12937 results about "Power transmission" patented technology

The invention relates to the technical field of power system monitoring, and discloses a wire and cable fault early warning system based on intelligent monitoring, which comprises a data sensing module, a multi-mode fusion module, a characteristic evolution module, an abnormal early warning module and a dynamic optimization module. The data sensing module collects multi-source heterogeneous data, the multi-modal fusion module processes the data to generate a spatial-temporal feature matrix, the feature evolution module extracts cable degradation features, the abnormity early warning module performs fault early warning based on the cable degradation features, and the dynamic optimization module optimizes system parameters by using federal learning. In addition, the system also comprises a digital twin mapping and topology analysis module for assisting decision making and enhancing positioning. According to the invention, real-time monitoring, accurate fault early warning and system performance optimization of the operation state of the wire and cable are realized, the stability and reliability of power transmission are improved, and the system has the advantages of comprehensive multi-source data acquisition, efficient data processing, accurate early warning, data privacy protection and the like.

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 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.

The invention relates to the technical field of circuit detection, discloses a power transmission line thermochromic wire clamp heating early warning method and system, effectively solves the problem of data acquisition distortion in a strong electromagnetic environment, and improves the accuracy of state evaluation through multi-source data fusion. The dynamically adjusted early warning model reduces the risk of false alarm and missing alarm caused by equipment aging, the intelligent decision support module shortens the fault handling response time, the data closed-loop mechanism ensures the reliability of the system in the whole life cycle, and the reliability of the system in the whole life cycle is improved through multi-sensor cooperative monitoring and edge calculation processing. And the influence of environmental factors on data acquisition is reduced. The two-channel transmission architecture guarantees the data transmission integrity under different network conditions, the CRC verification mechanism effectively recognizes and corrects transmission errors, a high-quality data basis is provided for a subsequent early warning model, the abnormal data recollection mechanism avoids data missing caused by single collection failure, and continuous and stable operation of the monitoring system is ensured.

The invention discloses a pre-twisted damper fatigue life prediction method for an extra-high voltage ground wire, and particularly relates to the technical field of power transmission line vibration prevention. Collecting multi-source vibration and environment data, and constructing a vibration characteristic time sequence; amplitude, frequency and strain energy are extracted based on the multi-frequency vibration response, and a multi-frequency collaborative vibration model is established; obtaining a damper structure and material parameters, establishing a finite element model, coupling the finite element model with a vibration model, and simulating local stress response; a Rainflow counting method and a Miner damage criterion are adopted to construct a fatigue damage factor distribution matrix; predicting the residual fatigue life of the damper based on the damage evolution trend, and evaluating the replacement opportunity; according to the method, accurate fatigue life prediction and optimal replacement strategy recommendation of the pre-twisted damper under complex working conditions can be realized, and the method has high precision, high adaptability and engineering practicability.

The invention discloses a line loss treatment method based on low-voltage power grid multi-source data fusion, and belongs to the technical field of information, and the method comprises the steps: reconstructing a power supply path based on a power grid topological structure and a changed power access point; extracting reverse power flow characteristics of the frequency points for correcting a node impedance matrix of the current power supply path to obtain a power supply access point impedance parameter set; updating a power transmission path through the reverse power flow characteristic to obtain a power distribution characteristic; and a power grid topological structure, a current power supply path, a power supply access point impedance parameter set and power distribution characteristics are fused to construct a line loss distribution prediction model and identify a line loss abnormal region, and the line loss abnormal region is treated. Through implementation of the method and the device, the problem that the line loss treatment effect is poor due to the fact that the influence of factors such as transformer area topological structure change, power access point change and load fluctuation on the line loss is not considered in the prior art, and the predicted line loss distribution has a large error compared with the actual situation can be solved.

The invention relates to power transmission line unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection path dynamic planning system, which comprises a quantum high-precision positioning unit, an environment perception and electromagnetic field modeling unit, a dynamic path planning unit, an unmanned aerial vehicle flight control unit and a ground station task management unit. The quantum positioning unit fuses quantum inertial navigation, GNSS and quantum magnetometer data to improve the positioning precision under strong interference; the environment sensing unit obtains a three-dimensional environment and electromagnetic field distribution of the power transmission line; the path planning unit dynamically optimizes an inspection path based on positioning and environment information; the flight control unit performs accurate execution; and ground station management tasks and data analysis. The anti-interference positioning precision of the unmanned aerial vehicle is remarkably improved, accurate path planning and electromagnetic field avoidance are realized, the inspection risk is effectively reduced, and the efficiency, the safety and the adaptability are improved.

The invention provides an electric power inspection path planning method and device and electronic equipment, and relates to the technical field of unmanned aerial vehicle electric power inspection. The method comprises the following steps: acquiring obstacle information of an electric power facility environment, wherein the obstacle information comprises the type and position of an obstacle; based on the type of the obstacle and a preset safety distance coefficient, determining a differentiated safety distance, the type of the obstacle including a power transmission line, a transformer substation, a tower and other obstacles; based on the obstacle information and the differentiated safety distance, obtaining an initial global path through a path search algorithm; based on a preset multi-objective optimization function, the initial global path is optimized, a Pareto optimal path set is generated, and the multi-objective optimization function comprises a path length objective, a safety margin objective and an electromagnetic safety objective; and determining a target global path from the Pareto optimal path set based on a preset inspection task mode. According to the invention, the inspection efficiency and adaptability can be improved while the safety is guaranteed.

The invention relates to the technical field of fault monitoring, in particular to a power transmission line fault monitoring and positioning method, system and device and a medium. The method comprises the following steps: firstly, performing digital conversion on an original current signal to obtain time sequence data, and then identifying and classifying fault events through threshold detection and a feature extraction algorithm to obtain classification labels of fault types; then, based on the classification label, establishing a traveling wave propagation model and carrying out time difference calculation, carrying out clustering analysis on a plurality of wave head data in a preset time window, and judging whether the fault is a single-point fault or a multi-point fault through a clustering result; and finally, a positioning result is transmitted to the central server for comprehensive verification and alarm management, and a final fault response is formed. By introducing a time series data analysis and clustering judgment mechanism, the fault type can be accurately identified, single-point and multi-point fault conditions can be effectively distinguished, and meanwhile, the reliability of a positioning result is improved through comprehensive verification of the central server.

The invention provides a power transmission network equipment fault diagnosis and life prediction method and system, and relates to the technical field of fault diagnosis, and the method comprises the steps: obtaining the time sequence electrical characteristic data of a plurality of monitoring nodes, carrying out the window segmentation and statistical characteristic extraction, building a dynamic association graph structure based on a space-time association constraint model, and obtaining the time sequence electrical characteristic data; and calculating the abnormal contribution degree of each node, marking candidate abnormal nodes, determining a fault propagation path through reverse tracing and path analysis, and finally outputting a fault positioning result. According to the invention, abnormal nodes can be accurately identified, a fault propagation path can be accurately tracked, and the accuracy and timeliness of power transmission network fault diagnosis are improved.

The invention discloses a forest fire hidden danger identification method and device for a power transmission line based on a multi-modal large model, and the method comprises the steps: constructing a forest fire hidden danger identification model for a smog or flame-containing image outputted by a conventional target detection model, and enabling the model to guide a dialogue set through the combination with forest fire hidden danger identification, thereby achieving the recognition of the forest fire hidden danger. The dialogue context and the image visual features are fused, so that the deep fusion of the text semantics and the firework image features is realized; and finally, inputting the deeply fused features into a large language model, so that a forest fire hidden danger output dialogue of the power transmission line can be obtained by utilizing the deep semantic understanding capability of the large language model, and then a forest fire hidden danger recognition result of the power transmission line is obtained. Therefore, according to the invention, the judgment of whether the smoke and fire can damage the power transmission line is realized, the technology upgrade from smoke and fire existence detection to equipment threat research and judgment is completed, and on the basis, the problems of resource waste and untimely crisis response caused by reporting all smoke and fire information in the traditional technology can be avoided.

The invention discloses a power transmission line fault diagnosis and operation and maintenance scheduling method and system based on networking learning, and relates to the technical field of power transmission line fault diagnosis operation and maintenance scheduling. Related data is extracted to construct a high-risk equipment area and a visual high-risk area thermodynamic diagram, a visual risk grading diagram is constructed in combination with electrical quantity data, and meanwhile, an intelligent recognition storage network and a fault type classification recognition model are constructed in combination with a convolutional neural network-long and short-term memory network hybrid model; the model is optimized through networking learning and an attention mechanism, maintenance teams and resources are autonomously allocated in combination with an operation and maintenance management system, then autonomous optimization and closed-loop operation are achieved, full-process coverage of fault sensing, intelligent decision making and efficient response is achieved, the response time after a line fault occurs is remarkably shortened, and the maintenance efficiency is improved. And the fault handling and operation maintenance capabilities of the power grid system are comprehensively enhanced.

The invention relates to the technical field of power transmission and transformation equipment state evaluation, in particular to a power transmission and transformation equipment state evaluation system based on multi-sensor fusion, which realizes accurate alignment of multi-modal data by acquiring temperature, current, infrared thermal images, visible light images and laser point cloud data and constructing a unified time and space coordinate system. Short-term fluctuation features, image hot spot and texture anomaly features and structure change features are extracted through time sequence modeling, image saliency extraction, point cloud residual clustering and other methods, a joint feature vector is generated through fusion, and integrity and collaboration of multi-modal information expression are enhanced. And on the basis of a fixed time period, calculating a joint feature difference value between adjacent periods to obtain a residual sequence, and carrying out normalization processing in a sliding time window to construct a multi-period state residual spectrum. And finally, the atlas is input into a pre-training classification model, dynamic identification and anomaly detection of the equipment operation state are realized, and the intelligent evaluation capability under complex working conditions is effectively improved.

The invention relates to the technical field of power transmission and distribution production, and discloses a data integration and multi-modal diagnosis method based on a power transmission and distribution scene, and the method comprises the following steps: S1, enhanced integration of multi-source heterogeneous data: collecting time sequence monitoring data, text procedures and image data of power transmission and distribution equipment, constructing an equipment topological correlation graph through a graph attention neural network, and carrying out the enhanced integration of the multi-source heterogeneous data; node feature embedding is optimized through self-supervised comparative learning, an adversarial variational auto-encoder is designed for edge data, and an enhanced sample is generated in combination with physical constraints of equipment. According to the data integration and multi-modal diagnosis method based on the power transmission and distribution scene, the field adaptability and reliability of a diagnosis result are improved while the model fine tuning cost is reduced, and the knowledge migration problem of a general model in the power transmission and distribution scene is solved; the introduction of a dynamic knowledge graph and a multi-dimensional evaluation system realizes the real-time integration of new regulation knowledge and the comprehensive evaluation of model performance, and ensures the sustainable evolution ability and decision transparency of the diagnosis model.

The invention discloses a power transmission line foreign matter detection method and system based on multi-modal image fusion, and relates to the technical field of intelligent operation and maintenance and state monitoring of a power system, a lightweight Ev-Mama architecture is introduced into a backbone network part of YOLOv13, the model keeps relatively low calculation complexity, and meanwhile, the power transmission line foreign matter detection efficiency is improved. And the modeling capability of the method on the long-range dependency relationship and the global semantic information is obviously enhanced. Besides, by using the CDIDF module, the EVCS module and the MHSAA module, on the basis of increasing a small amount of calculation, the scale sensing ability, the space structure modeling ability and the context understanding ability of the model are effectively improved, and the performance bottleneck of a traditional YOLO series network in the aspects of processing small targets, shielding targets and cross-scale information fusion is effectively relieved.

The invention discloses a branch topological structure automatic switching control method and system based on load balancing, and relates to the technical field of automatic switching control, and the method comprises the following steps: obtaining working condition data, and constructing a joint map; extracting a power conduction path from the trunk node to each load node based on the joint map to form a high-load segment set; aiming at the high-load transmission segment, constructing a multi-target switching feasibility set, and generating a coupling relation mapping graph; based on the coupling relation mapping map, constructing a path search model, and solving an optimal topology switching path by adopting an enhanced iterative algorithm; and generating a topology switching sequence according to the optimal topology switching path, and implementing power transfer regulation and control on a high-load transmission section. According to the method, the joint atlas is constructed, the optimal topology switching path is generated, local overload is effectively relieved, power fluctuation is weakened, and the overall load balancing level and the active regulation and control capability of the system are improved.

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 cradle head inspection dynamic regulation and control method and system for a power transmission line, a storage medium and a program product, and relates to the technical field of power transmission line monitoring, and the method comprises the steps: obtaining the multi-source data of a target power transmission line, inputting the multi-source data into a preset dynamic risk assessment model, obtaining a dynamic risk thermodynamic diagram output by the preset dynamic risk assessment model; establishing a spatial characteristic model for a plurality of cradle head preset positions of the target power transmission line, and performing spatial correlation analysis on the spatial characteristic model and the dynamic risk thermodynamic diagram to obtain spatial correlation characteristic parameters of each cradle head preset position in the plurality of cradle head preset positions; determining an equipment state weight value of each piece of pan-tilt camera equipment in a plurality of pieces of pan-tilt camera equipment of the target power transmission line, and determining a comprehensive inspection weight value of each pan-tilt preset position according to the equipment state weight value and the spatial correlation characteristic parameters; and converting the comprehensive inspection weight value into a target inspection period of each cradle head preset position by using a first preset inverse proportional function.

The invention discloses a power transmission line state simulation and prediction method based on digital twinning, and the method comprises the following steps: accessing meteorological data, electrical data, space and asset data and historical state data, carrying out the preprocessing, and generating a multi-source spatial-temporal feature sequence set; constructing a double-domain cross-coupling time convolution twinborn model, and obtaining corresponding feature representation through a quick response path and a slow response path; physical consistency state representation is generated through a physical constraint cross gating layer; outputting a prediction result set through a simulation engine; online observation data are obtained, and the model is corrected based on virtual and real residual errors; and generating a current prediction result set by using the corrected model, and converting the current prediction result set into a scheduling and operation and maintenance strategy set. According to the method, the double-domain cross-coupling time convolution twin model and a virtual-real closed-loop correction mechanism are adopted, multi-scale state simulation prediction of the power transmission line is achieved, and the method has the advantages of being high in precision, robustness and performability.

The invention discloses a distribution line first-aid repair operation risk assessment method, system and equipment based on multi-source perception data and a medium, and belongs to the technical field of first-aid repair operation risk assessment. The method comprises the steps that multi-source data of perception data, operation data and environment data are collected and subjected to time-space alignment, quality verification and missing repair are carried out, and a first-aid repair result is obtained; generating fusion features with credibility weights; constructing a dynamic graph structure taking the space nodes and the participation roles as vertexes in the operation scene; a physical consistency index constraint used for reflecting an operation safety boundary is introduced into the graph structure, and an interpretable risk characterization mechanism is formed; carrying out distribution robustness and interval calibration on the risk characterization result; carrying out anti-fact simulation on the plurality of candidate coping schemes based on digital twin modeling, and triggering the sensing equipment to execute active supplementary mining operation according to an information value maximization strategy; sensing and simulation results are fed back to the model for updating and iteration. According to the invention, intelligent risk identification and adaptive control of the whole operation process of the power transmission line are realized.

The invention provides a power distribution network operation risk intelligent identification and early warning method, which comprises the steps of identifying an equipment contact failure probability through an actual wear state, and when the equipment contact failure probability exceeds a safe operation requirement, determining an equipment fault early warning signal through historical fault statistical data, identifying potential equipment failure risk points and extracting risk distribution characteristics; identifying a high-risk equipment node through the equipment fault early warning signal, evaluating whether a cascading fault of adjacent equipment overload is caused after power flow redistribution of a power grid according to the identified high-risk node, extracting a fault propagation path, and determining a system risk level distribution diagram; and carrying out risk area division on the system risk level distribution diagram, identifying key equipment nodes in a high-risk area, extracting a load transfer scheme of the high-risk area, and determining a load distribution path and a power transmission direction.

This invention advances power grid operational planning by introducing a mid-term scheduling framework for integrated hydro-wind-solar systems that accounts for heterogeneous daily load profiles across multiple receiving-end power grids. The proposed approach utilizes an adaptive variable-step search algorithm to segment loads into peak, flat, and valley intervals. By synthesizing five key metrics, including mean daily load, daily load factor, peak-valley differential ratio, load rates during peak / valley periods, and timing of peak / valley occurrences, the method accurately captures region-specific load patterns and peak-shaving demands. This enables a refined reconstruction of load profiles of receiving-end power grids. A nested multi-temporal scheduling model that couples medium- and short-term horizons to simultaneously maximize total energy production and minimize transmission imbalances among power grids. The model is addressed by using the mixed-integer linear programming (MILP) to obtain medium- and short-term generation schedules and power transmission schedules.

The invention belongs to the field of bird repelling and equipment state monitoring, and particularly discloses a bird inhabitation multi-mode repelling and state monitoring device and method. The device is combined with a photosensitive device for day and night environment discrimination, is integrated with an intelligent hardware device of a multi-mode driving unit and a state monitoring module, and is suitable for long-term bird prevention operation in unattended scenes such as power transmission lines and transformer substations; further, accurate detection of bird approaching is realized by fusing various sensors, efficient repelling is performed by combining a multi-channel interference means of touch, hearing and vision, meanwhile, the running state of the anti-bird structure is monitored in real time, and data is remotely transmitted back, so that long-term, stable, protection and monitoring integration in an unattended scene is realized, and the safety and reliability of the system are improved. The threat of bird damage to safe operation of a power system is effectively reduced.

The invention discloses a power transmission channel forest fire risk early warning method and system based on spatial-temporal feature fusion, and relates to the technical field of risk early warning. The method comprises the following steps: constructing a power transmission channel risk grid and fusing historical multi-source data to generate a static flammability background map; the method comprises the following steps: collecting real-time visual and micrometeorological data, and after space-time alignment processing, respectively extracting visual risk features and environmental flammability trend features by using a double-branch space-time feature extraction network; inputting the static background and the dynamic characteristics into a space-time fusion module to obtain a comprehensive forest fire risk index; when the index exceeds a threshold, triggering a forest fire spreading deduction model corrected by combining a power transmission corridor effect, predicting fire spreading, evaluating a line tripping probability, and issuing a graded early warning and emergency strategy; according to the method, the defects of serious data islands and inaccurate early warning are overcome, the crossing from pure fire point monitoring to risk situation dynamic prediction is realized, and the accuracy of forest fire early warning and the intelligent level of power grid prevention and control are remarkably improved.

The invention discloses a power transmission line intelligent inspection method and system based on an unmanned aerial vehicle, and relates to the technical field of power transmission line robots, and the method comprises the steps: collecting three-dimensional space environment data of laser point cloud, visible light, infrared and meteorological data through an airborne multi-mode sensing module; constructing a dynamically updated environment digital twinborn model based on the environment digital twinborn model; online path re-planning is carried out by using an improved fast extended random tree algorithm, and static obstacles and dynamic meteorological risks are synchronously avoided; in a multi-machine cooperation scene, distributing inspection subtasks by adopting a greedy partitioning algorithm based on load balancing; performing real-time defect detection and positioning on the tower image by using a multi-scale feature pyramid convolutional neural network through an edge calculation unit; the whole-process automatic closed loop of power transmission line inspection from environment perception, intelligent planning, collaborative operation to defect identification is realized, and the safety, efficiency and accuracy of inspection operation are improved.

The invention relates to the technical field of power equipment state monitoring, in particular to an AI fusion processing method, system and equipment for power transmission and transformation equipment state monitoring data and a storage medium. Collecting multi-source monitoring data of the power transmission and transformation equipment, constructing an equipment health portrait, and identifying key feature parameters and a coupling relationship among the parameters through association mining; performing multi-dimensional consistency check on the key feature parameters through interactive verification to obtain an initial health degree evaluation value, extracting a historical feature reference and performing deviation degree calculation, and combining the initial health degree evaluation value to obtain a comprehensive health degree and a corresponding threshold value; and based on the time sequence change of the comprehensive health degree, identifying a degradation evolution trajectory, determining a degradation type and a degradation rate, predicting the future state evolution trend of the equipment, and generating graded early warning information. Fusion and multi-dimensional accurate evaluation of multi-source heterogeneous data are obtained, and static evaluation is converted into dynamic prediction.

The invention discloses a wind-solar complementary energy storage direct current charging method and system based on liquid cooling heat dissipation, and relates to the technical field of energy storage charging. Comprising the following steps: S1, collecting wind-solar complementary energy storage data in real time, and carrying out data preprocessing; s2, converting wind-solar power generation into direct current, judging the flow direction of wind-solar power generation energy, and performing corresponding wind-solar power generation energy transmission power supply; s3, judging the limit of the output power of the charging pile, and controlling and adjusting the output of the charging pile in stages; s4, quantifying the liquid cooling flow required by heat dissipation, and performing heat source heat dissipation on the energy storage system and the charging pile; and S5, carrying out coordinated regulation and control, strategy optimization and multi-dimensional visualization on the flow direction of wind and light power generation energy, charging pile output and heat source heat dissipation. The problems that an existing charging station is difficult to deploy in remote and power transmission difficult areas, high-power charging heat dissipation and energy efficiency bottlenecks are prominent, and the efficiency of traditional air cooling and alternating current conversion is low, so that efficient, safe and green energy supplementation is difficult to achieve are solved.

The invention relates to the field of power transmission line forest fire monitoring, and discloses a power transmission line multi-mode fusion intelligent forest fire monitoring method which is used for constructing an all-dimensional power transmission line forest fire prevention monitoring system and remarkably improving the intelligent level of power transmission line forest fire risk prevention and control. Comprising the steps of reasoning and identifying a smoke and fire target for a fused feature set by utilizing an improved YOLOv8 algorithm model, realizing accurate positioning of a fire point by adopting a haze penetration thermal imaging technology and a smoke cooperative monitoring technology in combination with spatial positioning, and establishing a temperature and humidity and forest fire occurrence probability correlation model based on historical data. According to the real-time temperature and humidity parameter dynamic adjustment algorithm model, the confidence threshold and the sensor acquisition frequency are detected, a dynamic threshold control method is adopted, an early warning threshold is adaptively adjusted according to parameters such as environment illumination, a final early warning result is output after cross validation of a multi-modal data fusion strategy, and the safety of the power transmission line is effectively guaranteed.

The invention discloses a power transmission line icing intelligent early warning method, system and device and a medium, and the method comprises the steps: obtaining icing state data and meteorological data, and dynamically adjusting the collection frequency and a dormancy strategy; performing data preprocessing and cleaning on the acquired data; extracting time-frequency features through wavelet packet transformation and a self-attention mechanism, and fusing the spatial dependency relationship and cross-modal interaction information of multiple monitoring points by using a graph neural network to obtain enhanced icing state characterization; performing icing risk prediction by adopting a gradient boosting decision tree model to obtain an icing risk prediction result; and analyzing an icing risk prediction result by using an interpretable tool, identifying a key factor which has the greatest influence on icing risk prediction, dynamically adjusting an early warning level according to the key factor, and generating an early warning and maintenance suggestion. Therefore, the monitoring real-time performance and the early warning timeliness are improved.

The invention discloses a power grid dynamic scheduling decision-making method and device based on multi-modal prediction, electronic equipment and a storage medium, and belongs to the field of power system regulation and control operation, and the method comprises the steps: obtaining internal state data and external working condition data of each target power grid device, and a future load change curve of a related power transmission and distribution line, and an equipment feature matrix is constructed through space-time alignment. And inputting the feature matrix into a multi-modal neural network, and outputting the health index, the remaining service life and the fault probability. When the equipment health index is lower than a threshold value, a multi-objective optimization model is constructed, a preventive scheduling strategy is generated, and scheduling is executed; and when the equipment fault probability exceeds a set threshold value, updating the power grid line weight based on load prediction, generating a topology reconstruction scheme of the minimum power failure range, and scheduling according to the topology reconstruction scheme. By implementing the method and the device, the problem that the long-term degradation trend and the short-term sudden risk of the equipment cannot be accurately predicted due to single data dimension in the prior art can be solved.