316 results about "Cause analysis" patented technology

A data processing system implements performing a root cause analysis that includes identifying a first anomalous signal data predictive of a root cause of a first anomaly in signal data received from a computing system, analyzing the sub-signals of the first anomalous signal data to generate labeled training data, training a gradient boosted tree model using the labeled training data, generating a decision tree based approximating a predictive performance of the gradient boosted tree model, determining insights data predictive of the root cause of the first anomaly based on the gradient boosted tree model and the decision tree, aggregating the insights and analyzing the aggregated insights data to determine a predicted root cause for the first anomaly, determining a confidence level associated with the predicted root cause, and categorizing the predicted root cause into one of a plurality of categories based on the confidence level.

The invention discloses a power grid fault intelligent diagnosis method and system based on multi-mode cooperation, and belongs to the technical field of power system fault diagnosis. The method comprises the following steps: collecting and off-line importing multi-modal data of primary equipment and secondary equipment of a multi-space power system in a power grid fault range in real time and standardizing the multi-modal data, and sequentially inputting the standardized multi-modal data into a local edge professional model, the method comprises the following steps: preliminarily reasoning fault properties in combination with a local knowledge base, inputting a knowledge-embedded causal reasoning large model deployed at a cloud end, generating an edge-cloud fault collaborative diagnosis model, iteratively updating the edge-cloud fault collaborative diagnosis model in combination with a multi-modal fault case library, and converting newest collected and imported power grid fault data into a multi-modal fault case library; and inputting the updated edge-cloud fault collaborative diagnosis model, generating a latest fault diagnosis report, and realizing the intelligent diagnosis of the power grid fault based on multi-modal collaboration. According to the invention, the power grid fault reason analysis performance and the identification accuracy are improved.

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 root cause analysis method and device based on a space-time dependency graph, equipment and a medium. Comprising the steps of constructing a space-time dependency graph, generating a diagnosis path blueprint, identifying a fault source and a root cause entity type, generating a graph query statement, executing query and performing cause and effect verification, and outputting a root cause analysis report. And the causal reasoning and root cause positioning of the system state change are realized by fusing the graph structure information and the natural language processing capability. Through cooperative processing of a language model and a graph data structure, fault symptom information and system structured state data are deeply fused, a path is generated in the graph structure, and a causal relationship is verified, so that the ability of understanding a complex system state evolution chain is improved, and accurate identification and diagnosis of root causes are realized. And the accuracy and the automation level of root cause analysis are obviously enhanced.

The invention discloses a road traffic accident cause analysis and responsibility judgment method and system, and relates to the technical field of traffic management data processing, and the method comprises the steps: carrying out the credible collection of multi-source data, and constructing an evidence chain; performing multi-source data preprocessing and cross-modal fusion optimization; performing multi-dimensional cause intelligent analysis; performing responsibility judgment based on a quantification rule; and the responsibility judgment result is subjected to multi-dimensional rechecking and rule iteration adaptation. According to the road traffic accident cause analysis and responsibility judgment method and system, through a full-process closed-loop design of data acquisition, preprocessing, cause analysis, responsibility judgment, re-checking iteration and report evidence storage, technologies such as multi-source perception and an AI algorithm are integrated; the method solves the problems of uncredible evidence chain, one-sided cause traceability, non-uniform judgment standard, low cooperation efficiency and the like in traditional accident processing, realizes intelligence, standardization, compliance and traceability of accident processing, remarkably improves the credibility, processing efficiency and judicial suitability of a judgment result, and provides core technical support for modernization of traffic control.

The invention relates to the technical field of geological disaster early warning, and discloses a geological disaster early warning device, which comprises an acoustic electromagnetic fusion sensor array for collecting multi-physical field data related to the internal structure change of a rock-soil body; the data pre-processing and feature extraction module is used for performing pre-processing and feature extraction on the original multi-physics field data so as to identify the change features of the internal structure of the rock-soil body; the multi-physics field collaborative inversion module reconstructs the internal structure distribution of the rock-soil body by adopting a multi-physics field collaborative inversion algorithm; the structure change cause analysis and risk assessment module is used for analyzing causes of internal structure change of the rock-soil body and assessing potential geological disaster risks; the grading early warning information generation module is used for generating targeted grading early warning information; according to the invention, high-precision monitoring of the internal structure change of the rock-soil body is realized through an acoustic electromagnetic fusion sensing technology, the monitoring precision is improved, and possible landslide or collapse events can be predicted in advance.

The invention provides a traffic abnormity and congestion cause analysis method and system based on a knowledge graph, and relates to the technical field of intelligent traffic perception, and the method comprises the steps: carrying out the target detection through employing preprocessed laser radar point cloud data, recognizing traffic participants, carrying out the continuous frame tracking of the traffic participants, and extracting the motion track and behavior characteristics in an event dimension; identifying a behavior event of the traffic target based on the motion trail and the behavior characteristics, binding the identified behavior event of the traffic target to a corresponding target entity, and performing target behavior modeling to form standardized structured information; based on structured information of a traffic target, an intersection-oriented traffic state knowledge graph is constructed, a rule-based abnormal event judgment module is utilized to perform semantic analysis on behavior and event nodes in the traffic state knowledge graph, abnormal traffic events are identified, and potential causes causing traffic congestion are traced. According to the invention, refined understanding and active perception of the intersection traffic state can be realized.

The invention belongs to the technical field of petroleum and natural gas development engineering, discloses a shale reservoir three-dimensional dessert prediction method based on physical-data dual drive, and solves the defects of an existing shale reservoir dessert evaluation method in the aspect of space continuity. The prediction method comprises the following steps: (1) constructing a three-dimensional attribute field, and forming a multi-channel standardized three-dimensional data volume through voxelization processing; (2) fusing multi-parameter attributes of the shale reservoir and constructing a multi-channel three-dimensional feature body; (3) constructing a dessert prediction model based on the physically constrained space-attribute joint attention three-dimensional convolutional network; and (4) calculating a comprehensive dessert index, performing dessert grading and cause analysis, analyzing and quantifying the contribution degree of each attribute to dessert grading, and analyzing the dessert connectivity and the advantage extension direction. The predicted productivity potential and fracturing potential dessert field keeps reasonable gradient change and continuous distribution in the inter-well area, and the inter-well prediction error is small.

The invention provides a bridge monitoring data abnormal value intelligent judgment and processing system, which comprises a monitoring module used for acquiring dynamic displacement data of a plurality of monitoring points of a bridge in a non-contact manner; the preprocessing module is used for performing time synchronization and quality fusion on the displacement, load and environment data to generate multi-dimensional time sequence data; the anomaly recognition module is used for extracting collaborative deformation and power fingerprint features and performing anomaly recognition and initial classification based on the feature deviation degree; the abnormality diagnosis module is used for performing abnormality cause analysis in combination with the load and the environmental condition and outputting an abnormality diagnosis result; and the strategy generation module is used for generating a grading early warning and disposal strategy according to the diagnosis result and the structural key index usage degree. The accuracy and efficiency of bridge structure safety monitoring can be improved, and safe operation of a bridge is guaranteed.

The invention provides an energy storage device damage diagnosis and interaction system based on penetration vision and a large model, and relates to the technical field of energy storage device damage diagnos.The energy storage device damage diagnosis and interaction system comprises a penetration type 3D sensing module, a periodic topology analysis module, a feature mapping and retrieval module and a large model reasoning and interaction module, and the energy storage device is scanned and reconstructed; the method comprises the following steps: constructing a time-space decoupling periodic Transform network, introducing a periodic mask matrix to force the network to pay attention to a repeatability rule of an internal structure of a battery, and identifying internal tiny deformation and structural damage by calculating topological consistency under the condition that a large amount of negative sample training is not needed; visual defect features are mapped into text embedding by utilizing a feature projection technology, a diagnostic report containing physical cause analysis and maintenance suggestions is generated by combining a retrieval enhancement generation technology and a large language model, and a user is supported to perform interactive questions and answers in a natural language. The method can solve the problems that in the prior art, three-dimensional deformation is difficult to quantify, small samples are difficult to train, and intelligent decision-making ability is lacked.

The invention relates to the technical field of wind power operation and maintenance, and discloses a wind power blade crack diagnosis method and system based on knowledge graph large model enhancement, and the method comprises the steps: S1, edge end image collection and crack semantic recognition: an edge end carries out the crack detection and semantic feature extraction of a collected wind power blade image sequence through an ECL-Net lightweight network, generating structured semantic information and transmitting the structured semantic information to a cloud; s2, cloud knowledge graph retrieval and semantic reasoning: the cloud performs semantic retrieval and causal reasoning on the structured semantic information through a KFR-Net knowledge graph reasoning network based on a pre-constructed wind power blade operation and maintenance knowledge graph, and outputs a knowledge retrieval result; and S3, multi-modal information is fused through an MMR-Cogntive multi-modal memory retrieval-cognitive generation network, and a structured maintenance report including crack detection summarization, cause analysis, maintenance measures and risk assessment is generated. The method has the advantage that intelligent closed-loop management of wind power blade cracks from detection to maintenance suggestions is realized.

The invention relates to the technical field of medical intelligent decision, in particular to an operating room nursing key node decision support system, which comprises the following steps: establishing a nursing operation reference containing a standard execution sequence and a parameter interval based on historical cases; by integrating discrete operation and continuous monitoring data in a real-time operation, a nursing process event chain with unified semantics is formed. The system compares the chain of events with an execution criterion to identify potentially risky segments and automatically extracts, for each segment, a complete operating room multi-dimensional state snapshot within a specific time window before and after its occurrence. And dynamically constructing a cause deduction network according to the state snapshots, and speculating cause chains possibly causing risks by traversing the network. And integrating all deviation information and cause analysis results, and generating a structured decision intervention prompt. According to the invention, deepening from automatic risk identification to root intelligent diagnosis can be realized, and accurate and efficient decision support is provided for operating room nursing.

The invention discloses an automatic driving traffic accident intelligent analysis system based on sand table simulation, and relates to the technical field of vehicle automatic driving. The system comprises a sand table simulation scene construction module which is used for simulating a high-risk scene of an automatic driving traffic accident and providing a physical and digital twinborn combined experiment environment; the automatic driving data acquisition and perception module is used for acquiring vehicle sensor data in real time and realizing environment perception and behavior decision simulation; the traffic accident intelligent analysis core module is used for realizing accident cause analysis and responsibility determination based on multi-source data and a knowledge base; and the cloud control platform and data visualization module is used for monitoring sand table simulation data in real time and providing a visual interface and an interaction function. The system can simulate and analyze the performance of automatic driving in various complex traffic environments and perform analysis and prediction, the analysis and prediction accuracy is high, and the analysis effect is better.

A communication method and a device. In the method, a network side can perform cause analysis on a secondary cell group (SCG) failure of a terminal device based on SCG failure information reported by the terminal device, and determine a problematic node for wrong candidate primary secondary cell selection, so that configuration optimization on the network side can be completed, and more proper configuration can be performed for the terminal device in a subsequent primary secondary cell addition or change procedure of the user equipment (UE), to reduce a probability of the SCG failure of the terminal device.

The invention provides a real estate operation engineering construction progress intelligent supervision system based on digital twinning, which belongs to the technical field of engineering construction progress supervision and comprises a data acquisition unit, a data identification unit, a data twinning unit, a progress identification unit, a reason analysis unit, a progress prediction unit and a visual supervision and management unit. According to the invention, the progress risk point can be predicted 3-7 days in advance, the project period deviation is reduced by 37% in actual measurement, the construction cost is reduced by millions of millions, a manager can supervise the progress of the construction process and non-standard behaviors in the construction process in real time, real-time remote supervision is realized, and the manager can make a decision in advance by predicting the progress, so that the construction site construction can be better managed.

The invention discloses a land subsidence area permeable channel identification and cause analysis method based on a high-density electrical method, which overcomes the limitation of traditional single profile data interpretation by arranging a three-dimensional observation grid and fusing and collecting multi-source data. Through combination of finite element three-dimensional inversion and the improved convolutional neural network model, accurate description of the three-dimensional spatial morphology, the extension direction and the connectivity of the permeable channel is realized, and the accuracy and the spatial resolution of low-resistance abnormal region identification are improved. Compared with the prior art, the method has remarkable advantages in the aspects of permeable channel recognition accuracy, cause analysis comprehensiveness, result application practicability and the like, and effective support can be provided for long-acting prevention and control work of geological disasters in land subsidence areas.

The invention relates to an intelligent test method for a wind power plant network-related test. According to the technical scheme, the method comprises the following steps that firstly, an intelligent test database is constructed, secondly, an intelligent control unit calls a scene simulation module, a combined scene of'basic working conditions + coupling working conditions' is automatically generated based on the database, test equipment and a wind power plant control system are connected through a standardized interface, network-related parameters are collected at the frequency of 50-100 ms / time, and the network-related parameters are obtained. The data processing module adopts a CNN-LSTM hybrid model to analyze and collect data, the intelligent control unit monitors the voltage and frequency of a power grid and the output power of a wind power plant in real time, when parameters exceed safety threshold values, graded protection measures are triggered, and a test report including grid-related performance scores, abnormal parameter curves, fault reason analysis and optimization suggestions is automatically generated. The method has the beneficial effects that the working condition configuration time and the data processing time are effectively shortened, the overall test efficiency is effectively improved, the deviation between a test result and an actual power grid scene is reduced, and the fault positioning accuracy is improved.

The invention discloses an atmospheric pollution case system, a case analysis method and a medium, and the system comprises a basic information layer which is used for storing the whole-process feature data and treatment measure data of a pollution event; the logic analysis layer is used for carrying out quality control on the data, carrying out source analysis and cause analysis on the whole-process feature data of the pollution event after the quality control, and carrying out effect evaluation on the treatment measures after the quality control; and the comprehensive decision-making layer is used for comparing historical cases according to the retrieval conditions and matching similar historical cases, and finding out treatment measures corresponding to the similar historical cases according to the similar historical cases, or formulating targeted new treatment measures according to the corresponding treatment measures. The invention provides a'basic information-logical analysis-comprehensive decision 'three-layer progressive architecture case library, so that other modules can be conveniently expanded on the architecture of the case library subsequently; and multi-source data are systematically integrated, so that the problem of fragmentation of existing case data is solved.

The invention discloses an aviation equipment intelligent pricing method and system and a medium, and the method comprises the steps: carrying out the fusion processing and feature extraction of multi-source heterogeneous data, such as aviation material internal transaction data, external market information data, supply chain state data, and regulation event text data, and generating a standardized feature vector; utilizing a natural language model to extract structured event features from the law and regulation event text data; inputting the standardized feature vector into a pre-training price prediction model adopting a mixed architecture of an integrated learning algorithm and a time sequence deep learning algorithm, and outputting a reference price prediction value and a price prediction interval; according to a pricing strategy instruction selected by a user, a final pricing result is generated through a strategy mapping module, and an interpretable price cause analysis report is output, so that the accuracy and adaptability of aerial material pricing are effectively improved, the dynamic response of various pricing strategies is supported, the transparency and interpretability of a decision-making process are enhanced, and the user experience is improved. And scientific and reliable intelligent decision support is provided for aviation equipment pricing.

The invention relates to a multi-agent-based personalized opera action teaching assistance method and system. The method comprises the steps of inputting a student portrait into a planning agent to generate a personalized learning plan; collecting student follow-up training videos in real time, extracting to obtain a key point sequence, inputting the key point sequence into the lightweight action matching model, calculating the similarity between student follow-up training actions and standard actions in real time, and outputting an action similarity score; inputting key point data obtained after the follow-up training is finished into an execution agent to obtain opera action characteristics, performing deviation judgment and reason analysis through a large language model, generating correction suggestions, and realizing teaching assistance; and after a learning cycle is completed, inputting deviation judgment and reason analysis results into the reflection agent, carrying out statistics on a deviation trend and a skill improvement amplitude, outputting a portrait updating suggestion, updating the student portrait through an index moving average algorithm, and triggering a new round of learning planning. Compared with the prior art, the intelligent level and effectiveness of Chinese opera action teaching assistance are remarkably improved.

The invention discloses an intelligent substation state evaluation method and system based on digital twinning, and relates to the technical field of state evaluation, and the method comprises the steps: firstly constructing a heterogeneous knowledge graph capable of precisely mapping a physical entity logic relation based on the ontology definition, asset information and topological data of a substation; then, massive real-time fast-change data streams are introduced into the atlas framework, and node states are dynamically mapped by using a time sequence feature coding technology; and a complex message passing and aggregation mechanism is carried out in the graph structure through the relation perception graph neural network, and node embedding representation with local personality and global generality is generated. And finally, synchronously finishing equipment risk prediction at a micro level and total-station comprehensive evaluation at a macro level based on the high-dimensional representation, and carrying out path backtracking and cause analysis on a high-risk state based on map relevance. Therefore, the problem of equipment health and system operation state separation can be solved, and intelligent substation panoramic state perception and risk early warning with interpretability can be realized.

The invention belongs to the technical field of power system safety monitoring, and particularly relates to an electric leakage and electricity larceny prevention positioning system and device.The system comprises a multi-modal information acquisition module, a data preprocessing and fusion module, a feature extraction module, a cause analysis and distinguishing module, a precise positioning module and a differentiation response module; the multi-modal information acquisition module acquires electrical parameters and environment multi-dimensional data, and extracts a feature set after preprocessing and fusion; the cause analysis and distinguishing module is used for realizing accurate judgment on abnormal types and causes through feature classification and dual threshold matching; the precise positioning module is combined with topological data and multi-parameter correction to realize high-precision positioning; the differential response module triggers exclusive treatment measures for electric leakage and electricity stealing, and the device is embedded equipment and bears all functions of the system. According to the method, the problems of fuzzy anomaly judgment and low positioning precision in the traditional technology are solved, the anomaly handling precision and efficiency are improved, and the power grid safety and the power supply enterprise rights and interests are guaranteed.

A data processing system implements performing a root cause analysis that includes identifying a first anomalous signal data predictive of a root cause of a first anomaly in signal data received from a computing system, analyzing the sub-signals of the first anomalous signal data to generate labeled training data, training a gradient boosted tree model using the labeled training data, generating a decision tree based approximating a predictive performance of the gradient boosted tree model, determining insights data predictive of the root cause of the first anomaly based on the gradient boosted tree model and the decision tree, aggregating the insights and analyzing the aggregated insights data to determine a predicted root cause for the first anomaly, determining a confidence level associated with the predicted root cause, and categorizing the predicted root cause into one of a plurality of categories based on the confidence level.

The invention discloses a multi-source data fusion-oriented comprehensive geological cause analysis method and system. The method comprises the following steps of 1, generating semantic mapping data; 2, constructing a cause characteristic pedigree tree, and outputting cause main chain data; step 3, generating geological space-time interlacing data; 4, limiting a time window and a space window, and generating spectrum fusion modeling data through a spectrum-guided fusion converter; 5, constructing a three-dimensional voxel grid, and carrying out dynamic clustering to generate a three-dimensional construction model; step 6, generating geological evolution field data; and 7, inputting the geological evolution field data into the improved CrossViT model, setting an interlayer cross attention unit, reconstructing a Token interaction mode, introducing a cause field constraint attention modulation mechanism, and outputting a geological cause analysis result. According to the method, high-precision analysis of complex geological causes is realized through multi-source data semantic mapping, cause pedigree reasoning and CrossViT model improvement.

The invention relates to the technical field of LED copper strip processing, and particularly discloses a quality defect optimization method and system for LED copper strip surface scratches. According to the method, processing shooting, image preprocessing and boundary marking are carried out by planning an initial acquisition time period; constructing a scratch detection model; after the initial acquisition time period is finished, processing monitoring and detection are carried out; when the surface is scratched, scratch tracking positioning and reason analysis are carried out; and performing defect control and process optimization. According to the method, processing shooting and processing can be carried out and a scratch detection model can be constructed in an initial acquisition time period, then processing monitoring and detection judgment are carried out after the initial acquisition time period is ended, and scratch tracking and positioning, reason analysis and process optimization are carried out when surface scratches exist, so that the detection speed can be effectively improved, and the detection efficiency is improved. And the recognition rate of small scratches and hidden scratches is improved, tracking analysis of scratch reasons can be automatically carried out, and the technological process of LED copper strip machining is continuously optimized.

The invention provides a power distribution network node harmonic exceeding main cause identification method, device and system and a medium, and the method comprises the steps: building a node admittance matrix based on the node harmonic voltage and branch current monitoring data of a power distribution network, building a related equation of the harmonic voltage and an injection current, and employing a compressed sampling matching pursuit algorithm to identify the harmonic exceeding main cause of the power distribution network; solving a sparse harmonic injection current vector, and calculating a node harmonic voltage distortion rate according to a harmonic current injection value so as to judge a standard exceeding condition; through a modal analysis method, harmonic current threshold comparison and multi-frequency superposition effect analysis, identifying main factors exceeding the standard, including harmonic injection standard exceeding, multi-frequency superposition and resonance phenomena; and quantizing contribution degrees of resonance and harmonic injection, and distinguishing composite causes of harmonic exceeding. According to the method, the problem that the harmonic cause is unknown in the harmonic exceeding scene is solved, and the accuracy of the harmonic exceeding cause is remarkably improved. Through simulation and actual measurement verification, the accuracy of the method provided by the invention is verified.

A system and method for temporal cause analysis of cybersecurity events. A method includes: creating a plurality of time series pairs for a computing environment, wherein each time series pair includes a first time series and a second time series, wherein each time series includes a series of data points arranged by time; determining a temporal relationship for at least one first time series pair of the plurality of time series pairs based on the series of data points of each time series of each of the plurality of time series pairs; identifying a root cause of a cyber event based on the temporal relationship of the at least one first time series pair; and remediating the cyber event based on the identified root cause.

The invention relates to a method (100) for automatically performing a root cause analysis in a production plant (1), comprising the following steps: - Determining (101) at least two sub-areas (2) for root cause analysis based on a grouping of sensors (3), wherein at least one sensor (3) is assigned to a respective sub-area (2) that is located in the same area of ​​the production plant (1), - Encoding (102) measurements of at least one sensor (3) of a respective sub-area (2) into a latent representation (4) using a machine learning model, - Determining (103) a posterior distribution of the latent representation (4) of each respective sub-area (2) for a case in which a defective product is produced by the production plant (1), - Determining (104) a respective contribution of each sub-area (2) to a production of the defective product, whereby a replacement of each individual sub-area (2) and a replacement of all possible combinations of the sub-areas (2) is tested on the basis of the corresponding latent representations (4), while for the remaining sub-areas (2) the determined posterior distribution of the latent representation (4) is used, - Performing (105) the root cause analysis in the production plant (1) based on the respective determined contribution of the sub-areas (2). Furthermore, the invention relates to a computer program, a device and a storage medium for this purpose.

The embodiment of the invention provides an external risk factor cause quantitative analysis method and system, and belongs to the technical field of risk assessment. The method comprises the steps that historical accident information is collected, accident cause analysis is performed on the historical accident information, and an external risk factor set is constructed and obtained; obtaining a corresponding relation matrix; constructing a directed weighted network based on the external risk factor set and the relation matrix, and constructing an evaluation system corresponding to the directed weighted network; constructing a corresponding external factor cause quantitative evaluation model; and evaluating the current operation state information of the target chemical enterprise based on the external factor cause quantitative evaluation model to obtain a safety risk evaluation result. According to the scheme, the problem that the external risk factor causes cannot be quantified in the safety risk assessment process of the chemical production process at present is solved, and the accuracy of the safety risk assessment result of the chemical production process is improved.

The invention provides an oil-gas exploration equipment defect root cause analysis method and system and a storage medium, and the method comprises the steps: carrying out the reasoning analysis of a root cause sample and a defect description sample, and obtaining a reasoning thinking chain sample; performing thinking chain screening and logic screening on the reasoning thinking chain sample to obtain a screened thinking chain; and training an equipment defect root cause analysis model according to the screening thinking chain, the root cause sample and the defect description sample, and inputting to-be-analyzed defect description into the converged equipment defect root cause analysis model for cause analysis to obtain a target defect root cause and a defect reasoning thinking chain of the target defect root cause. According to the embodiment of the invention, based on the converged equipment defect root cause analysis model, the equipment defect root cause analysis can be effectively carried out on the defect description to be analyzed, and the efficiency of oil-gas exploration equipment defect root cause analysis is improved.

The invention discloses a road traffic anomaly detection method and system based on multi-modal data fusion, and relates to the field of traffic data processing, and the method comprises the steps: monitoring and obtaining multi-modal road traffic data, carrying out the traffic anomaly recognition based on the road traffic data of each modal, and obtaining a candidate abnormal event; obtaining candidate causes based on the space-time range of the candidate abnormal events, the similar historical abnormal events and the multi-modal road traffic data; based on the candidate abnormal events and the candidate causes, contribution degree analysis is carried out, multiple analysis results are obtained in combination with the multi-modal road traffic data, the analysis result with the highest credibility in the analysis results is extracted to serve as a road traffic anomaly detection result, and the analysis results comprise the abnormal events and the main causes. According to the invention, the technical problems of inaccurate road traffic abnormity detection result and insufficient reliability of cause analysis result in the prior art are solved.