70 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 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 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 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 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 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 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.

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 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 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.

The embodiments of the present disclosure disclose a fault diagnosis and alarm method, apparatus, electronic device, and computer-readable medium. A specific implementation of the method includes: obtaining a termination point information set; selecting termination point information that meets a screening condition from the termination point information set as valid termination point information to obtain a valid termination point information set; determining the failure risk rate and failure risk growth rate of the termination point represented by each valid termination point information in the valid termination point information set; determining the overall risk coefficient of the termination point represented by each valid termination point information in the valid termination point information set based on the termination point type information and the termination point call information sequence; and executing an alarm operation in response to determining that there is a failure risk rate greater than a failure risk rate threshold, a failure risk growth rate greater than a failure risk growth rate threshold, or an overall risk coefficient greater than an overall risk coefficient threshold. This implementation can more accurately and comprehensively diagnose faults and issue alarms.

A method, method, and apparatus for generating a disk failure risk assessment model include: acquiring multiple sets of sample disk data; wherein each set of sample disk data contains data on one or more disk parameters; preprocessing the multiple sets of sample disk data; obtaining an actual failure risk level determined based on each set of sample disk data; and using the preprocessed multiple sets of sample disk data and the actual failure risk level corresponding to each set of sample disk data as a training sample set to train a pre-established machine learning model to generate a disk failure risk assessment model. Because this embodiment assesses disk failure risk based on the disk's inherent condition, the generated disk failure risk assessment model can accurately assess disk failure risk.

The invention discloses a cascade dam group system service risk matrix type evaluation method, which comprises the steps of collecting data information of a drainage basin where a cascade dam group system is located to construct a deep learning model, and performing dam break flood routing calculation; calculating a system failure risk rate of each unit dam, quantifying a risk transfer coefficient between adjacent unit dams, constructing a state transition probability matrix and a state transition loss matrix of the cascade dam group system, calculating a state transition risk matrix of the cascade dam group system, and obtaining a total risk rate of the system and three types of total risks of life, economy and social environment; the corresponding total loss is evaluated; and finally carrying out cascade dam group system risk rating. The method has higher calculation efficiency and precision, and can provide richer and more comprehensive calculation results; according to the system risk quantitative grading standard and method, theoretical preciseness is considered, meanwhile, higher engineering applicability and practical operability are achieved, and higher practical application value is shown.

The application provides a ship collision arc gate failure risk method, system, device and medium, and relates to the field of water conservancy engineering safety evaluation. The method comprises the following steps: constructing an index system for evaluating the failure risk of the arc gate; simulating the dynamic response of the arc gate after being hit by ships under different ship tonnages and ship speeds according to a finite element model, so as to extract actual values of various evaluation indexes; sending the actual values of the various evaluation indexes into a forward cloud generator model to generate corresponding cloud droplets, so as to calculate the membership degrees of the various evaluation indexes under different risk levels; calculating the combined weight values of the various evaluation indexes by using an entropy weight method and a variation coefficient method, and combining the membership degrees to obtain corresponding basic probability assignment functions; and fusing the basic probability assignment functions of the different evaluation indexes according to a Dempster combination rule, so as to determine the final failure risk evaluation grade of the arc gate under the ship collision. The technical scheme can accurately measure the failure risk of the gate under the dynamic load.

The invention discloses a peritoneal dialysis related peritonitis patient treatment failure risk prediction method, and relates to the technical field of intelligent medical information processing, and the method comprises the steps: carrying out the key feature screening based on a patient standardized spatio-temporal feature vector set, simulating the evolution trajectory under different clinical conditions through an anti-fact sample, and carrying out the prediction of the treatment failure risk of a peritoneal dialysis related peritonitis patient. Generating an anti-fact disease course evolution trajectory data set; performing feature fusion on the anti-fact disease course evolution trajectory data set and multi-source clinical data by adopting time window splicing and multi-dimensional alignment to form a three-dimensional fusion feature tensor with a time dimension, a feature dimension and a scene dimension; carrying out entropy risk assessment by using the three-dimensional fusion feature tensor, and obtaining a dynamic treatment failure risk value; and carrying out risk state division according to the dynamic treatment failure risk value, and generating an individualized risk prediction result and an intervention prompt. According to the method, the complexity and uncertainty of the course of disease are effectively quantified, and dynamic and accurate risk early warning and decision support are provided for clinic.

The invention discloses a shield tail sealing grease performance failure risk assessment method and system based on multiple factor influences, terminal equipment and a medium, and relates to the technical field of shield engineering. The method comprises the following steps: identifying shield tail sealing grease performance failure influence factors including environmental factors, material factors and exogenous medium mixing factors; quantifying the factors according to a preset score assignment standard to obtain a first performance index score set; constructing a simulation environment based on the environmental factors and testing to obtain a second performance index score set; and integrating the scores to obtain a comprehensive performance score, and dividing failure risk levels according to a preset threshold. Through multi-factor identification, double-path scoring and weight integration, the risk of shield tail sealing grease performance failure is scientifically quantified, limitation of single evaluation is avoided, an accurate basis is provided for grease operation and maintenance, engineering risks are reduced, and it is guaranteed that shield construction is safe and efficient.

The invention discloses an intelligent risk optimization method and system based on an FMEA5 seven-step method, and relates to the field of product design, and the method comprises the steps: carrying out the vectorization processing of product function description through a BERT model, storing historical failure data and improvement measure data, obtaining a dynamic knowledge base, carrying out the retrieval and matching of an input target product function in the dynamic knowledge base, and carrying out the retrieval and matching of the target product function. Generating an initial failure chain, wherein the initial failure chain comprises a failure reason, a failure mode and a failure influence which are associated with one another; determining a severity S value according to a failure influence in the initial failure chain, determining an occurrence degree O value according to a failure reason, determining a detection degree D value according to a detection scheme, inputting the severity S value, the occurrence degree O value and the detection degree D value into a three-dimensional logic matrix, and outputting an action priority AP value by adopting a conditional branch judgment mode; and judging the risk level of the target product according to the action priority AP value, and generating a risk assessment result. By implementing the method, the accuracy of failure risk assessment in the product design stage can be improved.

The application discloses a kind of transport path optimization method and device considering port cascading failure risk, belong to port logistics and transport network optimization technical field.For the problem that insufficient consideration is given to port systemic risk in existing transport path planning, and chain reaction is easily caused by single point failure, the application proposes a comprehensive evaluation and optimization mechanism, by constructing a weighted directed graph model to represent the port transport network, integrating the port carrying capacity, historical failure probability and multi-dimensional external risk factors, the quantitative calculation of port cascading failure risk is realized.On this basis, a multi-objective optimization function is designed, with the minimum time and cost product as the optimization goal, while constraining the lowest probability of full-path failure risk, and using gradient descent algorithm to solve the optimal transport path.By dynamically adjusting the transport volume distribution, effectively avoiding high-risk nodes, the network robustness and transport reliability are improved.The application method can be widely applied to international sea transport, container scheduling and other scenarios, significantly reducing transport cost and interruption risk, and enhancing the ability to respond to emergencies.

This application provides a risk identification method and electronic device based on entity connection relationships, comprising: acquiring multi-source data of a mechanical system's physical interface and identifying target entity information and target entity connection relationship information in the multi-source data; performing semantic similarity matching between the target entity information and target entity connection relationship information and a failure rule base to obtain a semantic similarity score; generating a risk seed for the failure risk entity connection relationship when the semantic similarity score is greater than a preset threshold; and performing a failure risk assessment based on the risk seed to obtain the failure risk level of the failure risk entity connection relationship. This solves the problems of existing risk identification designs for entity connection relationships in mechanical system physical interfaces, such as single data analysis dimensions, low accuracy of physical interface risk identification, and complex reasoning mechanisms. It achieves automated identification of risks in mechanical system physical interfaces, improves the completeness of physical interface risk identification, and significantly enhances the ability to discover hidden risks.

The invention discloses an integrated project risk assessment and resource adaptation management method and system, and relates to the technical field of project management methods. The integrated project risk assessment and resource adaptation management method comprises the following steps: S1, constructing and dynamically updating a project database; s2, calculating a comprehensive fitness score; s3, generating a construction instrument selection suggestion and calculating an instrument fault risk index; and S4, generating and outputting a resource allocation instruction. According to the integrated project risk assessment and resource adaptation management method, an original experience-dependent and qualitative decision-making process is converted into computable quantitative analysis based on multi-dimensional data such as load capacity, environment compatibility and health state; the limitation of human factors is effectively overcome, it is ensured that instrument selection is the optimal solution, and the safety risk caused by insufficient instrument capacity or mismatching with the environment is avoided from the source.

The invention provides a multi-component mechanical system competition risk health assessment method and system based on GMM-LSTM-DeepHit, and the method comprises the steps: carrying out the unsupervised clustering analysis of a two-dimensional feature time sequence matrix sample set through employing a Gaussian mixture model GMM, and obtaining a three-dimensional feature time sequence matrix sample set; an LSTM-DeepHit model is constructed, and the survival probability of the sample is obtained; and based on the sample survival probability, calculating a risk score mean value of the whole life cycle of the component, dividing a failure risk level of the component, converting the risk score mean value into a component service life prediction value, and combining the risk contribution degree of each feature to complete health assessment of the competition risk of the multi-component mechanical system. According to the method, automatic identification of potential risk events, accurate capture of time sequence degradation characteristics and quantitative modeling of risk conduction relations among components can be realized, and finally, the accuracy and engineering practicability of health assessment of a complex mechanical system are improved.

The invention relates to a risk early warning method of a risk potential energy fusion risk field model based on truck braking temperature rise, which comprises the following steps: S1, acquiring a current vehicle state and an environment state, and calculating to obtain total risk potential energy; s2, judging whether the total risk potential energy is larger than a pre-configured first risk threshold value or not, and if yes, executing the step S3; s3, receiving one or more risk avoiding measures; s4, for any risk avoiding measure, the current vehicle state and the environment state are updated based on the risk avoiding measure, and the difference of the total risk potential energy before and after the risk avoiding measure is executed is calculated; s5, respectively calculating the execution cost of each risk avoiding measure; s6, the risk avoiding measures of which the difference between the total risk potential energy before and after the risk avoiding measures is negative are removed, and the income value of each risk avoiding measure is obtained through calculation; and S7, sorting the risk avoiding measures according to the profit values, and judging and displaying risk levels. Compared with the prior art, the method has the advantages that accurate early warning can be carried out on the brake failure risk caused by brake temperature rise of the truck on the long and large downhill road section, and the like.

The invention discloses an air-ground integrated failure risk assessment method applied to a low-altitude airspace, and relates to the technical field of low-altitude risk assessment. The evaluation method comprises the following steps: S1, calculating an air failure risk probability; s2, deducing an air-ground risk evolution process, and establishing a falling trajectory model covering different failure types; s3, evaluating the number of potential casualties of the ground personnel; s4, integrating the number of casualties of the airborne passengers and the number of casualties of the ground personnel, and outputting an air-ground integrated failure risk assessment result in real time; the method is technically characterized in that various fault types possibly occurring in the operation process of an aircraft are systematically analyzed, a complete conduction path from air failure to ground accidents is deduced in combination with corresponding damage relieving measures, and an aircraft falling trajectory model considering the intervention conditions of the damage relieving measures is mainly constructed; therefore, accurate description and quantitative evaluation of the air-ground comprehensive risk caused by the air failure are realized.