78 results about "Fault tree analysis" patented technology

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 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 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 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 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 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 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.

The invention discloses a system information intelligent acquisition 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 the prior art, the efficiency of obtaining the topological structure and fault knowledge of a system is low, the accuracy is insufficient, and the cross-modal cooperative processing capability is lacked, the invention provides the following scheme: obtaining and preprocessing a system flow chart, and generating high-quality input data through functional decoupling and physical boundary decomposition; carrying out symbol recognition and text extraction to obtain preliminary topological information; forming a standardized system information file through a regular expression; inputting the file into a large language model to obtain a fault information set containing a fault mode and traceability information; integrating the topology file and the fault information to form system information data; and after consistency and integrity verification, outputting a standardized file which can be directly used for fault tree analysis and reliability evaluation. The method is suitable for system topology construction, fault tree analysis and safety and reliability evaluation work of a complex industrial system.

The invention discloses an unexpected electric braking fault control method and related equipment, and the method comprises the steps: determining a risk level and a fault cause corresponding to an unexpected electric braking fault of a vehicle, the unexpected electric braking fault being used for representing unexpected deceleration exceeding a quantitative safety boundary generated by the abnormal electric braking function of the vehicle, and determining the risk level and the fault cause corresponding to the unexpected electric braking fault; the quantitative safety boundary is a threshold value of unexpected deceleration and duration related to each risk level, the risk level is determined based on hazard analysis and risk assessment, and the fault cause is obtained through analysis and tracing of an architecture-level fault tree; safety requirements are determined based on the risk level and the fault reason, and the safety requirements comprise generation control of an electric braking request, execution process monitoring of the electric braking request and management and degradation when an unexpected electric braking fault occurs; an actual execution electric braking force of the vehicle is configured based on the safety demand to limit an unexpected electric braking force within a quantified safety boundary.

The embodiment of the application discloses a kind of fusion safety analysis method and storage medium of laser radar.The method comprises: determining the top-level safety target of laser radar according to the product definition of laser radar;Based on top-level safety target and fault tree analysis method, fusion safety analysis is carried out on laser radar, to obtain dangerous trigger event, wherein, fusion safety analysis includes functional safety analysis, expected functional safety analysis and information security analysis;Determine fusion safety mechanism based on dangerous trigger event;Based on fusion safety mechanism, fusion safety test is carried out on laser radar, and output user response strategy.The method makes the safety analysis of laser radar more complete and comprehensive, effectively reduces the safety risk of laser radar product.

This invention provides an intelligent CNC hydraulic system and control method, including a sensing and monitoring unit comprising a multi-source heterogeneous sensor network deployed at key nodes throughout the system. The invention is rationally designed, using a multi-source heterogeneous sensor network deployed throughout the system to simultaneously collect multi-dimensional operating parameters such as pressure, flow rate, temperature, and vibration. Edge computing is used to complete data preprocessing and fault-sensitive feature extraction. Relying on an SVM-LSTM dual-model fusion fault intelligent diagnosis module and integrating a fault tree FTA analysis submodule, it achieves accurate classification of fault types, rapid location of fault points, and root cause tracing. It also provides early warning of early system faults, significantly improving fault diagnosis accuracy compared to traditional threshold alarm methods. Furthermore, it can identify fault types that traditional methods cannot detect, such as directional valve sticking, predicting fault development trends in advance and effectively preventing equipment damage caused by fault escalation.

The invention discloses an aircraft environment control system fault diagnosis method, device and equipment and a storage medium. The method comprises the following steps: collecting historical operation parameters of each subsystem of the environmental control system, and performing data preprocessing through a traversal method, a Tukey fence method and a K-Means clustering algorithm based on Euclidean distance to construct a high-quality historical database; based on the database, establishing a BP neural network baseline model for each subsystem, and performing traversal optimization on a model interlayer structure by taking an average absolute deviation as an index to obtain an optimized baseline model; in the real-time diagnosis stage, real-time parameters are input into the model to obtain a predicted value, the predicted value is compared with a measured value, and if the difference exceeds a threshold value, fault early warning is triggered; and then, starting a corresponding fault tree analysis process to carry out root positioning. According to the method, a data driving method and a model driving method are fused, early warning and rapid root diagnosis of faults are achieved, and the accuracy, timeliness and interpretability of fault diagnosis of the environmental control system are remarkably improved.

A dynamic risk assessment method for a direct burial long-distance heat supply pipeline belongs to the technical field of heat supply pipeline safety management, solves the technical problem of poor accuracy of the existing risk assessment method, and comprises the following steps: S1, determining the initial probability of each basic event according to accident historical data; s2, identifying potential failure factors by using an accident tree analysis method, and constructing an accident tree model; s3, calculating a three-dimensional influence factor, and constructing a mathematical model for quantifying the weight of the basic event by adopting a multi-dimensional factor fusion formula; s4, mapping the accident tree model determined in the step S2 into a Bayesian network model, and correcting the initial probability of the basic event; and S5, calculating an occurrence probability of a top event, then taking real-time monitoring data of the heat supply system as input, updating a failure probability value of each network node after iterative calculation, and completing dynamic risk assessment. According to the method, the accuracy of the risk assessment of the direct-buried long-distance heat supply pipeline is improved, and the factors considered by the risk assessment of the direct-buried long-distance heat supply are more comprehensive.

The invention provides a CO2 leakage risk assessment method and device based on a fault tree, and relates to the technical field of carbon sequestration, and the method comprises the steps: setting CO2 leakage as a top event of fault tree analysis; determining a direct reason causing CO2 leakage as a first-stage intermediate event, analyzing the direct reason of the first-stage intermediate event as a second-stage intermediate event, and so on until a basic reason which cannot be decomposed is disassembled, and taking the basic reason as a bottom event; a general symbol of a fault tree analysis method is adopted to connect a top event, middle events of all levels and a bottom event to construct a fault tree, the top event is located at the topmost end of the fault tree, the bottom event is located at the bottommost end of the fault tree, and the middle events of all the levels are sequentially distributed between the top event and the bottom event according to the causal logic sequence; and determining the occurrence probability corresponding to each bottom event, calculating the occurrence probability of a top event based on the occurrence probability corresponding to each bottom event in combination with the fault tree, and generating CO2 leakage risk assessment information.

The invention relates to a shallow water Christmas tree development mode risk identification and quantitative evaluation method and system, processing equipment and a storage medium, and the method is characterized in that the method comprises the steps: carrying out the risk qualitative analysis of a shallow water Christmas tree drilling stage, constructing a fault tree analysis model of each drilling stage in a shallow water Christmas tree development mode, and determining potential risk factors; based on the risk factors in the fault tree analysis model, performing quantitative evaluation by adopting a strategy test and evaluation laboratory method, and determining the weight of each risk factor in the fault tree analysis model; based on the weight of each risk factor in the fault tree analysis model, high risk factors in the shallow water Christmas tree drilling stage are determined, risk identification and quantitative evaluation of the shallow water Christmas tree development mode are completed, potential risks can be more comprehensively identified and evaluated, quantitative evaluation is carried out, and the method can be widely applied to the technical field of risk analysis.