9231 results about "Transmission line" patented technology

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.

The invention provides an electric power line health state assessment prediction method and system based on big data, and relates to the technical field of electric power line health assessment, and the method comprises the steps: preprocessing historical operation data, generating standardized data, extracting fault features through employing a space-time heterogeneous graph network, constructing a basic hierarchical knowledge base for automatic marking, and carrying out the prediction of the health state of an electric power line. Finally generating a training sample set; a directed acyclic graph is modeled, a main transmission line is identified, risk sections are divided, a knowledge base is optimized, an inspection scheme is adaptively generated based on an inspection strategy model of reinforcement learning, the health state of a power line is evaluated, and a health state report is generated; according to the invention, the fault prediction accuracy and inspection efficiency of the power line are improved.

The invention relates to the technical field of wire disaster prevention analysis, and discloses an overhead transmission line icing monitoring and disaster prevention method and system. The method comprises the following steps: acquiring meteorological parameters, stress data and icing images through distributed sensing nodes; performing time-space synchronization processing on the data; calculating icing state information; evaluating an icing risk level; selectively starting the deicing device; and storing the data and extracting a law to optimize a disaster prevention plan. According to the invention, accurate monitoring of the icing state, scientific assessment of risks, intelligent selection of deicing measures and continuous optimization of disaster prevention strategies are realized, so that the ability of the power grid to deal with icing disasters is significantly improved, and economic losses and potential safety hazards are reduced.

The invention discloses a large-scale point cloud registration method, system and device based on a power grid power transmission line and a storage medium, and the method comprises the steps: collecting point cloud data of the power transmission line under different visual angles, and forming a source point cloud set and a target point cloud set; down-sampling point cloud data in the source point cloud set and the target point cloud set, dynamically adjusting the sampling granularity of different density regions, and obtaining representative point cloud data from the source point cloud set and the target point cloud set; extracting point cloud feature points, accelerating neighborhood search through a KD-Tree structure, and adaptively adjusting a neighborhood search radius; constructing a geometric topology model; performing coarse registration based on an improved RANSAC algorithm, selecting non-collinear feature points, and optimizing a transformation model; an improved ICP algorithm is adopted to carry out fine registration, and normal vector features are utilized to introduce weighting factors to optimize registration precision. According to the invention, large-scale power transmission line point cloud data can be efficiently and accurately processed, and the method is especially suitable for power inspection, three-dimensional modeling and maintenance scenes.

The invention discloses a power transmission line multi-mode warning system and a power transmission line multi-mode expelling method, belongs to the technical field of power transmission line safety monitoring, and aims at solving the problems that power transmission line invasion target monitoring is not accurate, and the warning and expelling effect is poor. An environment image is acquired through an acquisition unit, and redundancy compensation is carried out on a fault unit. In the aspect of image splicing, a database is constructed by using structural feature points of a power transmission line, rapid projection transformation of a fixed area is realized, incremental feature matching is adopted for a dynamic area, and an environment panoramic image is generated. The method comprises the following steps: establishing a background coordinate system based on an environment panoramic image by aligning a fixed structure region, extracting multi-modal features of an intrusion target, constructing a dynamic trajectory parameter set, completing species classification and behavior recognition, constructing a multi-dimensional evaluation index system, dividing threat levels, and generating a thermodynamic diagram. And finally, according to data such as threat levels, a multi-mode grading warning system is constructed, warning equipment is dynamically adjusted, a target track is tracked, a warning effect is evaluated, and accurate and efficient invasion target expelling is realized.

The invention discloses a power transmission line connection fitting fault detection method based on acoustics and electromagnetic induction, and belongs to the technical field of intelligent inspection and nondestructive detection of power transmission lines. A multi-mode sensor is carried by an unmanned aerial vehicle, ultrasonic wave, vibration, images and power frequency electromagnetic field data are synchronously collected, and a multi-dimensional original data set is constructed. Signal quality is improved by adopting wavelet noise reduction, beam forming and sound image fusion technologies, and flight vibration and electromagnetic interference are effectively suppressed by combining an adaptive filtering algorithm and a physical shielding structure. And acoustic, image and electromagnetic characteristics are extracted and normalized and fused, a dynamic threshold reference library is established, and intelligent grading discrimination of abnormity, defects and faults is realized through multi-stage early warning logic. A detection result automatically generates a report and is mapped to a three-dimensional line model, and operation and maintenance system linkage is supported. According to the method, synchronous identification of surface and internal defects is realized, the anti-interference capability is high, the detection accuracy is high, the inspection efficiency and safety are remarkably improved, and the method is suitable for intelligent operation and maintenance of the high-voltage transmission line.

The invention provides a power transmission line three-dimensional reconstruction method based on an unmanned aerial vehicle and a laser radar, and the method comprises the steps: dynamically adjusting the scanning field angle and scanning frequency of the laser radar according to the flight attitude of the unmanned aerial vehicle and the direction of a cable, obtaining the original point cloud data of the cable at different angles and different densities, carrying out the preprocessing of the obtained original point cloud data, and obtaining a pre-processed point cloud data; obvious noisy points and outliers are removed, and cable point cloud data after preliminary filtering are obtained; and in a local tangent coordinate system, point cloud simplification and curved surface reconstruction are carried out on each local cable point cloud segment to obtain a local cable three-dimensional model, and through coordinate system inverse transformation, the local cable three-dimensional models are spliced into a complete cable three-dimensional model to realize three-dimensional accurate reconstruction of the cable. According to the invention, the problem of accurate three-dimensional reconstruction of a power transmission line in a complex environment is effectively solved, the efficiency and precision of cable detection and maintenance are improved, and during later upgrading and reconstruction or new project construction, electric power personnel can conveniently and rapidly master cable information.

The invention belongs to the technical field of power system safety, and discloses a high-voltage transmission line safety assessment method based on an intelligent algorithm. A database is constructed through multi-source data acquisition and fusion processing, a deep learning model is utilized to analyze relevance between meteorological conditions and an icing formation mechanism, an evolution law of the meteorological conditions and the icing formation mechanism is mined, multi-scene simulation and sensitivity analysis are performed in combination with physical characteristics and landform information of a power transmission line, and a line icing risk partition assessment system is established. According to the method, a critical value of line mechanical strength and icing thickness is calculated based on a mechanical model, a grading early warning threshold standard is formulated, a time sequence prediction algorithm and a risk propagation model are constructed to realize intelligent early warning, and a prevention and control strategy is formed through optimal configuration of anti-icing resources and self-adaptive generation of a deicing scheme. According to the invention, the capability of resisting icing disasters of the high-voltage transmission line is effectively improved, and the safe and stable operation level of a power grid is enhanced.

A system for breakage early warning of high-voltage transmission lines based on acoustic prints, including a data acquisition unit, an edge processing unit, a cloud server, a warning execution unit, and a communication unit. The data acquisition unit is configured to collect environmental information of a high-voltage transmission tower in real time through a multimodal sensing device. The edge processing unit includes an adaptive filtering module configured to eliminate wind noise interference in the environmental information and compensate for frequency response deviation. The cloud server can generate a warning instruction based on the environmental information. The warning execution unit can perform warning and alarming actions based on the warning instruction. The communication unit is configured to enable data and information transmission among the units. A method implemented by such system is also provided.

The invention discloses a power transmission line icing real-time monitoring method and system based on multi-parameter fusion, and relates to the field of data processing, and the method comprises the steps: receiving conductor physical parameters at the same moment, and simulating an icing track of a target power transmission line section in combination with current environment data; determining a windward side of the icing track based on the current wind direction, and simulating a windward icing layer of the icing track by taking the windward side as a starting surface, taking the current wind speed as a reference wind speed and adopting an icing layer simulation strategy; based on the topographic data of the target power transmission line section, determining a high-risk icing area in the icing track, and determining icing layer data of the high-risk icing area; determining an icing galloping area in the icing track based on the vibration parameter and the strain parameter; and fitting the windward icing layer, the high-risk icing area and the icing galloping area with the icing track, and optimizing the fitted icing track according to the icing layer data to generate a visual interface. According to the invention, the accuracy of icing monitoring of the power transmission line can be effectively improved.

The invention provides a power transmission line inspection data asset management method and system, and the method comprises the steps: obtaining the multi-modal data of a power transmission line through a plurality of sensors disposed on the power transmission line and the surrounding environment, and sensors carried by a helicopter and an unmanned plane; and processing the multi-modal data based on the space-time correlation model to generate a fusion data set. Analyzing the fused data set by using an adaptive feature extraction algorithm, generating structured power transmission line data assets including environmental features, defect features and hidden danger features, and evaluating the possibility of triggering defects by environmental conditions based on the association of the environmental features and the defect features; according to the hidden danger characteristics and the environmental conditions, constructing a graph network model which takes a power transmission line as a node and sensor data as an edge weight and is used for analyzing a defect propagation path and predicting time and space distribution of hidden dangers; defect risk level early warning is generated based on a prediction result, a power transmission line hidden danger distribution diagram is generated in combination with environmental meteorological data, and accurate assistance is provided for patrol and maintenance plans.

The invention discloses a power transmission line inspection unmanned aerial vehicle obstacle intelligent detection and obstacle avoidance method. The method comprises the steps that multi-source sensing data is acquired, and alignment is completed through calibration and timestamp matching; heterogeneous data preprocessing and feature enhancement; constructing a high-precision environment fusing a geometric structure and a semantic tag, mapping a two-dimensional target detection result output by the recognition network to a three-dimensional coordinate system through spatial transformation, and fusing the two-dimensional target detection result with a point cloud structure to construct a semantic occupation grid map; performing preliminary route planning according to a preset power grid topological structure and task coverage requirements, and generating a barrier-free flight path covering the whole inspection area; reinforcing learning of a dynamic obstacle avoidance strategy; track dynamic reconstruction and energy consumption optimization scheduling are carried out; the technical problems that an existing technical system has defects in the aspects of obstacle recognition accuracy, complex environment adaptability, data fusion capacity and obstacle avoidance strategy intelligence, and the requirements for high-reliability, low-energy-consumption and high-efficiency unmanned aerial vehicle power transmission line inspection are difficult to meet are solved.

The invention discloses a power transmission channel risk early warning evaluation method and system, and relates to the technical field of power transmission channel abnormal condition early warning, and the method comprises the steps: analyzing the current waveform distortion degree caused by a nonlinear load in a power transmission line through monitoring the voltage harmonic distortion rate, and evaluating the influence of harmonic waves on the insulation aging and heating loss of the line and equipment; the real-time gradient of the temperature of the wire is analyzed, abnormal heating conditions of different sections of the wire are judged, and the abnormal rising degree of the temperature of the wire and potential thermal damage accumulation caused by overload and poor contact factors are evaluated; by monitoring and analyzing multi-dimensional indexes such as the voltage harmonic distortion rate, the lead temperature real-time gradient, the sag dynamic deviation rate and the corona discharge intensity, the defect of a traditional monitoring means in the aspect of abnormal heating risk assessment is overcome.

The invention relates to the technical field of power system monitoring, and discloses an infrared thermal image feature fusion-based power transmission line abnormal area identification method, which comprises the following steps of: acquiring infrared thermal image data of a power transmission line, and preprocessing the data; the invention further provides an infrared thermal image feature fusion power transmission line abnormal area identification system, which comprises a data acquisition module, an infrared thermal image feature fusion module, an infrared thermal image feature fusion module, an infrared thermal image feature fusion module, an infrared thermal image feature fusion module and an infrared thermal image feature fusion module, and is characterized in that the data acquisition module is used for acquiring infrared thermal image data of a power transmission line; and the preprocessing module is used for preprocessing the collected infrared thermal image data. According to the method, through combination of multi-scale wavelet transform, nonlinear dynamic modeling, graph neural network optimization and reinforcement learning, the abnormal region identification precision is improved, the robustness and adaptive ability of the system in a complex environment are enhanced, and it is ensured that the abnormal region can still be efficiently and accurately identified under dynamic change and noise interference.

The invention provides an online monitoring method and system based on a power transmission line, and relates to the technical field of power transmission line monitoring. According to the method, the heterogeneous sensing terminal, edge calculation, the graph neural network and Bayesian reasoning are combined, multi-source data acquisition, state identification and risk prediction are realized, the fault diagnosis accuracy and the risk early warning capability are improved, and the intelligent level and the safety guarantee capability of power transmission line operation are enhanced; the monitoring data is analyzed in real time through an edge calculation unit to generate a state label, a potential fault mode is recognized by combining graph neural network modeling space-time relevance, a risk factor library is further constructed, and a real-time fault probability graph is generated based on a Bayesian network. And dynamic identification and early warning of risk types such as wire strand breakage, icing overrun and mechanical fatigue can be realized.

The invention discloses an improved YOLOv11 power transmission line defect detection method, which introduces an attention mechanism to optimize the feature extraction capability, improves the performance of a model in a complex scene, remarkably enhances the understanding of the model for local and global information, and improves the target detection precision. Spatial offset is carried out on a feature map in a detection head by adopting feature extraction, feature association of different scales is enhanced, the detection precision of a small target is improved, redundant calculation is reduced, the calculation efficiency and performance of a model are further optimized, diversified requirements in a complex environment are met, and the method is suitable for popularization and application. The feature learning ability of a self-integrated attention head optimization target detection model is introduced, and the detection precision and robustness are improved under the scenes of small object detection and complex backgrounds. The method effectively improves the detection precision and training efficiency, can achieve excellent performance in a high-precision requirement and a real-time task, and meets the requirements of safety risk management and control in the power industry.

The invention is suitable for the technical field of power transmission line maintenance, and provides a line early warning judgment method and system based on multi-source data fusion, and the method comprises the steps: constructing a multi-dimensional knowledge graph, and building a semantic association network; collecting meteorological and equipment state data in real time, fusing the data into a knowledge graph space through a mapping function, identifying an abnormal meteorological-equipment combination mode by using fuzzy reasoning, and calculating a thickness change rate in combination with an icing growth prediction model; the icing thickness and the galloping probability are calculated, and a composite early warning level is generated; and dynamically generating a risk thermodynamic diagram on the GIS map, and visually displaying a risk propagation path. Through semantic fusion of multi-source data, composite risk modeling and spatialization decision making, the system breaks through single parameter criterion limitation of traditional early warning, realizes intelligent identification and dynamic early warning of meteorological-equipment coupling risks, and significantly improves accuracy of power transmission line disaster early warning and operation and maintenance decision making efficiency.

The device comprises a control system, the control system is elastically connected with a multi-stage energy consumption mechanism, the control system is connected with one end of a first stay wire, and the other end of the first stay wire is used for being connected with the power transmission line and is provided with a monitoring system; the monitoring system is connected with the control system to transmit collected information to the control system, the multi-stage energy dissipation mechanism is elastically connected with the supporting plate, the supporting plate is connected with one end of the second pull wire, the other end of the second pull wire is used for being connected with a ground foundation, the multi-stage energy dissipation mechanism comprises a shell, and the two sides of the shell are elastically connected with the control system and the supporting plate respectively. A collision friction damping assembly, an eddy current damping assembly and a magnetorheological fluid damping assembly are arranged in the shell, wherein the eddy current damping assembly and the magnetorheological fluid damping assembly are connected with the control system. By the adoption of the control device, the defects of traditional vibration reduction measures are overcome.

The invention provides an unmanned aerial vehicle high-precision autonomous navigation method and system based on GNSS and IMU deep fusion, and aims to solve the problems of insufficient navigation precision and poor robustness in a complex electromagnetic environment. Through a tight coupling architecture, GNSS original observed quantity and IMU pre-integration results are jointly modeled in an observation layer, and multi-source constraints are introduced in combination with factor graph optimization, so that the positioning precision and consistency in weak signal and shielding scenes are remarkably improved. For abnormal observation, a robust kernel function is adopted to dynamically adjust the weight, and the influence of electromagnetic interference and a multipath effect is effectively inhibited. Meanwhile, navigation calculation and model prediction control MPC are combined, and sub-meter hovering and high-precision trajectory tracking are achieved. According to the method, in high-voltage transmission line inspection, dependence on a high-cost sensor is reduced, the engineering application value is high, the method can be widely applied to the fields of electric power inspection, disaster emergency, infrastructure monitoring and the like, and reliable technical support is provided for high-precision autonomous navigation of the unmanned aerial vehicle in a complex environment.

The invention relates to the technical field of power detection, and discloses a vegetation risk hidden danger detection method and system based on sparse point cloud segmentation, and the method comprises the steps: collecting image data of a monitoring region at different angles, generating sparse point cloud data, and carrying out the preprocessing of the sparse point cloud data; and performing point cloud segmentation on the sparse point cloud data through a neural network, dividing the monitoring region according to region types, analyzing a spatial relationship between vegetation and power equipment in a target region, performing risk assessment, and generating an early warning signal in combination with multi-modal data. According to the method, the sparse point cloud data is precisely segmented, the three-dimensional space characteristics of the tree are extracted, and the potential risk of the tree and the power transmission facility can be dynamically monitored in real time by precisely calculating the space relation between the tree and the power transmission facility; according to the multi-modal fusion data, multi-level early warning information is generated, so that the efficiency and precision of power transmission line inspection are greatly improved, and the pre-judgment and timely treatment of hidden dangers are realized.

The invention provides a dynamic monitoring method for a soil mass seepage field of a power transmission tower slope. According to the method, related data of the slope are acquired through multi-source data acquisition and technologies of pressure sensing and static pressure measurement, displacement monitoring, multi-sensor fusion, Beidou / GPS positioning, resistivity method exploration, soil moisture content monitoring and the like; fusing the collected multi-source data, analyzing the dynamic change of the seepage field by using a risk assessment model and a machine learning algorithm, and predicting the evolution trend of the seepage field; a lightweight model is deployed at a monitoring terminal to realize local rapid processing of data, alarm information is immediately sent out once an abnormal state is monitored, and scientific and reasonable decision support is provided for protection and treatment of a transmission tower slope according to early warning information in combination with a pre-made decision scheme; according to the invention, the accuracy and timeliness of transmission tower slope soil seepage field monitoring can be effectively improved, and the threat of landslide and other disasters to a transmission line is reduced.

The invention provides an electric power engineering survey data processing method and a related device, and the method comprises the steps: obtaining a multi-source survey data set of a target region, the multi-source survey data set comprises geological topographic data, electromagnetic interference data and equipment layout data, carrying out the joint feature mapping of the multi-source survey data set through a pre-trained feature extraction network, and obtaining a feature mapping result; a multi-dimensional survey feature vector is generated, the multi-dimensional survey feature vector is input into an optimization decision model for iterative parameter adjustment, an electric power engineering optimization parameter set of the target area is generated, and the optimization decision model is obtained through training based on a mapping relation between historical survey data and a verification optimization result; and generating an electric power facility deployment scheme of the target area according to the electric power engineering optimization parameter set, wherein the deployment scheme comprises a power transmission line path planning result, substation site selection coordinates and an electromagnetic interference suppression strategy. According to the method, the environmental adaptability, the global coordination and the construction performability of a power facility deployment scheme can be improved.

The invention provides a high-voltage transmission line insulator defect detection method for unmanned aerial vehicle inspection. Firstly, by designing a multi-scale fusion and parameter sharing detection head, the feature fusion capability is effectively improved; secondly, an EMA attention mechanism is introduced, so that the model is more focused on information of a key area; the use of the depth separable convolution significantly reduces the parameter quantity and calculation complexity of the model. And finally, using Inner-IoU as a frame loss function, and more effectively improving the detection precision of the small target and enhancing the performance of the model in the aspect of prediction frame regression by focusing on the matching degree in the frame. The improved model can be used for accurately detecting the defects of the insulator, the detection precision is improved, and the operand of the model is effectively reduced through the lightweight design, so that the model provided by the invention is more suitable for being deployed to embedded equipment for real-time detection of the defects of the insulator, and the real-time detection of the defects of the insulator is facilitated. A new method is provided for the field of power transmission network insulator defect detection.

The invention provides an edge fusion diagnosis system and method for multi-source heterogeneous power transmission monitoring data, and belongs to the technical field of power transmission line monitoring. The multi-source heterogeneous power transmission monitoring data edge fusion diagnosis system comprises a multi-source heterogeneous data access module, a data preprocessing and standardization module, an edge computing node, an edge node collaborative diagnosis module, a data compression and feature extraction module and a cloud fusion analysis platform. The method comprises the following steps: accessing multi-source heterogeneous data, preprocessing and standardizing, locally diagnosing by an edge computing node, triggering multi-node collaborative diagnosis when the credibility is insufficient, and uploading key feature data to a cloud for global analysis and model optimization. According to the method, comprehensive integration and efficient processing of multi-source data are realized, the real-time performance, accuracy and comprehensiveness of power transmission monitoring diagnosis are improved, and the efficient monitoring requirement of a complex power transmission network can be met.

The invention discloses an automatic three-dimensional reconstruction method and system based on power transmission corridor point cloud. The method comprises the following steps: acquiring data and exporting point cloud data; noise points, outliers and ground points are removed from the acquired point cloud data of the power transmission corridor, and down-sampling is performed on the point cloud with a large data volume; performing clustering segmentation on the preprocessed point cloud of the power transmission corridor, positioning the position of a tower, determining a point cloud range of a power line, and removing point clouds of other objects on the ground; aiming at the structural characteristics of a power line and an insulator in the power transmission line, extracting point clouds of the power line, the insulator and a tower by adopting a geometric constraint principal component analysis (PCA) algorithm; performing automatic geometric model reconstruction on the tower, insulator and power line point clouds according to different reconstruction rules to generate each module grid model; and transforming the grid model of each module, restoring the grid model to the original position of the point cloud, and generating a power transmission line digital twin model conforming to the real size. According to the method, automatic segmentation, classification and real-time reconstruction can be accurately carried out on the point cloud of the power transmission corridor.

The invention relates to the technical field of intelligent operation inspection of power transmission lines, in particular to an intelligent detection method of a power grid frame and a satellite image processing system.The method comprises the steps that the power grid frame is detected according to collected multi-source monitoring information, information of a plurality of power grid structures is obtained, and a power grid topology model is established; performing connection analysis on the power grid topology model to obtain a power grid topology analysis result, and performing spatial distribution analysis on the power grid frame by collecting coordinate information of the power grid frame and the power grid topology analysis result to obtain power grid coverage data. And performing data visualization on the plurality of pieces of power grid structure information, the power grid topology analysis result and the power grid coverage data to generate a power grid frame detection result. According to the invention, the method effectively solves a problem that the weak edge region is liable to leak detection in the recognition of the power grid frame in a large-range and high-complexity scene, remarkably improves the recognition efficiency and accuracy of the power grid frame, and reduces the cost and time of manual inspection.

The invention provides an overhead transmission line defect detection method based on multi-scale feature fusion, and the method comprises the steps: carrying out the correction and registration of a lead image and a laser point cloud image, carrying out the data enhancement, and constructing a detection data set; the method comprises the following steps: respectively inputting two images into a target detection network for training, adding an attention mechanism in a feature extraction network Backbone so as to carry out correlation calculation on a target, and meanwhile, adopting a multi-scale feature fusion detection method in a backbone part of a YOLOV8 operation network, so that the improvement of precision and accuracy is realized on the premise of ensuring the size of a model, and the accuracy of the model is improved. And the requirement of actual overhead transmission conductor defect detection is met.

The invention discloses an efficient denoising power transmission line point cloud processing method and system, and relates to the technical field of three-dimensional point cloud data processing and intelligent analysis, and the method comprises the steps: carrying out the segmentation processing of original power transmission line point cloud data, dividing the point cloud into power equipment parts, carrying out the local density analysis of the segmented point cloud, and generating a density distribution diagram; based on the density distribution diagram and the local geometric features of the point clouds, multi-stage filtering is carried out, denoising model parameters are optimized, a statistical filtering method is combined to remove noise points, curvature features and normal vector consistency features of the point clouds are extracted, classification feature vectors are generated, and based on the classification feature vectors, fine classification is carried out on the point clouds. Through data preprocessing, multi-stage filtering, self-supervised learning, feature extraction and refined classification, the technical defects of insufficient multi-modal feature fusion efficiency and poor equipment feature adaptability in power transmission line point cloud denoising are effectively solved.

The invention provides a power transmission line mountain fire disaster risk assessment method and system based on space-time event driving, and the method comprises the steps: obtaining mountain fire monitoring data of a to-be-assessed region, and generating a space-time event according to the mountain fire monitoring data; according to an event association rule, the space-time event is associated with a power transmission line tower through a directional edge, and then a power transmission line mountain fire disaster hidden danger map is constructed; performing similarity matching on the time-space events generated in real time and the time-space events in the power transmission line mountain fire disaster hidden danger map, and searching the corresponding time-space events in the power transmission line mountain fire disaster hidden danger map by adopting a map traversal algorithm; and for the searched towers influenced by the space-time events, quantitatively calculating the risk score of each tower based on the data values of the real-time space-time events, and dividing the risk grades according to the risk scores. According to the method, the association precision of power transmission line risk assessment and the precision and efficiency of power transmission line forest fire prevention and control can be improved.

The invention belongs to the technical field of construction planning, and particularly relates to a power grid transmission line construction scheme planning method and system based on a knowledge graph, and the method comprises the steps: firstly constructing a knowledge graph model of a mechanical construction scheme and a standardized adaptation principle; obtaining a power transmission line project construction task, constructing a construction data integration set, inputting the construction data integration set into the mechanical construction scheme knowledge graph model, generating a power transmission line mechanical construction scheme, and screening out a standardized construction scheme based on an optimized MOPSO algorithm; according to the method, the knowledge graph and the three-dimensional construction scene are constructed, the multi-target problem is solved by optimizing the MOPSO algorithm, the optimal construction scheme is obtained, the construction process is simulated, the construction process is optimized, and the construction efficiency is improved. Therefore, standardized processing of different construction projects is achieved, the construction efficiency is improved, and the construction quality is guaranteed.