4030 results about "Dependability" patented technology

The invention belongs to the field of power system engineering, and discloses a power distribution automation terminal diagnosis method and system based on multi-source wave recording feature fusion, and the method comprises the steps: obtaining the electric quantity data and equipment operation state data collected by a power distribution automation terminal; performing adaptive decomposition on the electrical quantity data by using a variational mode decomposition algorithm to obtain an intrinsic mode function; constructing a deep residual network model, carrying out fusion analysis on the time-frequency domain features of the intrinsic mode function, and generating a fault feature vector; establishing a multi-dimensional evaluation matrix based on the fault feature vectors, and integrating a plurality of indexes to output fault types and credibility scores; according to the fault type and the credibility score, generating a fault isolation strategy based on a Petri network model; and executing a dynamically adjusted self-adaptive self-healing control algorithm. According to the method, complex and changeable fault modes can be effectively identified, a complete collaborative verification mechanism is formed, seamless connection from fault diagnosis to self-healing control is realized, and the operation reliability of the power distribution network is remarkably improved.

The invention discloses a drainage basin maintenance intelligent management system based on the Internet of Things and intelligent decision, and relates to the technical field of computers. Comprising a multi-source perception and edge access module which is used for realizing multi-protocol access, time and session alignment, data quality labeling, breakpoint resume and edge side anomaly preliminary screening of industrial data, low-power-consumption point location data and video and thermal image data. According to the invention, through the multi-source sensing and edge access module, various types of data such as industrial data, low-power-consumption point location data, video and thermal image data can be processed, the technical problems of multi-protocol access, time and session alignment, data quality labeling and breakpoint resume and the like are solved, the integrity and reliability of the data on the edge side are ensured, and the service life of the data is prolonged. The edge side anomaly preliminary screening function significantly improves the timeliness of early fault discovery, reduces the network bandwidth pressure, effectively solves the problems of anomaly cooperative detection and alarm flooding in a complex system, and improves the accuracy and timeliness of fault early warning.

The invention relates to the technical field of multi-agent collaboration, and discloses a multi-agent collaboration system with a task automatic decomposition and hierarchical rechecking mechanism. The system comprises a task planning framework construction module, a dynamic role matching module, a layered review triggering module and a cooperative execution monitoring module. Wherein the task planning framework construction module is used for generating a directed acyclic graph task framework consisting of a plurality of logic dependence subtasks after receiving a top-layer complex task; the dynamic role matching module screens skill matching degrees based on sub-node requirements, and dynamically binds execution agent identifiers and sub-task nodes; the hierarchical review triggering module is used for embedding configurable review nodes in a task framework, selecting a review hierarchy of AI cross validation or manual approval according to a sub-task risk level, and generating a node review strategy set; and the cooperative execution monitoring module generates a task execution track sequence according to the node state flow. According to the system, the efficiency and reliability of multi-agent cooperative processing of complex tasks are improved.

The invention relates to a reservoir dam operation safety sky-land project intelligent sensing system and an operation method, and relates to the technical field of hydraulic engineering safety monitoring. The system is composed of a sky-land water conservancy project integrated monitoring and sensing system, a self-adaptive sampling module, a layered distributed architecture and a software and hardware integrated module, and multi-source data such as deformation, seepage, stress strain, vibration and environmental quantity are cooperatively collected through five dimensions of sky domain, airspace, territory, water domain and work domain. The monitoring frequency is dynamically adjusted by using an adaptive sampling strategy, and data cleaning, standardization, space-time registration and fusion processing are completed through a distributed architecture to generate a high-quality comprehensive data set. The system can realize total-factor and whole-process refined monitoring, effectively eliminates data islands, improves data quality and monitoring efficiency, has high reliability, real-time performance and expandability, and provides powerful data support and decision basis for dam safety assessment and intelligent early warning.

The invention discloses a digital twin-driven bridge full life cycle damage prediction and evaluation method and system, and belongs to the field of bridge structure health monitoring, and the method comprises the steps: obtaining a monitoring data set of a bridge structure; an initial digital twinborn model embedded with a micro physical layer is constructed and trained, the micro physical layer constructs a damage evolution model applied with monotonic physical constraint based on multi-source monitoring data, and a damage evolution trajectory of the bridge in a future time period is predicted through the damage evolution model based on the physical parameter vector; in combination with an uncertainty quantification method, generating a time-varying reliability index of the bridge in a future time period; and based on the time-varying reliability index, constructing and solving a maintenance decision optimization model to generate a maintenance decision of the bridge. According to the invention, the physical authenticity and reliability of the long-term prediction result are ensured.

The invention relates to the technical field of informatization processing, and provides an IT asset intelligent management system and an implementation method thereof, and the system comprises an asset discovery subsystem which outputs a standardized asset list through a feature comparison and deduplication fusion program; the unified modeling subsystem forms an asset topological graph by processing association and dependence between information technology assets; the intelligent analysis and prediction subsystem outputs insight results and prediction indexes about resource capacity prediction, anomaly detection, cost optimization suggestions, risk trends and the like; the life cycle management subsystem drives an automatic operation and maintenance workflow through a strategy engine to realize closed-loop management of the information technology asset state; the security and compliance subsystem generates an alarm, a repair suggestion or executes an automated repair policy. According to the invention, intelligence, automation and safety compliance of information technology asset management are realized, and the efficiency and reliability of information technology operation, maintenance and management are significantly improved.

The invention belongs to the technical field of automatic control, and relates to a self-adaptive control method based on multi-physical modeling. According to the method, by collecting multi-source data of a controlled object, a thermal, electric and force coupling relation used for control analysis is established so as to describe dynamic responses under different operation conditions. And calculating stress, motor power, energy consumption and temperature rise change in the operation process based on a coupling relation to obtain system performance data, verifying stability and safety of different control parameter combinations in a simulation environment, and obtaining performance indexes including operation retardation risk, overload safety margin and safety response time limit. And according to a simulation result, under the condition of meeting safety constraints, taking energy consumption and temperature rise as optimization targets, adjusting control parameters, generating optimized control parameter configuration data, and feeding back the optimized control parameter configuration data to a control unit, so that closed-loop adaptive control and performance optimization are realized. According to the invention, through multi-physical coupling modeling and simulation optimization, the adaptability and reliability of the automatic control system are improved.

The invention discloses a building equipment fault rapid attribution and self-optimization method, which relates to the field of intelligent operation and maintenance of building equipment, establishes a multi-dimensional association relationship among equipment, building space, sensors and fault modes, and deeply combines a fault attribution engine with system dynamics. Data driving flexibility and physical logic preciseness are simultaneously realized in fault attribution, and reasoning from abnormal data to root cause analysis is realized. According to the method, the optimization efficiency is remarkably improved through a specific incremental learning / local updating mode, the weight of the knowledge graph and the Bayesian network conditional probability table are adjusted according to the maintenance result, physical equation parameters are calibrated, and dynamic updating of node attributes of the knowledge graph is achieved; through dynamic weight optimization, the adaptive ability of real-time reasoning confidence evaluation and scene context is improved; a physical equation is used as an executable knowledge unit to be embedded into a knowledge graph through establishment and deep integration of a system dynamics model, and a safety and reliability mechanism is designed for verification.

The invention relates to the technical field of application state diagnosis, in particular to an application state dynamic diagnosis and automatic repair method and system.The method comprises the steps that multi-source heterogeneous logs are collected and standardized in real time, key fields are extracted, context information is injected, and structured log data are generated; inputting the structured log data into a dynamic anomaly detection model, constructing a dual-path detection mechanism based on LSTM time sequence analysis and a graph neural network, identifying an abnormal mode and positioning a fault root cause; according to the output of the anomaly detection model, a repair action is triggered in a grading manner through an intelligent repair strategy engine; in the repairing process, system state changes are stored and recorded through a pre-writing type redundancy log, automatic rollback during abnormity is achieved on the basis of check point information, and data consistency and system stability are guaranteed. The problem of service interruption or data inconsistency possibly caused by traditional automatic repair is avoided, and the reliability of automatic operation and maintenance is improved.

The invention provides a code generation method and system based on a waterfall model and multi-agent collaboration, and the method comprises the steps: constructing a multi-agent collaboration framework which comprises a problem analysis agent, a solution agent, a pseudo-code agent, a coding agent and a restoration agent based on a software development waterfall model thought; and the interaction process of each agent is coordinated through a dynamic cooperation algorithm. Wherein the problem analysis intelligent body checks similar problems and solutions; the solution intelligent agent generates and evaluates candidate schemes; performing scheme conversion by the pseudo-code intelligent agent; the coding agent generates an executable code; and the repair agent performs fine-grained repair on the code in grammar, runtime and semantic levels through a two-dimensional repair mechanism. According to the method, the limitation of a single agent in a complex programming task is broken through, automatic code generation covering the whole process of demand analysis, scheme design, code implementation and test repair is realized, and the quality of generated codes and the reliability of operation are remarkably improved.

The invention discloses a streaming knowledge injection and adversarial self-optimization large language model training method and system, and the method comprises the steps: collecting the newest knowledge of an authoritative information source in real time, converting the newest knowledge into a structured constraint rule through a semantic analyzer, and dynamically updating a knowledge base; based on the updated knowledge base, adopting a PPO algorithm to optimize a generator, actively constructing a high-risk adversarial sample, and forcing the model to expose security vulnerabilities; after user input and adversarial samples are input into the model, total loss is calculated through constraint detection, gradient updating is blocked if the total loss exceeds a threshold value, and otherwise, a multi-level safety verification stage is started; and finally, fusing a security verification result and the adversarial loss, updating a total loss function and cooperatively adjusting model parameters to form a continuous self-optimization training cycle. According to the method, compliance is guaranteed through streaming knowledge injection, vulnerabilities are actively mined in combination with adversarial training, verification precision is improved by means of multi-level detection and dynamic threshold adjustment, and safety and reliability of a large language model in a complex scene are enhanced.

The invention relates to the technical field of machine tool fault diagnosis and maintenance, and discloses a machine tool fault predictive maintenance method based on vibration analysis, which comprises the following steps: collecting vibration, temperature and acoustic emission signals and machine tool working condition parameters through a multi-modal sensor, extracting multi-domain features after preprocessing the vibration signals, and combining the working condition parameters through feature fusion and dimension reduction to obtain a machine tool fault predictive maintenance result. And establishing a fault classification model by using transfer learning, and performing hierarchical optimization. Model parameters are updated in real time based on an online learning mechanism, a fault early warning agent model is constructed to predict fault probability distribution, a dynamic threshold strategy is designed to avoid false report and missing report, and finally, related models and strategies are integrated to edge computing equipment. According to the method, multi-source data are integrated, multiple advanced algorithms are applied, machine tool faults can be accurately predicted, real-time monitoring and maintenance decision output are achieved, the machine tool operation reliability is improved, and the maintenance cost is reduced.

The invention discloses a large model and knowledge graph dual-drive-based guided reasoning system and method, belongs to the technical field of artificial intelligence reasoning, and solves the problem of how to improve the process reasoning ability of a large language model for engineering subject courses and the reliability of solving complex engineering problems. According to the method, metadata is extracted from teaching materials, structured problem representation is constructed, and a knowledge graph is constructed to form a complete knowledge system; the method comprises the following steps: decomposing a complex engineering problem into a structured solving plan with knowledge marks, and carrying out iterative loop based on a Monte Carlo tree search algorithm to generate a search tree comprising a plurality of high-quality candidate problem solving paths; then determining an optimal answer and a corresponding reasoning path in all simulated paths in a voting mode, performing confidence evaluation on each node of each path in the candidate path set, and further selecting a path of an optimal solution; and the process reasoning capability of the large language model on engineering subject courses and the reliability of solving complex engineering problems are effectively improved.

The invention relates to the technical field of production logistics scheduling management, and discloses a digital twin workshop production logistics real-time scheduling management method and system, and the method comprises the steps: 1, building a unified state data and standard parameter baseline, calculating a consistency deviation value, and setting a two-stage deviation judgment threshold; 2, receiving a field event, and determining a scheduling time window; 3, determining task priorities, and generating a production to-be-executed list; 4, generating a carrying task set according to the production to-be-executed list, and determining carrying equipment and task allocation under constraint; step 5, performing channel conflict-free path planning, and generating a space-time occupation table; step 6, performing cross consistency check, and executing issuing or local recalculation according to a threshold; and step 7, executing field operation and updating unified state data, and executing consistency recovery when the deviation exceeds the limit. According to the invention, real-time collaborative scheduling and high-reliability operation of workshop production and logistics tasks are realized.

The invention discloses a multi-condition simulation capacitance detection relay life prediction method, system, equipment and medium, and relates to the technical field of relays, and the method comprises the steps: applying a plurality of simulation conditions to a relay through a programmable driving power supply, simulating the complex conditions in actual operation, and collecting the multi-dimensional parameters of the relay in real time in the execution process of the simulation conditions, extracting characteristic parameters from the collected multi-dimensional parameters, carrying out data preprocessing on the characteristic parameters, constructing a life prediction model, carrying out prediction calculation and reliability grade evaluation on the residual life of the relay by utilizing the life prediction model, and carrying out reliability grade evaluation on the residual life of the relay by monitoring capacitance change between contacts of the relay. And early performance degradation early warning is carried out in combination with a prediction result. According to the invention, through fusion of multi-working-condition simulation, hybrid modeling and capacitance detection, full-chain optimization from data acquisition, accurate prediction to early warning is realized, and the accuracy, timeliness and engineering application value of relay life prediction are improved.

The invention provides a double-machine lifting dynamic balance control method based on real-time load feedback, which comprises the following steps of: after a system is initialized, synchronously acquiring load, height, attitude, position, motion and environment data of two machines, and fusing and filtering; load, height, angle, position and motion coordination multiple deviations are calculated, and balance and safety are evaluated; generating a speed, position, attitude and safety comprehensive control strategy by using multi-objective optimization according to an evaluation result, and setting a priority and a look-ahead mechanism; executing instructions in a distributed manner and monitoring the instructions in real time; self-adaptive parameter adjustment and machine learning continuous optimization are carried out based on a feedback closed loop; and multi-stage safety monitoring and emergency processing are synchronized. According to the invention, the precision, safety and efficiency of hoisting operation can be improved, the operation difficulty and energy consumption are reduced, and the adaptability and reliability of the system in a complex environment are enhanced.

System and methods are disclosed for providing unforgeable telemetry on computer platforms. Mathematical modeling and theorem proving are utilized to guarantee the integrity of telemetry probe execution flow and trigger, thereby preventing circumvention and tampering of logged probe data. In contrast to current state-of-the-art solutions that rely implicitly on the operating environment, this approach provides a sound and complete assurance of telemetry output. The system enables organizations to map unforgeable telemetry probe data to industry and government cybersecurity regulatory controls, ensuring compliance therewith. This invention addresses the shortcomings of existing solutions, including their vulnerability to sophisticated attacks, operational complexity, and inability to provide unforgeable telemetry data, thereby providing a reliable and accurate monitoring output in the presence of cyberattacks on computer platforms.

The computer fault diagnosis method based on the causal reasoning and mapping knowledge domain hybrid architecture comprises the steps of obtaining and processing multi-source heterogeneous data of an embedded computer, constructing a system mapping knowledge domain integrating the multi-source data of the embedded computer, and forming a knowledge base containing components, functions, fault phenomena and association relationships thereof; the method is characterized in that a causal reasoning engine is designed, domain knowledge constraints are utilized, a real cross-level fault propagation causal chain is mined from atlas association, and root causes are verified through anti-fact reasoning. And the engine dynamically feeds back the mined causal knowledge to the atlas, so that the causal knowledge is continuously optimized. According to the hybrid architecture, accurate and rapid tracing with causal explanation from a fault phenomenon to a root cause is realized, and the diagnosis capability in high-reliability fields such as aerospace and industrial control is remarkably improved.

The invention relates to the technical field of equipment management, in particular to an equipment full-life-cycle digital twinborn decision-making system enabling a large model. Comprising a digital twin modeling unit; a large model enabling analysis unit, wherein the large model enabling analysis unit adopts a hydroelectric equipment multi-modal causal constraint analysis model; a whole-process closed-loop management and control unit; and an intelligent decision output unit. According to the method, the multi-modal causal constraint analysis model adaptive to the working condition of the hydroelectric equipment is constructed, and a causal chain verification backtracking mechanism is introduced, so that the problem that the reasoning result lacks logic verification is effectively solved, the logic consistency of a fault reasoning conclusion is guaranteed, and the reliability of decision output is improved; through a scene adaptation mode of'pre-training + fine tuning 'of a large model, multi-modal feature fusion processing and deep linkage of a workflow engine and a digital twinborn body, full-life-cycle management requirements of equipment are fully covered, and the refinement and intelligence level of hydroelectric equipment management is further improved.

The invention provides a storage type time-depth logging method, and relates to the technical field of logging control, and the method comprises the steps: obtaining an encoder, tension and acceleration data, and drawing an encoding curve, a tension curve and an acceleration curve. And receiving an effective movement time interval of single complete drilling rod operation intercepted by the user according to the curve characteristics. And for the original coding curve section of each interval, the total variation and the total duration of the coding value are kept unchanged, and the coding value and the time point are matched again according to the layered scale relationship between the encoder data and the actual displacement of the hook, so that a corrected target coding curve is obtained. And all the target coding curves are converted into time-depth curves according to the depth interval corresponding to each drill rod, and a time-depth file is generated through splicing according to the operation sequence of the drill rod operation event interval. According to the scheme, the technical problem of how to improve the precision of storage type time-depth logging depth measurement and the reliability of the whole system is solved.

The invention discloses a dynamic closed-loop management method for bridge-shaped contact performance, and relates to the technical field of power system protection. The method comprises the following steps: S1, a dynamic sensing process: acquiring multi-source dynamic parameters of a bridge-shaped contact in a non-intrusive mode; s2, a quantitative evaluation process: fusing the multi-source dynamic parameters and the static parameters, and calculating a health index or a health level of the contact through a preset model; s3, adaptively optimizing the process, dynamically adjusting the closing energy or protection parameters of the circuit breaker according to the health state, and compensating the performance change of the contact; and S4, a predictive maintenance process: performing trend extrapolation based on the historical data of the health index, predicting the residual life and generating a maintenance suggestion. The processes are sequentially connected and cyclically iterated to form dynamic closed-loop management, so that the problems of one-sided parameter sensing, inaccurate evaluation, fixed parameters and maintenance lag in the prior art are solved, the operation reliability of the bridge-shaped contact is improved, the service life of equipment is prolonged, and the operation and maintenance cost is reduced.

Embodiments manage context repositories in computing environments to enhance automated security analysis. Embodiments obtain context records containing supplemental information associated with security events and integrates them into prompts for large language models (LLMs) to generate severity scores for event classification. Embodiments apply criteria to invalidate outdated or unreliable context records based on age, source reliability, and usage frequency, then modifies prompts and repositories accordingly. Enhanced prompts incorporate context record summaries and entity relationship mappings to improve subsequent event analysis. Embodiments dynamically evaluates context records through quality filters, consolidates duplicates, and maintains audit trails with provenance tracking. User interfaces are dynamically transformed based on telemetry metrics and user feedback to optimize analyst workflows. Embodiments enable organizations to maintain curated, high-quality context repositories that continuously improve AI-assisted security analysis while reducing false positives and enhancing incident response effectiveness.

The invention discloses a method and a system for evaluating the reliability of a contact network system under an icing galloping condition. The method comprises the following steps: collecting icing galloping data of the overhead line system in real time, and carrying out load conversion and statistics on parts of the overhead line system in icing galloping by adopting a rain flow counting method; the reliability of parts under the icing galloping condition is calculated through the Miner cumulative damage theory, and weight distribution of the overhead line system is conducted through an analytic hierarchy process; based on the reliability and the weight distribution result of each part under the icing galloping condition, evaluating the reliability of the overhead line system of the data acquisition anchor section until the evaluation task is finished; the method and the system provided by the invention are suitable for evaluating the operation reliability of the contact network system in the icing galloping section, can provide support for operation and maintenance of the contact network system after icing galloping occurs, and have wide application prospects.

The invention discloses a dynamic script compiling and hot updating system based on Grouping and an implementation method of the dynamic script compiling and hot updating system. The system comprises a script source code management module, a dynamic compiling engine, an intelligent cache manager, an exception handling and recovery system and a hot update controller. And the script source code management module receives and preprocesses the Grouping script to generate a unique identifier for subsequent management. And the dynamic compilation engine executes compilation operation according to the identifier to ensure that a compilation result accords with a specification. And the intelligent cache manager efficiently stores and retrieves the compiling result, so that the repeated compiling cost is reduced. And the exception handling and recovery system monitors the execution state in real time, and automatically starts a recovery process in case of exception to guarantee stable operation of the system. The hot update controller realizes seamless update and version switching of scripts, and ensures service continuity. Through cooperative work of the modules, high-reliability, high-performance and zero-shutdown deployment is realized.

The invention relates to the technical field of surface defect detection, in particular to a hardware surface defect online detection method based on multi-modal sensing fusion, which comprises the following steps: step 1, arranging multi-modal sensor groups above and on the side of a conveyor belt of a hardware production line; 2, preprocessing the multi-modal sensor data acquired in the step 1; 3, extracting features from the multi-modal sensor data preprocessed in the step 2; 4, performing reliability evaluation on the multi-modal features extracted in the step 3; 5, generating a fusion feature vector, wherein adaptive weighted fusion adopts a weighted feature splicing method; and step 6, outputting defect types and position information to a production line control system. The combined use of the multi-modal sensor can comprehensively detect the surface of the hardware from multiple dimensions, and the accuracy and stability of the system are further improved through reliability evaluation and weighted fusion.

The invention discloses an engineering quality detection method based on big data analysis, which relates to the technical field of engineering quality detection, and comprises the following steps: according to an evidence contribution degree sequence, carrying out management judgment and merging processing on a conclusion stability index, outputting an initial detection conclusion, carrying out management backtracking association on the initial detection conclusion and a quality evidence set, and obtaining a quality detection result; constructing a quality evidence chain; counting the integrity index of the quality evidence chain, performing hierarchical output processing on the initial detection conclusion according to the integrity index to form a hierarchical detection conclusion set, extracting an evidence gap list and a conflict influence path abstract in the hierarchical detection conclusion set, and outputting rectification suggestions and reinspection strategies. According to the method, the quality evidence conflict graph is constructed, the conflict intensity and the propagation range are quantified, and the evidence contribution degree evaluation and conclusion judgment based on virtual elimination are synchronously used, so that the structured expression of the conflict relationship between the engineering quality evidences is realized, and the stability and the reliability of the detection conclusion are improved.

The invention discloses a casting shrinkage cavity process parameter optimization method and system based on deep learning, and relates to the technical field of parameter optimization, and the method comprises the steps: training an agent model, and outputting a predicted value and uncertainty of a product quality index; constructing an uncertainty fusion engine, and fusing the agent model prediction uncertainty, the process parameter fluctuation uncertainty and the material parameter interval uncertainty; defining and calculating a robustness evaluation index based on the predicted value of the product quality index and the comprehensive uncertainty envelope; by taking optimization of a product quality index and a robustness evaluation index as dual targets, searching to obtain a robust Pareto frontier solution set; and recommending a final process parameter scheme based on the robust Pareto frontier solution set. The technical problems that an existing casting shrinkage cavity process parameter optimization method depends on experience and trial and error, efficiency is low, and result accuracy and reliability are poor are solved.

The invention discloses an electricity meter metering error correction method and system based on time sequence anomaly detection and an electricity meter, relates to the technical field of electricity meter metering error correction, and provides an intelligent anomaly detection method integrating multi-source data preprocessing, online learning and federation cooperation for a power system in which distributed energy and diversified loads coexist. The method comprises the following steps of: 1, dynamically filtering and caching original data by adopting a suspicious degree function, and outputting high-quality input; secondly, online learning is carried out through multi-algorithm fusion and expert knowledge constraint, and accurate judgment of traditional and emerging modes is achieved; 3, realizing multi-region cooperative gain based on difference parameter sharing and global aggregation; and 4, deploying a lightweight model at the edge end by using feedback evaluation and model distillation, thereby improving the real-time performance and reliability. In conclusion, the false alarm rate and the missing report rate can be remarkably reduced, various changes of load behaviors are effectively adapted, the operation and maintenance efficiency of a power grid is improved, and meanwhile data privacy safety and collaboration are guaranteed.

The invention discloses an intelligent inspection and fault prediction system based on computing power service, and belongs to the technical field of power grid inspection. The instruction analysis module analyzes the power grid dispatching instruction and determines target equipment and an expected state path; the topology network construction module constructs an equipment operation intention topology network and marks key nodes; the task screening module generates a sensing set containing equipment state recognition and robot movement control tasks according to the environment data, and screens out tasks outputting key node state parameters as a core task set; the computing power distribution module calculates the minimum computing power of the core task according to the weight of the key node and the environmental interference coefficient, and triggers non-core task degradation when the minimum computing power is insufficient; the fault prediction module inputs equipment state parameters output by the core task into a prediction network to generate an abnormal coefficient and a maintenance sequence; the real-time performance of key tasks is guaranteed by dynamically allocating computing power resources, and the operation and maintenance efficiency and reliability of the power grid are improved.

The invention discloses an unattended substation autonomous operation and maintenance management platform based on multi-modal data fusion. Multi-source data such as equipment state, intrusion behavior, flood prevention safety and environmental parameters are acquired through a multi-modal data sensing module; the cross-modal space-time fusion module performs timestamp alignment, space coordinate mapping and feature level fusion on the data to generate a unified representation vector; the intelligent analysis module outputs analysis results such as equipment fault probability and intrusion threat level by using a multi-task parallel deep learning model; the autonomous decision-making module dynamically generates an operation and maintenance strategy set based on the rule base and the knowledge graph; the execution driving module converts the strategy into an executable instruction and issues the executable instruction to the edge computing node; the visual interaction module realizes strategy verification through a three-dimensional digital twinborn body; and the edge computing node drives an execution mechanism to complete specific operation and return a result. According to the invention, multi-source data fusion, intelligent analysis and autonomous decision making of the unattended substation are realized, and the operation and maintenance efficiency and reliability are improved.