Digital lightning protection multi-dimensional fusion intelligent control system based on artificial intelligence

Abstract

The present invention relates to the field of information and data fusion technology, and specifically provides a digital lightning protection multi-dimensional fusion intelligent control system based on artificial intelligence. The system includes: a data acquisition and fusion component, which is used to collect multi-source data, fuse and analyze multi-source data, and capture the characteristics and threat level of lightning activities; a control strategy adjustment component, which is used to mine the laws and potential risks behind lightning events according to the fusion analysis results, and automatically adjust the protection strategy; a visualization linkage component, which is used to provide an interface to display lightning data and protection status; and when lightning risks are detected, the power system, communication system, and security system are coordinated to respond. The present invention realizes real-time monitoring, intelligent evaluation, and rapid response to lightning activities by integrating advanced data acquisition, analysis, control, and visualization technologies, greatly improving the efficiency and effect of lightning protection and ensuring the safe and stable operation of key infrastructure.

Description

Technical Field

[0001] The present invention relates to the field of information and data fusion technology, and in particular to a digital lightning protection multi-dimensional fusion intelligent control system based on artificial intelligence. Background Art

[0002] As a natural phenomenon, lightning poses a serious threat to human life and various facilities due to its suddenness, randomness and destructiveness. Lightning not only causes direct physical damage, such as destroying buildings and equipment or causing fires, but also may affect the normal operation of key infrastructure such as communications and electricity through electromagnetic interference, and even cause broader social and economic impacts.

[0003] Existing lightning protection technologies mainly rely on a single data source and simple protection strategies. These technologies are incapable of coping with complex and changing lightning environments. Specifically, traditional lightning monitoring equipment can usually only provide basic information such as the time and location of lightning. These data dimensions are limited and cannot fully and accurately reflect the characteristics and threat level of lightning activities. For example, key information such as the intensity, duration, propagation path, and potential impact of lightning on specific facilities are often ignored or not effectively integrated.

[0004] In addition, traditional lightning protection technology is independent of the protection needs of other critical infrastructure such as power systems and communication systems, and lacks deep integration and collaborative work. This isolated working mode results in the inability to form an effective linkage response between the systems when facing lightning threats, and the protection effect is poor. For example, when lightning occurs, the power system may need to quickly cut off power supply to certain areas to prevent equipment damage, while the communication system needs to ensure that key communication lines are unobstructed. However, due to the lack of an effective coordination mechanism, these operations are often difficult to carry out simultaneously and may even cause conflicts.

[0005] With the advancement of science and technology and the development of society, the requirements for lightning protection of various facilities are increasing. In modern society, key infrastructure such as electricity, communications, transportation, and security have put forward higher requirements for the timeliness, accuracy, and intelligence of lightning protection. However, existing lightning protection technologies are difficult to meet these requirements and cannot provide comprehensive and reliable lightning protection for key infrastructure. Summary of the invention

[0006] In order to overcome the shortcomings of the prior art, the present invention aims to provide a digital lightning protection multi-dimensional fusion intelligent control system based on artificial intelligence. By integrating technical means such as multi-dimensional data fusion, intelligent protection strategy adjustment and visualized linkage response, the system realizes the leap from single monitoring to intelligent collaboration of lightning protection, and solves the problems existing in the existing lightning protection technology, such as insufficient multi-dimensional data fusion, single protection strategy and lack of intelligence, and lack of intuitive display and linkage response. It greatly improves the efficiency and effectiveness of lightning protection and ensures the safe and stable operation of key infrastructure.

[0007] In order to achieve the above object, the present invention adopts the following technical scheme: One aspect of the present invention provides a digital lightning protection multi-dimensional fusion intelligent control system based on artificial intelligence, comprising: The data acquisition and fusion component is used to collect multi-source data, calculate the weight of each data source through multi-dimensional parameter weighting and comprehensive evaluation of multi-source data, and combine multiple factors such as time decay, correlation analysis, uncertainty and expert knowledge; fuse and analyze multi-source data to capture the characteristics and threat level of lightning activities; The control strategy adjustment component is used to deeply explore the laws and potential risks of lightning events through data fusion and dynamic decision-making mechanisms, and optimize the protection strategy in real time by combining multi-dimensional economic and feasibility analysis; The visual linkage component is used to provide an interface to display lightning data and protection status; at the same time, when lightning risks are detected, it coordinates the power system, communication system and security system to respond.

[0008] In an optional implementation, the data acquisition and fusion component includes: The weight allocation module is used to collect information from various data sources, evaluate the importance of each data source, and determine its weight; the weight of each data source is calculated through weighted and comprehensive evaluation of multi-dimensional parameters, combined with time decay, correlation analysis, uncertainty and expert knowledge. The parameter extraction module is used to extract the frequency function of lightning characteristics, angle parameters, environmental parameters, parameters of equipment lightning resistance performance and geographical parameters from each data source after the weight is determined; A metric value calculation module is used to calculate the contribution of each data source to the lightning threat and obtain a comprehensive metric value through weighted summation; In an optional implementation, the parameter extraction module includes: The preprocessing submodule is used to preprocess the data from the data source, including missing value processing, noise removal, outlier monitoring and data standardization; The feature selection submodule is used to filter lightning features from the data based on the correlation of data sources, time decay and expert knowledge; The feature fusion submodule is used to integrate data features from different sources or types, map different features into the same space, and form a comprehensive feature vector.

[0009] In an optional implementation, the feature selection submodule extracts lightning characteristic parameters from each data source by weighted calculation of comprehensive frequency, angle, environment, equipment lightning resistance and geographical multi-dimensional parameters, combined with adjustment of data correlation, time attenuation and expert knowledge.

[0010] In an optional implementation, the metric value calculation module includes: The parameter processing submodule is used to combine the extracted frequency function value, angle parameter, environmental parameter, equipment lightning resistance performance parameter and geographical parameter, and calculate the weighted value in combination with the weight coefficient; adjust the timeliness of the data through the time decay factor; normalize the frequency records to reflect the frequency of lightning events; and adjust the correlation between data sources; The contribution value calculation submodule is used to combine all parameters and adjustment factors to calculate the contribution value of a single data source to the lightning threat; The result calculation submodule is used to comprehensively calculate the contribution values ​​of all data sources and perform normalization processing to obtain the final comprehensive measurement value.

[0011] In an optional implementation, the result calculation submodule comprehensively evaluates the frequency, angle, environment, equipment lightning resistance and geographical lightning characteristic parameters of multiple data sources, and introduces weight coefficients, data correlation, time attenuation, expert knowledge and noise processing factors to perform multi-dimensional weighted calculations to generate a comprehensive lightning threat measurement value.

[0012] In an optional implementation, the contribution value calculation submodule includes: A weighted value calculation unit is used to assign a weight coefficient to each parameter according to the importance of different parameters; and multiply the extracted parameter value by its corresponding weight coefficient to obtain a weighted value; A normalization processing unit is used to adjust the timeliness of data through a time decay factor; and to perform normalization processing on frequent records; The weighted summation unit is used to combine all processed parameter values ​​and adjustment factors, and calculate the contribution value of a single data source to the lightning threat through weighted summation.

[0013] In an optional implementation, the control strategy adjustment component includes: The suppression effect calculation module is used to assign a weight coefficient to each protection strategy to reflect its importance in risk suppression; combining the comprehensive measurement value of lightning threat and the risk suppression factor of the protection strategy to calculate the suppression effect of the strategy on the risk; The strategy optimization and adjustment module is used to evaluate the feasibility of the strategy through resource utilization, cost suppression factor, implementation difficulty coefficient and implementation cost; it dynamically optimizes the real-time response capability of the strategy by combining the dynamic adjustment factor of the strategy, risk response attenuation coefficient, response time difference and total resources; The priority adjustment module is used to calculate the optimization value of each protection strategy and select the optimal strategy; assign a weight coefficient to each response measure to reflect its importance in risk mitigation; evaluate the effectiveness of the measures based on the risk mitigation effect, resource consumption rate and time efficiency factor of the response measures; quantify the feasibility of the response measures through cost suppression factors, implementation difficulty coefficients and implementation costs; and dynamically adjust the system's response priority to risks based on the system's risk tolerance and risk response priority adjustment factor.

[0014] In an optional implementation, the strategy optimization and adjustment module includes: The basic optimization value calculation submodule is used for weight coefficient and suppression effect calculation. For the protection strategy, based on its weight coefficient, lightning threat comprehensive measurement value and suppression factor, its suppression effect is calculated; combined with resource utilization, cost suppression factor, implementation difficulty coefficient and implementation cost, the economy and implementation efficiency of the strategy are quantified, and the sum is obtained to obtain the basic optimization value; The dynamic optimization value calculation submodule is used to evaluate the response efficiency of the strategy in the time dimension through the risk response attenuation coefficient and the response time difference; it combines the basic protection capability and the expansion capability coefficient to quantify the comprehensive protection capability of the strategy under the available resources and calculate the dynamic optimization value; The protection strategy selection submodule is used to compare the basic optimization value and the dynamic optimization value and select the larger one as the final optimization value.

[0015] In an optional implementation, the visualization linkage component includes: Data display module, used to design a dynamic dashboard to display real-time data of lightning activity; The interactive operation module is used to enable users to interact through the interface, zoom and filter, view detailed information, and adjust control strategies. When a lightning risk is detected, the visualization interface will simultaneously trigger the following linkage displays; Implement an update module for real-time updates and warnings, continuously update according to real-time changes in lightning activities, and send warning information through sound, flashing prompts or text messages.

[0016] Compared with the prior art, the present invention achieves the following beneficial effects: (1) By integrating multi-source data acquisition and fusion technology, the present invention can comprehensively and accurately capture the multi-dimensional characteristics of lightning activities, such as time, location, intensity, duration, etc., thereby significantly improving the accuracy and timeliness of lightning monitoring.

[0017] (2) The present invention utilizes advanced data analysis and intelligent decision-making algorithms. The system can deeply explore the patterns and potential risks of lightning events, and combine multi-dimensional economic and feasibility analysis to optimize protection strategies in real time, thus realizing intelligent and dynamic adjustment of lightning protection.

[0018] (3) The system can form deep integration and collaborative work with various key infrastructures such as power systems, communication systems, and security systems to achieve rapid linkage response when lightning risks occur, effectively avoid isolated operations between systems, and improve the overall protection effect.

[0019] (4) By designing an intuitive and easy-to-use visual interface, the system can display lightning data and protection status in real time, allowing users to quickly understand lightning activity and system protection status, thereby improving decision-making efficiency and operational convenience.

[0020] (5) By improving the accuracy, timeliness, intelligence and coordinated response capabilities of lightning protection, the present invention can significantly reduce the impact of lightning disasters on key infrastructure such as electricity and communications, ensure their safe and stable operation, and reduce economic losses and social impacts.

[0021] In summary, the present invention realizes real-time monitoring, intelligent evaluation and rapid response to lightning activities by integrating advanced data acquisition, analysis, control and visualization technologies, which greatly improves the efficiency and effectiveness of lightning protection and ensures the safe and stable operation of critical infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a block diagram of a multi-dimensional fusion intelligent control system for digital lightning protection based on artificial intelligence provided in Example 1 of the present invention; Figure 2 This is a block diagram of the data acquisition and fusion component provided in Example 2 of the present invention; Figure 3 This is a block diagram of a parameter extraction module provided in Embodiment 3 of the present invention; Figure 4 This is a block diagram of a metric value calculation module provided in Embodiment 4 of the present invention; Figure 5 This is a block diagram of a contribution value calculation submodule provided in Embodiment 5 of the present invention; Figure 6 This is a block diagram of a control strategy adjustment component provided in Embodiment 6 of the present invention; Figure 7 This is a block diagram of a strategy optimization and adjustment module provided in Embodiment 7 of the present invention; Figure 8 This is a block diagram of a priority adjustment module provided in Embodiment 8 of the present invention; Fig. 9 This is a block diagram of a visualization linkage component provided in Example 9 of the present invention; Fig.10 A block diagram of an electronic device provided by the present invention; Fig.11 A block diagram of a computer-readable storage medium provided for the present invention. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention will be described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0024] In the following, the terms "first", "second", etc. are used only for convenience of description and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0025] In the present invention, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense, for example, "connection" can be a fixed mechanical connection, or a detachable mechanical connection, or integrated; or, "connection" can be a direct connection, or an indirect connection through an intermediate medium. In addition, unless otherwise clearly specified and limited, the term "coupling" should be understood in a broad sense, for example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components, and can also be understood as electrical connection between different components in a circuit structure through physical lines such as copper foil or wires on a printed circuit board (PCB) that can transmit electrical signals to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in an air-spaced / non-contact manner, for example, two components are electrically connected by capacitive coupling to transmit electrical signals.

[0026] In the embodiments of the present invention, directional terms such as "up", "down", "left" and "right" may be defined including but not limited to the orientation relative to the schematic placement of the components in the drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and may change accordingly according to the change of the orientation of the components in the drawings.

[0027] Embodiment 1:

[0028] like Figure 1 As shown, an embodiment of the present invention provides a digital lightning protection multi-dimensional fusion intelligent control system based on artificial intelligence, comprising: Data collection and fusion components are used to collect data from multiple sources such as meteorological monitoring stations, geographic information systems, power systems, and communication systems, and integrate and analyze multi-source data to capture the characteristics and threat level of lightning activities; The control strategy adjustment component is used to deeply explore the laws and potential risks of lightning events through data fusion and dynamic decision-making mechanisms, and optimize the protection strategy in real time by combining multi-dimensional economic and feasibility analysis; The visual linkage component is used to provide an interface to display lightning data and protection status; at the same time, when lightning risks are detected, it coordinates the power system, communication system, and security system to respond.

[0029] In the above embodiments, the data acquisition and fusion component ensures that the system can obtain comprehensive data support, improves the accuracy and real-time performance of lightning prediction, can timely capture the characteristics and potential threats of lightning activities, and provide a reliable data basis for the control strategy. The control strategy adjustment component uses data analysis and mining technology to identify the laws of lightning events, evaluate potential risks, and dynamically adjust the protection strategy to adapt to the ever-changing lightning activities; through automated control strategy adjustment, the system can respond to lightning threats more intelligently, reduce human intervention, improve response speed and protection efficiency, and effectively reduce the impact of lightning disasters on key infrastructure such as power and communications. The visualization linkage component provides a user interface for displaying lightning monitoring data and system protection status. At the same time, when lightning risks are detected, it can timely link multiple systems such as power systems, communication systems, and security systems for coordinated response; the visualization linkage component not only improves the convenience and intuitiveness of operation, allowing relevant personnel to quickly understand the operating status of the lightning protection system, but also can achieve rapid cross-system linkage when risks occur, enhancing the system's emergency response capability and overall protection capability.

[0030] To sum up, the artificial intelligence-based digital lightning protection multi-dimensional fusion intelligent control system of this embodiment realizes real-time monitoring, intelligent evaluation and rapid response to lightning activities by integrating advanced data acquisition, analysis, control and visualization technologies, greatly improving the efficiency and effectiveness of lightning protection and ensuring the safe and stable operation of critical infrastructure.

[0031] Embodiment 2:

[0032] like Figure 2 As shown, based on Example 1, the data acquisition and fusion component provided in this embodiment of the present invention includes: The weight allocation module is used to collect information from various data sources, evaluate the importance of each data source, and determine its weight; the weight of each data source is calculated through weighted and comprehensive evaluation of multi-dimensional parameters, combined with multiple factors such as time decay, correlation analysis, uncertainty and expert knowledge; The parameter extraction module is used to extract the frequency function of lightning characteristics, angle parameters, environmental parameters, parameters of equipment lightning resistance performance and geographical parameters from each data source after the weight is determined; The metric value calculation module is used to calculate the contribution of each data source to the lightning threat and obtain a comprehensive metric value through weighted summation.

[0033] Among them, the weight distribution calculation formula of the weight distribution module is:

[0034] In the formula, Indicates i The weight coefficient of each data source indicates the importance of the data source. The weight coefficient ranges from 1 to 1. The higher the weight coefficient, the more important the information provided by the data source. Indicates i The frequency function of the lightning characteristics of a data source indicates the frequency or probability of the lightning events recorded by the data source. The parameter can be a time series data or a statistical value based on historical data. Indicates i The angle parameter of the data source indicates the directionality or angle distribution of lightning characteristics, which helps to understand the spatial distribution characteristics of lightning events; Indicates i Environmental parameters of a data source, which represent environmental factors related to lightning events, such as humidity and temperature. Environmental parameters have an important impact on the occurrence and intensity of lightning events. Indicates i The geographical parameters of a data source indicate the geographical location where the lightning event occurs. Geographic parameters can help understand the spatial distribution characteristics of lightning events and provide important information for lightning warning and protection; Indicates i The lightning resistance performance parameters of the equipment of a data source indicate the lightning protection capability of the equipment or facilities at the location of the data source. The parameters can help evaluate the potential threat of lightning to the equipment or facilities. Indicates i The uncertainty parameter of each data source indicates the reliability and accuracy of the data recorded by the data source. The parameter can be estimated based on the errors in the data collection process, measurement errors, etc. Introducing uncertainty factors can more objectively evaluate the contribution of each data source; Represents the data source iThe correlation coefficient between the two data sources j. If the data collected by the two data sources are highly correlated, their relative importance in the weight distribution may need to be adjusted. This parameter can be determined by calculating the covariance or correlation coefficient of the data between the data sources; Indicates i The time decay factor of each data source is used to indicate the importance of data over time. For time-sensitive data, such as lightning events, newer data may be more important than older data, so the introduction of the time decay factor can better reflect the timeliness of the data. Indicates i Machine learning optimization weights of data sources, using machine learning algorithms such as random forests or neural networks to predict and optimize the weight of each data source based on historical data; Indicates i The expert knowledge parameter of a data source represents the subjective assessment of the importance of the data source by domain experts. This parameter can be set based on the experience of experts, adding additional flexibility and interpretability to the model. Indicates i The outlier and noise parameters of a data source are used to identify and process outliers and noise in the data. This parameter can be estimated through statistical detection methods or data cleaning techniques.

[0035] In the above embodiment, the weight allocation module assigns a weight value to each data source based on indicators such as the reliability, accuracy and relevance of the data source; the higher the weight value, the greater the influence of the data source on the final result; the weight allocation module can ensure that the contribution of different data sources is reasonably allocated, avoiding certain data sources from having too much influence on the results, and improving the accuracy and fairness of the comprehensive measurement value. The parameter extraction module extracts parameters related to lightning characteristics from each data source, including frequency function, angle parameters, environmental parameters, equipment lightning resistance performance parameters and geographical parameters; these parameters are key factors in assessing lightning threats; the parameter extraction module can comprehensively and accurately collect lightning-related parameters, and provide necessary input data for subsequent measurement value calculations. At the same time, the process of extracting parameters is also a process of screening and preprocessing the data source, which helps to improve the reliability of the final result. The metric calculation module uses the outputs of the previous two modules to calculate the contribution of each data source to the lightning threat; through the weighted summation method, the contribution values ​​of each data source are combined to obtain a comprehensive metric value to evaluate the overall lightning threat level; the core function of the metric calculation module is to integrate the information of each data source to obtain a comprehensive and objective lightning threat assessment result; the comprehensive metric value can provide an important basis for lightning protection decision-making, which helps to improve the effectiveness and pertinence of lightning protection.

[0036] In summary, this embodiment realizes the complete process from data collection, parameter extraction to comprehensive evaluation; improves the accuracy and reliability of lightning threat assessment, and provides strong technical support for lightning protection work.

[0037] Embodiment 3:

[0038] like Figure 3 As shown, based on Example 2, the parameter extraction module provided by the embodiment of the present invention includes: The preprocessing submodule is used to preprocess the data from the data source, including missing value processing, noise removal, outlier monitoring and data standardization; The feature selection submodule is used to filter lightning features from the data based on the relevance of data sources, time decay and expert knowledge. It extracts lightning feature parameters from each data source by weighted calculation of comprehensive multi-dimensional parameters (frequency, angle, environment, equipment lightning resistance performance and geography, etc.) and combined with the adjustment of data relevance, time decay and expert knowledge. The feature fusion submodule is used to integrate data features from different sources or types, map different features into the same space, and form a comprehensive feature vector.

[0039] Among them, the frequent function extraction formula is:

[0040] In the formula, Indicates i Extract values ​​of lightning frequency function from data sources; Indicates i Data source k The frequency of lightning events is recorded; Indicates i The average frequency of lightning events from the data sources; m Indicates i Total number of lightning events recorded by each data source; Represents the data source i and data sources j The correlation coefficient between Indicates i The time decay factor of each data source; Indicates i Expert knowledge parameters of data sources; Angle parameter extraction formula:

[0041] In the formula, Indicates i Extract values ​​from angle parameters of each data source; Indicates i Data sourcek Angular parameter records of lightning events; Indicates i Data source j Angular parameter records of lightning events; Environmental parameter extraction formula:

[0042] In the formula, Indicates i Extract values ​​of environmental parameters from data sources; Indicates i Data source k Record of environmental parameters of lightning events; Equipment lightning protection performance parameter extraction formula:

[0043] In the formula, Indicates i Extract the lightning protection performance parameters of the equipment from the data source; Indicates i Data source k Records of equipment lightning resistance performance parameters for each lightning event; Geographic parameter extraction formula:

[0044] In the formula, Indicates i Extract values ​​from geographic parameters of data sources; Indicates i Data source k The formula combines multiple factors, such as the relevance of data sources, time decay, expert knowledge, etc., to ensure that the extracted parameters can fully reflect the characteristics of lightning and take into account the timeliness and reliability of the data.

[0045] In the above embodiments, the preprocessing submodule improves data quality by means of missing value processing, noise removal, outlier monitoring, etc., laying the foundation for analysis; normalizes the data to eliminate the dimensionality effect, so that different features are comparable at the same scale, which is conducive to model convergence and performance improvement. The preprocessing submodule improves data quality, enhances the generalization and robustness of the model; and provides a high-quality data foundation for feature extraction. The feature selection submodule screens out features that are closely related to the target variable based on the correlation between data, and reduces feature dimensions; analyzes the changing trend of features over time, removes features that decay too quickly, and retains stable and effective features; combines the experience of domain experts to identify key features and improve the accuracy of feature selection. The feature selection submodule reduces feature dimensions, reduces redundant features, and improves model training efficiency; extracts key features, improves the interpretability and predictive power of the model; combines expert knowledge to improve the field applicability and accuracy of feature selection. The feature fusion submodule integrates data features from different sources or types, eliminates data islands, and achieves information complementarity; maps different features to the same space to form a comprehensive feature vector, enhances the comparability and aggregation of features; through feature fusion, improves the expressiveness of features and improves the prediction performance of the model. The feature fusion submodule realizes the fusion of multi-source data, makes full use of information from different data sources, improves the generalization ability of the model; improves the aggregation of features, facilitates the processing and application of features; enhances the prediction performance of the model, and improves the robustness of the model in complex scenarios.

[0046] In summary, this embodiment plays a role in data preprocessing, feature selection, and feature fusion, which jointly improves the performance and generalization ability of the model and lays a solid foundation for analysis and prediction tasks.

[0047] Embodiment 4: like Figure 4 As shown, based on Example 2, the metric value calculation module provided in this embodiment of the present invention includes: The parameter processing submodule is used to combine the extracted frequency function value, angle parameter, environmental parameter, equipment lightning resistance performance parameter and geographical parameter, and calculate the weighted value in combination with the weight coefficient; adjust the timeliness of the data through the time decay factor; normalize the frequency records to reflect the frequency of lightning events; and adjust the correlation between data sources; The contribution value calculation submodule is used to combine all parameters and adjustment factors to calculate the contribution value of a single data source to the lightning threat; The result calculation submodule is used to comprehensively calculate the contribution values ​​of all data sources and perform normalization processing to obtain the final comprehensive measurement value; comprehensively evaluate the lightning characteristic parameters of multiple data sources (including frequency, angle, environment, equipment lightning resistance performance, geography, etc.), and introduce factors such as weight coefficient, data correlation, time attenuation, expert knowledge and noise processing, perform multi-dimensional weighted calculations, and finally generate a comprehensive measurement value of lightning threat.

[0048] The calculation formula of the comprehensive metric value is as follows:

[0049] Where: TRI represents the comprehensive measure of lightning threat, indicating the overall lightning threat level. The larger the value, the higher the threat. Indicates i The weight coefficient of a data source indicates the importance of the data source, ranging from [0,1]; Indicates i The frequency function value extracted from each data source reflects the frequency or probability of lightning events; Indicates i The angle parameter values ​​extracted from the data sources are used to evaluate the spatial distribution characteristics of lightning events; Indicates i The environmental parameter values ​​extracted from the data sources represent the environmental factors related to lightning events. Indicates i The lightning protection performance parameter value of the equipment extracted from the data source indicates the lightning protection capability of the equipment or facility; Indicates i The geographical parameter values ​​extracted from the data sources reflect the geographical location characteristics of lightning events. Indicates i Data source k The frequency records of lightning events are used to calculate the frequency function; Represents the data source i With data source j The correlation coefficient between them is used to adjust the weights between data sources; Indicates i The time decay factor of each data source indicates the importance of data over time; Indicates i Expert knowledge parameters of data sources are used to introduce subjective evaluations of domain experts; Indicates i Outliers and noise parameters of each data source are used to identify and handle outliers in the data; m Indicates i Total number of lightning events recorded by each data source; nIndicates the total number of data sources.

[0050] In the above embodiment, the parameter processing submodule generates a weighted value by combining the frequency function value, angle parameter, environmental parameter, equipment lightning resistance performance parameter and geographical parameter, and combining the predefined weight coefficient; ensuring that the importance of each parameter in the evaluation matches its actual impact; introducing the time decay factor to attenuate the historical data to ensure that the latest data has a greater impact on the results, thereby reflecting the dynamic changes of lightning threats; normalizing the frequency records to eliminate dimensional differences so that the frequency degree can be intuitively reflected in the results; by analyzing the correlation between data sources, avoiding repeated calculations or information redundancy, and improving the accuracy and efficiency of calculations; providing standardized, timely and reasonably correlated input data for calculations, ensuring the scientificity and reliability of the evaluation results. The contribution value calculation submodule combines all parameters and their adjustment factors (such as weight coefficients, time decay factors, etc.) to calculate the contribution value of a single data source to the lightning threat, comprehensively considering the actual impact and dynamic changes of each parameter; realizing the quantitative evaluation of the lightning threat of a single data source, providing a basis for the comprehensive calculation of multi-source data, and enhancing the precision and pertinence of the evaluation. The result calculation submodule performs weighted summation or other forms of comprehensive calculation on the contribution values ​​of all data sources to generate preliminary comprehensive measurement values; normalizes the comprehensive measurement values ​​and maps them to a unified range to facilitate users' intuitive understanding and subsequent application; and provides the final comprehensive measurement values, which provide directly usable quantitative indicators for lightning risk assessment and support decision-making and risk management.

[0051] In summary, the metric value calculation module of this embodiment realizes a comprehensive, dynamic and quantitative evaluation of lightning threats through the synergistic effect of the three sub-modules of parameter processing, contribution value calculation and result calculation; the scientificity and accuracy of the evaluation results are ensured through weight coefficients, time decay factors and normalization processing; the introduction of time decay factors reflects the dynamic changes of lightning threats and improves the timeliness of the evaluation; through the comprehensive analysis of multi-source data, the limitations of a single data source are avoided and the comprehensiveness of the evaluation is enhanced; the final comprehensive metric value can be directly used for risk classification, warning issuance and formulation of protective measures, and has high practical value.

[0052] Embodiment 5: like Figure 5 As shown, based on Example 4, the contribution value calculation submodule provided in this embodiment of the present invention includes: A weighted value calculation unit is used to assign a weight coefficient to each parameter according to the importance of different parameters; and multiply the extracted parameter value by its corresponding weight coefficient to obtain a weighted value; A normalization processing unit is used to adjust the timeliness of data through a time decay factor; and to perform normalization processing on frequent records; The weighted summation unit is used to combine all processed parameter values ​​and adjustment factors, and calculate the contribution value of a single data source to the lightning threat through weighted summation.

[0053] In the above embodiment, the weighted value calculation unit assigns a weight coefficient to each parameter according to the contribution of different parameters to the lightning threat assessment; the extracted parameter value is multiplied by its weight coefficient to obtain a weighted value to provide a standardized input for the comprehensive calculation; through weight allocation, the influence of key parameters (such as frequency function value, equipment lightning resistance performance parameter) is strengthened to improve the accuracy of the assessment result; parameters of different dimensions are converted into a unified weighted value to facilitate mathematical operations and comprehensive analysis. The normalization processing unit dynamically adjusts the timeliness of the data through the time decay factor. For example, the weight of recent data is higher, and the weight of historical data decreases, ensuring that the assessment result reflects the current threat situation; mapping parameter values ​​such as frequency records to a unified range (such as 0 to 1), eliminating the dimensional differences between different parameters, and enhancing the comparability of data; timeliness adjustment ensures that the assessment results are updated over time, improving the real-time and adaptability of the system; normalization processing eliminates the dimensional differences between parameters to avoid assessment deviations caused by different parameter value ranges. The weighted summation unit combines all processed parameter values ​​(weighted values ​​and normalized values) with the adjustment factor, and calculates the contribution of a single data source to the lightning threat through weighted summation. The weighted summation algorithm is used to integrate multi-dimensional parameters into a comprehensive value to simplify the analysis. The weighted summation is used to combine the influence of multiple parameters into a quantitative value to fully reflect the contribution of the data source to the lightning threat. The weighted summation algorithm is simple and efficient, suitable for real-time calculation of large-scale data, and improves the response speed of the system.

[0054] In summary, the contribution value calculation submodule of this embodiment realizes the accurate calculation of the lightning threat contribution value through the division of labor and cooperation among the weighted value calculation unit, the normalization processing unit and the weighted summation unit. The technical effects of each unit jointly ensure the accuracy, real-time and consistency of the evaluation results, and provide reliable technical support for the lightning threat assessment system.

[0055] Embodiment 6:

[0056] like Figure 6 As shown, based on Example 1, the control strategy adjustment component provided by the embodiment of the present invention includes: The suppression effect calculation module is used to assign a weight coefficient to each protection strategy to reflect its importance in risk suppression; combining the comprehensive measurement value of lightning threat and the risk suppression factor of the protection strategy to calculate the suppression effect of the strategy on the risk; The strategy optimization and adjustment module is used to evaluate the feasibility of the strategy through resource utilization, cost suppression factor, implementation difficulty coefficient and implementation cost; it dynamically optimizes the real-time response capability of the strategy by combining the dynamic adjustment factor of the strategy, risk response attenuation coefficient, response time difference and total resources; The priority adjustment module is used to calculate the optimization value of each protection strategy and select the optimal strategy; assign a weight coefficient to each response measure to reflect its importance in risk mitigation; evaluate the effectiveness of the measures based on the risk mitigation effect, resource consumption rate and time efficiency factor of the response measures; quantify the feasibility of the response measures through cost suppression factors, implementation difficulty coefficients and implementation costs; and dynamically adjust the system's response priority to risks based on the system's risk tolerance and risk response priority adjustment factor.

[0057] In the above embodiment, the suppression effect calculation module assigns a weight coefficient to each protection strategy, quantifies its importance in risk suppression, and ensures that key strategies are given priority support; combines the comprehensive measurement value of lightning threat and the risk suppression factor of the protection strategy, dynamically calculates the suppression effect of the strategy on risk, and improves the protection accuracy; through a data-driven method, an accurate assessment of the risk suppression ability of the protection strategy is achieved, providing basic parameters for strategy optimization and priority adjustment, ensuring that the system can suppress lightning threats in a targeted manner and reduce risk losses. The strategy optimization and adjustment module quantifies the feasibility of the strategy through resource utilization, cost suppression factor, implementation difficulty coefficient and implementation cost, ensuring the economy and operability of the solution; combines the dynamic adjustment factor of the strategy, risk response attenuation coefficient, response time difference and total resources, dynamically optimizes the real-time response capability of the strategy, and improves the system's response efficiency. It integrates multi-dimensional factors such as resources, costs, and implementation difficulty to achieve dynamic optimization of the protection strategy, ensuring that the system can quickly generate the optimal protection plan and maximize the overall protection effectiveness when resources are limited. The priority adjustment module calculates the optimization value of each protection strategy and selects the optimal strategy to ensure that the system can accurately match the current lightning threat; assigns a weight coefficient to each response measure to reflect its importance in risk mitigation and ensure that key measures are implemented first; combines the risk mitigation effect of the response measures, resource consumption rate and time efficiency factor to quantify the effectiveness of the measures and improve protection efficiency; dynamically adjusts the system's response priority to risks through cost suppression factors, implementation difficulty coefficients, implementation costs and system risk tolerance to ensure that key systems receive priority protection support; through the priority adjustment mechanism, efficient allocation of protection resources is achieved and the overall protection capability of the system is improved; through dynamic optimization of priorities, it ensures that key targets and scenarios can obtain timely and effective protection support and reduce the impact of lightning risks on the system.

[0058] In summary, the control strategy adjustment component of this embodiment realizes accurate assessment of risk suppression capability, dynamic real-time optimization and efficient resource allocation of the protection strategy through three modules: suppression effect calculation, strategy optimization adjustment and priority adjustment. It not only improves the protection accuracy and adaptability of the system, but also provides a scientific and efficient solution for lightning protection in complex environments, which has important technical value and application significance.

[0059] Embodiment 7:

[0060] like Figure 7 As shown, based on Example 6, the strategy optimization and adjustment module provided in this embodiment of the present invention includes: The basic optimization value calculation submodule is used for weight coefficient and suppression effect calculation. For the protection strategy, based on its weight coefficient, lightning threat comprehensive measurement value and suppression factor, its suppression effect is calculated; combined with resource utilization, cost suppression factor, implementation difficulty coefficient and implementation cost, the economy and implementation efficiency of the strategy are quantified, and the sum is obtained to obtain the basic optimization value;

[0061] In the formula, Indicates the protection strategy. Weight coefficient, Represents inhibitory factor, Indicates the combined resource utilization, represents the cost suppression factor, Indicates the implementation difficulty coefficient, represents the implementation cost; Represents the basic optimization value; The dynamic optimization value calculation submodule is used to evaluate the response efficiency of the strategy in the time dimension through the risk response attenuation coefficient and the response time difference; it combines the basic protection capability and the expansion capability coefficient to quantify the comprehensive protection capability of the strategy under the available resources and calculate the dynamic optimization value;

[0062] In the formula, It represents the dynamic adjustment factor, reflecting the flexibility and adaptability of the strategy in different scenarios; represents the risk response attenuation coefficient; Indicates the response time difference; Indicates basic protection capability; represents the expansion capacity coefficient; Indicates available resources; Represents a dynamically optimized value; The protection strategy selection submodule is used to compare the basic optimization value and the dynamic optimization value and select the larger one as the final optimization value;

[0063] In the formula, Represents the final optimized value.

[0064] In the above embodiment, the formula for calculating the basic optimization value of the basic optimization value calculation submodule integrates the balance between the protection effect and resource consumption of the strategy, ensuring that the strategy has high cost-effectiveness and feasibility of implementation while suppressing the threat of lightning; by quantifying the comprehensive effect of the protection strategy, an objective numerical basis is provided for the strategy selection, avoiding the deviation of subjective judgment; a unified evaluation of the economy, resource utilization and implementation difficulty of the strategy is achieved, ensuring that the selected strategy has high operability and benefits in practical applications. The dynamic optimization value calculation submodule evaluates the response efficiency and comprehensive protection capability of the strategy in the time dimension; the formula for calculating the dynamic optimization value takes into account the dynamic adaptability and expansion capability of the strategy, ensuring that the strategy can respond efficiently in different application scenarios; by introducing dynamic adjustment factors under the time dimension and resource constraints, the flexibility and adaptability of the strategy are enhanced; a comprehensive performance evaluation of the protection strategy under real-time response and available resource constraints is achieved, ensuring that the strategy can still maintain efficient protection in high-risk scenarios. The protection strategy selection submodule ensures that the final selected strategy achieves the optimal balance between static economic evaluation and dynamic response efficiency; through comprehensive comparison of optimization values, multi-dimensional evaluation and selection of protection strategies are realized, ensuring that the selected strategy achieves the best in protection effect, economy, implementation efficiency and dynamic adaptability; it improves the overall performance and reliability of the lightning protection system and provides a scientific basis for lightning protection in complex environments.

[0065] In summary, the strategy optimization and adjustment module of this embodiment realizes the comprehensive optimization and selection of the protection strategy through the collaborative work of the basic optimization value calculation submodule, the dynamic optimization value calculation submodule and the protection strategy selection submodule. Its technical effects are embodied in: the basic optimization value calculation submodule quantifies the economy and implementation efficiency of the strategy; the dynamic optimization value calculation submodule evaluates the time response efficiency and comprehensive protection capability of the strategy; the protection strategy selection submodule selects the optimal strategy by comparing the static and dynamic optimization values. This embodiment improves the scientificity and systematicity of the lightning protection strategy; through multi-dimensional evaluation and optimization, it ensures the efficiency and reliability of the protection strategy in complex environments; and provides technical support for the intelligence and adaptability of the lightning protection system.

[0066] Embodiment 8: like Figure 8 As shown, based on Example 6, the priority adjustment module provided in this embodiment of the present invention includes: The comprehensive evaluation value calculation submodule is used to calculate the actual risk mitigation capability of the response measures based on their weight coefficients and risk mitigation effects; evaluate the resource utilization efficiency and response speed of the measures by combining the resource consumption rate and time efficiency factor; and quantify the economic efficiency and implementation difficulty of the measures by using the cost suppression factor, implementation difficulty coefficient and implementation cost;

[0067] In the formula, Indicates response measures; represents the weight coefficient; Indicates the risk mitigation effect; Indicates resource consumption rate; represents the time efficiency factor; represents the cost suppression factor; Indicates the implementation difficulty coefficient; represents the implementation cost; represents the comprehensive evaluation value; The risk adjustment value calculation submodule is used to quantify the system's tolerance for risk based on the system's risk tolerance and adjustment factors; evaluate the potential threats it faces in combination with the system's risk level; and calculate the system's risk adjustment value using a logarithmic function;

[0068] In the formula, Indicates risk tolerance; represents the regulating factor; Indicates the risk level; represents the risk-adjusted value; The response priority calculation submodule is used to subtract the risk adjustment value of the system from the comprehensive evaluation value of the response measures to obtain the final response priority;

[0069] In the formula, Indicates the final response priority.

[0070] In the above embodiment, the comprehensive evaluation value calculation submodule calculates through multi-dimensional parameters (weight coefficient, risk mitigation effect, resource consumption rate, time efficiency factor, cost suppression factor, implementation difficulty coefficient and implementation cost) to quantify the actual mitigation capability, resource utilization efficiency, response speed, economy and implementation difficulty of the response measures; the complex response measures are converted into comparable values ​​through formulas to provide data support for decision-making; it is ensured that the evaluation results not only consider the risk mitigation effect, but also integrate key factors such as resource consumption, time and cost to achieve more comprehensive optimization; according to the weight coefficients and adjustment factors of different scenarios, the evaluation results are dynamically adjusted to improve the flexibility and adaptability of the system. The risk adjustment value calculation submodule calculates the risk adjustment value based on the system's risk tolerance, adjustment factor and risk level, and quantifies the system's tolerance for risk and the impact of potential threats; the system's risk tolerance and risk level are converted into operational values ​​through formulas to provide a basis for priority adjustment; the introduction of logarithmic functions enables the risk adjustment value to more sensitively reflect the changes in high risk levels and enhance the system's ability to identify high risks; according to the risk tolerance and adjustment factors of different systems, personalized configuration of risk management is achieved. The response priority calculation submodule calculates the difference between the comprehensive assessment value and the risk adjustment value to obtain the final response priority, ensuring that the response measures match the actual risk requirements; through the difference calculation, the response measures are associated with the system risks to ensure that high-risk scenarios are handled first and improve the overall security of the system; according to the priority results, limited resources (such as manpower, funds, and time) are reasonably allocated to maximize the risk mitigation effect; and clear priority sorting is provided for decision makers to reduce decision-making time and improve the response speed and reliability of the system.

[0071] In summary, this embodiment realizes a complete closed loop from risk quantification to response optimization. Its technical significance lies in: through mathematical models and algorithms, complex risk management problems are transformed into quantifiable decision-making basis, improving the scientificity and accuracy of decision-making; through multi-dimensional evaluation and priority sorting, it ensures efficient use of resources and avoids resource waste and redundancy; the parameters in the system (such as weight coefficient, risk tolerance, etc.) can be adjusted according to actual needs, so that the module has a high degree of flexibility and adaptability; through the calculation and priority sorting of risk adjustment values, it ensures that the system can operate within the controllable range of risk and reduce potential losses.

[0072] Embodiment 9: like Fig. 9 As shown, based on Example 1, the visualization linkage component provided by the embodiment of the present invention includes: The data display module is used to design a dynamic dashboard to display real-time data of lightning activity; the dashboard includes the following elements: Lightning activity map: Use heat maps or dynamic markers to show the location and spread of lightning; Risk warning indication: Intuitively indicate the lightning threat level through colors (such as green, yellow, red) or icons; Protection status monitoring: Displays the operating status of the power system, communication system and security system, including whether protection measures have been activated; Historical data analysis: Provide historical data comparison of lightning activities to help users analyze changing trends; The interactive operation module is used to enable users to interact through the interface, zoom and filter, view detailed information, and adjust control strategies. When a lightning risk is detected, the visualization interface will simultaneously trigger the following linkage displays; Power system response: Displays the power outage or lightning protection measures activation status of power facilities; Communication system assurance: Demonstrate whether the switching or backup plan of the communication network has been activated; Security system protection: Prompt whether security equipment (such as surveillance cameras, alarm systems) is operating normally; Implement an update module for real-time updates and warnings, which are continuously updated according to real-time changes in lightning activities, and send warning information through sound, flashing prompts or text messages.

[0073] In the above embodiment, the dynamic dashboard of the data display module combines heat maps, color coding and historical data comparison to realize the intuitive display of lightning activities, risk levels and protection status, and improve the efficiency of data perception and the accuracy of decision-making basis; the interactive operation module users can realize interactive control through zooming, filtering and detailed information viewing, and trigger the linkage display of power, communication and security systems at the same time, optimize user experience and convenience of system operation; the implementation update module is based on real-time data update and multi-modal warning methods (such as prompt sound, flashing light, text message) to ensure the timely transmission of warning information and improve the system response speed and risk control ability. This embodiment realizes all-weather, multi-dimensional lightning monitoring and protection display, improves the real-time and linkage of the system, and provides users with efficient and reliable decision support and risk control means.

[0074] Fig.10 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown.

[0075] The electronic device may include a central processing unit / microprocessor / main control chip, etc. 1; a storage medium 2, coupled to the central processing unit / microprocessor / main control chip, etc. 1, and storing computer executable instructions therein, for performing the steps of each method of an embodiment of the present invention when executed by the processor.

[0076] The central processing unit / microprocessor / main control chip etc. 1 may include but is not limited to, for example, one or more processors or microprocessors etc.

[0077] The storage medium 2 may include, but is not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (such as hard disk, floppy disk, solid state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0078] In addition, the electronic device may also include (but not limited to) a data bus 3, an input / output bus / external bus / device bus 4, a display 5, and input / output devices 6 (eg, keyboard, mouse, speaker, etc.).

[0079] The central processing unit / microprocessor / main control chip etc. 1 can communicate with external devices ( 5 , 6 etc.) through the I / O bus 4 via a wired or wireless network (not shown).

[0080] The storage medium 2 may also store at least one computer executable instruction for executing the various functions and / or method steps in the embodiments described in the present technology when the instruction is executed by the central processing unit / microprocessor / main control chip 1.

[0081] In one embodiment, the at least one computer executable instruction may also be compiled into or constitute a software product, wherein one or more computer executable instructions are executed by a processor to perform the various functions and / or method steps in the embodiments described in the present technology.

[0082] Fig.11 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0083] like Fig.11 As shown, instructions are stored on the non-transitory computer-readable storage medium 8, and the instructions are, for example, computer-readable instructions 7. When the computer-readable instructions 7 are executed by the processor, the various methods described above can be executed. The non-transitory computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-transitory non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, the non-transitory computer-readable storage medium 8 can be connected to a computing device such as a computer, and then, when the computing device runs the computer-readable instructions 7 stored on the computer-readable storage medium 8, the various methods described above can be performed.

[0084] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0085] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0086] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0087] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for executing all or part of the steps of the various embodiments of the present invention through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk, etc. Various media that can store program codes.

[0088] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital lightning protection multi-dimensional fusion intelligent control system based on artificial intelligence, characterized in that: Include: Data collection and fusion component, used to collect multi-source data, calculate the weight of each data source through multi-dimensional parameter weighting and comprehensive evaluation of multi-source data, combined with time decay, correlation analysis, uncertainty and expert knowledge; Fusion analysis of multi-source data to capture the characteristics and threat level of lightning activities; The control strategy adjustment component is used to deeply explore the laws and potential risks of lightning events through data fusion and dynamic decision-making mechanisms, and optimize the protection strategy in real time by combining multi-dimensional economic and feasibility analysis; The visual linkage component is used to provide an interface to display lightning data and protection status; at the same time, when lightning risks are detected, it coordinates the power system, communication system and security system to respond.

2. The multi-dimensional fusion intelligent control system for digital lightning protection based on artificial intelligence according to claim 1, characterized in that: Data collection and fusion components, including: The weight allocation module is used to collect information from various data sources, evaluate the importance of each data source, and determine its weight; The weight of each data source is calculated by weighting and comprehensively evaluating multi-dimensional parameters, combining time decay, correlation analysis, uncertainty and expert knowledge. The parameter extraction module is used to extract the frequency function of lightning characteristics, angle parameters, environmental parameters, parameters of equipment lightning resistance performance and geographical parameters from each data source after the weight is determined; The metric value calculation module is used to calculate the contribution of each data source to the lightning threat and obtain a comprehensive metric value through weighted summation.

3. The multi-dimensional fusion intelligent control system for digital lightning protection based on artificial intelligence as claimed in claim 2 is characterized in that: Parameter extraction module, including: The preprocessing submodule is used to preprocess the data from the data source, including missing value processing, noise removal, outlier monitoring and data standardization; The feature selection submodule is used to filter lightning features from the data based on the correlation of data sources, time decay and expert knowledge; The feature fusion submodule is used to integrate data features from different sources or types, map different features into the same space, and form a comprehensive feature vector.

4. The multi-dimensional fusion intelligent control system for digital lightning protection based on artificial intelligence as claimed in claim 3 is characterized in that: in, The feature selection submodule extracts lightning characteristic parameters from each data source through weighted calculation of comprehensive frequency, angle, environment, equipment lightning resistance and geographical multi-dimensional parameters, combined with data correlation, time attenuation and adjustment of expert knowledge.

5. The multi-dimensional fusion intelligent control system for digital lightning protection based on artificial intelligence as claimed in claim 2, characterized in that: Metric calculation module, including: The parameter processing submodule is used to combine the extracted frequency function value, angle parameter, environmental parameter, equipment lightning resistance performance parameter and geographical parameter, and calculate the weighted value in combination with the weight coefficient; adjust the timeliness of the data through the time attenuation factor; normalize the frequency records to reflect the frequency of lightning events; Adjust the correlation between data sources; The contribution value calculation submodule is used to combine all parameters and adjustment factors to calculate the contribution value of a single data source to the lightning threat; The result calculation submodule is used to comprehensively calculate the contribution values ​​of all data sources and perform normalization processing to obtain the final comprehensive measurement value.

6. The multi-dimensional fusion intelligent control system for digital lightning protection based on artificial intelligence as claimed in claim 5, characterized in that: in, The result calculation submodule comprehensively evaluates the frequency, angle, environment, equipment lightning resistance performance and geographical lightning characteristic parameters of multiple data sources, and introduces weight coefficients, data correlation, time attenuation, expert knowledge and noise processing factors to perform multi-dimensional weighted calculations to generate a comprehensive measurement value of lightning threat.

7. The multi-dimensional fusion intelligent control system for digital lightning protection based on artificial intelligence as claimed in claim 5, characterized in that: Contribution value calculation submodule, including: A weighted value calculation unit is used to assign a weight coefficient to each parameter according to the importance of different parameters; and multiply the extracted parameter value by its corresponding weight coefficient to obtain a weighted value; A normalization processing unit is used to adjust the timeliness of data through a time decay factor; and to perform normalization processing on frequent records; The weighted summation unit is used to combine all processed parameter values ​​and adjustment factors, and calculate the contribution value of a single data source to the lightning threat through weighted summation.

8. The multi-dimensional fusion intelligent control system for digital lightning protection based on artificial intelligence according to claim 1, characterized in that: Control strategy adjustment components, including: The suppression effect calculation module is used to assign a weight coefficient to each protection strategy to reflect its importance in risk suppression; combining the comprehensive measurement value of lightning threat and the risk suppression factor of the protection strategy to calculate the suppression effect of the strategy on the risk; The strategy optimization and adjustment module is used to evaluate the feasibility of the strategy through resource utilization, cost suppression factor, implementation difficulty coefficient and implementation cost; it dynamically optimizes the real-time response capability of the strategy by combining the dynamic adjustment factor of the strategy, risk response attenuation coefficient, response time difference and total resources; The priority adjustment module is used to calculate the optimization value of each protection strategy and select the optimal strategy; assign a weight coefficient to each response measure to reflect its importance in risk mitigation; and evaluate the effectiveness of the response measure by combining its risk mitigation effect, resource consumption rate, and time efficiency factor; The feasibility of response measures can be quantified through cost suppression factors, implementation difficulty coefficients and implementation costs. The system's response priority to risks can be dynamically adjusted by combining the system's risk tolerance and risk response priority adjustment factors.

9. The multi-dimensional fusion intelligent control system for digital lightning protection based on artificial intelligence as claimed in claim 8, characterized in that: Strategy optimization and adjustment module, including: The basic optimization value calculation submodule is used for weight coefficient and suppression effect calculation. For the protection strategy, based on its weight coefficient, lightning threat comprehensive measurement value and suppression factor, its suppression effect is calculated; combined with resource utilization, cost suppression factor, implementation difficulty coefficient and implementation cost, the economy and implementation efficiency of the strategy are quantified, and the sum is obtained to obtain the basic optimization value; The dynamic optimization value calculation submodule is used to evaluate the response efficiency of the strategy in the time dimension through the risk response attenuation coefficient and the response time difference; Combine the basic protection capability and expansion capability coefficient to quantify the comprehensive protection capability of the strategy under available resources and calculate the dynamic optimization value; The protection strategy selection submodule is used to compare the basic optimization value and the dynamic optimization value and select the larger one as the final optimization value.

10. The multi-dimensional fusion intelligent control system for digital lightning protection based on artificial intelligence according to claim 1, characterized in that: Visual linkage components, including: Data display module, used to design a dynamic dashboard to display real-time data of lightning activity; The interactive operation module is used to enable users to interact through the interface, zoom and filter, view detailed information, and adjust control strategies. When a lightning risk is detected, the visualization interface will simultaneously trigger the following linkage displays; Implement an update module for real-time updates and warnings, continuously update according to real-time changes in lightning activities, and send warning information through sound, flashing prompts or text messages.

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