Power transmission line forest fire risk assessment and early warning system and method

Abstract

The invention relates to a power transmission line forest fire risk assessment early warning system and method, and belongs to the technical field of power transmission line risk assessment. The system comprises an environment DNA power transmission line forest fire risk assessment and early warning system, a quantum sensing power transmission line fire remote monitoring system, a power transmission line forest fire early warning emergency training system, a digital twin power transmission line fire risk dynamic management system, and a multi-node power transmission line fire early warning system of an edge AI and a low orbit satellite. The system comprises a thermal imaging and deep learning integrated power transmission line fire risk distribution prediction system, an AI-driven power load dynamic adjustment and fire prevention system, an intelligent fire-fighting equipment linked power transmission line fire spreading path prediction system and a distributed quantum computing power transmission line fire emergency simulation and optimization response system. According to the method, the inflammable vegetation density and biocenosis change can be dynamically analyzed through a plant and microorganism gene sequencing technology.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line risk assessment, and in particular to a transmission line wildfire risk assessment and early warning system and method. Background Art

[0002] As an indispensable and important part of the power system, transmission lines play a vital role in the energy supply system of modern society. They are widely and densely distributed in various geographical environments, and mountainous areas and forests are common areas for their laying. The mountainous areas have undulating terrain and dense forest vegetation, which brings many potential risks to the safe operation of transmission lines. Among them, the occurrence of wildfires is one of the most severe challenges.

[0003] At present, the monitoring and assessment of wildfires on transmission lines has the following main limitations:

[0004] Problem 1: Traditional monitoring methods are insufficient: ground inspections are time-consuming and labor-intensive, with low inspection frequencies, making it difficult to achieve real-time monitoring, and prone to delayed discovery of hidden dangers of wildfires. Although video surveillance can capture images in real time, its monitoring range is limited and it is difficult to cover the vast mountainous power transmission lines;

[0005] Problem 2: Low accuracy in fire risk prediction: Although the existing remote sensing and meteorological data monitoring systems can provide basic data, the accuracy of fire risk assessment is affected by many factors, such as meteorological conditions, terrain complexity, and vegetation types, and it is impossible to accurately predict fire risks. In particular, in the changeable mountain climate environment, the applicability of existing assessment methods is low.

[0006] Problem 3: Slow early warning response: Due to the lack of intelligent and automated early warning mechanisms, existing early warning systems often rely on manual intervention or simple rule settings, and are unable to quickly generate effective early warnings in the early stages of a fire, resulting in serious consequences for the spread of wildfires and increased operational risks for power transmission lines.

[0007] Problem 4: Lack of multi-level, coordinated emergency response capabilities: The current system rarely considers the multi-level coordinated processing and dynamic response functions of transmission line wildfires. A single data source and a single system are difficult to provide a comprehensive emergency response strategy in complex fire situations. Summary of the invention

[0008] In view of the shortcomings of the prior art, the present invention provides a transmission line wildfire risk assessment and early warning system, which solves the problems of low inspection frequency, low fire risk prediction accuracy and slow early warning response speed in the prior art.

[0009] Another object of the present invention is to provide a transmission line wildfire risk assessment and early warning method with multi-level collaborative processing and dynamic response functions.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0011] Transmission line wildfire risk assessment and early warning system, Environmental DNA transmission line wildfire risk assessment and early warning system, used to collect, analyze and process transmission line environmental information, generate risk distribution maps, adjust risk assessment results, and provide early warning signals;

[0012] Quantum sensing transmission line fire remote monitoring system, used to detect remote environmental change information, generate real-time data, and integrate it with historical data to predict the development of fire;

[0013] A digital twin transmission line fire risk dynamic management system is set up to create a virtual digital twin of the transmission line, set monitoring indicators and thresholds for fire risks, provide response strategies for high-risk areas based on monitoring indicators and thresholds, integrate multi-source data, dynamically adjust monitoring indicators and thresholds for fire risks based on the amplitude and frequency of data changes, and optimize early warning plans and resource allocation;

[0014] The multi-node power transmission line fire warning system based on edge AI and low-orbit satellites is used to detect abnormal data along the transmission line, transmit abnormal data across regions, analyze abnormal data at different scales between data nodes, and self-organize and collaborate;

[0015] A transmission line fire risk distribution prediction system that integrates thermal imaging and deep learning to collect heat source images, identify fire hotspots, predict fire development, and generate a distribution map of risk areas;

[0016] AI-driven power load dynamic adjustment system, which is used to monitor the power load of transmission lines in real time, reduce the dimension and extract features of power load data, and adaptively adjust the line load according to the set fire risk threshold;

[0017] The intelligent fire-fighting equipment linkage system is used to predict the direction of fire spread and link fire-fighting equipment based on the above-mentioned risk area distribution map.

[0018] The method for assessing and warning the risk of wildfires on power transmission lines includes the following steps:

[0019] Collect DNA samples of plants and microorganisms around power transmission lines, analyze and process the collected data, generate risk distribution maps, adjust risk assessment results, and provide early warning signals;

[0020] Detect remote environmental change information, generate real-time data, and integrate it with historical data to predict fire development;

[0021] Create a virtual digital twin of the transmission line, set monitoring indicators and thresholds for fire risks, provide response strategies for high-risk areas based on the monitoring indicators and thresholds, integrate multi-source data, dynamically adjust the monitoring indicators and thresholds for fire risks based on the amplitude and frequency of data changes, and optimize early warning plans and resource allocation;

[0022] Detect abnormal data along the transmission line, transmit abnormal data across regions, analyze abnormal data at different scales between data nodes and self-organize and collaborate;

[0023] Collect heat source images, identify fire hotspots, predict fire development, and generate risk area distribution maps;

[0024] Real-time monitoring of the power load of the transmission line, dimension reduction and feature extraction of the power load data, and adaptive adjustment of the line load according to the set fire risk threshold;

[0025] Based on the above risk area distribution map, the direction of fire spread is predicted and fire-fighting equipment is linked.

[0026] A computer device comprising:

[0027] A processor; and a memory configured to store machine-readable instructions, which, when executed by the processor, execute the above-mentioned power transmission line wildfire risk assessment and early warning method.

[0028] A storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned power transmission line wildfire risk assessment and early warning method.

[0029] The present invention has the following beneficial effects:

[0030] 1. The environmental DNA transmission line wildfire risk assessment and early warning system in the present invention can dynamically analyze the density of flammable vegetation and changes in biological communities through plant and microbial gene sequencing technology, and provide high-precision fire risk maps in combination with meteorological and geographic data, thereby improving the accuracy of fire risk prediction along the transmission lines.

[0031] 2. The quantum sensing transmission line fire remote monitoring system of the present invention uses quantum sensing technology to detect tiny temperature differences and humidity changes, realizes remote data transmission through quantum networks, and sends out early warning signals when abnormalities are detected, ensuring early detection and treatment of hidden dangers of wildfires.

[0032] 3. The digital twin transmission line fire risk dynamic management system in the present invention can simulate fire situations in real environments, generate emergency strategies through cognitive algorithms, and realize dynamic resource allocation in combination with resource optimization modules, making fire prevention and response of transmission lines more intelligent and efficient.

[0033] 4. The edge AI and low-orbit satellite multi-node transmission line fire warning system in the present invention can achieve rapid warning through node self-organization at the early stage of fire through collaborative analysis of data from edge AI nodes and low-orbit satellites, thereby improving the system's response speed and coverage.

[0034] 5. The power transmission line fire spread path prediction system linked to the intelligent fire fighting equipment in the present invention uses a neural network to predict the direction of fire spread and automatically controls the fire extinguishing equipment to achieve accurate positioning and real-time control of the fire, thereby minimizing the fire losses of the power transmission line.

[0035] 6. The present invention monitors in real time, increases inspection frequency, covers a wide monitoring range, and makes accurate predictions on fire risks, especially in the changeable mountain climate environment, with high applicability; it has an intelligent and automated early warning mechanism, which can quickly generate effective early warnings in the early stages of a fire, prevents wildfires from spreading, and increases the operational safety of transmission lines; it has multi-level, coordinated emergency response capabilities, taking into account the multi-level coordinated processing and dynamic response functions of wildfire situations on transmission lines, and can provide comprehensive emergency response strategies in complex fire situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic block diagram of the structure of the evaluation and early warning system of the present invention;

[0037] Figure 2 It is a schematic flowchart of the evaluation and early warning method process of the present invention;

[0038] Figure 3 It is the overall system mind map of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Example 1

[0041] like Figure 1As shown, the transmission line wildfire risk assessment and early warning system includes an environmental DNA transmission line wildfire risk assessment and early warning system, a quantum sensing transmission line fire remote monitoring system, a transmission line wildfire early warning emergency training system, a digital twin transmission line fire risk dynamic management system, a multi-node transmission line fire early warning system of edge AI and low-orbit satellites, a transmission line fire risk distribution prediction system integrating thermal imaging and deep learning, an AI-driven power load dynamic adjustment and fire prevention system, a transmission line fire spread path prediction system linked to intelligent fire fighting equipment, and a distributed quantum computing transmission line fire emergency simulation and optimization response system;

[0042] The environmental DNA transmission line wildfire risk assessment and early warning system includes an environmental DNA acquisition module, a data analysis module, a risk assessment module, a vegetation distribution detection module, a meteorological integration module and an early warning response module. The environmental DNA acquisition module arranges sampling equipment along the transmission line and regularly collects DNA samples of plants and microorganisms around the line. The data analysis module processes the sampling data through gene sequencing technology to identify the density of flammable vegetation and changes in biological communities in the area. The risk assessment module combines biological community data with environmental information to form a fire risk map. The vegetation distribution detection module generates a vegetation distribution heat map in high-risk areas. The meteorological integration module adjusts the fire risk assessment results through real-time meteorological data. The early warning response module provides fire warning signals in high-risk areas based on risk prediction results.

[0043] The quantum sensing transmission line fire remote monitoring system includes a quantum sensing module, a remote data transmission module, a sensitivity detection module, a cloud processing module, a risk analysis module and an early warning module. The quantum sensing module is installed on the transmission tower to detect ultra-long-range tiny temperature differences and humidity changes. The remote data transmission module transmits detection data through a quantum computing network. The sensitivity detection module generates real-time data when environmental conditions change. The cloud processing module integrates quantum sensor data with historical fire data. The risk analysis module predicts the development of fire based on quantum sensor data. The early warning module sends an early warning signal when an abnormality is detected.

[0044] The transmission line wildfire warning and emergency training system includes a simulated fire scene module, a virtual environment simulation module, a training simulation module, a real-scene identification module and a real-time warning module. The simulated fire scene module reproduces the fire spread path and its impact on the line, the virtual environment simulation module dynamically simulates the development of fire risks, the training simulation module provides immersive emergency training, the real-scene identification module is used to identify and warn high-risk areas during inspections, and the real-time warning module provides warning prompts in high-risk areas.

[0045] The digital twin transmission line fire risk dynamic management system includes a virtual environment construction module, a data fusion module, a cognitive algorithm module, a strategy generation module, a simulated fire module and a resource optimization module. The virtual environment construction module creates a virtual digital twin of the transmission line, the data fusion module integrates geographical, meteorological and electrical multi-source data, the cognitive algorithm module uses historical data to optimize fire prediction, the strategy generation module provides response strategies for high-risk areas, the simulated fire module is used for fire simulation in a virtual environment, and the resource optimization module is used for early warning plans and resource allocation optimization.

[0046] The multi-node transmission line fire warning system of edge AI and low-orbit satellite includes edge AI node module, low-orbit satellite communication module, self-organizing network module, anomaly detection module, collaborative data processing module and real-time warning module. The edge AI node module is arranged along the transmission line to analyze the fire signal in real time. The low-orbit satellite communication module transmits data across regions. The self-organizing network module realizes self-organizing collaboration between data nodes. The anomaly detection module automatically triggers an early warning when a fire signal is detected. The collaborative data processing module shares information between multiple nodes. The real-time warning module realizes rapid fire early warning through node self-organization.

[0047] The transmission line fire risk distribution prediction system that integrates thermal imaging and deep learning includes a thermal imaging acquisition module, an image recognition module, a risk prediction module, a distribution map generation module, a meteorological analysis module and an early warning response module. The thermal imaging acquisition module collects heat source images through drones and satellites. The image recognition module identifies fire hotspots based on deep learning algorithms. The risk prediction module predicts the development of fires based on terrain, vegetation and other data. The distribution map generation module automatically generates high-precision distribution maps of risk areas. The meteorological analysis module corrects risk predictions based on real-time meteorological data. The early warning response module sends risk maps to the transmission line management system.

[0048] The AI-driven power load dynamic adjustment and fire prevention system includes a load monitoring module, a weather monitoring module, a dynamic adjustment module, a heat load reduction module, a fire prevention module and a risk prediction module. The load monitoring module monitors the power load of the transmission line in real time, the weather monitoring module monitors meteorological conditions, the dynamic adjustment module adaptively adjusts the line load according to the fire risk, the heat load reduction module reduces the heat generated by the cable under high-risk conditions, the fire prevention module achieves fire prevention and control by optimizing the power load, and the risk prediction module issues adjustment instructions when a high risk is predicted.

[0049] The transmission line fire spread path prediction system linked with intelligent fire-fighting equipment includes a path prediction module, a linkage control module, a fire-fighting equipment module, an isolation equipment module, a risk analysis module and an early warning module. The path prediction module predicts the spread direction of the fire based on a neural network algorithm. The linkage control module controls the activation of the fire-fighting and isolation equipment. The fire-fighting equipment module automatically extinguishes the fire when the fire spreads to the dangerous area. The isolation equipment module establishes a fire barrier during the peak of the fire. The risk analysis module continuously analyzes the spread risk. When the early warning module predicts a fire threat, it links the fire-fighting equipment.

[0050] The distributed quantum computing transmission line fire emergency simulation and optimization response system includes a quantum computing simulation module, a fire scenario generation module, an emergency response strategy module, an inspection optimization module, a resource allocation module and a data feedback module. The quantum computing simulation module simulates the spread of fire at high speed, the fire scenario generation module simulates the development of fire under different environmental conditions, the emergency response strategy module generates multi-dimensional optimization response strategies, the inspection optimization module dynamically adjusts the inspection and maintenance plans, the resource allocation module optimizes the allocation of fire prevention and control resources, and the data feedback module uses the simulation results to improve the subsequent prediction accuracy.

[0051] The risk assessment module in the environmental DNA transmission line wildfire risk assessment and early warning system adopts a new combination algorithm that combines the random forest algorithm, support vector machine (SVM) and hierarchical clustering algorithm. The algorithm first uses the random forest algorithm to preliminarily screen and classify the vegetation and microbial DNA feature data, and extracts subsets with potential wildfire risk-related characteristics. Then, the support vector machine (SVM) is used to further accurately divide these subsets and construct preliminary boundaries between high-risk and low-risk areas. Finally, the classified data is hierarchically analyzed through the hierarchical clustering algorithm to optimize the division of risk areas, so as to identify high-risk areas from vegetation and microbial DNA characteristics. At the same time, through the fusion of multi-dimensional biological community and environmental data, including but not limited to the deep fusion of temperature, humidity, wind speed environmental data with biological community data, weighted fusion and other methods are used to improve the accuracy of wildfire risk identification.

[0052] The risk analysis module in the quantum sensing transmission line fire remote monitoring system is based on a combination of quantum adaptive neural network algorithm, reinforcement learning (RL) algorithm and Kalman filtering algorithm. The quantum adaptive neural network algorithm adaptively adjusts the network structure and parameters according to the special properties of the quantum sensing signal to better adapt to the processing of quantum signals. The reinforcement learning (RL) algorithm continuously interacts with the environment to learn the optimal decision-making strategy under different quantum sensing signal states to optimize the response to small signal fluctuations. The Kalman filtering algorithm filters the quantum sensing signal to remove noise interference. These three algorithms work together to cope with small fluctuations in the quantum sensing signal and enhance the sensitivity and robustness of the fire prediction model.

[0053] The virtual environment simulation module in the power transmission line wildfire warning emergency training system adopts a simulation algorithm that combines convolutional neural network (CNN), generative adversarial network (GAN) and Markov decision process. The convolutional neural network (CNN) is used to extract image features in the fire spread process, including the shape, color and spread speed visual features of the flame. The generative adversarial network (GAN) is responsible for generating more realistic fire scenes. Through adversarial training of the generator and the discriminator, the generated scenes are closer to the actual fire in details and authenticity. The Markov decision process simulates the decision-making behavior of emergency personnel and its impact on the spread of fire under different environmental conditions and fire development stages.

[0054] The cognitive algorithm module in the digital twin transmission line fire risk dynamic management system is based on the algorithm framework of multimodal data fusion, combining the long short-term memory (LSTM) network, particle swarm optimization algorithm (PSO) and adaptive algorithm based on Bayesian reasoning. The long short-term memory (LSTM) network processes time series data, analyzes the trend of fire risk changes over time, and captures potential patterns in historical data. The particle swarm optimization algorithm (PSO) optimizes the model parameters and finds the optimal solution in the parameter space by simulating the foraging behavior of bird flocks to improve the accuracy of the model. The adaptive algorithm based on Bayesian reasoning continuously updates the prior probability of the model according to the newly acquired data to achieve adaptive adjustment of the model. These three algorithms form an adaptive dynamic control mechanism to adjust the risk model parameters in real time. During operation, by setting reasonable monitoring indicators and thresholds, parameter adjustment is triggered in time according to the amplitude and frequency of data changes to optimize the accuracy of fire prediction and the response speed of the system.

[0055] The anomaly detection module in the multi-node power transmission line fire warning system of edge AI and low-orbit satellite adopts a multi-layer hybrid algorithm, including genetic algorithm (GA), ant colony optimization algorithm (ACO) and multi-scale deep neural network. The genetic algorithm (GA) optimizes and selects the parameters of the anomaly detection model by simulating the biological evolution process and retains the optimal parameter combination. The ant colony optimization algorithm (ACO) uses the pheromone transmission principle of ants in the foraging process to find abnormal patterns in the data and improve the sensitivity to abnormal data. The multi-scale deep neural network analyzes the data from different scales, extracts multi-level feature information, and enhances the processing ability of complex data. Through these three algorithms, an adaptive anomaly detection mechanism is constructed to realize the joint optimization processing of data between different nodes. During the data transmission and processing process, a data quality assessment and correction mechanism is set up. According to the consistency and correlation of data between nodes, the algorithm parameters are adjusted to improve the recognition accuracy and response efficiency of fire signals.

[0056] The risk prediction module in the transmission line fire risk distribution prediction system integrating thermal imaging and deep learning adopts a multi-algorithm integration framework, combining the deep convolutional neural network (DCNN), the K nearest neighbor (KNN) algorithm and the gradient boosting decision tree (GBDT). The deep convolutional neural network (DCNN) extracts and classifies the thermal imaging data to identify areas where there may be fire risks. The K nearest neighbor (KNN) algorithm further determines the spread range of the risk by calculating the distance and similarity with the surrounding data points based on the data features around the identified area. The gradient boosting decision tree (GBDT) conducts a comprehensive analysis of multi-source data and optimizes the risk prediction results in combination with other environmental data and historical fire data. Through this combination, the fire distribution in high-risk areas is intelligently predicted and updated based on multi-source data. In the prediction process, a dynamic weight adjustment mechanism is set to adjust the weight of each algorithm in the prediction according to the reliability and timeliness of different data sources, thereby improving the reliability of fire distribution prediction.

[0057] The dynamic adjustment module in the AI-driven power load dynamic adjustment and fire prevention system combines the optimization algorithms of the adaptive neural fuzzy inference system (ANFIS), the variational autoencoder (VAE) and the Monte Carlo tree search algorithm. The adaptive neural fuzzy inference system (ANFIS) combines fuzzy logic and neural networks, and can process data with strong ambiguity such as power load and temperature. According to the input power system parameters and environmental conditions, it outputs a reasonable adjustment strategy. The variational autoencoder (VAE) reduces the dimension and extracts features of the power load data, mines the potential patterns in the data, and provides richer information for the adjustment strategy. The Monte Carlo tree search algorithm searches for the optimal solution in the decision space of power load adjustment through simulation. Through this optimization algorithm, real-time dynamic optimization and adjustment of the power load is realized. During operation, a real-time monitoring and feedback mechanism is set up to adjust the algorithm parameters in time according to the actual changes in cable temperature and load, so as to ensure that the cable load and temperature remain within a safe range under high-risk fire conditions, thereby effectively reducing the probability of fire.

[0058] The path prediction module in the transmission line fire spread path prediction system linked with intelligent fire fighting equipment adopts a fusion prediction model based on three algorithms, including long short-term memory network (LSTM), reinforcement learning (RL) and conditional random field (CRF) algorithm. The long short-term memory network (LSTM) is used to process the time series data of fire development to capture the trend and periodic characteristics of fire spread. The reinforcement learning (RL) algorithm learns what actions to take under different fire conditions through interaction with the environment to most accurately predict the spread path, and obtains corresponding rewards or penalties according to the prediction results, thereby continuously optimizing the prediction strategy. The conditional random field (CRF) algorithm takes into account the spatial correlation and contextual information in the process of fire spread, and further constrains and optimizes the prediction results. Through this fusion method, the intelligent prediction of the fire spread direction is realized. During the prediction process, real-time communication is carried out with the control system of the fire fighting equipment. According to the prediction results, the deployment and startup sequence of the fire fighting equipment are adjusted in time to ensure that the corresponding fire fighting equipment is linked in time at the early stage of fire spread to improve the prevention and control effect. At the same time, according to the feedback data of the actual fire situation, the algorithm parameters are continuously adjusted to improve the accuracy of the prediction.

[0059] Example 2

[0060] like Figure 2 As shown, the transmission line wildfire risk assessment and early warning method includes the following steps:

[0061] Step 1: Collect DNA samples of plants and microorganisms around the transmission line, analyze and process the collected data, generate a risk distribution map, adjust the risk assessment results, and provide early warning signals;

[0062] Step 2: Detect remote environmental change information, generate real-time data, and integrate it with historical data to predict the development of the fire;

[0063] Step 3: simulate fire scenarios and provide emergency training;

[0064] Step 4: Create a virtual digital twin of the transmission line, integrate multi-source data, provide response strategies for high-risk areas, and optimize early warning plans and resource allocation;

[0065] Step 5: Analyze fire signals in real time, transmit data across regions, realize self-organization and collaboration among data nodes, and share information;

[0066] Step 6: Collect heat source images, identify fire hotspots, predict fire development, and generate high-precision distribution maps of risk areas;

[0067] Step 7: monitor the power load of the transmission line in real time and adjust the line load adaptively according to the fire risk;

[0068] Step 8: predict the direction of fire spread and link fire fighting equipment;

[0069] Step 9: simulate the development of fire under different environmental conditions, generate multi-dimensional optimization response strategies, and feed back the simulation results to improve the subsequent prediction accuracy.

[0070] Preferably, in step 1, a novel combined algorithm combining a random forest algorithm, a support vector machine (SVM) and a hierarchical clustering algorithm is used. First, the vegetation and microbial DNA feature data are preliminarily screened and classified using the random forest algorithm to extract subsets with potential wildfire risk-related features. Then, the support vector machine (SVM) is used to further accurately divide these subsets to construct preliminary boundaries between high-risk and low-risk areas. Finally, the classified data is hierarchically analyzed by a hierarchical clustering algorithm to optimize the division of risk areas, so as to identify high-risk areas from vegetation and microbial DNA features. At the same time, multi-dimensional biome and environmental data are integrated;

[0071] The formula of the random forest algorithm is as follows:

[0072] The random forest algorithm integrates multiple decision trees, and the prediction result is the average or voting result of all tree results:

[0073]

[0074] Where T is the number of trees, h t (x) is the prediction result of the t-th tree for sample x, is the final prediction output, which represents the average prediction value obtained by integrating multiple decision tree models. To sum the prediction results of each decision tree, Factor for averaging;

[0075] The specific formula of the support vector machine is as follows:

[0076] Data classification is achieved by finding the optimal hyperplane, which satisfies the maximum boundary distance:

[0077]

[0078] Among them, min means minimization, w is the normal vector of the hyperplane used to define the direction of the boundary, b is the bias term that determines the position of the hyperplane in space, and y i is the sample label, x i is the sample feature vector, ||w|| is the square norm of the weight vector w;

[0079] The specific formula of the hierarchical clustering algorithm is as follows:

[0080] Hierarchical clustering creates a hierarchy of clusters by merging or splitting groups, typically using the following distance metric formula:

[0081]

[0082] Where X and Y are two data groups or clusters used to calculate the distance between them, x∈X and y∈Y represent the data points belonging to cluster x and cluster Y respectively, and d(x,y) is the distance measure between data points x and y.

[0083] Preferably, in step 2, based on the combined quantum adaptive neural network algorithm, reinforcement learning (RL) algorithm and Kalman filter algorithm, the quantum adaptive neural network algorithm adaptively adjusts the network structure and parameters according to the special properties of the quantum sensor signal to better adapt to the processing of the quantum signal, and the reinforcement learning (RL) algorithm learns the optimal decision-making strategy under different quantum sensor signal states by continuously interacting with the environment to optimize the response to small fluctuations of the signal. The Kalman filter algorithm filters the quantum sensor signal to remove noise interference. Through the cooperation of these three algorithms, the small fluctuations in the quantum sensor signal can be dealt with, and the sensitivity and robustness of the fire prediction model can be enhanced;

[0084] The quantum adaptive neural network algorithm is as follows:

[0085]

[0086] Among them, ψ(x) is the linear superposition of quantum states, α i is the adaptive weight, φ i (x) is the basis function of the i-th quantum state;

[0087] The Kalman filter algorithm formula is as follows:

[0088]

[0089] P k|k-1 =FP k-1|k-1 F T +Q

[0090] Among them, the first line of formula is the predicted state estimation, is the prior estimate of the state at time k, F is the state transfer matrix, is the k-1 state estimate, B is the control input matrix, u k is the control input vector at time k;

[0091] In the second line of the formula, Q is the process noise covariance, P k|k-1 is the prior error covariance matrix of the state estimate at time k, P k-1|k-1 is the state estimation error covariance matrix at time k-1, F T is the transposed matrix of the state transfer matrix.

[0092] Preferably, in step 3, a simulation algorithm combining a convolutional neural network (CNN), a generative adversarial network (GAN) and a Markov decision process is used, wherein the convolutional neural network (CNN) is used to extract image features during the fire spread process, and the generative adversarial network (GAN) is responsible for generating a more realistic fire scene. Through adversarial training of the generator and the discriminator, the generated scene is closer to the actual fire in terms of details and authenticity, and the Markov decision process simulates the decision-making behavior of emergency personnel and its impact on the spread of fire under different environmental conditions and fire development stages.

[0093] Preferably, in step 4, based on the algorithm framework of multimodal data fusion, the long short-term memory (LSTM) network, the particle swarm optimization algorithm (PSO) and the adaptive algorithm based on Bayesian reasoning are combined. The long short-term memory (LSTM) network processes the time series data, analyzes the trend of fire risk changes over time, and captures the potential patterns in the historical data. The particle swarm optimization algorithm (PSO) optimizes the model parameters, and finds the optimal solution in the parameter space by simulating the foraging behavior of bird flocks to improve the accuracy of the model. The adaptive algorithm based on Bayesian reasoning continuously updates the prior probability of the model according to the newly acquired data to achieve adaptive adjustment of the model. These three algorithms form an adaptive dynamic control mechanism to adjust the risk model parameters in real time. During operation, by setting reasonable monitoring indicators and thresholds, the parameter adjustment is triggered in time according to the amplitude and frequency of data changes, so as to optimize the accuracy of fire prediction and the system response speed.

[0094] Preferably, in step 5, a multi-layer hybrid algorithm is adopted, including a genetic algorithm (GA), an ant colony optimization algorithm (ACO) and a multi-scale deep neural network. The genetic algorithm (GA) optimizes and selects the parameters of the anomaly detection model by simulating the biological evolution process, and retains the optimal parameter combination. The ant colony optimization algorithm (ACO) uses the pheromone transmission principle during the foraging process of ants to find abnormal patterns in the data and improve the sensitivity to abnormal data. The multi-scale deep neural network analyzes the data from different scales, extracts multi-level feature information, and enhances the processing capability of complex data. An adaptive anomaly detection mechanism is constructed through these three algorithms to realize the joint optimization processing of data between different nodes. During the data transmission and processing, a data quality assessment and correction mechanism is set. According to the consistency and correlation of the data between nodes, the algorithm parameters are adjusted to improve the recognition accuracy and response efficiency of the fire signal.

[0095] Preferably, in step 6, a multi-algorithm integration framework is used to combine a deep convolutional neural network (DCNN), a K-nearest neighbor (KNN) algorithm and a gradient boosting decision tree (GBDT), wherein the deep convolutional neural network (DCNN) performs feature extraction and classification on thermal imaging data to identify areas where fire risks may exist, and the K-nearest neighbor (KNN) algorithm further determines the spread range of the risk by calculating the distance and similarity with surrounding data points based on the data features around the identified area, and the gradient boosting decision tree (GBDT) performs a comprehensive analysis of multi-source data, and optimizes the risk prediction results in combination with other environmental data and historical fire data. Through this combination, the fire distribution in high-risk areas is intelligently predicted and updated based on multi-source data. During the prediction process, a dynamic weight adjustment mechanism is set to adjust the weight of each algorithm in the prediction according to the reliability and timeliness of different data sources, thereby improving the reliability of fire distribution prediction.

[0096] Preferably, in step 7, by combining the optimization algorithm of adaptive neural fuzzy inference system (ANFIS), variational autoencoder (VAE) and Monte Carlo tree search algorithm, the adaptive neural fuzzy inference system (ANFIS) combines fuzzy logic and neural network, and can process data with strong fuzziness such as power load and temperature, and output reasonable adjustment strategies according to the input power system parameters and environmental conditions. The variational autoencoder (VAE) performs dimension reduction and feature extraction on the power load data, mines the potential patterns in the data, and provides richer information for the adjustment strategy. The Monte Carlo tree search algorithm searches for the optimal solution in the decision space of power load adjustment through simulation search. Through this optimization algorithm, real-time dynamic optimization adjustment of the power load is realized. During the operation, a real-time monitoring and feedback mechanism is set, and the algorithm parameters are adjusted in time according to the actual changes in cable temperature and load to ensure that the cable load and temperature remain within a safe range under high fire risk conditions, thereby effectively reducing the probability of fire.

[0097] Preferably, in step 8, a fusion prediction model based on three algorithms is adopted, including a long short-term memory network (LSTM), a reinforcement learning (RL) and a conditional random field (CRF) algorithm. The long short-term memory network (LSTM) is used to process the time series data of the fire development, capture the trend and periodic characteristics of the fire spread, and learn what actions to take under different fire conditions through interaction with the environment through the reinforcement learning (RL) algorithm to most accurately predict the spread path, and obtain corresponding rewards or penalties according to the quality of the prediction results, thereby continuously optimizing the prediction strategy. The conditional random field (CRF) algorithm takes into account the spatial correlation and contextual information in the process of fire spread, and further constrains and optimizes the prediction results. Through this fusion method, intelligent prediction of the direction of fire spread is achieved. During the prediction process, real-time communication is carried out with the control system of the fire-fighting equipment, and the deployment and startup sequence of the fire-fighting equipment is adjusted in time according to the prediction results to ensure that the corresponding fire-fighting equipment is linked in time at the early stage of the fire spread to improve the prevention and control effect. At the same time, according to the feedback data of the actual fire situation, the algorithm parameters are continuously adjusted to improve the accuracy of the prediction.

[0098] The key algorithms mentioned in Example 1 are analyzed in detail below, including their core mathematical formulas and explanations:

[0099] 1. Random Forest (RF)

[0100] Core formula: The random forest algorithm integrates multiple decision trees, and the prediction result is the average or voting result of all tree results:

[0101]

[0102] Where T is the number of trees, ht (x) is the prediction result of the t-th tree for sample x, is the final prediction output, which represents the average prediction value obtained by integrating multiple decision tree models. To sum the prediction results of each decision tree, The factor for averaging.

[0103] Random forests train multiple decision trees to reduce the overfitting risk of a single tree and improve model stability for classification and regression tasks.

[0104] 2. Support Vector Machine (SVM)

[0105] Core formula: SVM achieves data classification by finding the optimal hyperplane, which satisfies the maximum boundary distance:

[0106]

[0107] Among them, min means minimization, w is the normal vector of the hyperplane used to define the direction of the boundary, b is the bias term that determines the position of the hyperplane in space, and y is i is the sample label, x i is the sample feature vector, ||w|| 2 is the square norm of the weight vector w.

[0108] SVM distinguishes samples of different categories by constructing a hyperplane. It is suitable for processing high-dimensional data and has good classification effect.

[0109] 3. Hierarchical Clustering

[0110] Core formula: Hierarchical clustering creates a cluster hierarchy by merging or splitting groups, usually using the following distance metric formula:

[0111]

[0112] Or the average distance of the group.

[0113] Hierarchical clustering does not require a preset number of clusters. It forms a cluster tree by gradually merging or splitting groups, which is suitable for analyzing the internal hierarchical structure of data.

[0114] Where X and Y are two data groups or clusters used to calculate the distance between them, x∈X and y∈Y represent the data points belonging to cluster x and cluster Y respectively, and d(x,y) is the distance measure between data points x and y.

[0115] 4. Quantum Adaptive Neural Network Algorithm

[0116] Core formula: This algorithm uses the superposition of quantum states and the adaptive update mechanism. The typical formula is as follows:

[0117]

[0118] Among them, ψ(x) is the linear superposition of quantum states, α i is the adaptive weight, φ i (x) is the basis function of the ith quantum state.

[0119] Quantum neural networks process massive amounts of data through quantum properties and adaptively adjust model parameters with high sensitivity.

[0120] 5. Kalman Filter

[0121] Core formula: Recursive process of prediction and update:

[0122]

[0123] P k|k-1 =FP k-1|k-1 F T +Q

[0124] Among them, the first line of formula is the predicted state estimation, is the prior estimate of the state at time k, F is the state transfer matrix, is the k-1 state estimate, B is the control input matrix, u k is the control input vector at time k;

[0125] In the second line of the formula, Q is the process noise covariance, P k|k-1 is the prior error covariance matrix of the state estimate at time k, P k-1|k-1 is the state estimation error covariance matrix at time k-1, F T is the transposed matrix of the state transfer matrix.

[0126] 6. Convolutional Neural Network (CNN)

[0127] Core formula: The output calculation formula of the convolutional layer is:

[0128]

[0129] Among them, x is the input feature, w is the convolution kernel weight, b is the bias, and f is the activation function. is the output of the kth convolution kernel at position (i, j), x i+m,j+n is the pixel value of the input feature map, is the weight of the kth convolution kernel at position (m,n), b (k) is the bias term of the kth convolution kernel.

[0130] CNN extracts local feature information of images through convolution operations and is the mainstream algorithm for computer vision tasks.

[0131] 7. Generative Adversarial Networks (GANs)

[0132] Core formula: GAN consists of a generator G and a discriminator D, and its optimization goal is:

[0133]

[0134] Where G is the generator and D is the discriminator. is the adversarial target between the generator and the discriminator, is the expected value, x~p data (x) is the distribution of real data, logD(x) is the logarithmic value of the output probability of the discriminator for the real data, z~p z (z) is random noise, G(z) is a pseudo sample generated by the generator based on the random noise z, and log(1-D(G(z))) is the logarithmic value of the output probability of the discriminator for the generated data.

[0135] GAN generates synthetic data that is close to the real data distribution through the game between the generator and the discriminator.

[0136] 8. Long Short-Term Memory (LSTM)

[0137] Core formula: The output calculation formula of the LSTM network is:

[0138] h t =o t ⊙tanh(c t )

[0139] Among them, t is the output gate, c t is the cell state, h t is the hidden state at time t, tanh is the hyperbolic tangent activation function, c t is the cell state at time t.

[0140] LSTM is a variant of RNN, which can effectively solve the gradient vanishing problem of long sequences and is suitable for time series prediction.

[0141] 9. Ant Colony Optimization (ACO)

[0142] Core formula: The pheromone update formula of the ant colony on the path is:

[0143]

[0144] Where ρ is the pheromone evaporation coefficient, represents the pheromone increment of the kth ant on the path, (1-ρ)τ ij Indicates the amount of pheromone remaining after evaporation. Added section for pheromones.

[0145] ACO solves combinatorial optimization problems by simulating the foraging process of ants.

[0146] 10. Gradient Boosted Decision Tree (GBDT)

[0147] Core formula: GBDT fits the residual at the mth iteration:

[0148]

[0149] Among them, L(y,F(x)) is the loss function, is the residual of the i-th sample in the m-th iteration, F(x) is the predicted value of the model on x, and x i is the eigenvalue of the i-th sample, The loss function L is the predicted value F(x i ) is used to calculate the residual.

[0150] GBDT gradually optimizes the decision tree prediction error through gradient descent.

[0151] 11. Monte Carlo Tree Search (MCTS)

[0152] Core formula: The formula of MCTS is based on maximizing the expected reward:

[0153]

[0154] Among them, R i is the reward of the ith simulation, N(s,a) is the number of visits to the state-action pair (s,a), Q(s,a) is the average reward value for executing action a in state s, is the reward value obtained by executing action a for the i-th time in state s.

[0155] MCTS solves policy optimization problems through simulation and tree search.

[0156] The specific application logic steps of each module and algorithm in the present invention are described as follows.

[0157] 1. Logical steps of the risk assessment module: First, the random forest algorithm is used to preliminarily screen and classify the vegetation and microbial DNA feature data, and the subsets with potential wildfire risk-related characteristics are extracted. Then, the support vector machine (SVM) is used to further accurately divide these subsets and construct the preliminary boundaries of high-risk and low-risk areas. Finally, the classified data is hierarchically analyzed through the hierarchical clustering algorithm.

[0158] 2. Logical steps of the risk analysis module: The quantum adaptive neural network algorithm adaptively adjusts the network structure and parameters according to the special properties of the quantum sensor signal to better adapt to the processing of quantum signals. The reinforcement learning (RL) algorithm learns the optimal decision-making strategy under different quantum sensor signal states by continuously interacting with the environment to optimize the response to small signal fluctuations. The Kalman filter algorithm filters the quantum sensor signal to remove noise interference.

[0159] 3. Logical steps of the virtual environment simulation module: The convolutional neural network (CNN) is used to extract image features during the fire spread process, including the shape, color and spread speed visual features of the flame, and the generative adversarial network (GAN) is responsible for generating more realistic fire scenes through adversarial training of the generator and the discriminator.

[0160] 4. Logical steps of cognitive algorithm module: The long short-term memory (LSTM) network processes time series data, analyzes the trend of fire risk changes over time, and captures potential patterns in historical data. The particle swarm optimization algorithm (PSO) optimizes model parameters and finds the optimal solution in the parameter space by simulating the foraging behavior of bird flocks to improve the accuracy of the model. The adaptive algorithm based on Bayesian reasoning continuously updates the prior probability of the model according to the newly acquired data to achieve adaptive adjustment of the model.

[0161] 5. Logical steps of the anomaly detection module: The genetic algorithm (GA) optimizes and selects the parameters of the anomaly detection model by simulating the biological evolution process and retains the optimal parameter combination. The ant colony optimization algorithm (ACO) uses the pheromone transmission principle of ants in the foraging process to find abnormal patterns in the data and improve the sensitivity to abnormal data. The multi-scale deep neural network analyzes the data from different scales, extracts multi-level feature information, and enhances the ability to process complex data.

[0162] 6. Logical steps of the risk prediction module: The deep convolutional neural network (DCNN) extracts and classifies the thermal imaging data to identify areas where fire risks may exist. The K-nearest neighbor (KNN) algorithm further determines the spread of the risk by calculating the distance and similarity with the surrounding data points based on the data features around the identified area. The gradient boosting decision tree (GBDT) conducts a comprehensive analysis of multi-source data and combines it with other environmental data and historical fire data.

[0163] 7. Logical steps of dynamic adjustment module: The adaptive neuro-fuzzy inference system (ANFIS) combines fuzzy logic and neural networks, and can process data with strong ambiguity such as power load and temperature. According to the input power system parameters and environmental conditions, it outputs a reasonable adjustment strategy. The variational autoencoder (VAE) reduces the dimension and extracts features of the power load data, mines the potential patterns in the data, and provides richer information for the adjustment strategy. The Monte Carlo tree search algorithm searches through simulation.

[0164] 8. Logical steps of the path prediction module: Use the long short-term memory network (LSTM) to process the time series data of fire development, capture the trend and periodic characteristics of fire spread, and use the reinforcement learning (RL) algorithm to interact with the environment to learn what actions to take under different fire conditions to most accurately predict the spread path.

[0165] The module composition and description of each system in the above embodiments are as follows.

[0166] 1. Hardware composition of environmental DNA transmission line wildfire risk assessment and early warning system:

[0167] Environmental DNA collection module, data analysis module, risk assessment module, vegetation distribution detection module, meteorological integration module and early warning response module.

[0168] The environmental DNA collection module arranges sampling equipment along the transmission lines and regularly collects DNA samples of plants and microorganisms around the lines. The data analysis module processes the sampling data through gene sequencing technology to identify the density of flammable vegetation and changes in biological communities in the area. The risk assessment module combines biological community data with environmental information to form a fire risk map. The vegetation distribution detection module generates a vegetation distribution heat map for high-risk areas. The meteorological integration module adjusts the fire risk assessment results through real-time meteorological data. The early warning response module provides fire warning signals for high-risk areas based on risk prediction results.

[0169] 2. The quantum sensing transmission line fire remote monitoring system includes the quantum sensing module hardware components:

[0170] Remote data transmission module, ultra-high sensitivity detection module, cloud processing module, risk analysis module and early warning module.

[0171] The quantum sensing module is installed on the transmission tower to detect ultra-long-range tiny temperature differences and humidity changes. The remote data transmission module transmits the detection data through the quantum computing network. The ultra-high sensitivity detection module generates real-time data when environmental conditions change. The cloud processing module integrates the quantum sensor data with historical fire data. The risk analysis module predicts the development of the fire based on the quantum sensor data. The early warning module sends an early warning signal when an abnormality is detected.

[0172] 3. Hardware composition of the power transmission line wildfire early warning and emergency training system:

[0173] High-simulation fire scene module, virtual environment simulation module, training simulation module, real-scene identification module and real-time warning module.

[0174] The high-simulation fire scene module reproduces the fire spread path and its impact on the line. The virtual environment simulation module dynamically simulates the development of fire risks. The training simulation module provides immersive emergency training. The real-scene identification module is used for inspection and high-risk area identification and warning. The real-time warning module provides warning prompts in high-risk areas.

[0175] 4. Hardware composition of the digital twin transmission line fire risk dynamic management system:

[0176] Virtual environment construction module, data fusion module, cognitive algorithm module, strategy generation module, fire simulation module and resource optimization module.

[0177] The virtual environment construction module creates a virtual digital twin of the transmission line, the data fusion module integrates geographical, meteorological, and electrical multi-source data, the cognitive algorithm module uses historical data to optimize fire prediction, the strategy generation module provides response strategies for high-risk areas, the simulated fire module is used for fire simulation in a virtual environment, and the resource optimization module is used for early warning plans and resource allocation optimization.

[0178] 5. Hardware composition of the multi-node power transmission line fire warning system based on edge AI and low-orbit satellites:

[0179] Edge AI node module, low-orbit satellite communication module, self-organizing network module, anomaly detection module, collaborative data processing module and real-time warning module.

[0180] Edge AI node modules are arranged along the transmission lines to analyze fire signals in real time. The low-orbit satellite communication module transmits data across regions. The self-organizing network module realizes self-organizing collaboration between data nodes. The anomaly detection module automatically triggers an early warning when a fire signal is detected. The collaborative data processing module shares information between multiple nodes. The real-time early warning module realizes rapid fire early warning through node self-organization.

[0181] 6. Hardware composition of the power transmission line fire risk distribution prediction system integrating thermal imaging and deep learning:

[0182] Thermal imaging acquisition module, image recognition module, risk prediction module, distribution map generation module, meteorological analysis module and early warning response module.

[0183] The thermal imaging acquisition module collects heat source images through drones and satellites. The image recognition module identifies fire hotspots based on deep learning algorithms. The risk prediction module predicts the development of fires based on terrain, vegetation and other data. The distribution map generation module automatically generates high-precision distribution maps of risk areas. The meteorological analysis module corrects risk predictions based on real-time meteorological data. The early warning response module sends the risk map to the transmission line management system.

[0184] 7. AI-driven dynamic power load adjustment and fire prevention system hardware composition:

[0185] Load monitoring module, weather monitoring module, dynamic adjustment module, heat load reduction module, fire prevention module and risk prediction module.

[0186] The load monitoring module monitors the power load of the transmission line in real time, the weather monitoring module monitors meteorological conditions, the dynamic adjustment module adaptively adjusts the line load according to the fire risk, the heat load reduction module reduces the heat generated by the cable under high-risk conditions, the fire prevention module achieves fire prevention and control by optimizing the power load, and the risk prediction module issues adjustment instructions when a high risk is predicted.

[0187] 8. Self-learning mechanism module hardware composition:

[0188] Path prediction module, linkage control module, fire extinguishing equipment module, isolation equipment module, risk analysis module and early warning module.

[0189] The path prediction module predicts the spread direction of the fire based on the neural network algorithm. The linkage control module controls the activation of the fire extinguishing and isolation equipment. The fire extinguishing equipment module automatically extinguishes the fire when the fire spreads to the dangerous area. The isolation equipment module establishes a fire barrier during the peak of the fire. The risk analysis module continuously analyzes the spread risk. When the early warning module predicts a fire threat, it links the fire-fighting equipment.

[0190] 9. Hardware composition of the distributed quantum computing transmission line fire emergency simulation and optimized response system:

[0191] Quantum computing simulation module, fire scenario generation module, emergency response strategy module, inspection optimization module, resource allocation module and data feedback module.

[0192] The quantum computing simulation module simulates the spread of fire at high speed, the fire scenario generation module simulates the development of fire under different environmental conditions, the emergency response strategy module generates multi-dimensional optimization response strategies, the inspection optimization module dynamically adjusts the inspection and maintenance plans, the resource allocation module optimizes the allocation of fire prevention and control resources, and the data feedback module uses the simulation results to improve the accuracy of subsequent predictions.

[0193] A computer device comprising:

[0194] A processor; and a memory configured to store machine-readable instructions, which, when executed by the processor, execute the above-mentioned power transmission line wildfire risk assessment and early warning method.

[0195] A storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned power transmission line wildfire risk assessment and early warning method.

[0196] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0197] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Transmission line wildfire risk assessment and early warning system, characterized by: include: Environmental DNA transmission line wildfire risk assessment and early warning system, which is used to collect, analyze and process transmission line environmental information, generate risk distribution maps, adjust risk assessment results, and provide early warning signals; Quantum sensing transmission line fire remote monitoring system, used to detect remote environmental change information, generate real-time data, and integrate it with historical data to predict the development of fire; A digital twin transmission line fire risk dynamic management system is set up to create a virtual digital twin of the transmission line, set monitoring indicators and thresholds for fire risks, provide response strategies for high-risk areas based on monitoring indicators and thresholds, integrate multi-source data, dynamically adjust monitoring indicators and thresholds for fire risks based on the amplitude and frequency of data changes, and optimize early warning plans and resource allocation; The multi-node power transmission line fire warning system based on edge AI and low-orbit satellites is used to detect abnormal data along the transmission line, transmit abnormal data across regions, analyze abnormal data at different scales between data nodes, and self-organize and collaborate; A transmission line fire risk distribution prediction system that integrates thermal imaging and deep learning to collect heat source images, identify fire hotspots, predict fire development, and generate a distribution map of risk areas; AI-driven power load dynamic adjustment system, which is used to monitor the power load of transmission lines in real time, reduce the dimension and extract features of power load data, and adaptively adjust the line load according to the set fire risk threshold; The intelligent fire-fighting equipment linkage system is used to predict the direction of fire spread and link fire-fighting equipment based on the above-mentioned risk area distribution map.

2. The power transmission line wildfire risk assessment and early warning system according to claim 1 is characterized in that: The environmental DNA transmission line wildfire risk assessment and early warning system includes an environmental DNA acquisition module, a data analysis module, a risk assessment module, a vegetation distribution detection module, a meteorological integration module and an early warning response module. The environmental DNA acquisition module arranges sampling equipment along the transmission line and regularly collects DNA samples of plants and microorganisms around the line. The data analysis module processes the sampling data through gene sequencing technology to identify the density of flammable vegetation and changes in biological communities in the area. The risk assessment module combines biological community data with environmental information to form a fire risk map. The vegetation distribution detection module generates a vegetation distribution heat map of the risk area. The meteorological integration module adjusts the fire risk assessment results through real-time meteorological data. The early warning response module provides a fire early warning signal for the risk area based on the risk prediction results.

3. The power transmission line wildfire risk assessment and early warning system according to claim 1, characterized in that: The quantum sensing transmission line fire remote monitoring system includes a quantum sensing module, a remote data transmission module, a sensitivity detection module, a cloud processing module, a risk analysis module and an early warning module. The quantum sensing module is installed on the transmission tower to detect remote temperature differences and humidity changes. The remote data transmission module transmits detection data through a quantum computing network. The sensitivity detection module generates real-time data when environmental conditions change. The cloud processing module integrates quantum sensor data with historical fire data. The risk analysis module predicts the development of fire based on quantum sensor data. When an abnormality is detected, the early warning module sends an early warning signal.

4. The power transmission line wildfire risk assessment and early warning system according to claim 1, characterized in that: It also includes a transmission line wildfire warning and emergency training system, which includes a simulated fire scene module, a virtual environment simulation module, a training simulation module, a real-scene identification module and a real-time warning module. The simulated fire scene module reproduces the fire spread path and the impact on the line, the virtual environment simulation module dynamically simulates the development of fire risks, the training simulation module provides immersive emergency training, the real-scene identification module is used for inspection and high-risk area identification and warning, and the real-time warning module provides warning prompts in high-risk areas.

5. The power transmission line wildfire risk assessment and early warning system according to claim 1, characterized in that: The digital twin power transmission line fire risk dynamic management system includes a virtual environment construction module, a data fusion module, a cognitive algorithm module, a strategy generation module, a simulated fire module and a resource optimization module. The virtual environment construction module creates a virtual digital twin of the transmission line, the data fusion module integrates geographical, meteorological and electrical multi-source data, the cognitive algorithm module uses historical data to optimize fire prediction, the strategy generation module provides response strategies for high-risk areas, the simulated fire module is used for fire simulation in a virtual environment, and the resource optimization module is used for early warning plans and resource allocation optimization.

6. The power transmission line wildfire risk assessment and early warning system according to claim 1, characterized in that: The multi-node power transmission line fire warning system of edge AI and low-orbit satellite includes an edge AI node module, a low-orbit satellite communication module, a self-organizing network module, an anomaly detection module, a collaborative data processing module and a real-time warning module, wherein the edge AI node module is arranged along the power transmission line to detect abnormal data along the power transmission line, the low-orbit satellite communication module transmits abnormal data across regions, the collaborative data processing module collaboratively calculates abnormal data at different scales between data nodes, the anomaly detection module automatically triggers an early warning when a fire signal is detected, the self-organizing network module realizes self-organizing collaboration between data nodes, and the real-time warning module realizes rapid fire early warning through node self-organization.

7. The power transmission line wildfire risk assessment and early warning system according to claim 1, characterized in that: The transmission line fire risk distribution prediction system that integrates thermal imaging and deep learning includes a thermal imaging acquisition module, an image recognition module, a risk prediction module, a distribution map generation module, a meteorological analysis module and an early warning response module. The thermal imaging acquisition module collects heat source images through drones and satellites, the image recognition module identifies fire hotspots based on deep learning algorithms, the risk prediction module predicts the development of fire in combination with terrain and vegetation data, the distribution map generation module automatically generates a risk area distribution map, the meteorological analysis module corrects the risk prediction in combination with real-time meteorological data, and the early warning response module sends the risk area distribution map to the intelligent fire linkage system.

8. The power transmission line wildfire risk assessment and early warning system according to claim 1, characterized in that: The AI-driven power load dynamic adjustment system includes a load monitoring module, a weather monitoring module, a dynamic adjustment module, a heat load reduction module, a fire prevention module and a risk prediction module. The load monitoring module monitors the power load of the transmission line in real time, the weather monitoring module monitors meteorological conditions, the dynamic adjustment module adaptively adjusts the line load according to the set fire risk threshold, the heat load reduction module adjusts the cable heating value according to the cable temperature, the fire prevention module realizes fire prevention and control by optimizing the power load, and the risk prediction module issues an adjustment instruction when it predicts that the power load reaches the fire risk threshold.

9. The power transmission line wildfire risk assessment and early warning system according to claim 1, characterized in that: The intelligent fire fighting equipment linkage system includes a path prediction module, a linkage control module, a fire fighting equipment module, an isolation equipment module, a risk analysis module and an early warning module. The path prediction module predicts the spread direction of the fire based on the risk area distribution map and the neural network algorithm. The linkage control module controls the activation of the fire fighting and isolation equipment. The fire fighting equipment module automatically extinguishes the fire when the fire spreads to the dangerous area. The isolation equipment module establishes a fire barrier during the peak of the fire. The risk analysis module continuously analyzes the spread risk. When the early warning module predicts a fire threat, it links the fire fighting equipment.

10. The power transmission line wildfire risk assessment and early warning system according to claim 1, characterized in that: It also includes a distributed quantum computing transmission line fire emergency simulation and optimization response system, which includes a quantum computing simulation module, a fire scenario generation module, an emergency response strategy module, an inspection optimization module, a resource allocation module and a data feedback module. The quantum computing simulation module simulates the spread of fire, the fire scenario generation module simulates the development of fire under different environmental conditions, the emergency response strategy module generates a multi-dimensional optimization response strategy, the inspection optimization module dynamically adjusts the inspection and maintenance plan, the resource allocation module optimizes the allocation of fire prevention and control resources, and the data feedback module feeds back the simulation results.

11. The risk assessment and early warning method of the power transmission line wildfire risk assessment and early warning system according to claim 1, characterized in that The steps include: Collect DNA samples of plants and microorganisms around power transmission lines, analyze and process the collected data, generate risk distribution maps, adjust risk assessment results, and provide early warning signals; Detect remote environmental change information, generate real-time data, and integrate it with historical data to predict fire development; Create a virtual digital twin of the transmission line, set monitoring indicators and thresholds for fire risks, provide response strategies for high-risk areas based on the monitoring indicators and thresholds, integrate multi-source data, dynamically adjust the monitoring indicators and thresholds for fire risks based on the amplitude and frequency of data changes, and optimize early warning plans and resource allocation; Detect abnormal data along the transmission line, transmit abnormal data across regions, analyze abnormal data at different scales between data nodes and self-organize and collaborate; Collect heat source images, identify fire hotspots, predict fire development, and generate risk area distribution maps; Real-time monitoring of the power load of the transmission line, dimension reduction and feature extraction of the power load data, and adaptive adjustment of the line load according to the set fire risk threshold; Based on the above risk area distribution map, the direction of fire spread is predicted and fire-fighting equipment is linked.

12. The method for assessing and warning the risk of wildfires on power transmission lines according to claim 11, characterized in that: In the DNA sample collection step, a combined algorithm of a random forest algorithm, a support vector machine and a hierarchical clustering algorithm is adopted. First, the random forest algorithm is used to preliminarily screen and classify the vegetation and microbial DNA feature data, and a subset with potential wildfire risk-related characteristics is extracted. Then, the support vector machine is used to further divide the above subset to construct the preliminary boundaries of high-risk and low-risk areas. Finally, the hierarchical clustering algorithm is used to perform a hierarchical analysis on the classified data, optimize the division of risk areas, identify high-risk areas from vegetation and microbial DNA characteristics, and at the same time, integrate multidimensional biological community and environmental data.

13. The method for assessing and warning the risk of wildfires on power transmission lines according to claim 12, characterized in that: The formula of the random forest algorithm is as follows: The random forest algorithm integrates multiple decision trees, and the prediction result is the average or voting result of all tree results: Where T is the number of trees, h t (x) is the prediction result of the t-th tree for sample x, is the final prediction output, which represents the average prediction value obtained by integrating multiple decision tree models. To sum the prediction results of each decision tree, Factor for averaging; The specific formula of the support vector machine is as follows: Data classification is achieved by finding the optimal hyperplane, which satisfies the maximum boundary distance: Among them, min means minimization, w is the normal vector of the hyperplane used to define the direction of the boundary, b is the bias term that determines the position of the hyperplane in space, and y i is the sample label, x i is the sample feature vector, ||w|| 2 is the square norm of the weight vector w; The specific formula of the hierarchical clustering algorithm is as follows: Hierarchical clustering creates a hierarchy of clusters by merging or splitting groups, typically using the following distance metric formula: Where X and Y are two data groups or clusters used to calculate the distance between them, x∈X and y∈Y represent the data points belonging to cluster x and cluster Y respectively, and d(x,y) is the distance measure between data points x and y.

14. The method for assessing and warning the risk of wildfires on power transmission lines according to claim 11, characterized in that: In the step of detecting remote environmental change information, based on the combined quantum adaptive neural network algorithm, reinforcement learning algorithm and Kalman filtering algorithm, the quantum adaptive neural network algorithm adaptively adjusts the network structure and parameters according to the quantum sensor signal, the reinforcement learning algorithm learns the optimal decision-making strategy under different quantum sensor signal states through interaction with the environment, and optimizes the response method to small signal fluctuations, and the Kalman filtering algorithm filters the quantum sensor signal to remove noise interference.

15. The method for assessing and warning the risk of wildfires on power transmission lines according to claim 14, characterized in that: The quantum adaptive neural network algorithm is as follows: Among them, ψ(x) is the linear superposition of quantum states, α i is the adaptive weight, φ i (x) is the basis function of the i-th quantum state; The Kalman filter algorithm formula is as follows: Among them, the first line of formula is the predicted state estimation, is the prior estimate of the state at time k, F is the state transfer matrix, is the k-1 state estimate, B is the control input matrix, u k is the control input vector at time k; In the second line of the formula, Q is the process noise covariance, P k|k-1 is the prior error covariance matrix of the state estimate at time k, P k-1|k-1 is the state estimation error covariance matrix at time k-1, F T is the transposed matrix of the state transfer matrix.

16. The method for assessing and warning the risk of wildfires on power transmission lines according to claim 11, characterized in that: It also includes simulating fire scenarios, providing emergency training steps, and using a simulation algorithm that combines convolutional neural networks, generative adversarial networks, and Markov decision processes. Convolutional neural networks are used to extract image features during the spread of fire. Generative adversarial networks generate fire scenarios through adversarial training of generators and discriminators. The Markov decision process simulates the decision-making behavior of emergency personnel and its impact on the spread of fire under different environmental conditions and fire development stages.

17. The method for assessing and warning the risk of wildfires on power transmission lines according to claim 11, characterized in that: In the step of creating a dynamic adjustment of a virtual digital twin of a transmission line, a long short-term memory network, a particle swarm optimization algorithm, and an adaptive algorithm based on Bayesian reasoning are combined based on an algorithm framework of multimodal data fusion. The long short-term memory network processes time series data, analyzes the trend of fire risk changes over time, and captures potential patterns in historical data. The particle swarm optimization algorithm optimizes model parameters and finds the optimal solution in the parameter space by simulating the foraging behavior of bird flocks. The adaptive algorithm based on Bayesian reasoning updates the prior probability of the model according to the newly acquired data. During operation, by setting monitoring indicators and thresholds, parameter adjustments are triggered in a timely manner according to the amplitude and frequency of data changes, thereby optimizing the accuracy of fire prediction and the response speed of the system.

18. The method for assessing and warning the risk of wildfires on power transmission lines according to claim 11, characterized in that: In the self-organizing and collaborative step of detecting abnormal data along the transmission line, a multi-layer hybrid algorithm is adopted, including a genetic algorithm, an ant colony optimization algorithm and a multi-scale deep neural network. The genetic algorithm optimizes and selects the parameters of the abnormal detection model by simulating the biological evolution process and retains the optimal parameter combination. The ant colony optimization algorithm uses the pheromone transmission principle during the foraging process of ants to find abnormal patterns in the data. The multi-scale deep neural network analyzes the data from different scales and extracts multi-level feature information. In the process of data transmission and processing, a data quality assessment and correction mechanism is set to adjust the algorithm parameters according to the consistency and correlation of the data between nodes.

19. The method for assessing and warning the risk of wildfires on power transmission lines according to claim 11, characterized in that: In the step of collecting heat source images to generate a risk distribution map, a multi-algorithm integration framework is used to combine a deep convolutional neural network, a K-nearest neighbor algorithm and a gradient boosting decision tree, wherein the deep convolutional neural network performs feature extraction and classification on thermal imaging data to identify areas with fire risks, and the K-nearest neighbor algorithm further determines the spread of the risk by calculating the distance and similarity with surrounding data points based on the data features around the identified area, and the gradient boosting decision tree performs a comprehensive analysis of multi-source data, and optimizes the risk prediction results in combination with other environmental data and historical fire data. During the prediction process, a dynamic weight adjustment mechanism is set to adjust the weight of each algorithm in the prediction according to the reliability and timeliness of different data sources.

20. The method for assessing and warning the risk of wildfires on power transmission lines according to claim 11, characterized in that: In the power load detection and adjustment step, by combining the optimization algorithm of the adaptive neural fuzzy inference system, the variational autoencoder and the Monte Carlo tree search algorithm, the adaptive neural fuzzy inference system combines fuzzy logic and neural network to process power load and temperature data, and outputs a reasonable adjustment strategy according to the input power system parameters and environmental conditions. The variational autoencoder reduces the dimension and extracts features of the power load data to mine the potential patterns in the data. The Monte Carlo tree search algorithm searches for the optimal solution in the decision space of power load adjustment through simulation search. During the operation, a real-time monitoring and feedback mechanism is set to adjust the algorithm parameters according to the actual changes in cable temperature and load.

21. The method for assessing and warning the risk of wildfires on power transmission lines according to claim 11, characterized in that: In the step of linking fire-fighting equipment, a fusion prediction model based on three algorithms is adopted, including long short-term memory network, reinforcement learning and conditional random field algorithm. The long short-term memory network is used to process the time series data of fire development, capture the trend and periodic characteristics of fire spread, and learn what actions to take under different fire conditions through interaction with the environment through the reinforcement learning algorithm to most accurately predict the spread path. According to the good or bad prediction results, corresponding rewards or penalties are obtained to optimize the prediction strategy. The conditional random field algorithm takes into account the spatial correlation and contextual information in the process of fire spread, and further constrains and optimizes the prediction results. During the prediction process, real-time communication is carried out with the control system of the fire-fighting equipment, and the deployment and startup sequence of the fire-fighting equipment are adjusted in time according to the prediction results. At the same time, the algorithm parameters are continuously adjusted according to the feedback data of the actual fire situation.

22. A computer device, characterized in that: include: processor; and a memory configured to store machine-readable instructions, which, when executed by the processor, execute the power transmission line wildfire risk assessment and early warning method as described in any one of claims 11-21.

23. A storage medium, characterized in that: The storage medium stores a computer program, and the computer program is executed by a processor as the power transmission line wildfire risk assessment and early warning method according to any one of claims 11-21.

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