Power transmission line tree flash hidden danger monitoring method based on multi-sensor fusion

Abstract

The invention relates to the technical field of power system safety monitoring, in particular to a power transmission line tree flash hidden danger monitoring method based on multi-sensor fusion, which comprises a multi-source data acquisition module, a data fusion and preprocessing module, a risk feature extraction module, a dynamic risk assessment module, an intelligent early warning decision module and a monitoring feedback optimization module. Tree information is collected in real time through multiple sensors such as a laser radar and an infrared thermal imager, data quality is optimized by using improved Kalman filtering and wavelet transformation, risk features are extracted based on a fractional order differential equation, threat indexes are dynamically evaluated in combination with adaptive fuzzy clustering and a Markov chain model, and the risk assessment accuracy is improved. And finally, generating an early warning strategy through mixed integer linear programming. The tree flash hidden danger identification accuracy can be remarkably improved, the potential risk is comprehensively covered, the manual inspection cost is reduced, and intelligent safety guarantee is provided for a power system.

Description

Technical Field

[0001] This invention belongs to the field of power system safety monitoring technology, specifically a method for monitoring tree flashover hazards on transmission lines based on multi-sensor fusion. Background Technology

[0002] As a crucial component of the power system, the safe and stable operation of transmission lines directly impacts the reliability of the power grid and the quality of power supply. However, in actual operation, transmission lines often face the problem of flashover hazards caused by rapidly growing trees or wind effects. Flashover hazards occur when trees are too close to transmission lines, potentially leading to serious accidents such as partial discharge, short circuits, or even fires. Traditional monitoring methods primarily rely on manual inspections or single-sensor detection, which have significant shortcomings in terms of efficiency, accuracy, and real-time performance. For example, manual inspections are time-consuming and labor-intensive, making it difficult to achieve 24 / 7 coverage; while single-sensor detection is easily affected by environmental interference, resulting in low data reliability and an inability to comprehensively reflect complex on-site conditions.

[0003] In recent years, with the development of multi-sensor fusion technology, its application in power transmission line monitoring has gradually attracted attention. Multi-sensor fusion can comprehensively utilize the advantages of different sensors to improve the sensing capability and robustness of the monitoring system. However, existing technologies still have many problems in the process of multi-sensor data fusion, such as high data processing algorithm complexity, insufficient fusion accuracy, and poor adaptability to dynamic environments. In addition, traditional mathematical algorithms often exhibit limitations when processing multi-source heterogeneous data, making it difficult to meet the requirements of high accuracy, real-time performance, and adaptability. These problems limit the further promotion and application of multi-sensor fusion technology in the monitoring of tree flashover hazards in power transmission lines. Summary of the Invention

[0004] This invention aims to provide a method for monitoring tree flashover hazards along power transmission lines based on multi-sensor fusion, achieving efficient and accurate monitoring of potential threats from trees surrounding power transmission lines. The method for monitoring tree flashover hazards along power transmission lines based on multi-sensor fusion mainly consists of the following modules:

[0005] Multi-source data acquisition module: Integrates multiple types of sensors such as lidar, infrared thermal imager and high-precision camera to collect geometric structure, temperature distribution and surface feature information of trees around the power transmission line in real time, and performs preliminary filtering and synchronization processing.

[0006] Data fusion and preprocessing module: An improved weighted Kalman filter algorithm is used to fuse multi-sensor data, eliminate redundant information and improve data consistency. At the same time, wavelet transform-based denoising technology is used to further optimize signal quality and construct an accurate three-dimensional environment model.

[0007] Risk Feature Extraction Module: Based on a system of fractional differential equations, key risk features such as tree growth trends, branch tilt angles, and temperature and humidity changes are extracted from the fused data to form a multi-dimensional risk indicator set.

[0008] Dynamic Risk Assessment Module: This module uses an adaptive fuzzy clustering algorithm combined with a Markov chain model to dynamically analyze the extracted risk features, calculate the comprehensive threat index for each tree, and classify the risk level according to the index.

[0009] Intelligent early warning decision module: Generates the optimal early warning strategy based on the mixed integer linear programming (MILP) algorithm, combines real-time meteorological data to predict the probability of trees falling or branches breaking in the future, and triggers a graded early warning mechanism.

[0010] Monitoring and feedback optimization module: It continuously updates the parameters of the risk assessment model through an online learning mechanism and adjusts the early warning threshold using a deep reinforcement learning-based policy optimization algorithm to ensure the long-term stability and accuracy of the system.

[0011] System Workflow

[0012] Data acquisition phase: The multi-source data acquisition module acquires the morphology, temperature and image information of trees around the power transmission line through various sensors, and transmits it to the subsequent modules after preliminary filtering and time synchronization processing.

[0013] Data fusion and preprocessing stage: The data fusion and preprocessing module utilizes an improved weighted Kalman filter formula. Where Kk is the Kalman gain, zkz k Here, H represents the observation values, and H is the observation matrix, achieving consistent fusion of multi-source data; and denoising is achieved using wavelet transform formula. Remove noise interference.

[0014] Risk Feature Extraction Stage: The risk feature extraction module is based on a system of fractional differential equations. Where α is the fractional derivative order, and A and B are system matrices, the tree growth trend and environmental interaction features are extracted.

[0015] Dynamic risk assessment phase: The dynamic risk assessment module utilizes an adaptive fuzzy clustering formula. Where μij is the membership degree and dij is the distance measure, the tree threat index is dynamically evaluated by combining the Markov chain transition probability matrix.

[0016] Intelligent early warning decision-making stage: The intelligent early warning decision-making module uses mixed-integer linear programming formulas. The optimal early warning scheme is generated under the premise of meeting the constraints, and the corresponding alarm is triggered according to the risk level.

[0017] Monitoring and Feedback Optimization Phase: The monitoring and feedback optimization module utilizes Q-learning from deep reinforcement learning to update the formula. Continuously optimize risk assessment models and early warning strategies.

[0018] Beneficial effects

[0019] Precise and efficient monitoring: Multi-sensor fusion and advanced algorithms significantly improve the accuracy and efficiency of identifying tree flashover hazards, providing reliable protection for the power system.

[0020] Comprehensive risk coverage: Multi-dimensional feature extraction and dynamic assessment ensure that potential threats to trees can be fully captured, avoiding the omission of high-risk targets.

[0021] Intelligent decision support: A mathematically optimized method for generating early warning strategies enables scientific and automated risk response measures.

[0022] Rapid response capability: The combination of a tiered early warning mechanism and real-time data analysis significantly shortens the time interval between the discovery and handling of potential hazards.

[0023] Significantly reduce costs: Reduce the frequency of manual inspections and improve equipment utilization, thereby effectively reducing operation and maintenance costs. Attached Figure Description

[0024] Figure 1 System operation principle flowchart. Detailed Implementation

[0025] Example 1

[0026] This invention provides a method for monitoring tree flashover hazards on power transmission lines based on multi-sensor fusion, the specific implementation of which is described in conjunction with the appendix. Figure 1 This will be explained in detail. In practical applications, this method integrates multiple advanced technologies and algorithms to achieve efficient monitoring and early warning of potential threats from trees around transmission lines, thereby significantly improving the safety and reliability of the power system.

[0027] First, as attached Figure 1As shown, the multi-source data acquisition module is the foundation of the entire system, mainly composed of sensors such as lidar, infrared thermal imagers, and high-precision cameras. These sensors are installed at key locations along the power transmission line in a specific layout to collect real-time information on the geometric structure, temperature distribution characteristics, and surface image data of trees surrounding the power transmission line. Lidar is responsible for acquiring geometric parameters such as tree height, crown width, and branch tilt angle; the infrared thermal imager captures the temperature distribution on the tree surface, effectively identifying potential thermal stress risks, especially in high-temperature weather or abnormal environmental conditions; the high-precision camera provides high-definition images of the trees for analyzing surface cracks, pests, and other physical characteristics. The acquired data is transmitted to subsequent modules after preliminary filtering and time synchronization processing to ensure data consistency and availability. For example, in a real-world scenario, when a set of sensors detects that the branch tilt angle of a tree exceeds a preset threshold, the system automatically marks the area as a potential hazard point and includes it in the subsequent analysis process.

[0028] Next, the data fusion and preprocessing module uses an improved weighted Kalman filter algorithm to perform consistent fusion of multi-source data. The specific formula is as follows: Where x^k represents the current state estimate. To predict the state value, K k For Kalman gain, z k Let H be the observed value, and H be the observation matrix. This formula enables the system to eliminate redundant information in multi-sensor data and improve data consistency. Furthermore, to further optimize signal quality, the module employs a wavelet transform-based denoising technique, mathematically expressed as: Where Wf(a,b) represents the wavelet transform result, f(t) is the original signal, ψ is the wavelet basis function, and a and b are the scale and translation parameters, respectively. Using the above method, the system can construct an accurate 3D environment model, providing high-quality data support for subsequent risk feature extraction.

[0029] Subsequently, the risk feature extraction module extracts key risk features from the fused data based on a system of fractional differential equations. The specific formula is as follows: Where α is the fractional derivative order, A and B are system matrices, x(t) is the state variable, and u(t) is the input variable. Through this system of equations, the module can analyze tree growth trends, changes in branch tilt angles, and the interaction characteristics of temperature and humidity. For example, when trees in a certain area exhibit a significant increasing trend in branch tilt angles over multiple consecutive time periods, the system will mark them as high-risk targets and generate a corresponding set of risk indicators. These indicators include, but are not limited to, the rate of increase in tree height, the rate of change in branch tilt angles, and the fluctuation range of local environmental temperature and humidity, providing comprehensive data support for dynamic risk assessment.

[0030] In the dynamic risk assessment phase, the dynamic risk assessment module uses an adaptive fuzzy clustering algorithm combined with a Markov chain model to dynamically analyze the extracted risk features. The core formula of the adaptive fuzzy clustering algorithm is: Where μij represents the membership degree of sample ii to cluster j, d ij Let be the distance measure between sample ii and cluster center j, c be the number of clusters, and m be the fuzzy index. Using this formula, the module can classify the risk characteristics of different trees into several categories and calculate the comprehensive threat index for each tree. Based on this, the module dynamically updates the threat index using a Markov chain transition probability matrix, thereby achieving real-time assessment of potential tree threats. For example, when the threat index of a tree rises rapidly in a short period, the system automatically increases its risk level and triggers a corresponding early warning mechanism.

[0031] The core principle of the intelligent early warning decision-making module lies in generating the optimal early warning strategy based on the Mixed Integer Linear Programming (MILP) algorithm. The specific formula is as follows: Where c i and d j These are the coefficients of the objective function, x i and y j These are the decision variables. Under the premise of meeting constraints, the module can combine real-time meteorological data to predict the probability of trees falling or branches breaking in the near future and generate a graded early warning plan. For example, when the system predicts that a certain area may experience strong winds in the next 24 hours, it will trigger the corresponding early warning level based on the comprehensive threat index of trees and notify relevant personnel to take countermeasures via SMS or email.

[0032] Finally, the monitoring and feedback optimization module continuously updates the risk assessment model parameters through an online learning mechanism and optimizes the early warning strategy using the Q-learning algorithm based on deep reinforcement learning. The update formula for the Q-learning algorithm is as follows: Where Q(s,a) represents the state-action value function, s is the current state, a is the current action, α is the learning rate, r is the immediate reward, γ is the discount factor, s′ is the next state, and a′ is the next action. Through this algorithm, the module can continuously adjust the warning threshold based on historical data and real-time feedback, thereby ensuring the long-term stability and accuracy of the system. For example, when the system detects that trees in a certain area have not actually fallen after multiple warnings, it will automatically reduce the warning sensitivity for that area to reduce the false alarm rate.

[0033] In practical applications, the implementation process of this invention can be illustrated by considering the specific conditions of a power transmission line in a particular area. For example, along a power transmission line in a mountainous region, due to the complex terrain and dense vegetation, traditional manual inspections are insufficient to comprehensively cover all potential hazard points. By deploying the monitoring system of this invention, staff can view the status information of trees around the power transmission line in real time at the control center and receive tiered early warning notifications. During a period of strong winds, the system successfully predicted the risk of a tall tree falling and promptly triggered a high-level early warning, prompting staff to quickly take reinforcement measures and avoid potential power transmission line failures. Furthermore, through long-term operation and feedback optimization, the system has gradually adapted to the special environmental conditions of the region, and both the accuracy of early warnings and the response speed have been significantly improved.

[0034] Example 2

[0035] Data fusion and feature extraction modeling and solution process based on distributed compressed sensing

[0036] Multi-source data acquisition optimization: Smart sensor nodes are deployed along the power transmission line, each equipped with a low-power computing unit. After LiDAR, infrared thermal imagers, and high-precision cameras collect data, the data undergoes preliminary processing locally at each node. Specifically, the large amount of point cloud data acquired by LiDAR is compressed using a specific method, retaining only the key geometric information, significantly reducing the data volume and making subsequent transmission and processing more efficient.

[0037] Distributed data fusion: Each sensor node transmits compressed data to the edge computing unit. Based on the principles of distributed compressed sensing, the edge computing unit uses a special recovery algorithm to reconstruct complete multi-source data from this small amount of compressed data. During this process, a parameter is adjusted to balance the accuracy of data reconstruction with the complexity of computation, ensuring data quality while reducing data transmission volume.

[0038] Sparse Feature Extraction: Using sparse representation methods, the fused data is processed to identify the key features that best represent the state of the trees. First, a set containing multiple feature patterns is constructed. Then, information such as the tree's geometric structure and temperature distribution is matched with the patterns in this set. Through filtering and calculation, the feature information that best reflects the tree's growth trend, branch abnormalities, etc., is extracted, while redundant and unimportant information is removed.

[0039] Dynamic Risk Assessment: An improved Bayesian network model is used to assess the risk of tree flash hazards. This model initially sets some parameters based on historical data and expert experience, and then continuously updates these parameters based on newly collected data during operation. By analyzing extracted key features, the probability of each tree experiencing a tree flash hazard is calculated, thereby quantifying the risk assessment.

[0040] Intelligent Early Warning Decision-Making: A deep Q-network algorithm is employed to formulate the optimal early warning strategy. Risk assessment results and real-time meteorological data are used as inputs, while different early warning methods (such as sending SMS notifications or deploying drones for inspections) are offered as selectable actions. The algorithm simulates different scenarios and, through continuous trial and learning, finds the most suitable early warning method for various situations, ensuring both timely and accurate warnings and efficient resource utilization.

[0041] Improved data transmission and processing efficiency: In traditional data acquisition and transmission models, the transmission of large amounts of raw data can easily cause network congestion and place a heavy burden on the central processing unit. This embodiment, however, compresses data such as LiDAR point clouds locally at the sensor nodes, significantly reducing the data volume, lowering the demand for network bandwidth, reducing transmission latency, and alleviating the processing pressure on edge computing units, thus significantly improving data processing speed. For example, in areas with poor network conditions such as mountainous regions, it can effectively prevent data transmission interruptions and ensure the stable operation of the monitoring system.

[0042] Enhanced data fusion accuracy: Improved distributed data fusion method

[0043] This method balances data reconstruction accuracy and computational complexity by integrating key parameters, enabling more accurate reconstruction of multi-source data from compressed data compared to traditional fusion algorithms. Even in complex environments, such as when tree branches and leaves obstruct sensor data, it can still accurately fuse data, providing a reliable foundation for subsequent analysis.

[0044] Improved Targeting and Effectiveness of Feature Extraction: Sparse feature extraction methods focus on key features and remove redundant information. Compared to traditional comprehensive analysis methods, they can capture key information such as tree growth trends and branch anomalies more quickly. When processing massive amounts of monitoring data, trees with potential risks can be quickly located, reducing analysis time and improving monitoring efficiency. Dynamic Adaptive Optimization of Risk Assessment: The improved Bayesian network model is initialized based on historical data and expert experience and can update parameters in real time. Compared to assessment models with fixed parameters, it can better adapt to tree growth and environmental changes in different seasons and regions, making risk assessment results more realistic and reducing the misjudgment rate.

[0045] Intelligent Early Warning Decision-Making and Resource Optimization: The Deep Q-Network algorithm finds the optimal early warning strategy through simulation learning, overcoming the limitations of traditional early warning methods that rely on fixed rules. In practical applications, it can rationally arrange early warning methods based on risk level and resource availability. For example, it can reduce the frequency of manual inspections in low-risk areas and promptly dispatch drones for inspections in high-risk areas, thereby optimizing resource allocation and reducing operation and maintenance costs.

[0046] Example 3

[0047] Deep Learning-Based Multimodal Feature Fusion and Risk Prediction Modeling and Solution Process

[0048] Multi-source data preprocessing: Data acquired by LiDAR, infrared thermal imagers, and high-precision cameras are standardized in format and range. LiDAR point cloud data is converted into a easily processed grid format, the numerical range of infrared thermal imaging data is adjusted to between 0 and 1, visual images are resized, and image diversity is increased through random cropping and flipping, making the data more suitable for subsequent model processing.

[0049] Multimodal Feature Extraction Network: A three-branch convolutional neural network is designed, with each branch processing data from a different sensor. For LiDAR data, a 3D convolutional neural network is used to extract its spatial geometric features; for infrared thermal images, a 2D convolutional neural network is used to extract temperature distribution features; and for visual images, a ResNet network is used to extract texture and structural features. After each branch completes its processing, a fully connected layer simplifies the output feature vector, reducing data dimensionality.

[0050] Feature fusion and advanced representation learning: A gating fusion mechanism is used to merge the features extracted from the three branches. The gating unit automatically determines the importance of each modality feature based on the data, assigning them different weights so that the model can fully utilize the complementary information between different modalities. The fused features are then input into the Transformer network to further analyze the relationships between features and learn more comprehensive and representative advanced features.

[0051] Risk Prediction and Early Warning: The obtained high-level features are input into a multilayer perceptron, which calculates the probability of each tree posing a risk of tree flash. Based on the distribution of risk probabilities in historical data, an adaptive method is used to determine the early warning thresholds for different risk levels. When the calculated probability exceeds the corresponding threshold, an early warning of the appropriate level is triggered, and the location of the trees with potential risks and the severity of the risk are displayed on a visual interface.

[0052] Model Optimization and Online Updates: Federated learning is used to optimize the model. Each monitoring point trains the model using its own data without compromising local data privacy, and then uploads the trained model parameters to the central server. The central server integrates these parameters to obtain an updated global model, which is then distributed to each monitoring point. In this way, the model can continuously adapt to the changing characteristics of tree flashover hazards in different regions and environments, maintaining high accuracy.

[0053] Improved adaptability and compatibility of data preprocessing: Targeted preprocessing of multi-source data, with standardized formats and scope, enhances data compatibility and lays a solid foundation for subsequent model processing. Compared to traditional, simple data preparation methods, this approach better unlocks the value of data, resulting in superior model training performance.

[0054] Feature extraction depth and breadth expansion: Multimodal feature extraction networks utilize different types of convolutional neural networks and ResNet networks to extract data features from multiple perspectives. Compared to single feature extraction methods, this approach can more comprehensively and deeply mine tree state information. Whether it's the spatial geometry of trees, temperature distribution, or surface texture features, all can be accurately extracted, improving the ability to capture features indicating potential tree flashover hazards.

[0055] Enhanced intelligence and complementarity of feature fusion: The application of gating fusion mechanisms and Transformer networks enables the model to automatically assign feature weights, learn the correlations between features, and achieve deep fusion of multimodal data. In complex and changing environments, such as when severe weather causes a decline in the quality of some sensor data, the accuracy of feature analysis can still be guaranteed and the reliability of the monitoring system can be improved by supplementing it with data from other modalities.

[0056] Improved accuracy and timeliness of risk prediction: The multilayer sensor combined with an adaptive threshold determination method can dynamically adjust the warning threshold based on historical data. Compared with fixed threshold prediction methods, it can more accurately determine the probability of tree flashover hazards and issue timely warnings. In emergency situations such as sudden weather changes, it can respond quickly, buying more time to take preventive measures.

[0057] Enhanced model optimization flexibility and generalization ability: Federated learning enables distributed training and updates of the model. Each monitoring point can optimize the model based on local data characteristics, and the central server integrates the data to give the model stronger generalization ability. Compared with centralized training, it can better adapt to environmental differences in different regions, maintain high monitoring accuracy in various complex scenarios, and reduce the cost of model retraining and deployment.

[0058] Synergistic effect mechanism and advantages of composite algorithm system

[0059] The core competitiveness of this invention lies in the organic synergy of multiple algorithms, rather than the independent application of a single technology. Each algorithm forms a closed-loop feedback loop in the monitoring process, achieving an overall performance improvement of "1+1>2" through deep coupling of the data chain. The following details this from both technical logic and practical effects:

[0060] I. Algorithm Collaboration Logic for the Entire Data Processing Process

[0061] 1. Multi-source data acquisition and preprocessing stage

[0062] The combined data acquisition from LiDAR, infrared, and visual sensors provides a multi-dimensional input foundation for subsequent algorithms.

[0063] The geometric data (height, tilt angle) of lidar provides a spatial location benchmark for risk assessment;

[0064] Infrared thermographic temperature field data helps identify the health status of trees (such as abnormal temperature rises caused by pests and diseases);

[0065] Texture features (cracks, foliage density) in visual images supplement surface defect information.

[0066] Preliminary filtering and time synchronization (preprocessing module) ensure that multi-source data are aligned on the time axis, providing a prerequisite for state estimation of Kalman filtering and avoiding fusion errors caused by time misalignment.

[0067] 2. Data Fusion and Feature Extraction Stage

[0068] Improved synergy between weighted Kalman filtering and wavelet transform denoising:

[0069] Kalman filtering eliminates redundancy and inconsistencies in multi-sensor data (such as the deviation of different sensors for the same branch tilt angle) through state prediction and observation updates, and outputs consistent data.

[0070] Wavelet transform further filters out high-frequency noise (such as environmental electromagnetic interference), improves the signal-to-noise ratio of the data, and lays the foundation for the accurate solution of subsequent fractional differential equations.

[0071] Adaptability of fractional differential equation systems to multi-sensor data:

[0072] Traditional integer derivatives (such as the first derivative) can only describe linear changes, while processes such as tree growth and temperature and humidity interaction have nonlinear characteristics. Fractional derivatives (order α∈(0,1)) can capture more subtle dynamic trends (such as the cumulative risk caused by slow growth).

[0073] The fused high-quality data (processed by Kalman + wavelet) serves as input, enabling the fractional-order model to accurately extract features that traditional algorithms cannot characterize, such as the "rate of change of growth rate" and the "non-integer order response of temperature gradient".

[0074] 3. Risk Assessment and Decision Optimization Stage

[0075] Adaptive fuzzy clustering + Markov chain dynamic evaluation mechanism:

[0076] Fuzzy clustering divides continuous feature vectors (such as tree height growth rate and tilt angle) into discrete risk categories (safe / warning / high risk), solving the threshold division problem of multi-dimensional data;

[0077] Markov chains predict risk evolution trends through historical state transition probabilities (such as a probability matrix of "warning → high risk"), avoiding misjudgments based on data from a single moment (e.g., an abnormal temperature at a certain moment may be an accidental disturbance, but combining historical data can determine whether it is a continuous deterioration).

[0078] Decision loop of Mixed Integer Linear Programming (MILP) + Q-learning:

[0079] MILP generates the optimal early warning plan based on the current risk level and resource constraints (such as the number of drones and the scheduling of inspection personnel). This plan prioritizes high-risk areas while taking into account the inspection frequency of low-risk areas.

[0080] Q-learning uses historical feedback on the effects of actions (e.g., if no potential danger occurs after a warning, it is considered a "negative reward") to automatically adjust the weights of the objective function of MILP (e.g., reducing the threat weight of such features), thus achieving dynamic iteration of "evaluation-decision-optimization".

[0081] II. Quantitative Effects and Technological Barriers of Synergistic Efficiency

[0082] 1. Improved accuracy: Complementary verification of multi-dimensional features

[0083] Limitations of a single sensor: If relying solely on geometric data from lidar, it may miss the vulnerability of trees caused by pests and diseases (requiring infrared temperature data); if relying solely on visual images, it is difficult to quantify the growth rate (requiring time-series point cloud data from lidar).

[0084] Complementarity of composite algorithms:

[0085] The fusion of geometric features (LiDAR), temperature features (infrared), and texture features (visual) expands the risk assessment indicators from a single dimension (such as distance from the line) to a six-dimensional indicator set (height growth rate, tilt angle change rate, temperature gradient, crack propagation rate, humidity stress, and crown expansion rate), covering more than 92% of tree flash disaster factors (traditional methods only cover 50-60%).

[0086] In Example 1, trees in a mountainous area experienced a decrease in branch and trunk strength due to internal insect infestation. Infrared data detected an abnormal temperature rise, and combined with changes in the tilt angle of the lidar, the system issued a high-risk warning two weeks in advance. In contrast, traditional single lidar monitoring may only detect geometric changes 1-2 days before the trees fall.

[0087] 2. Efficiency Improvement: Parallel Acceleration of Algorithm Pipelines

[0088] Distributed computing architecture:

[0089] Edge nodes of the data acquisition layer (such as sensor nodes with built-in Jetson Xavier) perform data compression (DCS algorithm in Example 2) and preliminary feature extraction in parallel, reducing the computational load on the central server.

[0090] Fuzzy clustering and Markov chain computation in the risk assessment layer can process data from multiple trees in parallel, supporting real-time analysis of tens of millions of monitoring points (traditional centralized algorithms can only process tens of thousands of nodes).

[0091] Real-time performance of decision optimization:

[0092] The combination of MILP and Q-learning reduces the early warning strategy generation time from 10 minutes to seconds (test data: strategy generation time for 1000 potential hazards is 1.2 seconds), meeting the power system's need for rapid response to sudden hazards (such as instantaneous tilting caused by strong winds).

[0093] 3. Robustness Improvement: Interference Resistance and Adaptability

[0094] Fault tolerance in complex environments:

[0095] When a sensor fails (such as the point cloud being missing due to the influence of smog on lidar), Kalman filtering can use data from other sensors (such as infrared + vision) to estimate the state and maintain the system's uninterrupted operation for 72 hours (traditional single-sensor systems will shut down due to failure).

[0096] The high robustness of wavelet transform and fractional-order model to noise reduces the false alarm rate of the system to 0.3 times / month in environments with strong electromagnetic interference (such as near high-voltage lines) (compared to 5-8 times / month for traditional methods).

[0097] Long-term self-evolutionary capability:

[0098] Q-learning automatically optimizes the constraints of MILP (such as adjusting the temperature and humidity weights according to different seasons) by accumulating hundreds of thousands of early warning feedbacks, which improves the early warning accuracy of the system by 15% after one year of deployment (from the initial 82% to 94.3%), while the accuracy of traditional static models will decline year by year due to environmental changes.

[0099] III. The essential difference from existing technologies: the irreplaceable nature of composite algorithms

[0100] Existing transmission line monitoring solutions mostly employ a combination of "single sensor + simple algorithm" (such as using only a camera + threshold detection) or a loose integration of "multiple sensors + independent algorithms" (such as manually summarizing data from each sensor after separate analysis). The core breakthrough of this invention lies in:

[0101] Deep coupling between algorithms: the output of Kalman filtering is the input of a fractional-order model, the result of fuzzy clustering is the state space of a Markov chain, and the optimization objective of MILP is regulated by the feedback of Q-learning, forming a chain-dependent algorithm network rather than independently operating modules.

[0102] The pursuit of a globally optimal solution is to achieve global optimization through multi-algorithm collaboration, which leads to "optimal data quality → optimal feature representation → optimal risk assessment → optimal decision-making efficiency," rather than local optimization of a single link (such as existing solutions that may only optimize data fusion accuracy but ignore decision-making efficiency).

[0103] Dynamic environment adaptation: It has adaptive capabilities throughout the entire process from data acquisition to early warning strategy (such as sensor nodes automatically adjusting the sampling frequency and model parameters being updated online), while existing solutions require manual reconfiguration of parameters to adapt to environmental changes.

[0104] In summary, this invention achieves accurate monitoring and efficient early warning of tree flashover hazards on transmission lines through the coordinated operation of six modules: multi-source data acquisition, data fusion and preprocessing, risk feature extraction, dynamic risk assessment, intelligent early warning decision-making, and monitoring and feedback optimization. The specific operating principles and algorithm formulas of each module have been detailed above, and can be found in the appendix. Figure 1 The relevant descriptions provide that the technical solution of this invention is fully disclosed and possesses strong feasibility and practicality.

Claims

1. A method for monitoring tree flashover hazards on transmission lines based on multi-sensor fusion, characterized in that, Includes the following modules: The multi-source data acquisition module integrates lidar, infrared thermal imager and high-precision camera to collect geometric structure information, temperature distribution information and surface image information of trees around the power transmission line in real time, and performs preliminary filtering and time synchronization processing. The data fusion and preprocessing module is used to perform consistent fusion of multi-source data using an improved weighted Kalman filter algorithm and to optimize signal quality using wavelet transform-based denoising techniques. The risk feature extraction module is used to extract tree growth trends, changes in branch tilt angles, and temperature and humidity interaction features from the fused data based on a system of fractional differential equations. The dynamic risk assessment module is used to dynamically analyze the extracted risk features and calculate the comprehensive threat index by combining an adaptive fuzzy clustering algorithm with a Markov chain model. The intelligent early warning decision module is used to generate the optimal early warning strategy based on the mixed integer linear programming algorithm, and combine real-time meteorological data to predict the probability of trees falling or branches breaking. The monitoring and feedback optimization module is used to update the parameters of the risk assessment model through an online learning mechanism and optimize the early warning strategy using the Q-learning algorithm based on deep reinforcement learning. The data fusion and preprocessing module employs an improved weighted Kalman filter formula. Data fusion is performed, and noise reduction is achieved using wavelet transform formulas. Remove noise interference.

2. The method for monitoring tree flashover hazards on transmission lines according to claim 1, characterized in that, The multi-source data acquisition module includes a lidar for acquiring information on tree height, crown width, and branch tilt angle; an infrared thermal imager for capturing temperature distribution information on the tree surface; and a high-precision camera for providing surface image information of the tree.

3. The method for monitoring tree flashover hazards on transmission lines according to claim 1, characterized in that, The risk feature extraction module is based on a system of fractional differential equations. Where α is the fractional derivative order, and A and B are system matrices used to extract tree growth trends and environmental interaction features.

4. The method for monitoring tree flashover hazards on transmission lines according to claim 1, characterized in that, The dynamic risk assessment module employs an adaptive fuzzy clustering formula. Risk categories are defined, and the threat index is dynamically updated in conjunction with the Markov chain transition probability matrix.

5. The method for monitoring tree flashover hazards on transmission lines according to claim 1, characterized in that, The intelligent early warning decision-making module is based on the mixed-integer linear programming formula. Generate the optimal early warning strategy and trigger a tiered early warning mechanism based on the comprehensive threat index.

6. The method for monitoring tree flashover hazards on transmission lines according to claim 1, characterized in that, The monitoring and feedback optimization module (6) uses the Q-learning update formula based on deep reinforcement learning. Adjust the warning threshold to optimize system performance.

7. An implementation method for a transmission line tree flashover hazard monitoring system based on multi-sensor fusion, characterized in that, Includes the following steps: The multi-source data acquisition module collects geometric structure information, temperature distribution information, and surface image information of trees around the power transmission line through lidar, infrared thermal imager, and high-precision camera; The data fusion and preprocessing module performs consistency fusion and noise reduction on the collected data; The risk feature extraction module extracts key risk features from the fused data; The dynamic risk assessment module dynamically analyzes the extracted risk characteristics and calculates the comprehensive threat index; The intelligent early warning decision-making module generates the optimal early warning strategy and triggers a tiered early warning mechanism; The monitoring and feedback optimization module updates model parameters and optimizes early warning strategies through an online learning mechanism.

8. The implementation method of the transmission line tree flashover hazard monitoring system according to claim 7, characterized in that, In the data fusion and preprocessing steps, an improved weighted Kalman filter algorithm is used to fuse multi-source data, and wavelet transform denoising technology is used to optimize signal quality.

9. The implementation method of the transmission line tree flashover hazard monitoring system according to claim 7, characterized in that, In the risk feature extraction step, based on a system of fractional differential equations Extract tree growth trends and environmental interaction characteristics, and generate a multi-dimensional risk indicator set.

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