118 results about "Fault tree analysis" patented technology

The invention discloses a vehicle communication fault source determination method and related equipment, and relates to the technical field of vehicle communication, the method comprises the following steps: obtaining communication operation information of a plurality of processing levels in a vehicle communication system, the plurality of processing levels comprising a hardware layer, a middle layer, a security layer and a network layer; based on a preset log format, recording the communication operation information, and generating an encrypted log; and performing fault root cause analysis on the encrypted log through a preset fault tree model to obtain a target fault source. Through multi-level log acquisition and fault tree analysis, in combination with a structured format and encrypted storage, vehicle communication fault diagnosis with accurate and efficient fault positioning, data traceability, safety and credibility can be realized.

The invention discloses an adaptive direct-current partition protection method based on a reconfigurable topology perception fault tree and an improved LSTM (Long Short Term Memory), which is suitable for fault diagnosis and partition protection of a direct-current system of a transformer substation. According to the algorithm, improved dynamic fault tree analysis and a long short-term memory (LSTM) network model are combined, and rapid positioning and selective isolation protection of direct-current system faults are achieved. The method comprises the following steps: firstly, constructing an updatable dynamic fault tree model based on fault historical data, and correcting a fault event weight in real time by adopting a Bayesian method to realize dynamic reconstruction of a fault propagation path; secondly, a multi-target particle swarm optimization algorithm is introduced, optimization is carried out between the minimum protection response time and the minimum isolation range, and an optimal partition scheme is generated; multi-dimensional time sequence characteristics such as fault current and bus voltage are extracted through a multi-channel LSTM model, and the fault development trend is predicted; and finally, dynamically adjusting a trigger threshold and a delay parameter of a protection action in combination with an online reinforcement learning strategy network of an Actor-Critic architecture. The method has the advantages of quick fault response, flexible partition, adaptive protection strategy and the like, and the safety and the stability of the direct current system can be remarkably improved.

The invention discloses a water and electricity intelligent management method and platform based on electric power prediction, and belongs to the technical field of water and electricity management, and the method specifically comprises the steps: collecting hydrological forecast data, electric power load data, equipment operation state data and historical scheduling records, building a unified data storage library, and guaranteeing the real-time updating and format standardization of various types of data; the method comprises the following steps: generating uncertainty quantitative indexes of each source by adopting a probability distribution model, error sequence analysis and fault tree analysis methods according to three sources of hydrological forecast errors, load prediction deviations and equipment fault probabilities; constructing a mapping model; according to the mapping result, establishing a risk grade evaluation system, and outputting the specific grade of each scheduling risk in real time; and according to the risk level, formulating a targeted scheduling strategy, executing the generated scheduling strategy, synchronously monitoring and predicting data change and risk level fluctuation, and immediately triggering dynamic adjustment of the scheduling strategy when an uncertainty index exceeds a threshold value.

The invention discloses a construction whole-process carbon emission metering method based on multi-source data fusion and dynamic feedback correction. The method comprises the steps that multi-source data such as construction equipment operation, building material logistics and environmental parameters are collected through Internet of Things equipment and preprocessed; using the improved neural network model to classify the associated data, and outputting the comprehensive energy consumption and carbon emission potential of the construction link; a metering model covering transportation, equipment and personnel activities is constructed, and link calculation is achieved in combination with dynamic parameters; and through real-time CO2 monitoring and comparison with a predicted value, analyzing a positioning deviation and correcting the model based on a fault tree. Through multi-source data fusion and dynamic feedback correction architecture, total element coverage and real-time self-adaptive correction of carbon emission in each construction stage are realized, the metering precision is improved, a construction unit is supported to quickly identify a high-carbon link and optimize the process, and the method is high in compatibility, can adapt to different construction projects and has remarkable engineering application value.

The invention discloses a power equipment operation intelligent monitoring analysis method based on artificial intelligence, and relates to the technical field of power system operation and maintenance. Equipment operation parameters, equipment maintenance and operation records, equipment appearance state data, vibration waveform data and sound characteristic data are covered. According to the power equipment operation intelligent monitoring analysis method based on artificial intelligence, by implementing a multi-stage fault prediction scheme, the fault development stages are divided, the feature threshold value of each stage is determined, the change rate of the real-time fusion feature and the equipment load are combined, and the power equipment operation intelligent monitoring analysis method based on artificial intelligence is realized. Predicting the remaining time of the fault developing to the critical stage through time sequence analysis; meanwhile, relying on a part association relationship in the digital simulation model, an improved fault tree analysis method is adopted to trace direct and indirect inducements, and a visual traceability map is generated; and fault prediction is changed from post-event identification to pre-event early warning, so that non-planned shutdown is effectively avoided.

The invention discloses a hydropower station GIL equipment insulation state detection and evaluation method based on an industrial internet platform, and the method comprises the steps: collecting the characteristic parameters of voltage, current, temperature and the like through an online monitoring system, a live detection device or a power failure high-voltage test, and generating an original characteristic set; through photoelectric conversion, filtering and other processing, key features are extracted to generate a structured feature set, and the structured feature set is serialized and uploaded to a data center. A phase resolution graph and a dielectric spectrogram are calculated by utilizing technologies such as partial discharge analysis and a knowledge graph, fault diagnosis is carried out, and information such as fault positions and types is determined. And based on a diagnosis result, evaluating an insulation state risk through methods such as fault mode and influence analysis and fault tree analysis, quantifying defects, safety and economic risks, and outputting an alarm level and a processing suggestion. And if the risk is triggered, performing convolutional neural network analysis on the specific signal to update the fault position, and finally generating a diagnosis report according to the fault influence range, the diagnosis result and the alarm level, thereby realizing comprehensive monitoring and accurate evaluation of the insulation state of the GIL equipment.

The invention provides a new energy automobile high-voltage accident grading response and emergency rescue operation guiding method and system, and is applied to the technical field of data processing. Multi-source data of voltage, current and battery states of a high-voltage system are collected, a standardized association sequence is generated after denoising, then the standardized association sequence is converted into a three-dimensional risk map, electric leakage, overvoltage and thermal runaway scenes are analyzed and subdivided through a fault tree, and a classified accident grading model is constructed. Extracting hazard weight factors to establish a dynamic evaluation matrix, and cooperatively calculating multi-source data to obtain key response indexes; parameters are extracted according to accident stages, response levels are divided, and a feature matrix is generated; real-time data and historical data are compared, and correction factors are generated by means of the accident curve slope. And in combination with vehicle types, architecture and environment grouping, key factors are screened by using a support vector machine, a personalized rescue model is established by fusing multiple constraints, and finally, a real-time graded response instruction and scene-divided rescue guidance are output.

The invention discloses a distributed photovoltaic power station intelligent operation and maintenance method based on fault tree analysis. The method comprises the following steps: S1, constructing a distributed photovoltaic power station fault tree model; s2, collecting operation data of the distributed photovoltaic power station in real time; s3, calculating the occurrence probability of each bottom event of the fault tree according to the collected operation data by using a fault tree analysis method; s4, identifying key fault factors according to the bottom event occurrence probability; and S5, making an intelligent operation and maintenance strategy according to the key fault factors. According to the distributed photovoltaic power station intelligent operation and maintenance method based on fault tree analysis, key fault factors are accurately identified, the operation and maintenance strategy is dynamically adjusted, the fault positioning and repairing efficiency is improved, the operation and maintenance cost is reduced, and the distributed photovoltaic power station is promoted to be upgraded to active intelligent operation and maintenance.

The invention discloses a medical equipment remote fault diagnosis, maintenance and guidance system based on a cloud platform. According to the invention, the deep reconstruction of the medical equipment maintenance ecology is realized by constructing the digital service network covering the whole process. The AR cooperation platform of the maintenance engineer end and the real-time data intercommunication of the hospital end break through the space-time limitation of technical support in a traditional maintenance mode, so that remote experts can directly participate in field fault diagnosis and cooperate with historical maintenance archives and fault tree analysis provided by the intelligent work order system, and the first repair rate is greatly improved. An intelligent order sending algorithm of the management background is combined with an order grabbing mechanism of the mobile terminal, so that the geographical distribution efficiency of engineer resources is optimized, precise docking is realized through skill label matching, the industrial pain point of scarcity of special equipment maintenance resources is solved, and the service life of the mobile terminal is prolonged. Modular design and data interconnection characteristics bring systematic improvement to medical equipment operation and maintenance management, and fine control of equipment management cost and continuous improvement of service quality are realized.

The invention relates to the technical field of chemical production safety, in particular to a chemical production safety risk intelligent reasoning method and system and a storage medium, and the method comprises the steps: building a multi-level flow model according to a chemical production flow chart, and constructing a fault tree analysis qualitative model for the multi-level flow model through a danger and operability analysis technology; fusing and correcting the multiple groups of evidence sources according to the D-S evidence theory to obtain the initial abnormal probability of each basic event; and establishing a dynamic Bayesian network quantitative model in combination with a logic gate rule, and performing forward reasoning and backward reasoning. According to the invention, forward reasoning and backward reasoning can be carried out on abnormal event nodes in the chemical production process, causal traceability is completed, an abnormal propagation path is explored, and an effective management means is provided for safe production in the chemical process.

The embodiment of the invention provides an intelligent coal bunker safety protection system and method. The device comprises the steps that sensing parameters are detected; wherein the sensing parameters comprise contact type temperature measurement data, infrared thermal imaging data and coal seam three-dimensional point cloud data; the contact type temperature measurement data and the infrared thermal imaging data are fused, and a three-dimensional temperature gradient model of the coal bunker is established; inverting a smoke traceability path based on a computational fluid mechanics algorithm; calculating a coal pile slippage risk coefficient according to the coal seam three-dimensional point cloud data; a multistage threshold triggering mechanism associated with a temperature gradient extreme value, an inversion smoke traceability path and a coal pile slippage risk coefficient is configured; if the multi-level threshold triggering mechanism is triggered, an emergency disposal scheme based on fault tree analysis is called, and a corresponding emergency device is controlled to operate; real-time and comprehensive monitoring of parameters of the coal bunker is realized, the prediction precision is high, potential safety hazards are found in time, and safe operation of a coal yard is guaranteed.

The invention relates to the technical field of automotive electronics and intelligent control, and discloses a domain controller failure mode judgment and redundancy control method and system, and the method comprises the steps: collecting the operation state information of a chassis domain controller; a failure tree analysis method is adopted, a failure mode library of the chassis domain controller is constructed according to the operation state information, failure modes are classified, and the failure mode library comprises controller failures, communication link anomalies, sensor failures and power supply anomalies; in combination with real-time monitoring data, the health state is judged by utilizing a machine learning algorithm, the fault mode is predicted, and the severity of the fault mode is determined; according to the method, the corresponding redundancy control strategy is determined according to the severity of the fault mode, the redundancy control strategy is used for redundancy control, the redundancy control is carried out according to the severity of different fault modes, continuous and stable operation of key chassis functions is ensured, and the safety and reliability of the chassis domain controller are improved.

The invention belongs to the technical field of multi-level RCM, and discloses a multi-level RCM analysis and reliability structure construction method and a related device. The multi-level RCM analysis and reliability structure construction method comprises the following steps: establishing a unit system function model after constructing a unit system database, performing multi-level classification on equipment of a unit system in combination with a fuzzy clustering algorithm, and then determining levels and weights of various types of equipment in the unit system function model, analyzing a failure mode of each type of equipment after multi-level classification through a harmfulness analysis method in combination with a fault tree analysis technology, establishing a correlation model between the equipment failure mode and a unit system function, determining an influence weight of the equipment failure mode on the reliability of the unit system, and establishing a unit system reliability model; inputting the unit system database into the unit system reliability model to obtain a unit system maintenance strategy library; the analysis result accuracy and the system reliability can be greatly improved.

The application discloses an embedded control system fault self-recovery method and system, and belongs to the technical field of embedded systems. The method comprises the following steps: setting and dynamically adjusting a fault threshold value based on historical data and current working conditions; triggering a pre-warning when a real-time parameter exceeds the dynamic threshold value, and confirming by comparing main and redundant sensor data to distinguish between sensor abnormalities and real equipment faults; intelligently diagnosing based on a knowledge base using methods such as fault tree analysis to determine the fault type, location and severity; the system matches candidate self-recovery strategies from a strategy library, selects and executes the optimal strategy by calculating the value and execution priority of each strategy, and completes self-recovery control through software and hardware execution mechanisms. The application significantly improves the accuracy and reliability of fault detection through dynamic fault threshold values and redundant data confirmation, effectively avoiding false positives; precise positioning of faults and optimal self-recovery strategy selection are achieved.

The invention relates to the technical field of equipment management, and discloses an intelligent equipment state monitoring and operation and maintenance management system based on the Internet of Things, which comprises a system module assembly, and the system module assembly comprises an edge data acquisition unit, a fault tree analysis engine, an edge intelligent reasoning node, a distributed collaborative analysis platform and a cloud edge collaborative operation and maintenance center. The edge data is deployed in a sensor network of an equipment end, collects operation data in real time, supports multi-protocol access and adapts to heterogeneous equipment, and the fault tree analysis engine constructs a dynamic fault tree model based on historical fault data, optimizes an event association rule through machine learning, calculates the probability of a top event in real time, and analyzes the fault data in real time. And risk levels are divided. According to the method, a dynamic fault tree evolution mechanism is adopted, a cooperative fault is combined to predict the equipment degradation trend, the fault tree structure is adaptively adjusted, and the multi-modal data is fused with the vibration signal, the infrared thermal imaging and the current harmonic data to improve the diagnosis precision.

The invention belongs to the technical field of fault detection and recovery, and particularly relates to a fault self-diagnosis and self-adaptive recovery method for an embedded controller, which comprises the following steps of: starting a fault self-diagnosis process by adopting a periodic triggering mode and an event triggering mode, and acquiring, processing and generating a diagnosis data source with time sequence characteristics in real time; step-by-step anomaly recognition is carried out through a three-level anomaly recognition mechanism, an anomaly feature set is extracted, cooperative fault positioning is carried out in combination with a weighted cosine similarity algorithm and fault tree analysis, fault types and severity levels are judged, and a fault positioning report is generated; the optimal self-adaptive recovery strategy is matched to carry out preliminary recovery, the strategy is divided into light, medium and deep levels according to serious levels and is provided with priorities, and the scheme with the minimum influence on the core service is preferentially selected; the recovery effect is verified through multi-dimensional comparison, and processing is carried out according to the recovery effect. According to the method, the real-time accurate diagnosis, intelligent positioning and self-adaptive recovery of the fault of the embedded controller are realized, and the reliability and self-healing capability of the system are improved.

The invention discloses an equipment failure risk analysis method based on a probability hesitant fuzzy evidence theory, and relates to the field of failure risk analysis, and the method comprises the steps: S1, building a fault tree structure, and constructing a reference failure rate set of basic events in a fault tree; s2, acquiring evaluation data of an expert on a reference failure rate set of the basic event, and obtaining an adjustment score and a tendency degree of the basic event; s3, converting the adjustment score into an evaluation probability by using a score conversion rule; s4, establishing a qualification weight according to the qualification of the expert; quantizing and adjusting the uncertainty of the score and the tendency degree to obtain an evaluation uncertainty weight, and carrying out weighted summation on the two weights to synthesize a total weight; s5, based on the evaluation probability and the total weight, synthesizing a final evaluation probability through a Dempster-Shafer evidence theory; and S6, performing fault tree analysis based on the final evaluation probability. According to the method, the probability hesitant fuzzy set and the Dempster-Shafer evidence theory are introduced, the diversity and uncertainty of expert evaluation are reserved, and conflicts and consistency between evidences are effectively processed.

The invention provides a soft start cooperative control method and system for a large industrial air conditioning unit, and the method comprises the steps: a central cooperative controller collects the operation parameters and start dependence conditions of all parts through a high-speed real-time communication network, builds a fault feature database, and carries out the real-time recognition of an abnormal signal; a fault tree analysis module is adopted, faults are divided into three levels according to the types and severity of the faults, if communication interruption occurs, a hardware watchdog circuit of a safety protection unit executes emergency shutdown within 500 ms, out-of-control is prevented, and the method relates to the technical field of large-scale industrial air conditioning unit control. The fault grade and the influence range are dynamically evaluated, rapid positioning and collaborative response of faults are achieved, fault spreading is avoided, the safety and reliability of the large industrial air conditioning unit starting process are improved, the problems of power grid impact and system reliability during multi-device starting are solved, and the method is suitable for large air conditioning units of data centers, factories and the like.

The invention discloses an intelligent detection method, system and device for a core control board of a distribution network hot-line work robot and a storage medium, and relates to the technical field of detection of the core control board of the distribution network hot-line work robot, and the method comprises the steps: building a key grade evaluation model based on the functional characteristics of each assembly of the core control board, and executing a dynamic detection process according to a grading result; an exception handling engine is built by combining a state machine model, and the equipment state is monitored in real time; the root cause of equipment state data before abnormality is analyzed and positioned through a fault tree, fault information is recorded by adopting an enhanced watchdog mechanism, and the waiting time length of function detection is dynamically adjusted; and dynamically adjusting the waiting time of function detection based on the real-time monitoring of the response states of the control panel and the equipment. According to the method, the detection efficiency and reliability are improved, and the large-scale deployment of the distribution network hot-line work robot is met.

This invention relates to a method and system for decision-making on retaining wall deformation control measures based on fault tree analysis. The method includes the following steps: using foundation pit monitoring data as input parameters for a retaining wall maximum deformation prediction model, and obtaining the predicted maximum deformation value of the retaining wall through the model; acquiring the retaining wall deformation in real time using the foundation pit monitoring data; when the retaining wall deformation exceeds the predicted maximum deformation value, making a deformation control scheme decision based on the foundation pit monitoring data and the retaining wall deformation control measure decision-making model, thereby obtaining the retaining wall deformation control measures. Compared with existing technologies, this invention achieves both the prediction of retaining wall deformation and the decision-making on retaining wall deformation control measures, improving construction efficiency.

The present invention belongs to the technical field of aviation airborne system safety analysis, and specifically relates to a method and system for generating FMES data for airborne system safety analysis. The method comprises: S1: acquiring FMEA data of the airborne system, performing data processing, and extracting data features using the BERT model; S2: using sinusoidal similarity to measure the FEMA structured data feature vectors and calculate the similarity of the FEMA structured data features; S3: obtaining the FEMA structured data feature similarity matrix and performing a first agglomerative hierarchical clustering analysis; S4: performing a second clustering analysis on the tree-like clusters of the FEMA structured data features based on fault tree analysis (FTA); and S5: generating structured Failure Mode and Effect Summary (FMES) data based on the second clustering results. The present invention proposes a complete framework for automatically generating FMES data, and improves the quality of the automatic generation through secondary clustering.

The invention relates to the technical field of fault diagnosis, in particular to a rapier loom fault diagnosis system. According to the system, fault tree analysis based on whale algorithm optimization and a probabilistic neural network are fused; the fault diagnosis method comprises the following steps: step 1, establishing a rapier loom fault tree diagnosis model; step 2, optimizing parameters of the fault tree model by using a whale algorithm; step 3, constructing a sample data set and carrying out normalization processing; and step 4, carrying out probabilistic neural network training and fault diagnosis. According to the diagnosis model based on fusion optimization of the probabilistic neural network and the fault tree algorithm, on the basis of a rapier loom fault tree diagnosis model, real-time newly-added state monitoring data of a monitoring system is introduced; and converting the data set into a sample feature vector, performing normalization processing, importing the sample feature vector into a probabilistic neural network algorithm model for training, and calculating and outputting a fault symptom probability, thereby realizing real-time, complete and rapid fault identification and positioning of the rapier loom fault diagnosis system.

The application discloses a civil aircraft minimum risk bomb position structure reliability analysis method, which comprises the following steps: S10, establishing a finite element model of an LRBL structure, and determining dangerous positions of the civil aircraft minimum risk bomb position structure; S20, determining uncertainty input variables of the LRBL structure, and performing explosion simulation and solving in combination with the uncertainty input variables; S30, determining failure criteria of the dangerous positions of the LRBL structure under explosion action, and obtaining a limit state function; S40, determining a failure probability of the dangerous positions of the LRBL structure according to the limit state function; and S50, obtaining reliability of the LRBL structure by using a fault tree analysis. Explosion simulation is used to avoid the work that can be completed only by a large number of real explosion tests, efficient estimation of the failure probability of the civil aircraft minimum risk bomb position structure is realized, and the efficiency of reliability analysis and calculation is improved.

This invention provides a method and system for assessing the reliability of wind turbine generator sets based on a directed fault graph. Addressing the challenges of complex structures and multiple failure modes in wind turbine generator sets, it employs fault tree analysis to establish a directed fault graph model. To address the difficulty of polymorphism at the root nodes of the directed fault graph, it considers the uncertain logical relationships between components and the system within the wind turbine generator set and uses probability values ​​to measure these relationships in a conditional confidence quality table. Then, it takes all importance rankings into account and performs directed fault graph reasoning according to the same importance ranking, selecting the maximum value to complete the multi-fault state reliability assessment of the wind turbine generator set. This invention effectively assesses the reliability of wind turbine generator sets while avoiding the low efficiency and low accuracy problems of the Monte Carlo method and confidence interval method, respectively.

The invention relates to a method for improving double-clamping reliability when a rod control system breaks down and the rod control system. The method comprises the following steps that rod falling information when the rod control system breaks down is obtained; based on the rod falling information and the double-clamping principle of the rod control system, a fault tree analysis method is adopted for analysis, and a fault analysis result is obtained; determining a rod falling reason according to the fault analysis result; determining an improvement strategy according to the rod falling reason; and outputting an improved double-clamping control instruction based on the improved strategy. According to the invention, the fault tree analysis method is adopted to carry out reason analysis to determine the rod falling reason, so that the corresponding improvement strategy is formed based on the determined rod falling reason, and the corresponding improved double-clamping control instruction is output according to the generated improvement strategy, so that the effective execution of the double-clamping function is ensured, and the falling of the control rod drive rod is avoided.

The invention provides an operating system fault diagnosis method, device and equipment based on an FTA fault tree, and relates to the technical field of operating system fault diagnosis, and the method comprises the steps: carrying out the FTA fault tree building of a target operating system based on the architecture dimension of the target operating system, and obtaining the FTA fault tree corresponding to the target operating system; obtaining a to-be-analyzed system log corresponding to a target operating system uploaded by a user and a target fault corresponding to the target operating system; according to the to-be-analyzed system log, the target fault and an FTA fault tree corresponding to the target operating system, performing matching analysis on the to-be-analyzed system log to obtain a plurality of fault nodes; generating a diagnosis result corresponding to the target fault according to the plurality of fault nodes; the diagnosis result at least comprises a fault node position and processing information corresponding to the fault node; the problems of low diagnosis efficiency, inaccurate positioning, high cost and the like in the prior art can be effectively solved.

An abnormality diagnosis device includes an abnormality determination unit configured to determine whether or not there is an abnormality with respect to a state quantity acquired from equipment and a cause estimation unit configured to estimate a cause of the abnormality in the equipment from a state quantity determined to be abnormal by the abnormality determination unit using a cause correspondence table in which a cause of an abnormal mode of the equipment identified in fault tree analysis is associated with the state quantity that is abnormal when the cause has occurred.

The invention discloses an intelligent fault tree generation method based on cooperation of a multi-modal large model and a large language model, and relates to the field of fault tree analysis. In order to solve the defects of low tree building efficiency, insufficient cross-modal information processing and incomplete fault causal chain reasoning in the prior art, the technical scheme provided by the invention comprises the following steps: carrying out topological structure analysis on a system flow chart by adopting a multi-modal large model to obtain an equipment network table; taking the equipment network list as input, and performing fault mode directional retrieval on the equipment information base by adopting a large language model to obtain a fault mode list; taking the equipment network list and the fault mode list as input, performing failure reason reasoning by adopting a large language model, and establishing a fault causal chain from a system layer to an equipment layer; and taking the fault causal chain as input, constructing a fault tree by adopting a large language model, and generating a complete fault tree structure from a system top event to an equipment basic event. The method is suitable for safety and reliability analysis work of a complex industrial system.

The invention discloses a pressurized water reactor system fault tree building method based on a large language model, and relates to the field of fault tree analysis. In order to solve the defects that in the prior art, a fault tree construction process depends on manpower, efficiency is low and logic consistency is insufficient, the technical scheme provided by the invention comprises the following steps: outputting structured module failure reason items according to a system success criterion, a system hierarchical structure, an equipment list, a connection relationship and an equipment failure mode of a pressurized water reactor system; on the basis of the module failure reason items, the overall failure of the system is set as a top event, and a subsystem failure event is output; on the basis of the subsystem failure event, continuing to expand and map to a module failure intermediate event causing subsystem failure, and outputting a module failure event; based on module failure events, tracing to specific equipment and failure forms thereof one by one, generating equipment failure bottom events, and constructing and outputting a preliminary fault tree; and generating a compact final fault tree.

The application discloses a kind of based on power prediction's hydropower wisdom management method and platform, belong to hydropower management technical field, specifically include: collection hydrological forecast data, power load data, equipment operating condition data and historical scheduling record, establish unified data storehouse, and ensure that all kinds of data are real-time updated and format standardization;According to the error of hydrological forecast, load prediction deviation, equipment failure probability three kinds of sources, respectively using probability distribution model, error sequence analysis, fault tree analysis method, generate each source uncertainty quantification index;Mapping model is constructed;According to mapping result, establish risk grade evaluation system, real-time output each scheduling risk specific grade;According to risk grade, formulate targeted scheduling strategy, execute generated scheduling strategy, synchronously monitor prediction data change and risk grade fluctuation, uncertainty index exceeds threshold value, immediately trigger the dynamic adjustment of scheduling strategy.