2902 results about "Power substation" patented technology

The invention discloses a substation equipment rare defect simulation and identification method and system and a storage medium. The method comprises the following steps: constructing an equipment reference feature library; marking dynamic features of rare defects in historical inspection according to a spatial-temporal feature enhancement algorithm, generating a knowledge graph, and constructing a dynamic defect learning library; the method comprises the following steps: learning space association and environmental factor influence of defects and equipment through a bimodal generation network, and generating initial defect data matched with a weak area of the equipment; generating high-credibility defect fusion data through physical constraint-intelligent detection double screening; constructing a three-dimensional mixed data set, and screening high-quality training samples through a dynamic defect evolution algorithm and hierarchical cognitive evaluation; and constructing a multi-algorithm collaborative fine tuning network by using a federated learning framework, simulating and labeling defect information, and outputting a multi-dimensional identification prediction report. The method aims at solving the problems that the model is insufficient in rare defect recognition precision and lack of evolution prediction ability, and high-quality simulation of rare defect samples and high-precision recognition of the model are achieved.

The invention relates to the technical field of smart power grids and artificial intelligence, in particular to a substation autonomous intelligent inspection method and system based on multi-modal data, and the method comprises the steps: obtaining inspection data of multiple modals, and generating fusion features; identifying system alarm information; performing intention recognition and task classification to generate an executable task sequence; generating a multi-device cooperative scheduling scheme; executing the multi-device cooperative scheduling scheme; iterative optimization is carried out; according to the intelligent inspection method provided by the invention, more accurate and more robust multi-mode perception and diagnosis are realized, and deep understanding of complex instructions and safe and efficient cooperation of multiple devices are also realized; and meanwhile, through dynamic re-planning and a verification type feedback learning mechanism, high real-time performance and robustness are ensured, and meanwhile, the system is endowed with the capability of iterative optimization.

The invention relates to the technical field of transformer substation intelligent inspection, in particular to a meta-learning-based small-sample transformer substation equipment state adaptive inspection system, which comprises a state acquisition module for acquiring the current feature vector and environmental parameter data of a target node; the drift detection module is used for comparing environment parameters to judge data drift and dynamically adjusting a confidence coefficient threshold value; the risk assessment module inputs the feature data into a meta-learning model to output an initial risk probability, and generates an effective risk probability based on threshold filtering; the blind area measurement module is used for acquiring unobserved nodes and calculating system state blind area entropy; the scheduling decision-making module is used for comparing the blind area entropy with a threshold value and generating an entropy reduction bottom instruction or a self-adaptive routing inspection distribution instruction; the strategy updating module is used for extracting an actual inspection result and feeding back to the model for parameter updating; according to the invention, the scheduling difficulty when the resources are limited is solved, and the self-adaptive capability of the system under different environment interferences is improved.

The invention relates to the technical field of substation abnormity identification, and discloses a substation switch cabinet abnormity identification method and system, and the method comprises the steps: obtaining the real-time temperature data of a plurality of monitoring points in a switch cabinet, and uploading the real-time temperature data to a big data analysis platform through a wired or wireless communication mode; and continuous monitoring of the operation state of the equipment is realized. The highest temperature of the monitoring area, the highest temperature difference value of the adjacent monitoring periods and the temperature sudden change value are calculated based on the real-time temperature data, and when the parameters exceed preset threshold values, a corresponding alarm strategy can be triggered in time. And through the measures of generating an infrared thermogram, extracting a temperature distribution difference area, marking high-risk monitoring points and the like, the accuracy and timeliness of anomaly recognition are further improved. Meanwhile, the temperature trend prediction model is used for predicting the future temperature development trend, an early warning signal is generated in advance, a preventive alarm mechanism is started, and a powerful guarantee is provided for safe and stable operation of a power system.

The invention discloses a storage battery capacity prediction method based on multi-source asynchronous perception and time hybrid modeling, and the method comprises the steps: collecting electrochemical parameters of a storage battery pack, carrying out the preprocessing, obtaining a time sequence sample, and extracting a capacity time sequence; constructing a multi-channel convolution encoder family module, and performing independent feature extraction on the electrochemical parameters by taking a time sequence sample as input to obtain spatial feature mapping; constructing a time hybrid modeling module, and obtaining capacity time sequence feature mapping by taking the capacity time sequence as input; constructing a cross-parameter feature fusion layer, and inputting spatial feature mapping and capacity time sequence feature mapping to obtain health state features; inputting the health state characteristics into a regression prediction head, and outputting a prediction value of the storage battery pack; and constructing a main loss function to carry out model training. The method realizes an integrated prediction mechanism for the transformer substation storage battery capacity degradation process, can effectively adapt to the complex operation environment of a transformer substation, and has relatively high engineering feasibility and popularization and application values.

The invention relates to the technical field, and discloses a power distribution network multi-time scale fault scene deduction method comprising the following steps: obtaining meteorological and power grid operation data, establishing a time sequence fault tree model, and analyzing the trigger probability of multi-line disconnection and rainstorm short circuit faults in a target time period; extracting cross-level fault propagation features, analyzing a dynamic coupling relationship among multi-line disconnection, transformer substation flooding and cascading trip, forming a fault feature mode set, and identifying a single fault and a cascading fault in combination with time sequence analysis; historical fault data are analyzed, time sequence features and topological features of single and cascading faults are extracted, if a single fault propagation path is in a single level, a support vector machine is used for being combined with the features to judge fault types, and a classification result is output; and performing anomaly detection and confidence evaluation on a fault classification result, outputting a fault evolution path and risk evaluation, and analyzing the contribution degree of cascading trip to a large-area power failure risk in combination with historical blackout data to obtain power failure risk probability distribution.

The invention discloses an automatic auditing and checking system and method for substation operation safety. The system comprises an automatic data acquisition module, a data semantic fusion analysis module, a dynamic auditing rule generation module, a risk collaborative prediction module, an automatic information pushing module and a visual interaction configuration module. All the modules are connected through an intranet data interface and a system internal communication protocol, aiming at the prominent problems that the auditing process depends on manpower, the information system is split, the system semantics is difficult to understand, the risk identification is not comprehensive, the rule adaptability is poor, the response and push chain is broken and the like commonly existing in the existing substation operation auditing work. The invention provides an automatic auditing and checking system and method for substation operation safety, and aims to construct a full-process closed-loop intelligent auditing platform with semantic comprehension capability, multi-source data fusion capability, risk collaborative prediction capability and dynamic rule evolution capability.

The invention provides a substation safety intelligent monitoring method based on infrared binocular vision, which comprises the following steps: acquiring an infrared radiation image, a visible light image and environmental parameters of equipment, and accurately inverting the real temperature of the equipment after eliminating environmental interference in combination with a depth distance calculation and physical compensation model; a historical time sequence of a real temperature is used to construct a multi-dimensional state vector fusing a thermal feature, a spatial feature and an environment feature, and intelligent classification evaluation of the security risk of the transformer substation is realized through an improved decision tree model. Meanwhile, a time sequence prediction model is introduced to predict the temperature rise trend of the equipment, the remaining time for reaching an early warning threshold value is calculated, and a dynamic grading early warning report is generated. According to the method, the temperature measurement precision and the risk identification accuracy are effectively improved, early warning and active prevention and control of the operation state of the equipment are realized, and the operation safety and the intelligent operation and maintenance level of the transformer substation are remarkably improved.

The invention discloses an electromagnetic transient simulation and mapping method and system oriented to multi-physics field coupling, and belongs to the technical field of substation digital operation and maintenance and electromagnetic simulation. Deploying multiple types of sensors in a transformer substation, preprocessing the acquired multi-physical field data, and fusing with the three-dimensional twinborn model; a coupling algorithm is adopted to carry out multi-physical field coupling analysis, multi-physical quantity display and thermodynamic diagram superposition are realized, and scene evolution change simulation under various typical working conditions is supported; a micro-service architecture is adopted to construct a digital twin system, double closed-loop verification of simulation and actual measurement is performed through actual measurement data and simulation data of a real-model test field, model optimization is completed, and real-time mapping of electromagnetic transient dynamic simulation and a transformer substation physical entity is realized. The method improves the precision of model and entity mapping, meets the requirements of real-time tracking and response of the electromagnetic transient change of the transformer substation, adapts to the digital transformation of different transformer substations, and provides a basis for the operation and maintenance of the transformer substations.

The invention discloses a power transformation and distribution station room operation method and device based on a force control dexterous hand, and belongs to the technical field of robots. According to the technical scheme provided by the embodiment of the invention, the distributed multi-source sensor array is utilized to obtain the consistent environment data, the operation situation description is dynamically generated to adapt to the environment change, the complete operation scheme is intelligently generated in combination with the cross-domain strategy knowledge graph, and real-time feedback is performed through the second multi-mode sensing data flow acquired during control, so that the control accuracy is improved. The problems of low efficiency and safety risk coexistence in the operation of the power transformation and distribution station room are solved.

The invention discloses an automatic cruise system for a cleaning robot of a power insulator of a transformer substation, and relates to the technical field of intelligent operation and maintenance of the transformer substation, the automatic cruise system comprises a data extraction module, a processing module, a decision module and an execution module, and solves the technical problems of excessive shutdown when a wind field approaches danger and discontinuous operation when the wind field is safe. The decision-making module sets safety indexes according to the weights of the wind speed and the wind direction, divides the wind speed into four risk intervals, divides the wind direction into three grades, calculates the total safety score of the wind field through weighted summation, corresponds to three operation grades of safety, yellow early warning and red early warning, generates differential instructions according to different grades, and sends the differential instructions to the wind field. And real-time path updating during early warning level switching is supported, and safe operation under the dynamic change of the wind field is realized.

The invention provides a photovoltaic consumption-oriented electric vehicle battery swap station collaborative planning method, and the method comprises the steps: obtaining planning input data which comprises power distribution network operation parameters, user charging demand data, electric vehicle battery swap station battery data, electricity price subsidy data and photovoltaic power generation data; planning constraint conditions are determined, wherein the planning constraint conditions comprise an electric vehicle battery swap station operation constraint, a node power balance constraint, a transformer substation output constraint, a line power flow limit value constraint, a node voltage constraint and a photovoltaic consumption constraint; determining a target function, wherein the target function is used for cooperatively optimizing the construction position and energy storage configuration of the battery swap station, a charging subsidy gear trigger strategy and a photovoltaic consumption level; and according to the planning input data, the planning constraint condition and the objective function, establishing and solving a mixed integer linear programming model to obtain an electric vehicle battery swap station collaborative programming scheme. According to the obtained electric vehicle battery swap station collaborative planning scheme, the consumption level of local photovoltaic electric energy is effectively improved.

The invention provides a demand side response power consumption management method based on a power gap architecture, and relates to the technical field of information, the power gap architecture especially relates to a distributed intelligent communication framework oriented to a novel power system, and multi-source power consumption and environment data are collected through power gap edge computing nodes of a transformer substation; through cleaning, feature extraction and clustering, classification results of three types of power consumption subjects of industry, business and residents are obtained, and the nodes carry computers and auxiliary equipment repair adaptation modules to guarantee operation; a classification result is encrypted and transmitted to a trusted execution environment, a power consumption behavior model is constructed by fusing multiple factors, a dynamic electricity price strategy and a response rule are generated and pushed to a terminal, and a regional response strategy is optimized by combining a computer and an auxiliary equipment repair intelligent operation and maintenance mechanism; a final management instruction is generated through load balancing judgment, optimized data are obtained after execution, and the model and the strategy are dynamically updated. Accurate regulation and control of power utilization are realized, the power grid load is balanced, and the power utilization efficiency is improved.

The invention discloses a substation safety distance monitoring system based on multi-modal data fusion, and relates to the technical field of substation safety distance monitoring. Comprising a laser point cloud data acquisition module, a visible light image data acquisition module, a data fusion module, a model construction module, a target identification module, a target sensing module, a safe distance calculation module, a danger alarm module, an intrusion area identification module, an alarm grading module, a sound-light alarm module, a broadcast alarm module and an unmanned aerial vehicle expelling module. According to the transformer substation safety distance monitoring system based on multi-modal data fusion, security and protection are upgraded from static alarm to dynamic active intervention through the unmanned aerial vehicle expelling module, an unmanned aerial vehicle can quickly arrive at a site and track, warn and expel from an optimal perspective, the problem that security personnel arrive at the site slowly is solved, and the security and protection efficiency is improved. And the active defense capability and the emergency disposal efficiency of the system are greatly improved.

The invention discloses a method and a system for positioning in a transformer substation based on Beidou and video deep fusion, and relates to the technical field of intelligent safety management and control of power system transformer substations. Firstly, three-dimensional point cloud data and a plurality of ground control point world coordinates of a target transformer substation are acquired, and the control point world coordinates are used as constraints; and registering the three-dimensional point cloud data to an east-north-sky coordinate system, and constructing a unified space reference model. And synchronously acquiring real-time video streams of the transformer substation and Beidou world positioning data of the target object, and positioning and resolving the video streams based on the reference model to obtain video world positioning data. And performing space-time alignment on the two types of positioning data, optimizing Beidou data through adaptive correction, combining video positioning data and adopting Kalman filtering to complete deep fusion, and finally converting fused data into standardized world positioning data and performing visual output. The method effectively solves the problems of poor positioning continuity and unstable precision in the complex environment of the transformer substation in the prior art, and gives consideration to the positioning reliability and practicability.

The invention discloses an intelligent fire-fighting inspection system and method for a main transformer of a transformer substation, and the method comprises the steps: collecting fire-fighting inspection related data of an automatic fire extinguishing system, and uploading the data to a cloud for storage; classifying the collected fire-fighting inspection related data of the automatic fire extinguishing system according to different time sequence change types; establishing a corresponding neural network prediction model according to fire-fighting inspection related data of the automatic fire extinguishing system and different classification results of time sequence change types; inputting the classified fire-fighting inspection related data of the automatic fire extinguishing system into a corresponding neural network prediction model, and calculating to obtain a fire-fighting inspection prediction result of the automatic fire extinguishing system; and comparing the fire-fighting inspection prediction result of the automatic fire extinguishing system with a preset fire-fighting inspection data threshold value, and judging whether the fire-fighting inspection prediction result of the automatic fire extinguishing system has a fault risk or not. The working state of the fire extinguishing system is intelligently predicted, and the automatic fire extinguishing system of the transformer substation main transformer is intelligently inspected.

The invention relates to the technical field of deep learning, and discloses a substation equipment maintenance and replacement method and system based on deep learning. The method comprises the following steps: acquiring historical and real-time data of equipment and topological information of a power grid; constructing an equipment health state evaluation model, and outputting the residual life and the fault risk level; establishing a power grid stability constraint model, and quantifying the equipment failure influence; multi-dimensional features are fused to construct a multi-target deep reinforcement learning decision model, and a maintenance and replacement instruction considering safety, stability and economical efficiency is generated; and scheduling resources to execute closed-loop operation and maintenance. The system comprises a data acquisition module, a health assessment module, a stability modeling module, a multi-objective decision engine module and an execution scheduling module. According to the system, through a multi-objective collaborative optimization and adaptive learning mechanism, the decision scientificity and the power grid operation reliability are remarkably improved.

The invention discloses a ground wire full life cycle management method, relates to the technical field of power equipment asset management, and aims to solve the problems that manual recording is easy to tamper, cloud diagnosis delay is high and an alarm mode is single and easy to neglect in traditional ground wire management. According to the method, operation fingerprints and spatio-temporal information during grounding wire hooking are recorded through the block chain technology, and it is ensured that data cannot be tampered; a built-in lightweight AI model of the edge computing unit is used for analyzing sensor data in real time to carry out localized fault diagnosis; and triggering a multi-mode sound-light alarm controlled by the PWM signal according to the diagnosis result grade. The system realizes full-life-cycle credible traceability, sub-second fault response and high-recognition-rate alarm of the state of the grounding wire, and is suitable for intelligent operation and maintenance of the grounding wire in the field of a transformer substation and the like.

The invention discloses a transformer substation anti-error logic verification method, system and device and a storage medium. The method comprises the following steps: configuring secondary locking primary logic for primary equipment; only secondary locking secondary logic needs to be configured for the protection pressing plate, and primary locking secondary logic does not need to be configured; when the primary equipment is operated, secondary locking primary logic is verified; when the protection pressing plate is operated, the secondary locking secondary logic is verified, and meanwhile, the secondary locking primary logic is verified based on the expected state of the simulation pressing plate, so that primary locking secondary verification is achieved. According to the method, the problem that secondary anti-error configuration of an existing transformer substation is complex can be solved, the engineering configuration efficiency is greatly improved, the logic rule acceptance time is shortened, the configuration error risk caused by human errors is reduced, the system reliability is remarkably improved, and the practical level of secondary anti-error configuration of the transformer substation is improved.

The invention relates to the technical field of substation equipment state monitoring, in particular to a substation switch cabinet state online monitoring device and method based on multi-source data fusion, and the monitoring device comprises a multi-dimensional sensing acquisition system, an edge intelligent processing unit, a multi-mode communication system and an intelligent early warning and decision-making system. The multi-dimensional sensing acquisition system acquires various types of operation parameters, the edge intelligent processing unit processes the parameters, the multi-modal communication system transmits data, and the intelligent early warning and decision-making system performs early warning and generates a maintenance scheme; according to the monitoring method, sensor deployment and calibration, multi-source data acquisition and synchronization, data preprocessing and feature extraction, state diagnosis and health assessment, data transmission and cloud collaboration, and early warning triggering and maintenance decision making are carried out in sequence. The switch cabinet state can be comprehensively monitored, the fault diagnosis accuracy is improved, and safe and stable operation of a power grid is guaranteed.

The invention discloses a rapid fault isolation method integrating protection, measurement and control of a box-type substation, particularly relates to the technical field of fault isolation of a power distribution network, and aims to solve the problem that an existing box-type substation is not complete in protection action information, disturbance exists in measurement and a primary wiring relation is complex. The problems of difficulty in timely and accurate judgment of a fault section, large isolation range, insufficient supply guarantee of important users and lack of traceable evidence chain in a fault processing process are solved. The method comprises the following steps: dividing sections according to primary switches and cable joints in a box-type substation, establishing a section-level topology model for unified time service and fault evidence fingerprints bound with protection configuration, scoring operation slices based on section fault hypothesis and a theoretical event mode, and solving a minimum isolation switch set under isolation constraint, so as to obtain a minimum isolation switch set; therefore, when the protection action is incomplete, the measurement is disturbed and the wiring is complicated, the fault section can still be locked, and an isolation scheme considering the user supply guarantee and the electrical safety boundary is generated, so that the fault isolation correctness and rapidity are improved.

The invention relates to the technical field of risk early warning, and provides an operation risk early warning method, device and system for substation operation and maintenance personnel and a medium. According to the implementation scheme, the method comprises the steps of training a behavior recognition model based on multi-modal historical data to obtain a target recognition model; based on the current operation data of the operation and maintenance personnel, performing risk identification on the current operation behavior represented by the current operation data through a target identification model to obtain an identification result; if the identification result is that the current operation behavior belongs to the dangerous behavior, triggering a multi-dimensional risk assessment mechanism to perform risk assessment on the current operation behavior to obtain a risk assessment result; and generating risk early warning information based on the risk assessment result. According to the embodiment of the invention, the intelligent early warning of the operation risk of the operation and maintenance personnel of the transformer substation can be realized, and the safety guarantee level of the operation and maintenance personnel during operation is improved.

The invention discloses a standby power supply system, and relates to the technical field of transformer substation power supply, a hydrogen production module is used for producing hydrogen by using commercial power or renewable power and then storing the hydrogen in a high-pressure gas storage tank of a hydrogen storage module, and the hydrogen storage module is provided with a one-way valve for the hydrogen to pass through in one way and a gas compressor for compressing the hydrogen to realize hydrogen conveying; after hydrogen stored in the hydrogen storage module is subjected to power generation through a fuel cell through the power generation module, the hydrogen is input into a storage battery pack of an uninterruptible power supply system of the transformer substation, and in the whole process, the control module detects the state of a main power supply and controls starting and stopping of the power generation module. The hydrogen production rate of the hydrogen production module and the hydrogen storage pressure of the hydrogen storage module are controlled, only water is generated in the power generation process, harmful emission is avoided, direct current generated by the fuel cell is directly matched with a UPS storage battery direct current bus, the response speed is high, conversion loss is reduced, the energy utilization efficiency is improved, the system automation degree is high, and the operation and maintenance cost is low.

The invention discloses a transformer substation abnormity intelligent diagnosis method based on a self-attention mechanism, and relates to the technical field of transformer substation abnormity identification, and the method comprises the following steps: S2, employing a computer vision technology, a sensor, an ultrasonic detector, and a current and voltage detector to collect the electrical parameters of equipment, and completing the collection of multi-source data; s3, realizing a virtual mirror image of the physical equipment by using a digital twinning technology; and S5, analyzing and processing the important information in the database through a self-attention mechanism, and predicting the health trend, risk point and service life of the substation equipment. According to the method, image information and equipment parameters of substation equipment are collected in real time, an attention weight matrix of data is obtained through calculation, multi-source data are associated, the fault probability is output, then a regression device is used, key parameter values of multiple time points in the future and the remaining service life are output, and the service life of the substation equipment is calculated. And predicting the health trend, the risk point and the service life of the substation equipment.

The invention discloses a transformer substation global digital twinborn fusion system, which comprises a data fusion module, a model fusion module and an artificial intelligence fusion module, and is characterized in that the data fusion module is used for collecting multi-source data of a transformer substation, preprocessing the data and performing data fusion on the preprocessed data; the model fusion module is used for constructing a bidirectional dynamic mapping model of a transformer substation physical entity and a digital virtual body, constructing a multi-field model and constructing a model increment updating mechanism, and the artificial intelligence fusion module is used for correcting the deviation of an AI model, performing simulation verification on the output of the AI model and generating a visual alarm. According to the method, the technical problems of incomplete data fusion, poor model fusion collaboration and insufficient AI and digital twinning fusion depth in the existing transformer substation digital twinning system are solved, real-time bidirectional accurate mapping of the transformer substation physical entity and the digital virtual body is realized, and the operation and maintenance efficiency and the decision-making precision are improved.

The invention relates to the technical field of network data security, in particular to a power fgOTN power low-delay secure transmission method. Obtaining a service bearing network topology and link transmission parameters, and constructing a network connection matrix; simulating a flow redistribution process after a fault of a single node, and calculating a topology betweenness centrality index of the substation node; the method comprises the following steps: analyzing payload contents of power business data frames at a fgOTN mapping preceding stage, extracting continuous monitoring features and discrete control features, and calculating a real-time business criticality index of each data frame in combination with a frequency deviation and a voltage fluctuation state of a current network; establishing a two-dimensional decision matrix, and generating a defense strategy according to mapping positions of the topological betweenness centrality index and the criticality index value in the matrix; processing the service data according to the generated defense strategy, and mapping the service data to a fine granularity time slot of the fgOTN; and embedding a security state signaling in the frame overhead, and sending the security state signaling to opposite-end equipment through an optical transmission unit.

The invention discloses a substation double-pump rotation automatic drainage control method and system and a medium, and relates to the technical field of double-pump rotation automatic drainage control, and the method comprises the following steps: obtaining a liquid level signal and a double-pump start-stop action signal of a double-pump drainage system, and recognizing a disturbance area of the liquid level signal according to the double-pump start-stop action signal; performing signal reconstruction on the disturbance area to obtain a reconstructed liquid level sequence, and establishing a double-pump rotation evaluation model based on the reconstructed liquid level sequence; the next round of double-pump start-stop action time is evaluated based on the double-pump rotation evaluation model, and the optimal double-pump start-stop action state is obtained; coupling the optimal double-pump start-stop action state with a liquid level signal to obtain an equivalent liquid level value; the double-pump alternating control is executed through the equivalent liquid level value, the double-pump starting and stopping control signal is output, and the problem that when the pumps are started or stopped, the liquid level signal jumps disorderly due to instantaneous water flow disturbance, and consequently double-pump alternating misoperation is caused is solved.

The invention belongs to the field of transformer substation design, and provides a transformer substation building parameterization intelligent design method and system, and the method comprises the steps: constructing a parameterization component library, building a component model, and configuring adjustable parameters and constraint rules of components; obtaining a station area general map and geological data of a target transformer substation, and generating a three-dimensional model containing terrain and a foundation; corresponding building components are selected from the parameterized component library, a building contour is arranged, and a preliminary model is formed; a design rule is called, corresponding parameters of the preliminary model are adjusted in a parameterized mode under the design rule, a structure calculation model is generated, and structure finite element calculation analysis is conducted; whether conflicts exist in the adjusted model is determined, if yes, conflict positions are marked, and an optimization scheme is produced; and if not, generating a drawing of the adjusted model, calculating an analysis result according to the structural finite element, and outputting a material list and an engineering quantity. The problem of multi-specialty collision can be solved, and the design efficiency and the standardization level are improved.

The invention discloses a transformer substation insulator pollution positioning method, device and system fusing a multispectral image and a laser radar point cloud, and belongs to the technical field of power equipment multispectral detection and intelligent operation and maintenance. The method comprises the steps that a multispectral imaging device and a laser radar device are arranged in a measurement area facing the outdoor insulator of the extra-high voltage transformer substation, and the laser radar device is controlled to collect three-dimensional point clouds of the insulator and a surrounding structure of the insulator, controlling the multispectral imaging device to acquire multispectral images of the outer insulation surface of the insulator under a plurality of working wavebands; performing spatial registration on the three-dimensional point cloud and the multispectral image based on a calibration plate and geometric constraints, and mapping multispectral spectral information to point cloud sampling points; by introducing a multispectral three-dimensional point cloud and a virtual cleaning operator and combining multi-view joint optimization under an umbrella skirt structure parameter model, the problem that pollution assessment is unstable and not fine due to a single-view image or a simple intensity threshold value in the prior art is solved; and a reliable quantitative basis is provided for substation insulator electrical risk assessment and intelligent operation and maintenance decision.

The invention relates to the technical field of substation inspection and maintenance intellectualization, in particular to a substation inspection and maintenance multi-expert agent collaborative task generation method based on an Agent2Agent protocol, which comprises the following steps of: receiving a substation inspection and maintenance natural language instruction, analyzing the instruction into a task tree containing a root node and a leaf node based on the physical topology of a substation, and labeling a capability label; calculating the semantic similarity between the current task tree and the historical task, and multiplexing and finely adjusting a historical agent capability chain or calling a capability graph to form a current capability chain; the intelligent agent is controlled to execute subtasks according to a capability chain, detection equipment is deployed in combination with a point location type, the intelligent agent performs standardized interaction through an Agent2Agent protocol, and secondary scheduling is performed when a capability gap appears; and performing streaming processing on task data, completing incremental indexing, performing fine tuning on a correlation model, and endowing high-quality feedback data with an initial weight so as to realize continuous optimization. According to the invention, the generation efficiency and execution accuracy of the substation inspection and maintenance task are improved, and intelligent operation and maintenance requirements are met.