terminal devices and the connection relationship between them in the WAPI network by using an improved depth-first search algorithm combined with the network layering concept, and to construct a network topology structure; The parameter configuration unit is used to determine the corresponding parameters based on the collected physical entity data, such as the transmission power parameters of the access point, the frequency band parameters of the channel, the communication protocol related parameters of the device, etc., and assign these parameters to the corresponding elements in the digital twin model; The model update unit is used to monitor the changes in newly incoming data in real time. When the collected data reflects changes in the status of network equipment, changes in network topology, or other factors affecting the network, the constructed digital twin model is updated accordingly to ensure that the model can consistently and accurately reflect the real-time status of the physical network and ensure the synchronization of the model with the actual network.

[0024] The model calibration and verification module includes: a data comparison unit and a parameter adjustment unit; The data comparison unit is used to use a difference detection algorithm based on time series analysis to compare and analyze the network performance indicators output by the digital twin model, such as throughput and latency, with the actual network operation data at each time point, and calculate the difference value and trend change between the two; The parameter adjustment unit is used to optimize and adjust the model parameters by using a genetic algorithm based on the above-mentioned difference detection results, and encode the key parameters in the model, such as the attenuation coefficient and the device transmission power in the signal propagation model, as chromosomes. Through genetic operations such as selection, crossover and mutation, the parameter combination that minimizes the difference between the model and the actual data is found. Among them, the genetic algorithm is the prior art content in this field and is not the inventive solution of this application, and will not be elaborated here.

[0025] The network optimization strategy formulation module includes: a problem diagnosis unit and a strategy generation unit; The problem diagnosis unit is used to conduct in-depth analysis of the network performance data in the digital twin model using a method based on a combination of expert systems and data analysis, and quickly and accurately diagnose problems in the network, such as signal coverage blind spots and areas with severe channel interference. The strategy generation unit is used to adopt a non-dominated sorting genetic algorithm to integrate multiple performance indicators to generate a network optimization strategy, wherein the non-dominated sorting genetic algorithm is the prior art content in this field, is not the creative solution of this application, and is not described here.

[0026] In summary, the overall implementation process of the WAPI network optimization and simulation system based on digital twins includes: S1: Use sensors and network monitoring equipment to collect physical entity data of the WAPI network, including access point signal strength, channel utilization, number of user connections, device operation status, etc., and transmit this data to the data processing center; S2: The data processing center builds a digital twin model of the WAPI network based on the collected physical entity data, and compares the output of the digital twin model with the actual network operation data. At the same time, error analysis is used to verify the comparison results; S3: Based on the digital twin model, use the optimization algorithm to evaluate and analyze the performance of the WAPI network to identify bottlenecks and potential problems in the network, such as insufficient signal coverage and severe channel interference. According to the analysis results, formulate corresponding network optimization strategies, including access point location adjustment, channel allocation optimization, and transmit power adjustment. S4: Conduct simulation experiments on network optimization strategies on the digital twin model, simulate the operation status of the optimized network, evaluate the changes in various performance indicators such as throughput, latency, and packet loss rate, and adjust and improve the network optimization strategy based on the simulation results to ensure that it can achieve the expected optimization effect in the actual network; S5: Apply the verified network optimization strategy to the actual WAPI network, and continuously monitor the network operation status through the digital twin model. At the same time, collect the actual data after network optimization, compare it with the data before optimization, analyze the optimization effect, and use the feedback information to further optimize the digital twin model and network optimization strategy.

[0027] For example, in a WAPI network in an office area, first, through the data acquisition module, sensors are deployed at various access points and key locations to collect data such as signal strength, channel quality, and user connection status. After these data are transmitted to the data processing center, the digital twin model construction module uses these data to construct a digital twin model of the WAPI network in the office area, including the location distribution of access points and the signal propagation range; then, the model calibration and verification module compares the network performance indicators output by the digital twin model with the actual network data, and adjusts the parameters in the signal propagation model to make the model accurately reflect the actual situation of the network. For example, through multiple comparisons, it is found that there is a deviation between the signal strength predicted by the model in a certain area and the actual measured value. After analyzing factors such as the obstacle characteristics of the area, the model parameters are adjusted to make the deviation within an acceptable range; secondly, the network optimization strategy formulation module is based on the calibrated model It was found that the dense user population near several access points resulted in excessive channel utilization and network congestion, so a network optimization strategy was formulated, including adjusting the channels of some access points, appropriately increasing the transmission power of some access points to expand the coverage, and reducing the access point load in user-dense areas. At the same time, the simulation and effect evaluation module simulated these network optimization strategies on the digital twin model, simulated the optimized network operation status, and found that the throughput was significantly improved, the delay was reduced, and the packet loss rate was reduced, indicating that the network optimization strategy was effective; finally, the network optimization strategy implementation and feedback module applied these network optimization strategies to the actual network, and continuously monitored the network operation status through the digital twin model, collected actual data and compared it with the data before optimization, and further optimized the model and strategy. For example, it was found that a certain area still had a weak signal problem after optimization, so the access point location and parameters in the area were further adjusted, thereby continuously improving the performance of the WAPI network.

[0028] Example 2

[0029] See also Figure 2 In this embodiment, the sensor unit adopts an intelligent sensing strategy, and the specific steps of the intelligent sensing strategy include: A1: Start the sensor and record the current network traffic value as the initial traffic value ; A2: Set the initial sampling interval , according to the initial sampling interval Carry out data collection, and obtain the current network traffic value after each collection is completed , and calculate the network traffic change rate based on the current network traffic value ,in, i Indicates the number of collections index. Indicates i The network traffic change rate when collecting data, Indicatesi -Network traffic value recorded during 1 collection; A3: Based on the calculated network traffic change rate , calculate the sampling interval for the next data collection ,in, Indicates i The sampling interval when collecting data, k represents a constant; A4: Set the threshold fluctuation range of the sampling interval to ; like , then set the sampling interval to ; like , then set the sampling interval to ; like , then set the sampling interval to ; A5: The sensor waits for the next data collection time according to the updated sampling interval, and repeats A2 to A4 to complete data collection when the time arrives.

[0030] Example 3 See also Figure 3 In this embodiment, the data transmission unit adopts an automatic repeat request hybrid dynamic adjustment retransmission number strategy, and the specific steps include: B1: When the data transmission unit is started, the channel quality monitoring function is turned on to obtain the initial channel bit error rate through communication protocol interaction with the data processing center. , at the same time, set a timer , used to periodically update the measured value of the channel bit error rate; B2: Obtain the physical entity data collected by the sensor unit, classify the physical entity data using a predefined method, and assign an initial data importance coefficient to each type of data ; In the present invention, the k-means clustering algorithm is used for classification. The k-means clustering algorithm is a prior art in the art and is not an inventive solution of the present application, so it will not be described in detail here.

[0031] For example, key performance indicators of an access point, such as signal strength, channel utilization, etc., may be assigned a higher coefficient, such as 5; while some auxiliary device status information, such as device temperature, may be assigned a lower coefficient, such as 1; these coefficients can be stored in a lookup table for quick query during data transmission.

[0032] B3: After the sensor unit collects the physical entity data, the data transmission unit packages the collected physical entity data into a data packet format suitable for transmission, and adds a data type identifier as header information; B4: Based on the current channel bit error rate And the corresponding data importance coefficient , calculate the number of retransmissions for each transmission , and sends the data packet to the data processing center. At the same time, a timer is started to wait for the confirmation feedback signal from the data processing center. Indicates i The number of retransmissions of the physical entity data transmission collected, Express Round up. Indicates i The importance coefficient of the physical entity data transmission collected, Indicates i The channel bit error rate of the physical entity data transmission collected, represents the logarithmic function; B5: Get the feedback signal confirmation time from the data processing center ; like , it means that the data transmission is successful, the timer is stopped, and the transmission time and number of retransmissions of this transmission are recorded; like , then enter the retransmission operation; B6: The number of retransmissions calculated , retransmit the data packet, restart the timer each time it is retransmitted, and wait for the confirmation feedback signal; If a confirmation feedback signal is received, retransmission is stopped; If the upper limit of retransmission times is reached and no confirmation feedback signal is received, the transmission failure is recorded; B7: According to the set timer , re-measure the channel bit error rate and update it to , and dynamically adjust the data importance coefficient.

[0033] For example, if a certain type of data is found to play a key role in network fault diagnosis within a certain period of time, the importance coefficient of the data is increased accordingly; then, according to the new and , calculate the number of retransmissions for the next data transmission , continue the data transfer process.

[0034] The topology construction unit adopts an improved depth-first search algorithm. The specific steps of the improved depth-first search algorithm include: C1: Mark all access points and terminal device network nodes as unvisited, and create an empty data structure to store network topology information, where the created empty data structure is an adjacency list; C2: The access point is regarded as the first layer, and the terminal devices directly connected to the access point are regarded as the second layer. An initial layer tag is set for each node; C3: Select the access point as the starting node of the depth-first search, then start visiting the starting access point, mark it as visited, and record the starting access point hierarchy information; C4: Find neighbor nodes connected to the starting access point and Determine the connection weights of neighbor nodes w , where b and c represent weight coefficients, s represents signal strength, Indicates bandwidth; C5: Determine the level of neighbor nodes according to their types; If the neighbor node is a terminal device, the level of the neighbor node is the current access point level plus 1; If the neighbor node is an access point other than the access point currently being visited, the level of the neighbor node is the level of the current access point; C6: For each unvisited neighbor node, in the recursive process, the access status, hierarchy information and connection relationship of the node are continuously updated and recorded in the topology data structure; C7: After completing the depth-first search of all reachable nodes, the adjacency list data structure is converted into an intuitive topology graph representation, where nodes represent access points and terminal devices, edges represent the connection relationship between them, and edge weights represent connection weights, to obtain a complete network topology structure.

[0035] The data comparison unit adopts a difference detection algorithm. The specific steps of the difference detection algorithm include: D1: Collect network performance indicator data and actual network operation data in the digital twin model in the same time interval, and assign a unique performance indicator identifier to each network performance indicator, wherein the network performance indicators include throughput, delay, and packet loss rate. At the same time, the addition and assignment of unique identifiers is the prior art content in this field, which is not the inventive solution of this application and will not be elaborated here; D2: Starting from the first time point, the network performance indicator data output by the digital twin model is compared with each performance indicator in the actual network operation data; D3: For each performance indicator, use the formula Calculate the difference value, where Indicates at a point in time t Performance indicators j The difference value of Represents the digital twin model at a point in time t Output performance indicators j The value of Indicates the actual network at a point in time t Performance indicators in real-time operation data j The value of D4: Store the difference value, corresponding timestamp and performance indicator identifier of each time point into a pre-built difference data list; D5: Calculate trend changes using the difference method , and according to the trend calculation results, determine whether the trend is rising, falling or stable, where Indicates at a point in time t -1 performance index j The difference value of Among them, if If it is greater than 0 for multiple consecutive time points, it is considered that the difference value of the performance indicator is on an upward trend, indicating that the deviation between the digital twin model and the actual network data is gradually increasing.

[0036] D6: Organize the analysis results of the difference values ​​and trend changes into a report format, where the report includes: the average difference value, the maximum difference value, and trend change charts for each performance indicator, such as a line chart showing the trend of difference values ​​changing over time.

[0037] Network optimization strategies include: adjusting access point locations, optimizing channel allocation, enhancing network security, performing load balancing, and adjusting transmission power.

[0038] Furthermore, the access point location adjustment strategy includes: Macro layout adjustment: Within the WAPI network coverage area, the location of the access points is replanned based on the coverage blind spots or weak signal areas analyzed by the digital twin model. For example, for a WAPI network in a large shopping mall, if the digital twin model shows that the signal coverage in the corner area of ​​a certain floor is insufficient, the strategy generation unit may generate a strategy to appropriately move nearby access points to that area to improve coverage. Micro-position fine-tuning: In addition to macro-position movement, it can also include adjustments to micro-position parameters such as the height and angle of the access point. For example, by adjusting the antenna angle of the access point, its signal can better cover specific user-dense areas, reduce signal waste in open areas, and improve signal utilization.

[0039] Channel allocation optimization strategies include: Frequency band selection and allocation: Consider the frequency band resources available in the WAPI network and allocate the optimal frequency band to the access point based on the characteristics of different frequency bands, such as interference conditions and transmission rates. For example, in an environment with many interference sources, frequency bands with strong anti-interference capabilities are allocated to access points that are most affected, avoiding channel interference between adjacent access points and improving channel multiplexing efficiency. Dynamic channel adjustment: Based on the dynamic changes in network traffic and the real-time usage of channels, a strategy for dynamically adjusting channels is formulated. For example, when the channel utilization of a certain access point is too high and the channels of other access points are relatively idle, the strategy generation unit generates a strategy to transfer some users or traffic to idle channels to balance the channel load and improve the overall network performance.

[0040] Transmit power adjustment strategies include: Power boost strategy: For areas with insufficient signal coverage, the strategy generation unit may generate a strategy to increase the transmit power of the access point. However, it should be noted that the increase in transmit power must be within the prescribed range to avoid interference with other wireless devices. For example, in the edge area of ​​an industrial park, in order to ensure that logistics vehicles outside the factory can also access the network stably, the transmit power of the access point in the area can be appropriately increased. Power reduction strategy: In areas with good signal coverage and relatively sparse user distribution, the transmit power of the access point is reduced to reduce unnecessary energy consumption and potential interference to other frequency bands. For example, during non-working hours at night in an office building, when most users have left and the network load is low, the transmit power of the access point can be reduced.

[0041] Load balancing strategies include: User access balance: By adjusting the parameters of the access point (such as signal strength threshold, association priority, etc.), user devices are guided to access each access point in a balanced manner. For example, in a hotel's WAPI network, a large number of users are prevented from accessing a certain access point near the front desk. Instead, new users are guided to access points with lighter loads based on the load conditions of each access point to prevent local network congestion.

[0042] Balanced traffic distribution: For data traffic in the network, formulate strategies to reasonably distribute traffic to different access points and links. For example, in a campus network with multiple access points, large data traffic services such as video streaming and file downloading are evenly distributed to different access points based on the server resources and bandwidth of each access point, to avoid the performance degradation of a certain access point due to excessive traffic.

[0043] Cybersecurity enhancement strategies include: Authentication policy optimization: WAPI network has a unique authentication mechanism. The policy generation unit can optimize the authentication policy, for example, adjust the parameters of the authentication server to improve the efficiency and security of authentication and ensure that only legitimate user devices can access the network. This may include measures such as shortening the authentication response time and strengthening the management of authentication keys; Encryption strategy adjustment: adjust the encryption algorithm and encryption strength according to the network security and performance requirements. Under the premise of ensuring data privacy, minimize the impact of encryption on network performance. For example, for the WAPI network access part of the corporate finance department with extremely high data security requirements, use a higher level of encryption algorithm and a longer key length; and for some public area network access that is sensitive to performance but has relatively low security requirements, use appropriate encryption methods to balance security and performance.

[0044] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, and all of these are within the protection of the present invention.

Claims

1. The WAPI network optimization and simulation system based on digital twins is characterized by: include: Data acquisition module, digital twin model construction module, model calibration and verification module, network optimization strategy formulation module, simulation and effect evaluation module, implementation and feedback module; The data acquisition module includes a sensor unit and a data transmission unit. The sensor unit is configured with an intelligent perception strategy, which is used to perceive the physical entity data of the network; the data transmission unit is configured with an automatic retransmission request hybrid dynamic adjustment retransmission number strategy, which is used to transmit the physical entity data collected by the sensor unit to the data processing center; The digital twin model construction module includes a topology construction unit, in which an improved depth-first search algorithm is configured. The improved depth-first search algorithm, combined with the network layering idea, identifies the connection relationship in the WAPI network and constructs a network topology structure; The model calibration and verification module includes a data comparison unit, in which a difference detection algorithm is configured. The difference detection algorithm is used to compare the network performance indicators output by the digital twin model with the actual network operation data at each time point, and calculate the difference value and trend change between the two; The network optimization strategy formulation module includes a problem diagnosis unit and a strategy generation unit. The strategy generation unit is used to integrate multiple performance indicators to generate a network optimization strategy.

2. The WAPI network optimization and simulation system based on digital twins according to claim 1, characterized in that: The sensor unit adopts an intelligent sensing strategy, and the specific steps of the intelligent sensing strategy include: A1: Start the sensor and record the current network traffic value as the initial traffic value ; A2: Set the initial sampling interval , according to the initial sampling interval Carry out data collection, and obtain the current network traffic value after each collection is completed , and calculate the network traffic change rate based on the current network traffic value ,in, i Indicates the number of collections index. Indicates i The network traffic change rate when collecting data, Indicates i -1 Network traffic value recorded during collection.

3. The WAPI network optimization and simulation system based on digital twins according to claim 2, characterized in that: The specific steps of the intelligent perception strategy also include: A3: Based on the calculated network traffic change rate , calculate the sampling interval for the next data collection ,in, Indicates i The sampling interval when collecting data, k represents a constant; A4: Set the threshold fluctuation range of the sampling interval to ; like , then set the sampling interval to ; like , then set the sampling interval to ; like , then set the sampling interval to ; A5: The sensor waits for the next data collection time according to the updated sampling interval, and repeats A2 to A4 to complete data collection when the time arrives.

4. The WAPI network optimization and simulation system based on digital twins according to claim 3, characterized in that: The data transmission unit adopts an automatic repeat request hybrid dynamic adjustment retransmission number strategy, and the specific steps include: B1: When the data transmission unit is started, the channel quality monitoring function is turned on to obtain the initial channel bit error rate through communication protocol interaction with the data processing center. , at the same time, set a timer , used to periodically update the measured value of the channel bit error rate; B2: Obtain the physical entity data collected by the sensor unit, classify the physical entity data using a predefined method, and assign an initial data importance coefficient to each type of data ; B3: After the sensor unit collects the physical entity data, the data transmission unit packages the collected physical entity data into a transmission data packet format and adds a data type identifier as header information.

5. The WAPI network optimization and simulation system based on digital twins according to claim 4, characterized in that: The specific steps of the automatic retransmission request hybrid dynamic adjustment retransmission number strategy also include: B4: Based on the current channel bit error rate And the corresponding data importance coefficient , calculate the number of retransmissions for each transmission , and sends the data packet to the data processing center, and at the same time, starts a timer to wait for the confirmation feedback signal from the data processing center; wherein, Indicates i The number of retransmissions of the physical entity data transmission collected, Express Round up. Indicates i The importance coefficient of the physical entity data transmission collected, Indicates i The channel bit error rate of the physical entity data transmission collected, represents the logarithmic function; B5: Get the feedback signal confirmation time from the data processing center ; like , it means that the data transmission is successful, the timer is stopped, and the transmission time and number of retransmissions of this transmission are recorded; like , then enter the retransmission operation.

6. The WAPI network optimization and simulation system based on digital twins according to claim 5, characterized in that: The specific steps of the automatic retransmission request hybrid dynamic adjustment retransmission number strategy also include: B6: The number of retransmissions calculated , retransmit the data packet, restart the timer each time it is retransmitted, and wait for the confirmation feedback signal; If a confirmation feedback signal is received, retransmission is stopped; If the upper limit of retransmission times is reached and no confirmation feedback signal is received, the transmission failure is recorded; B7: According to the set timer , re-measure the channel bit error rate and update it to , and dynamically adjust the data importance coefficient.

7. The WAPI network optimization and simulation system based on digital twins according to claim 6, characterized in that: The topology construction unit adopts an improved depth-first search algorithm, and the specific steps of the improved depth-first search algorithm include: C1: Mark all access points and terminal device network nodes as unvisited, and create an empty data structure to store network topology information, where the created empty data structure is an adjacency list; C2: The access point is regarded as the first layer, and the terminal devices directly connected to the access point are regarded as the second layer. An initial layer tag is set for each node; C3: Select an access point as the starting node of the depth-first search, visit the starting access point, mark it as visited, and record the starting access point hierarchy information; C4: Find neighbor nodes connected to the starting access point and Determine the connection weights of neighbor nodes w , where b and c represent weight coefficients, s represents signal strength, Indicates bandwidth.

8. The WAPI network optimization and simulation system based on digital twins according to claim 7, characterized in that: The specific steps of the improved depth-first search algorithm also include: C5: Determine the level of neighbor nodes according to their types; If the neighbor node is a terminal device, the level of the neighbor node is the current access point level plus 1; If the neighbor node is an access point other than the access point currently being visited, the level of the neighbor node is the level of the current access point; C6: For each unvisited neighbor node, in the recursive process, the access status, hierarchy information and connection relationship of the node are continuously updated and recorded in the topology data structure; C7: After completing the depth-first search of all nodes, the adjacency list data structure is converted into a topology graph, where nodes represent access points and terminal devices, edges represent connection relationships, and edge weights represent connection weights, to obtain a complete network topology structure.

9. The WAPI network optimization and simulation system based on digital twins according to claim 8, characterized in that: The data comparison unit adopts a difference detection algorithm, and the specific steps of the difference detection algorithm include: D1: Collect network performance indicator data and actual network operation data in the digital twin model in the same time interval, and assign a unique performance indicator identifier to each indicator; D2: Starting from the first time point, the network performance indicator data output by the digital twin model is compared with each performance indicator in the actual network operation data; D3: For each performance indicator, use the formula Calculate the difference value, where Indicates at a point in time t Performance indicators j The difference value of Represents the digital twin model at a point in time t Output performance indicators j The value of Indicates the actual network at a point in time t Performance indicators in real-time operation data j The value of D4: Store the difference value, corresponding timestamp and performance indicator identifier of each time point into a pre-built difference data list; D5: Use the difference method to calculate the trend change, and determine whether the trend is rising, falling or stable based on the trend calculation results; D6: Organize the analysis results of difference values ​​and trend changes into a report format.

10. The WAPI network optimization and simulation system based on digital twins according to claim 9, characterized in that: The network optimization strategy includes: adjusting the location of access points, optimizing channel allocation, enhancing network security, performing load balancing, and adjusting transmission power.

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