682 results about "Failure probability" patented technology

The invention relates to the technical field of pipeline stress fatigue detection, and discloses a natural gas station elbow tee joint stress fatigue digital intelligent analysis method and system and a product. The method comprises the following steps: establishing a multi-physics field coupling digital twinborn model of the elbow tee joint, integrating a geometric structure, material attributes and a fluid-solid coupling mechanism, and simulating flow-induced vibration stress and corrosion fatigue interaction; collecting real-time multi-source data of the elbow tee joint; real-time multi-source data is utilized to update boundary conditions and parameters of the digital twin model, and material constants are dynamically corrected through machine learning, so that self-calibration of the model is realized; performing stress distribution analysis based on the updated model to obtain stress field data, and performing fatigue crack propagation prediction in combination with real-time multi-source data to generate a prediction result; and based on the prediction result, evaluating fatigue failure risks, including crack growth rate, residual life evaluation and failure probability, and outputting alarm information or optimization decision information.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a processing environment switching and recovery method, device, equipment and medium. The method comprises the steps that multi-source heterogeneous data in a main processing environment and a standby processing environment are acquired, and the system fault probability is obtained through multi-model collaborative prediction; a dynamic threshold value is generated in combination with a historical service period mode and a real-time service load, when the fault probability exceeds the threshold value, a switching strategy is generated based on the fault scene knowledge base and the service priority, and flow scheduling between the main processing environment and the standby processing environment is executed; and monitoring the business index of the standby processing environment during the scheduling period, and triggering the fusing rollback when the business index is lower than the health standard. According to the method, the fault identification precision is improved through multi-source data fusion and multi-model prediction, adaptive scheduling is realized in combination with a dynamic threshold and a switching strategy, and fusing rollback is triggered to guarantee high availability and data consistency, so that the continuity and stability of key services are enhanced.

The invention discloses an intelligent coordination control method and system for power plant equipment based on multi-source heterogeneous data, and belongs to the technical field of intelligent manufacturing and industrial automation, and the method comprises the steps: deploying a multi-mode sensor network in the power plant equipment, collecting the multi-source heterogeneous data in real time, and carrying out the real-time data preprocessing through an edge calculation node; carrying out collaborative modeling on the preprocessed data by adopting a hybrid analysis framework, predicting an equipment state trend, identifying a fault propagation path, positioning a root cause and optimizing a maintenance decision scheme; the equipment failure probability is evaluated through a fault diagnosis result, a grading early warning mechanism is triggered, and a rule base is updated and optimized in combination with a dynamic knowledge base; a three-dimensional model is constructed by using a digital twinning technology to carry out virtual simulation and remote control, and maintenance guidance is carried out through an augmented reality auxiliary technology. According to the method, efficient real-time monitoring and fault prediction are achieved, the fault diagnosis time and the operation and maintenance cost are remarkably reduced by combining the fault tree model and the digital twinning technology, and the equipment operation safety and reliability are improved.

The invention discloses an automatic driving test scene set optimization method, apparatus and device, and a storage medium. The method comprises the steps of generating a simulation scene file in an OpenSCENARIO format through preprocessing data; analyzing the simulation scene file through a teacher model, outputting a risk description text, receiving a scene feature vector and the risk description text through a student model, and outputting a failure probability; determining a comprehensive value index according to the failure probability, dynamically updating a scene library of automatic driving test scenes, collecting failure data in an AUT test, performing incremental fine tuning on the student model, and obtaining an optimized target test scene set; the problem of gradient estimation variance explosion caused by sparseness disasters can be effectively solved, the stability of model training is improved, the recognition capability of a rare long-tail failure scene is enhanced, the accuracy of failure probability prediction is improved, the judgment accuracy of the model to a boundary scene is improved, and the accuracy and comprehensiveness of scene value quantification are improved.

The invention provides a production line equipment fault prediction method and system, and relates to the technical field of fault prediction.The method comprises the steps that multi-source time sequence signals are synchronously collected, a multi-dimensional health feature sequence is extracted, the comprehensive sensing capacity covering multiple states of equipment is established, and further, the fault prediction efficiency is improved. By selecting a key health feature sequence for each device and performing linear fitting to quantify the performance degradation severity, accurate description and early recognition of the performance degradation trend of the device are realized, and in addition, the causal influence intensity between the devices is quantified by adopting a transfer entropy algorithm, so that the accuracy of the performance degradation trend is improved. Scientific modeling and visual analysis of a fault propagation path and a linkage effect in a production line are realized, and a comprehensive risk index is generated through weighted fusion of performance degradation severity and a global impact factor, so that a comprehensive risk assessment index is formed. Based on the cumulative failure probability function, the comprehensive risk index is converted into a fault probability curve changing along with time, a visual mathematical expression of the risk trend is formed, and a scientific basis is provided for decision making.

The invention provides a task alarm processing method and system based on intelligent grading, and relates to the technical field of task scheduling alarm, and the method comprises the steps: collecting execution behavior data of each task in a task scheduling system in a plurality of time windows, carrying out the time sequence coding, and converting the execution behavior data into a state transition matrix, identifying an abnormal task and quantifying an alarm intensity value by analyzing non-stationary features; constructing a data flow direction and resource competition relation heterogeneous graph between the tasks, and mapping the abnormal tasks to corresponding nodes; generating a context sensing vector through multi-hop neighborhood aggregation, forming an alarm cluster based on semantic distance clustering, and determining a core node; and determining a control instruction according to the alarm intensity of the core node, performing conflict detection in combination with a resource competition relationship, adjusting a task execution time sequence, and predicting a failure probability output risk type based on a state transition path. According to the invention, intelligent grading and accurate processing of alarms can be realized, and the reliability and resource utilization efficiency of a task scheduling system are improved.

The invention discloses a power distribution equipment health assessment method and system based on multi-source data, and the method comprises the steps: mapping each modal feature into a comparable measure, constructing a learnable cost containing power flow and heat consistency, outputting a modal weight through scene gating, and forming a fusion representation of physical consistency; driving a neural differential equation by fusing the stress force obtained through representation decoding, adopting monotone weight parameterization and introducing equipment-level damage budget, and obtaining damage and health indexes which are irreversible along with time; under the constraint of physical baseline life, combining a working condition input time-scene gating danger rate model, performing causal consistency correction through virtual intervention, and outputting an interval failure probability and residual life; and calibrating a dynamic threshold value in the working condition cluster, and triggering routing inspection, sampling and load shedding based on the risk sensitivity and the topological linkage risk priority. According to the method, multi-source physical consistent fusion, individualized health modeling and dynamic closed-loop optimization can be realized, and the accuracy and interpretability of health assessment of the power distribution equipment are improved.

The invention relates to the technical field of electric digital data processing, and particularly provides a method for judging seepage failure of an immersed roadbed under the action of repeated seepage, which comprises the following steps of: constructing a water-force-chemical coupling test mechanism, monitoring dynamic change of a permeability coefficient, a fine particle loss rate and three-dimensional reconstruction of a pore structure in real time through a sensor, and determining seepage failure of the immersed roadbed under the action of repeated seepage. A scanning technology is combined to quantify an evolution rule of a seepage path; key parameters such as seepage path fractal dimension, fine particle mobility and dynamic porosity change are extracted on the basis of an evolution rule, the weight of each index is determined by introducing fuzzy analytic hierarchy process, and a seepage failure comprehensive index is constructed; and establishing a time sequence prediction model, inputting a seepage failure comprehensive index and an environment variable, outputting a seepage failure probability, and dividing safety-critical-danger three-level early warning. According to the method, the limitation of a traditional static evaluation method is broken through, and the whole-process monitoring and prediction of seepage failure under the dynamic action of repeated seepage are realized; and the discrimination precision is obviously improved.

The invention provides an AI-driven equipment health state assessment method and system, and relates to the technical field of intelligent operation and maintenance. The method comprises the following steps: acquiring equipment operation data, and performing time and dimension unification and quality control to form a multi-source operation data set and an environment context; generating an initial state feature based on the mechanism feature library, and obtaining a general representation through self-supervised pre-training; executing calibration learning by using a preset health label, and establishing a fusion evaluation model containing time sequence consistency and physical boundary constraint; carrying out distribution alignment and uncertainty estimation on the basis of scene differences to obtain alignment characterization and credibility scores so as to optimize a model gating strategy; performing joint mapping on the new data, outputting health index, fault probability and residual life estimation, and generating a root cause clue; lightweight online updating is executed under drifting detection, health indexes and root cause clues are written back to a mechanism feature library, early warning levels and maintenance suggestions are generated, and therefore complete-cycle intelligent sensing and self-adaptive optimization of the equipment state are achieved.

The invention discloses a rotor reliability constrained rolling bearing assembly parameter robust design method. The method comprises the following steps: constructing a dynamic model for an actual rotor-bearing system; constructing an uncertainty parameter vector and a design variable vector; a target function based on robustness and a constraint function based on reliability are constructed, so that an uncertainty optimization model is obtained; constructing an augmented input variable, and establishing a candidate orthogonal polynomial basis function set; on the basis, constructing and evaluating polynomial chaos-Kriging models for the target function and the constraint function respectively, and screening out an optimal polynomial chaos-Kriging model; calculating the expectation and the standard deviation of the target function and the failure probability of the constraint function under each design variable vector; and converting the uncertainty optimization model into an unconstrained single-target optimization model, randomly generating population individuals of a heuristic optimization algorithm in a feasible region of design variables, and iteratively searching an optimal solution of the unconstrained single-target optimization model as a rolling bearing assembly scheme.

The invention discloses a cable-stayed bridge cross brace optimization system based on system reliability and an intelligent algorithm, which relates to the technical field of bridge engineering, and comprises a multi-source parameter input module used for collecting and processing data such as a main beam section, stay cable parameters, a strain sequence, a vibration sequence, a satellite cloud picture, a traffic monitoring flow, laser point cloud and the like; and outputting the structured design data, the monitoring data flow, the dynamic load spectrum, the probability distribution model and the construction error correction parameters. According to the invention, all-dimensional data such as design, monitoring, load, materials and construction errors are integrated through the multi-source parameter input module, and multi-level and dynamic reliability evaluation is carried out on the component, the subsystem and the system level by using the system reliability analysis module, so that the component failure probability, the system reliability index time sequence and the like can be output; therefore, the real reliability level of the cross brace of the cable-stayed bridge in the whole life cycle is reflected more accurately, and the one-sidedness of a traditional method is avoided.

The invention discloses a post-pumping air tank robustness collaborative design method considering hydraulic parameter time-varying characteristics, which comprises the following steps: defining a hydraulic parameter uncertainty fluctuation interval of a water delivery system in a full life cycle, and setting a body structure decision variable range of a post-pumping air tank; performing combined sampling in the water conservancy parameter uncertainty fluctuation interval and the body structure decision variable range by using a test design method to generate an initial sample set; performing steady-state and transient-state coupling simulation on the initial sample set, constructing a constant-flow operation condition of the water delivery system by using a hydraulic equation, updating a water pump working point and pipeline pressure distribution, performing transient simulation by using a characteristic line method to obtain a hydraulic response index, and training to obtain a water hammer response agent model; constructing a robustness optimization objective function based on failure probability constraint; and performing global optimization on the target function by using an intelligent optimization algorithm, calling the water hammer response agent model to perform random simulation, evaluating a failure probability, and outputting a target design scheme.

The invention discloses a low-sample neural network structure reliability evaluation system and evaluation method, and relates to the technical field of engineering structure safety monitoring, and the evaluation system comprises a cloud server which is used for constructing a recurrent neural network model containing a static variable embedding mechanism, completing model training and converting a model format; the edge calculation terminal is used for receiving and preprocessing real-time data of the sensor, executing multi-step prediction to output a future time period response sequence, and calculating a future failure probability through virtual Monte Carlo simulation; the sensor assembly is used for collecting structure state time sequence data; and the communication module is used for realizing data interaction and alarm signal transmission operation. According to the method, collaborative modeling of time-varying and static uncertainty is realized by adopting a static variable embedded recurrent neural network model, failure probability distribution is generated at an edge computing terminal in combination with a virtual Monte Carlo technology, failure risk prediction in a future time period is supported, and real-time and accurate reliability early warning can be realized in a resource limited scene.

The invention provides an internal damage assessment method and system based on a multi-modal graphite component, and relates to the technical field of material deterioration assessment, and the method comprises the steps: generating a standardized multi-modal data stream through synchronously collecting five-modal data; extracting acoustic and electro-thermal physical features from the multi-modal data stream, and constructing a multi-dimensional damage sensitive feature vector; inputting the multi-dimensional damage sensitive feature vectors into a deep learning network corrected by a physical constraint layer, and extracting crack quantization parameters from the deep learning network; inputting the crack quantization parameters into a digital twin driven irradiation-creep-fatigue coupling damage model, and calculating the residual life and failure probability of the overall structure; and in combination with the space coordinate information of the three-dimensional crack probability field, a preventive maintenance instruction and schedule for the high-risk modular component are generated and optimized. According to the method, accurate perception, quantitative evaluation, accurate prognosis and optimized decision making of the internal damage of the graphite component are realized, the resource utilization efficiency is maximized, and unnecessary shutdown loss is reduced.

The invention relates to the technical field of geotechnical engineering structures, in particular to a multi-factor coupling CFG pile full-life failure probability prediction and optimization method. Comprising the following steps; the method comprises the following steps: S1, integrating resistance degradation, pile-soil interaction, geological evolution and load time-varying characteristics to construct a limit state model; s2, on the basis of the limit state model constructed in the step S1, Monte Carlo simulation circulation is used, the failure probability is calculated, and a CFG pile system failure probability change graph is obtained, S3, parameters in the limit state model are optimized, after each parameter is optimized, the steps S1 to S2 are carried out again, and the optimized CFG pile system failure probability change graph is obtained; according to the method, the accuracy and the adaptability of full-life prediction and optimization of the CFG pile are 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 present invention relates to an adaptive assessment method and apparatus for nuclear power plant component reliability, a storage medium, and an electronic device. The method comprises the following steps: constructing a Kriging initial model; gradually updating the Kriging initial model on the basis of first-layer samples of ICE to obtain a Kriging surrogate model; gradually updating the Kriging surrogate model on the basis of last-layer samples of the ICE; after the updating of the Kriging surrogate model is completed, obtaining a current Kriging model; and on the basis of the current Kriging model, assessing the reliability of a component to be assessed. In the present invention, first-layer adaptive Kriging gradually updates and explores a failure domain on the basis of the first-layer samples of the ICE until a set stopping criterion is satisfied, and on the basis of a currently constructed Kriging model, the last-layer samples of the ICE are also updated accordingly. Thus, it is ensured that an estimated failure probability converges unbiasedly to a real failure probability, and unnecessary updating of Kriging is avoided, thereby greatly improving calculation efficiency and saving calculation costs.

The invention discloses an electronic detonator detonation dynamic regulation and control method and system based on performance monitoring, and relates to the technical field of electronic detonators. The method comprises the following steps: acquiring environmental stress parameters and detonator performance parameters in real time, and performing time sequence analysis to realize dynamic feature extraction; identifying an abnormal fluctuation time period, analyzing and extracting fluctuation characteristics, and quantifying the interaction influence of the environment and performance parameters; a performance degradation model is constructed based on voltage / resistance time sequence data, the degradation rate is dynamically calculated in combination with batch characteristics and acceleration test data, and the detonation failure probability is simulated and predicted; a detonation parameter safety interval is generated through reliability level mapping, and optimization design of trigger parameters and energy is achieved through timing compensation, discreteness grouping, network topology optimization and energy dynamic adjustment strategies; a closed-loop dynamic regulation and control strategy is formed, and high-reliability detonation of the electronic detonator under complex working conditions is guaranteed. According to the method, multi-dimensional data analysis and an intelligent optimization algorithm are fused, and the safety of blasting engineering and the system stability are effectively improved.

The invention discloses a containment failure probability curve fitting method and system based on a random forest, and relates to the technical field of nuclear power safety. Comprising the following steps: acquiring key parameters influencing ultimate bearing capacity of a containment, determining a distribution interval of the key parameters, and establishing a key parameter table; performing finite element calculation on the basis of the key parameter table, obtaining ultimate bearing capacity of the containment vessel under different parameter combinations and temperature loads, and establishing a database; performing normalization preprocessing on the database to form a training data set; establishing a random forest model and performing regression model training by using the training data set; inputting actual design parameters of the containment and the temperature required to be evaluated into the trained model to obtain an ultimate bearing capacity predicted value of the containment; and fitting of a failure probability curve is carried out based on the central limit theorem. According to the method, complex probabilistic safety analysis is converted into a standardized calculation task, and the implementation threshold of nuclear power plant design verification and safety review is greatly reduced.

The invention belongs to the technical field of computers, and discloses a predictive retry method for a service request, and the method comprises the steps: predicting the failure probability of the service request based on multi-dimensional data before a response result of the service request is returned, and determining that the retry success probability of the service request is high if the predicted failure probability is below a first probability threshold value, if yes, retry is initiated when the service request fails, so that the processing efficiency of the service request is improved; if the failure probability is greater than a first probability threshold and less than a second probability threshold, determining that the probability of successful retry of the service request is relatively low but has a certain success rate, determining a retry time interval of the service request based on a preset parameter at least comprising the failure probability, and initiating retry based on the retry time interval when the service request fails. The processing efficiency of the service request is improved, the trial and error cost is reduced, and the retry time interval of the service request is determined based on the preset parameter, so that the retry time interval adapts to the current real-time environment.

The invention discloses an offshore wind turbine supporting structure reliability evaluation method based on a reduced-order digital twin model, and the method comprises the steps: obtaining the response data of an offshore wind turbine supporting structure through multi-dimensional load analysis; and finally, setting a limit state function of the offshore wind turbine support structure in an extreme state, and calculating the failure probability of the offshore wind turbine support structure through a first-order reliability method under the condition of combining response data of the offshore wind turbine support structure so as to evaluate the reliability of the offshore wind turbine support structure. According to the method, the problems of insufficient model precision, calculation efficiency bottleneck, reliability evaluation limitation and the like of an existing reliability evaluation method for the offshore wind turbine supporting structure can be solved, and the requirements of offshore wind power engineering for high precision, high efficiency and comprehensiveness of reliability evaluation of the supporting structure can be met.

The invention relates to the technical field of data processing, in particular to a pipeline digital twin modeling and working condition deduction method and system and a storage medium. Comprising the following steps: acquiring temperature, pressure and corrosion data through a multi-source sensor, and performing fusion processing to obtain a calibration measurement value; comparing with historical reference data to determine systematic error characteristics, and if an error exceeds a preset threshold value, utilizing Monte Carlo simulation to generate an error distribution sample and calculating an accumulated deviation sequence to obtain an error amplification coefficient; based on this, adjusting risk assessment model parameters, constructing a digital twinborn model, generating a simulation scene, and assessing a preliminary risk level; further analyzing the trend characteristics of the risk quantitative indexes, calculating the potential failure probability in combination with the prediction model, and generating a pipeline maintenance priority sequence; and optimizing a propagation path according to the priority sequence, reasonably distributing maintenance resources, and formulating an accurate maintenance scheduling scheme. The accuracy and efficiency of pipeline maintenance are effectively improved, and the failure risk is reduced.

The invention discloses a BIM-based deep foundation pit collapse risk probability assessment method. The method relates to the technical field of risk assessment, and comprises the following steps of multi-failure mode coupling failure probability modeling, coupling risk dynamic regulation and control, and construction stage dynamic risk probability and dynamic risk closed-loop control and visualization. According to the method, the BIM model is constructed, the failure probability of multi-failure mode coupling is calculated, and whether chain type damage risk dynamic regulation and control are triggered or not is judged; calculating a dynamic risk probability value in combination with a construction monitoring data updating model, and judging whether self-adaptive closed-loop intervention is triggered or not; according to the method, a visual risk module is constructed in a BIM model, risk grading early warning and accurate management and control are realized, the deep foundation pit collapse risk probability assessment accuracy is improved, and the problems of model distortion caused by insufficient coupling mechanism cognition, flow stiffness caused by data flow-model flow-decision flow mutual separation, and risk assessment accuracy caused by deep foundation pit collapse risk probability assessment in the prior art are solved. And the deep foundation pit collapse risk probability assessment accuracy is low.

The invention relates to the technical field of landslide monitoring, and discloses a probability early warning method based on random fluctuation of a landslide deformation monitoring curve, and the method comprises the steps: obtaining landslide deformation monitoring data; establishing an accumulative displacement-time (S-t) curve to calculate the average deformation rate of the constant-speed deformation stage and the deformation rates vi of two adjacent measurements within the equal time interval; the dimensions of the vertical and horizontal coordinates of the S-t curve are subjected to consistent processing; if the improved deformation rate accords with normal distribution, calculating an improved rate mean value mu v and a standard deviation sigma v in equal time intervals; calculating a reliability index beta in equal time intervals; and calculating a damage probability according to the reliability index, and dividing a landslide early warning grade according to the damage probability. According to the scheme, the probability of landslide instability is reflected by monitoring the random change of the data, and the purpose of landslide monitoring and early warning is achieved; the tedious data processing of a traditional method is avoided, and the reliability and accuracy of early warning and forecasting are higher.

The invention discloses a risk assessment method for a supercritical carbon dioxide conveying pipeline and related equipment, and the method comprises the steps: constructing a fault tree model for a failure event of the pipeline; obtaining a consequence event corresponding to the failure event, and constructing a consequence model according to the consequence event; based on the fault tree model and the consequence model, obtaining a bow-tie model of the supercritical carbon dioxide conveying pipeline; obtaining a plurality of groups of evaluation data of the plurality of evaluation policies for the basic event in the bow-tie model, and obtaining a failure probability corresponding to the basic event based on the plurality of groups of evaluation data; and obtaining an occurrence probability of a leakage consequence corresponding to the basic event based on the failure probability, and performing hazard assessment on the leakage consequence based on the occurrence probability to obtain an assessment result. According to the method, the occurrence frequency of consequences in various risk scenes is accurately calculated, the problem that the risk in the actual conveying process cannot be accurately reflected is avoided, and the design and implementation of subsequent risk management decisions and optimization measures can be effectively supported.

The invention relates to a pole-mounted circuit breaker monitoring method and system, electronic equipment and a medium. The method comprises the following steps: acquiring a voltage recovery waveform characteristic and a mechanical vibration characteristic during operation of a vacuum arc-extinguishing chamber, and performing timestamp alignment; calculating a contact gap conductive channel risk value based on the aligned waveform characteristics; generating breaking capacity attenuation trend characteristics by combining the historical breaking current peak value, the breaking times and the risk value; calculating the remaining service life according to the trend characteristics, and synchronously obtaining a fault probability value according to the conductive channel risk value; and finally, according to the residual service life and the fault probability value, determining a maintenance early warning level and outputting a maintenance early warning signal. Therefore, according to the method, the risk of the conductive channel is evaluated through fusion of the operation waveform and the mechanical signal, and the breaking capacity attenuation trend is extrapolated along the historical breaking data, so that online evaluation and advanced early warning of the health state of the pole-mounted circuit breaker are realized, and the operation reliability and the operation and maintenance efficiency of the key node of the power distribution network are improved.

The invention discloses a safety valve service life evaluation method based on pressure history statistics and dynamic damage accumulation. According to the method, only safety valve inlet pressure time sequence data is needed, a load cycle is extracted through an improved rain flow counting method, and the nonlinear damage increment is calculated according to an S-N curve model dynamically degraded along with the accumulated damage degree. According to the method, a self-adaptive failure probability threshold is established based on historical failure data, and probabilistic prediction of the residual life is realized. The model is clear in physical significance, only needs a single data source, significantly reduces the implementation complexity and cost, greatly improves the precision of life prediction by considering the degradation of material performance, and is especially suitable for predicting the accelerated failure risk of the safety valve in the later period of long-term service.

The invention discloses an oil and gas pipeline fault prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining and preprocessing pipeline associated data corresponding to an oil and gas pipeline based on sensing equipment, and obtaining pipeline target data; defining an entity type and a relationship type, and constructing a dynamic knowledge graph based on the pipeline target data, the entity type and the relationship type; when the leakage point is detected, determining a target sub-graph corresponding to the leakage point based on the dynamic knowledge graph, generating a migration sequence of the target sub-graph, and converting the migration sequence into a vector to be matched; and matching the to-be-matched vector with a sub-graph in a historical graph library to obtain a similar sub-graph, and determining a target fault probability of the oil and gas pipeline based on the similar sub-graph. The problem that an existing method is difficult to meet the requirements for dynamism, accuracy and the like of corrosion prediction in a complex pipeline system is solved, a dynamic knowledge graph is constructed as a core drive, Internet of Things perception, multi-modal data analysis and artificial intelligence are fused, and real-time prediction of the pipeline state is achieved.

The invention discloses an underground packer coupling integrity diagnosis system and a digital twinborn life prediction method. The system comprises a data acquisition layer, an edge computing layer, a cloud analysis layer with a built-in L4-level digital twin system and an application layer which are connected in sequence. According to the method, vibration, corrosion and other parameters are collected through a sensor, after preprocessing of an edge layer, a cloud calls a three-field coupling dynamic model, multi-field parameters, the failure probability and the remaining life are calculated in combination with an LSTM and Bayesian fusion algorithm, and an application layer outputs diagnosis results and maintenance suggestions and forms a closed loop. The problems that traditional methods depend on experience, multi-field coupling is difficult to quantify and the digital twinborn level is low are solved, the maintenance cost can be reduced, the well control accident risk is reduced, and a key technical support is provided for predictive maintenance of an oil and gas well.

The invention is suitable for the technical field of distribution network risk assessment, and provides a distribution line risk assessment method and system based on a lightning and associated disaster coupling probability model, and the method comprises the steps: obtaining meteorological data, geographic data and distribution line parameters of a target region; calculating a first fault probability of the lightning disaster lower line section or the tower and a second fault probability of the associated disaster lower line section or the tower; combining the disaster type conversion probability and the disaster accompanying time coefficient, determining the third fault probability of the lower segment or the tower of the comprehensive disaster, calculating the failure probability of the distribution line, and screening a typical fault scene set by using the system information entropy; calculating a line fault consequence value, and generating a line risk index in combination with the failure probability; and finally, generating a target risk line list according to the sorting of the line risk indexes. Based on the influence on the line fault probability under the condition of expected associated disasters of lightning disasters, the accuracy of line fault prediction in extreme weather is improved.