105 results about "Failure risk" patented technology

The invention discloses a bridge detection method and system based on a digital twin technology, and relates to the field of bridge structure health monitoring. The method comprises the following steps: acquiring a strain distribution value, a vibration spectrum value and an environmental load spectrum value in real time through a sensor network deployed in a physical bridge, generating a structural response data set, and synchronizing the structural response data set to a digital twinborn body; calculating a damage index value and an accumulated damage quantity value based on the structural response data set; inputting the damage index value and the accumulated damage quantity value into a preset safety criterion, and calculating a safety margin coefficient value and a failure risk grade value; calculating a residual life prediction value based on the safety margin coefficient value and the environmental load spectrum value, and synchronously correcting a degradation rate value of the digital twin; and generating a priority maintenance instruction according to the failure risk grade value, the residual life prediction value and the safety margin coefficient value, and feeding back maintenance effect data to the digital twinborn body to complete updating after execution. The bridge operation and maintenance efficiency and safety are remarkably improved.

The invention provides a bridge structure risk early warning method based on Bayesian decision and information value. According to the method, Bayesian prior analysis is carried out through structure monitoring data, a cost model comprises structure failure cost, maintenance cost and management and control cost, and a structure state model is obtained through structure fatigue reliability analysis. Bayesian prognosis test analysis is carried out in the next step, short-term future monitoring data distribution is predicted based on current monitoring data, then structural fatigue reliability analysis is carried out on the possibility of each kind of monitoring data, corresponding failure risk indexes are calculated, and information values are calculated based on a cost model. And finally, through maximizing the information value, obtaining an optimal structure early warning threshold value under Bayesian prognosis test analysis so as to determine the optimal early warning time. According to the self-adaptive structure risk early warning method, the early warning cost and the structure performance degradation are comprehensively considered, and rapid early warning decisions with safety guarantee and economic benefits can be achieved.

The invention discloses a living body target intelligent identification method based on multi-mode heterogeneous data collaboration, and particularly relates to the technical field of target intelligent identification. Multi-mode heterogeneous data are acquired in real time by using multiple types of sensors, and high-precision timestamps of all modes are recorded to construct multi-mode asynchronous mismatch coefficients; the time sequence mismatch risk caused by collection and transmission asynchronization is dynamically recognized, a cross-modal similarity matrix is constructed, joint modeling is conducted on the cross-modal similarity matrix and a modal feature significance conflict area to calculate a cross-modal feature conflict enhancement coefficient, the enhancement effect of cross-modal feature conflicts is quantitatively evaluated from the feature level, and the time sequence mismatch risk caused by collection and transmission asynchronization is evaluated. The cross-modal feature conflict enhancement coefficient and the multi-modal asynchronous mismatch coefficient form a complementary output multi-modal fusion risk assessment index, the stability and risk level of the current multi-modal fusion state are quantified, and the potential time sequence and feature collaborative failure risk in the fusion process is accurately reflected; and dynamically adjusting the fusion weight of each mode based on a risk assessment result.

The dynamic prediction and inducement identification method for the shaft integrity failure risk is characterized in that different influence sequences of shaft integrity failure risk factors under different working conditions are fully considered, and key risk factors are determined by adopting a principal component analysis method; constructing a shaft integrity failure fault tree comprising a top event, a middle event and a bottom event by utilizing a failure mode and an effect analysis method; determining a monitoring index according to the service condition of the predicted well; according to real-time changes of field data, dynamic updating of a prior probability, a conditional probability and a transition probability is realized in combination with a sliding window and a long-short-term memory network; combining the shaft integrity failure fault tree and the dynamic Bayesian network to establish a shaft integrity failure dynamic risk prediction model; and based on the dynamic changes of the prior probability, the conditional probability and the transition probability, utilizing the shaft integrity failure dynamic risk prediction model to realize shaft integrity failure dynamic risk prediction and inducement identification.

The invention discloses a shield tail sealing failure risk evaluation method based on numerical simulation, which comprises the following steps: identifying failure influence factors including an external load resultant force influence factor and an internal pressure influence factor; obtaining to-be-evaluated external load resultant force and internal pressure of the shield tail sealing device; the external load resultant force to be evaluated and the internal pressure are input into a numerical simulation model, a numerical simulation result is output, and the numerical simulation model is composed of a shield tail brush and a sealing grease cavity; and analyzing the numerical simulation result in combination with the influence factors to obtain the risk level of the shield tail sealing device. According to the method, the influence factors of the external load resultant force of the shield tail sealing device and the internal pressure of the shield tail sealing system are determined, the shield tail sealing possibility judgment standard is determined based on the numerical simulation model, corresponding countermeasures are given, and monitoring and reasonable early warning of the shield tail sealing state are effectively guaranteed.

The invention relates to the technical field of pressure-bearing structure welding defect identification, and discloses a pressure-bearing structure welding defect automatic identification and risk prediction method and system, and the method comprises the steps: obtaining an initial defect feature data set, carrying out the serialization analysis according to the initial defect feature data set, determining the initial defect extension trend, and carrying out the risk prediction of the initial defect extension trend; carrying out stress response analysis according to the defect initial expansion trend to obtain a defect stress response sequence, carrying out microscopic difference extraction analysis based on the defect stress response sequence to obtain a defect microcrack expansion trend, and carrying out time sequence prediction processing according to the defect microcrack expansion trend to obtain an expansion prediction vector; and carrying out characteristic evolution analysis and fatigue life calculation on the extended prediction vector, determining a potential failure time point, and carrying out risk index fusion calculation according to the potential failure time point to obtain a final failure risk prediction report. The method can solve the problem of insufficient dynamic monitoring in the prior art.

The invention relates to the technical field of big model data training, in particular to a method and system for intelligently checking hidden risks based on original big data, and the method comprises the following steps: 1) collecting and preprocessing data; 2) feature extraction; 3) constructing a risk model; 4) risk investigation; 5) risk reporting; 6) feedback and optimization; the method has the beneficial effects that through deep analysis of massive original data, the intelligent troubleshooting system can accurately capture subtle risk signals which are easily neglected by people. In industrial equipment monitoring, a large amount of original data collected by various sensors in the equipment operation process is analyzed, and tiny abnormal changes, such as temperature, pressure and vibration frequency, of equipment operation parameters can be accurately recognized, so that the equipment fault risk is warned in advance, and production interruption and economic loss caused by sudden equipment faults are avoided.

The invention provides a dual-drive deepwater wellbore integrity failure risk evaluation method, and belongs to the technical field of deepwater drilling safety, and the dual-drive deepwater wellbore integrity failure risk evaluation method comprises the following steps: based on fault tree analysis in a bowknot model, identifying a wellbore integrity failure mode; constructing a differentiated evaluation path according to the failure mode, and quantifying a static equipment failure risk and a dynamic structure failure risk respectively; constructing a wellbore system-level failure probability model through a fault tree and Bayesian network dynamic mapping mechanism; based on event tree analysis, modeling is conducted on the safety barrier state after shaft integrity failure, accident consequences are deduced, and the occurrence probability of the accident consequences is calculated; and outputting the system-level failure probability, the key path contribution degree, the bottom event sensitivity sequence and the accident consequence probability, and solving the technical problem that the system-level failure risk is difficult to accurately predict and dynamically prevent and control because static empirical data and a dynamic mechanical mechanism are separated and cannot be coupled and updated in real time.

The invention relates to a building risk monitoring and evaluating method and system, and the method comprises the steps: collecting the full life cycle data of a building, carrying out the preprocessing, extracting the failure mode of the building and key impact factors, designing an orthogonal test to simulate the accelerated aging process of the building, and determining the critical state of the accelerated failure. Selecting a dynamic covariable which is closely related to a failure mechanism and changes along with time from test conditions of an equivalent acceleration failure critical state, and constructing a risk regression model according to dynamic covariable data; and inputting the real-time dynamic covariable into the risk regression model, and calculating the relative failure risk of the current building. According to the method, the accelerated aging process of the building is simulated through the orthogonal test, the critical state of accelerated failure is accurately determined, observation of the aging process is greatly accelerated, interaction among different factors is comprehensively revealed, the relative failure risk of the building can be accurately quantified and evaluated in combination with the risk evaluation model, and the risk assessment efficiency is improved. And the real-time performance and the accuracy of the evaluation result are ensured.

The invention relates to the technical field of task scheduling control, and discloses a big data control system based on artificial intelligence and a block chain, and the system comprises a task data collection module which is used for collecting feature data of a to-be-executed task, and the feature data comprises a historical execution error record and an executor experience value; the task risk assessment module is used for predicting the failure risk probability of each to-be-executed task; the high-risk task identification module is used for marking high-risk tasks according to the failure risk probability; by introducing multi-dimensional indexes such as historical execution error records, executor experience value weights and task difficulty scores, a comprehensive assessment model of risk scores is established, and the potential failure risk of each to-be-executed task is accurately quantified. And through weight adjustment, management policy requirements of different scenes and different stages can be flexibly adapted. The accuracy of risk identification is improved, and the capability of early warning potential failure risks in advance is enhanced.

The invention belongs to the technical field of nuclear power plant DCS application and maintenance, and discloses a sodium-cooled fast reactor DCS risk analysis and safety measure intelligent analysis method and system. The method comprises the steps of data conversion, structure display, analysis object confirmation, signal isolation analysis, controller fault and controller configuration offline downloading safety measure analysis, module state analysis, I / O card fault safety measure analysis and the like. The system comprises a data conversion module, a structure display module, an analysis object confirmation module, a signal isolation analysis module, an offline downloading safety measure analysis module, a module state analysis module and an I / O card fault safety measure analysis module. According to the method, potential influence equipment and alarms can be efficiently judged, IO bit number forced risk analysis, IO card fault risk analysis, controller fault risk analysis and controller configuration offline downloading risk analysis are effectively assisted, and the risk analysis efficiency and accuracy are greatly improved.

The invention provides a component failure risk monitoring and early warning method and related equipment, classification of performance parameter test data sets of components is completed based on hierarchical clustering, variables with large correlation are clustered into a class cluster, and a plurality of clustering results are obtained. Modeling each clustering result based on a Gaussian mixture model, calculating an envelope analysis statistical magnitude according to the definition of the statistical magnitude after the modeling is completed, determining a target monitoring statistical magnitude by integrating the statistical magnitude values of all the clustering results, determining a target envelope baseline based on the target monitoring statistical magnitude, and testing data of the current performance parameters of the component sample, according to the comparison condition of the corresponding current statistical magnitude and the target envelope baseline, the existing failure risk type is determined and early warning is carried out, parameter tracing can be carried out on the component with the first type of failure risk, comprehensive monitoring of the mean value, the variance value and the correlation coefficient between the variables of the multiple variables is realized, the component with potential risk can be found in time, and the reliability of the component is improved. And the quality reliability of the component is improved.

The invention provides a risk positioning method and system for waterproof engineering and a storage medium, and relates to the technical field of risk positioning, and the method comprises the steps: obtaining the types of waterproof structures, including rigid, flexible and composite waterproof structures; segmenting the waterproof project to generate a plurality of sub-projects; waterproof construction features are collected, and a plurality of waterproof feature sets are generated and comprise waterproof layer materials, waterproof connection environments, waterproof layer connection structures and environment permeation features; constructing a waterproof failure prediction model, performing failure probability prediction according to a preset service life, and generating a plurality of waterproof failure probabilities; and positioning the risk sub-project of which the waterproof failure probability is greater than a preset probability, and performing failure risk reminding of the waterproof layer. The technical problems that in the prior art, a systematic classification and management means for waterproof engineering is lacked, waterproof layers of different structures are difficult to maintain in a targeted mode, and consequently management is improper, the leakage risk is increased, and the maintenance cost is increased are solved.

The invention provides a motor stator potting structure failure risk assessment and optimization design method. The method comprises the following steps: firstly, establishing a simplified numerical calculation domain based on a potting structure, carrying out grid and time step length independence verification, carrying out parameter sensitivity analysis and experimental measurement, and establishing a high-precision thermal-mechanical coupling numerical calculation model; then, in combination with the simulation result and the temperature change constitutive characteristics of the ultimate strength of the material, a node-level failure risk calculation method is provided, failure risk indexes of different stress forms are obtained, and a weighted comprehensive failure risk evaluation standard is determined according to the contribution degree of the stress forms to failure behaviors; and finally, proposing a parameterized structure optimization scheme according to a dangerous area and a dominant failure mechanism, carrying out quantitative evaluation and optimization on the candidate scheme under a weighted comprehensive failure risk evaluation standard, and determining a final structure optimization scheme. The method has the advantages of closed flow loop and quantifiable indexes, and is suitable for reliability design and life evaluation of the motor encapsulation structure.

The invention discloses a method and system for evaluating the failure risk of a buried pipeline in a disaster area, and the method comprises the steps: dividing a target pipeline into a plurality of segments, and determining a plurality of segments of to-be-evaluated pipelines; the total probability of leakage risks caused by the internal corrosion risk and the ground disaster risk of each to-be-evaluated pipeline is calculated, so that the failure possibility of the corresponding pipeline is obtained; determining the severity of the leakage risk consequence of each section of the to-be-evaluated pipeline; and determining the risk level of the corresponding pipeline according to the failure possibility and the leakage risk consequence severity of each section of to-be-evaluated pipeline. According to the invention, pertinence of failure risk assessment of the buried pipeline in the disaster area can be enhanced, and the level of safe operation management and technology of the buried pipeline is improved.

The invention discloses an MES-based industrial production data intelligent analysis and management system, and belongs to the field of industrial production data analysis. The system comprises a central module used for synchronously acquiring a time sequence of man-machine interaction event data and machine tool sensor data; the evaluation unit is used for calculating a cognitive load index and a processing load entropy; and the resonance risk assessment module is used for calculating a comprehensive risk level. Wherein the calculation of the comprehensive risk level depends on an instantaneous risk item based on a cognitive load index and a processing load entropy and a mode matching item based on form similarity between a time sequence and a predefined out-of-step mode template. According to the method, man-machine is regarded as a dynamic coupling system for analysis, predictive intervention on potential processing failure risks can be achieved, and the stability and robustness of the production process are improved.

The invention relates to the technical field of oil field drilling engineering, in particular to a while-drilling measurement-based rotary guide system risk identification method and identification terminal, and the method comprises the steps: obtaining while-drilling measurement data; establishing a deep well wall surrounding rock stress-strain model, and constructing a well wall stability discrimination equation; establishing a random nonlinear disturbance force action model, and determining random dynamic response process parameters of the system with the rotary guide drill column under the deep well condition; determining the well wall stability according to the well wall stability discrimination equation, and determining the failure risk of the drill column system with the rotation guide according to the random dynamic response process; according to the method, effective strain and effective stress at the position, r away from a borehole, of a near-well-wall zone are calculated, and a well wall stability judgment equation is obtained through fitting to judge the possibility of well wall instability; and a Newmark method is adopted to carry out time discretization on the stochastic nonlinear dynamic model of the drill string system to invert the stochastic dynamic response process of the stochastic nonlinear dynamic model, and risk factors are clarified.

The invention discloses a real-time risk monitoring and decision collaboration method for an edge intelligent terminal, and particularly relates to the field of industrial Internet of Things security monitoring and edge computing. The method comprises the following steps: collecting monitoring sub-region data through a multi-edge intelligent terminal, and calculating a local risk value; when the local risk value exceeds a threshold value, marking as a first edge intelligent terminal and broadcasting a collaboration request; the response edge intelligent terminal returns self risk data; the first edge intelligent terminal generates a final risk level through a weighted fusion algorithm according to a decision weight distribution strategy, and queries a rule base to generate a collaborative decision instruction; and sending a control instruction to the associated terminal based on the instruction, triggering equipment shutdown or data encryption operation, and feeding back an execution state. Through edge terminal localization calculation and collaborative decision, the problems of high response delay and high single-point fault risk of a centralized architecture are solved, millisecond risk response is realized, and the system stability and decision accuracy 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 graph theory-based shipping man-machine coupling failure risk assessment method and system, and relates to the technical field of shipping risk assessment, and the method comprises the steps: extracting structured data with a timestamp, generating an effective data set, screening the effective data set, and generating a key risk factor set; constructing a weighted undirected network through a graph theory based on the key risk factor set, performing sliding segmentation on the effective data set through setting time windows with fixed lengths and a preset step length, recalculating a standardized mutual information value between nodes in each time window, updating an edge weight, and generating a time-varying weighted undirected network topology sequence; performing global efficiency analysis on the time-varying weighted undirected network topology sequence, calculating a network global efficiency decline rate after each node is removed, and sorting the key risk factors according to the decline rate to determine the priority of the key risk factors; and outputting corresponding accident prevention suggestions and sailor training schemes based on the key risk factor priorities.

The application discloses a failure analysis method and related device, electronic equipment and storage medium, wherein the failure analysis method comprises: detecting based on the description document of the target agent to obtain the design elements of the target agent; analyzing based on the failure mode knowledge base and each design element to determine the first element suspected of existing failure risk and the first risk degree of the first element in each design element; selecting the first element as the second element based on the first risk degree of each first element; generating the test session and the adjudication rule of the second element based on the attack strategy knowledge base and the second element; determining the test reply of the second element based on the adjudication rule of the second element to obtain the second risk degree of the second element. The above scheme can identify the failure risk of the agent in advance before the agent goes online, and quantize the failure risk.

The embodiment of the present application provides a fault risk monitoring method, device, storage medium and program product, the method includes obtaining the current operating state of the system, the current operating state includes the current values ​​of multiple operating indicators, matching the current operating state with multiple historical operating states respectively, obtaining the matching degrees corresponding to the multiple historical operating states respectively, different historical operating states correspond to different sampling times, the historical operating state includes the historical values ​​of multiple operating indicators at the corresponding sampling time, obtaining the fault event under the historical operating state corresponding to the maximum value of the multiple matching degrees, and generating a fault risk prompt according to the fault event. The method provided by this embodiment can improve the comprehensiveness and accuracy of monitoring.

This application discloses a control method and device, electronic device, and storage medium for a storage system, relating to the field of computer technology. By using a preset risk prediction model, the failure risk index of each disk in the storage system is calculated to identify high-risk disks. Then, based on the failure risk index of each disk, the failure risk index of disk groups is calculated to identify high-risk disk groups. From the high-risk disk groups where no high-risk disks exist, disks to be marked are selected. Subsequently, all high-risk disks and disks to be marked in the storage system are replaced. This allows for timely handling of disks and disk groups with failure risks in the storage system, achieving rapid identification of high-failure-risk disks and high-failure-risk disk groups, and timely data backup and replacement. This ensures that the number of failed disks in the storage system does not exceed the system's fault tolerance capacity, effectively reducing the failure risk of the storage system and ensuring system reliability.

The invention provides intelligent hardware for production safety decision, and relates to the technical field of data processing, and the intelligent hardware comprises a data collection module which is used for collecting physical quantities in a production environment and production equipment in real time; the data processing module is used for carrying out preprocessing and production bottleneck prediction on the acquired physical quantity data; the intelligent analysis and decision module is used for carrying out fault risk analysis and carrying out scheduling processing according to production bottleneck data and a fault risk analysis result, the fault risk analysis is divided into subjective risk factor analysis and objective risk factor analysis, and the subjective risk factor analysis adopts an intuitive fuzzy number to distribute a specific risk value; according to the method, uncertainty and fuzziness in the evaluation process can be processed, the evaluation result is closer to the actual situation, and objective risk factor analysis standardizes fault states at different time points to the same scale for comparison by calculating the relative value of the fault strength, so that the change trend of the equipment fault strength is reflected more accurately, and the evaluation accuracy is improved. And the risk assessment precision is improved.

The invention discloses a natural gas pipeline failure risk assessment method and system based on data driving, and relates to the technical field of pipeline safety. The method comprises the following steps: firstly, calculating and correcting the failure probability by establishing a pipeline failure probability evaluation model, extracting key features by constructing a corrosion and design five types of failure factor index systems and principal component analysis, and calculating the failure probability by utilizing an improved correction coefficient formula; secondly, establishing a consequence evaluation model based on a risk matrix, and realizing differentiated evaluation of failure consequences in different geographical environments by combining GIS data; setting an online learning mechanism, and automatically updating a risk matrix when sensor data triggers a threshold value; the problems that a traditional method is high in subjectivity, insufficient in data fusion and low in simulation precision under complex conditions are solved, the accuracy and dynamic adaptability of risk assessment are improved through machine learning and space-time correlation analysis, accurate decision support is provided for pipeline operation and maintenance, and the safety accident rate is effectively reduced.

The present application discloses a method, device, storage medium and electronic device for determining the failure risk of an event. The method includes: obtaining a process template corresponding to a target event, and multiple failure mode information corresponding to the target event; matching multiple failure mode information with process information in the process template to obtain a target template, and determining the failure index value corresponding to each failure mode information; obtaining the characteristic value corresponding to the failure mode information matched by each process in the target event, comparing each characteristic value with the failure index value corresponding to the failure mode information, obtaining multiple comparison results, and determining the failure risk result of the target event based on the multiple comparison results. Through the present application, the problem that the failure risk in the product production process cannot be obtained quickly and accurately in the related art is solved.

The invention discloses an overhead line system part failure association classification method based on a three-way decision theory. The method comprises the following steps: acquiring historical failure data of overhead line system parts; defining failure association between the parts; constructing a failure association data set among the contact network parts; constructing a failure association risk assessment model; constructing a risk amplification function model; evaluating a failure association risk level between the parts, and calculating a risk amplification function value; constructing a failure association classification model based on a three-way decision theory; classifying the failure association into attraction association, mutual exclusion association and uncertain association; according to the method, the risk level of failure association between the parts can be evaluated, and the failure propagation mechanism of the parts can be revealed from the perspective of how to cause failure risk changes of other parts after one part fails.

The invention provides a risk grading and evaluating method and system for a compressed air energy storage cavern, and the method comprises the steps: extracting key risk factors through principal component analysis, building a failure function for a typical failure mode, calculating a failure probability through a first-order second-moment method, and dividing a threshold value in combination with an overall comprehensive failure probability to determine the risk grade. Quantitative evaluation and intelligent grading of multi-mode failure risks are realized, accurate identification and dynamic management of full life cycle risks of the compressed air energy storage cavern can be realized, and the operation safety and decision-making efficiency of an energy storage system are improved.

This application discloses a method and apparatus for quantitatively evaluating the failure risk of oil and gas gathering and transportation pipelines, relating to the field of data processing. The method includes: dividing the pipeline under test into multiple first pipe segments; classifying multiple typical failure types into internal corrosion failure and other failures; for each first pipe segment, determining the basic risk values ​​for internal corrosion failure and other failures based on a preset risk scoring table, obtaining a first basic risk value and a second basic risk value; correcting the first basic risk value based on a risk correction factor, obtaining a corrected risk value; the risk correction factor is dynamically determined based on the risk of slug flow occurring in the pipe segment; determining the comprehensive risk value of the pipe segment based on the corrected risk value and the second basic risk value; and obtaining the failure risk assessment result of the pipeline under test based on the comprehensive risk value of each first pipe segment. The scheme provided by this application can improve the accuracy and reliability of the assessment results and enhance the generalization ability of the assessment system.

The embodiment of the invention provides an existing building safety assessment method introducing a transfer learning mechanism. The method is applied to the technical field of constructional engineering safety and comprises the steps of constructing a three-dimensional evaluation model and performing finite element simulation on stress distribution; synthesizing the damage image and the simulation data through a conditional generative adversarial network to form a training data set; utilizing a damage detection model with double-branch attention and an HPC module to identify damage and mapping the damage to a stress sensitive feature library; and the risk assessment model calculates a failure risk and a structure degradation trend index, and dynamically adjusts an early warning threshold. In this way, by fusing multi-source heterogeneous data processing, dynamic model migration and risk feature extraction mechanisms, comprehensive perception, accurate evaluation and dynamic early warning of the building safety state are achieved, the knowledge experience migration reuse problem is effectively solved, and the accuracy, timeliness, self-adaptability and generalization ability of evaluation are remarkably improved.