769 results about "Root cause analysis" patented technology

The invention relates to the technical field of power equipment state monitoring, and discloses a power equipment state evaluation and early warning method and system. The method comprises the following steps: collecting multi-source monitoring data of power equipment, and obtaining an equipment state data set by adopting a collaborative preprocessing method; a multi-dimensional feature extraction method is adopted to extract feature parameters reflecting the operation state and the degradation degree of the equipment; constructing an equipment health degree evaluation model, and obtaining the equipment health degree through a multi-time scale evaluation method; predicting a future deterioration trend and state transition time; establishing a grading early warning decision-making mechanism to realize early warning of the state of the power equipment; and identifying factors of equipment state degradation by adopting a root cause analysis method, and generating operation and maintenance decision suggestions according to historical cases. According to the invention, the health state of the power equipment can be accurately evaluated, and degradation trend prediction and fault early warning are realized.

The invention relates to the technical field of business data insight, and discloses a method for disassembling insight business data through indexes. The method comprises the following steps: firstly, constructing a multi-dimensional index system, determining key business indexes and associated dimensions, and outputting an index disassembling framework; the method comprises the following steps: collecting multi-source data in a business process, performing cleaning and standardization processing, extracting index characteristics and dimension attributes, and constructing a business data association graph; introducing an adaptive weight distribution mechanism and a hierarchical iterative algorithm to optimize a preset attribution model, and generating a preliminary index contribution degree sequence based on an index disassembly framework; setting a monitoring threshold value of each dimension index, and tracking service data in real time; dynamically adjusting an index weight and a model parameter according to a tracking result, and recalculating and updating an index contribution degree sequence; and evaluating the service health degree, identifying abnormal index nodes, and starting a root cause analysis process for the abnormal index nodes. According to the method, multi-dimensional and dynamic insight of the business data can be realized.

The invention relates to an alarm event root cause analysis method, device and equipment based on a large language model and business topology, and the method comprises the steps: based on an analysis engine, according to an alarm event sent by an alarm system, obtaining an association node set, collecting multi-source data, generating a preliminary context packet, carrying out the extraction of abnormal information according to the preliminary context packet, and carrying out the analysis of the abnormal information. And generating a refined context package by removing irrelevant node information, and generating an interpretable root cause analysis report through large language model constraint and reasoning based on the constructed large language cue word template. According to the method, the initial context packet covering the fault propagation link is constructed based on the service topological graph, rapid modeling of the context is achieved, the high-frequency error mode template is generated through clustering, irrelevant nodes are dynamically pruned to generate the refined context packet, accurate focusing and data filtering are achieved, and the fault propagation efficiency is improved. And embedding the refined evidence chain through a structured cue word template, and driving the large language model to output an interpretable root cause report.

The invention belongs to the technical field of data analysis, and provides a multi-source operation and maintenance data anomaly analysis method and device, equipment, a medium and a product, the method comprises the steps that multi-source operation and maintenance data is acquired, and the multi-source operation and maintenance data comprises at least two of index time sequence data, application logs, call link tracking data, configuration change records, alarm events and work orders; carrying out joint anomaly modeling on the preprocessed multi-source operation and maintenance data based on a multi-model fusion architecture to identify an abnormal event in the multi-source operation and maintenance data; based on the operation and maintenance knowledge graph and the structured causal model, fault influence path tracing and root cause positioning are carried out on the abnormal event, a root cause analysis result is obtained, and the root cause analysis result is used for indicating a fault root cause node and a fault propagation path in the abnormal event. Therefore, the accuracy of anomaly analysis of the multi-source operation and maintenance data is remarkably improved.

The invention discloses a building equipment fault rapid attribution and self-optimization method, which relates to the field of intelligent operation and maintenance of building equipment, establishes a multi-dimensional association relationship among equipment, building space, sensors and fault modes, and deeply combines a fault attribution engine with system dynamics. Data driving flexibility and physical logic preciseness are simultaneously realized in fault attribution, and reasoning from abnormal data to root cause analysis is realized. According to the method, the optimization efficiency is remarkably improved through a specific incremental learning / local updating mode, the weight of the knowledge graph and the Bayesian network conditional probability table are adjusted according to the maintenance result, physical equation parameters are calibrated, and dynamic updating of node attributes of the knowledge graph is achieved; through dynamic weight optimization, the adaptive ability of real-time reasoning confidence evaluation and scene context is improved; a physical equation is used as an executable knowledge unit to be embedded into a knowledge graph through establishment and deep integration of a system dynamics model, and a safety and reliability mechanism is designed for verification.

The embodiment of the invention provides an equipment fault prediction management method and device based on a large model, and the method and device achieve the accurate evaluation of a state through the innovative design of a multi-modal feature fusion model, data integration and feature learning. A fault diagnosis system is constructed, and a reliable root cause analysis mechanism is established in combination with semantic analysis and case retrieval. Maintenance guidance is introduced, and the feasibility of a maintenance scheme is ensured through experience precipitation and priority evaluation. According to the method, the defects of the traditional technology in the aspects of feature extraction, fault diagnosis, maintenance guidance and the like are effectively overcome, and technical guarantee is provided for equipment management.

The invention provides a wind power plant unit state monitoring and fault early warning system and method based on deep learning, and belongs to the field of wind power generation and artificial intelligence. According to the system, a cloud edge collaborative architecture is adopted, an edge computing terminal operates a data-driven space-time prediction model and a physical digital twinborn model in parallel, and abnormity is preliminarily screened by calculating a double-track residual error and comparing the double-track residual error with a dynamic early warning threshold value. And when an exception occurs, the cloud platform receives multi-modal data including a sensor, a model state and an operation and maintenance text, performs deep root cause analysis by using a diagnosis model fused with a wind power fault knowledge graph, and generates an interpretable diagnosis report. According to the method, deep fusion of data and a physical model is realized, and the accuracy of fault monitoring, the interpretability of diagnosis and the intelligent level of operation and maintenance decision are remarkably improved through a data-physical double-track driving mode.

The invention discloses a heterogeneous system integration and fault diagnosis operation and maintenance system based on big data analysis, and the system is characterized in that the system comprises an acquisition cleaning module which is used for collecting structured data, semi-structured data and non-structured data, and carrying out the data cleaning; the mapping calculation module is used for dynamically mapping the cleaned data, and storing the data into a database after federal calculation; the feature extraction module is used for performing multi-modal extraction on the data in the database, constructing a knowledge graph and generating features for fault diagnosis; the model training module is used for constructing a fault diagnosis model and performing fault prediction and root cause analysis by using fault diagnosis features; the collaborative decision-making module is used for carrying out collaborative decision-making on the edge and the cloud according to the analysis result; and the feedback optimization module is used for feeding back the response processing result to the data center and carrying out updating iteration on the diagnosis model.

The invention belongs to the technical field of artificial intelligence, and relates to an attribution analysis method and device based on artificial intelligence, computer equipment and a storage medium, and the method comprises the steps: carrying out the information analysis of an alarm notification based on a data perception agent when the alarm notification corresponding to a target system is monitored, and obtaining key entity information; acquiring associated data corresponding to the key entity information based on the data acquisition agent; performing data fusion on the associated data based on the data processing agent to obtain a comprehensive data set; constructing a target prompt text corresponding to the comprehensive data set based on the data interaction agent; based on a large language model, performing association analysis and intelligent reasoning on the comprehensive data set according to the target prompt text, and generating a root cause analysis result; and outputting an attribution analysis result. In addition, the attribution analysis result can be stored in the block chain. The method can be applied to attribution analysis scenes in the financial field and the medical health field, and the processing efficiency and accuracy of attribution analysis are improved.

The invention discloses a lithium battery diaphragm production quality tracing method and system based on the Internet of Things, and relates to the field of lithium battery diaphragm production quality tracing, and the method comprises the steps: collecting historical diaphragm production information and real-time diaphragm production information; key nodes of lithium battery diaphragm production are determined, and a unique quality safety traceability code is set for each key node; constructing a quality anomaly knowledge graph in combination with historical diaphragm production information and quality safety traceability codes; inputting the real-time diaphragm production information into the quality anomaly knowledge graph to obtain quality anomaly parameters; carrying out credibility verification on the quality abnormal parameters; determining a quality abnormality cause by using the root cause analysis model, and generating a compensation parameter according to the quality abnormality cause; and performing parameter compensation on the quality anomaly parameter based on the compensation parameter, and performing quality tracing in the quality anomaly knowledge graph according to the compensated quality anomaly parameter. The management efficiency of lithium battery diaphragm production can be effectively improved.

A method of diagnosing a software system of a vehicle includes receiving data related to the software system of the vehicle, identifying an anomalous event based on a pattern of the received data, and collecting contextual information related to the anomalous event. The method also includes inputting the anomalous event and the contextual information to a machine learning model, determining a root cause of the anomalous event by the machine learning model, and based on determining that the anomalous event corresponds to the malfunction, performing a mitigating action.

The invention discloses an automatic data modeling and optimizing system and method fusing a knowledge graph and ChatBI, and relates to the technical field of data analysis. In order to solve the problems of high interaction threshold and insufficient analysis depth of a traditional BI tool, the scheme adopted by the invention comprises a data layer which has the capabilities of multi-source data access, domain knowledge graph construction, intelligent mapping recommendation, federal calculation and data mild governance, and realizes data integration and semantic unification; the analysis layer realizes accurate conversion from a natural language to an SQL and semantic reasoning of a knowledge graph through graph vectorization, multi-model cooperation, natural language understanding, intelligent SQL generation and graph dynamic updating, and supports efficient data query and analysis; and the application layer has the functions of natural language interaction, visual recommendation and generation, root cause analysis and intelligent early warning, and provides a visual interaction interface and data display service for a user. According to the invention, intelligent data analysis and visualization based on natural language interaction can be realized.

The invention discloses an AI-based automatic production line scheduling system in an industrial internet, which relates to the technical field of production scheduling and comprises a production plan management module S1, a dynamic scheduling engine module S2, a resource scheduling module S3, a real-time monitoring system module S4, an exception handling center module S5 and a data optimization platform module S6. In the industrial internet, an AI-based automatic production line scheduling system, an X dynamic scheduling engine millisecond response and a multi-agent reinforcement learning engine based on a federated learning architecture realize millisecond response scheduling, each device is used as an autonomous decision-making unit, and dynamic coordination is performed through a distributed Q learning algorithm, so that the vacancy rate of the devices is greatly reduced, and the scheduling efficiency is improved. According to a long-short-term memory network deep analysis model of order delivery cycle compression, emergency order insertion response speed improvement, multi-modal AI quality monitoring, fusion of vibration, thermal imaging and current spectrum, the detection rate is greatly improved compared with a unified sensor, causal reasoning and root cause analysis are performed, a fault causal graph is constructed to position a deep problem, and the average repair time is shortened.

The invention provides a root cause analysis method combining eBPF and traditional observability data, and belongs to the technical field of root cause analysis, the method comprises the following steps: S1, collecting events to obtain a structured eBPF index sequence; s2, time axis alignment and entity matching are carried out, a node calling graph is constructed, and a fused observation entity structure body is generated; s3, trend change detection and anomaly screening are carried out on the fused observation entity structure body; and S4, based on a node calling graph, the abnormal index list, the abnormal timestamp record and the abnormal score matrix, performing root cause path derivation to obtain a root cause service node and an abnormal propagation path. The eBPF and traditional observable data are fully combined, and the root cause positioning efficiency and precision are effectively improved.

The embodiment of the invention provides a service exception root cause positioning method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a real-time node topological graph corresponding to a target service in response to the detection of the exception of the target service, the real-time node topological graph at least comprising a plurality of nodes; acquiring a real-time node weight corresponding to each node; positioning a first candidate node from the nodes based on the real-time node weight; performing reverse traversal along the topological edge of the first candidate node to obtain a first fault propagation path; the root cause analysis is performed according to the first fault propagation path to obtain the abnormal root cause corresponding to the target service, the limitation of single service is broken through based on the total service calling topology, the accuracy of cross-layer fault identification is improved, the omission rate is reduced, the node weight is introduced, processing is performed according to the real-time node weight of the node, and the fault identification efficiency is improved. Therefore, the fault positioning accuracy is improved, and the fault identification efficiency is 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 relates to the field of multi-source data management, in particular to an intelligent security collaborative management system based on multi-source perception and a large language model. Comprising a multi-source data acquisition module which is accessed to various intelligent monitoring devices and is output and converted into a unified standard tuple format through a mapping function; the information fusion module is used for screening a candidate alarm set according to the space-time tolerance, and generating composite alarm information by adopting confidence ranking and semantic embedding weighted averaging; the RAG knowledge base module is used for generating a composite event description through a large language model and vectorizing the composite event description as a retrieval index; the intelligent retrieval module is used for acquiring related historical events by adopting a mixed retrieval strategy and constructing structured cue words; the LLM decision module is used for outputting root cause analysis and classification disposal suggestions based on the composite alarm and the priori knowledge; the double-path response module is used for distributing decision suggestions to management personnel and an agent system to realize collaborative execution; and the feedback optimization module is used for collecting disposal data and adjusting the weight of the knowledge base and the decision template to realize continuous optimization.

The invention relates to the technical field of chemical enterprise production management, in particular to a chemical enterprise safety production informatization management system which comprises a sensing network layer architecture, a digital twin management platform, a risk management and control and decision center, a process closed-loop management center and an emergency cooperative processing platform. According to the chemical enterprise safety production informatization management system and method, through construction of a chemical knowledge graph, on the basis of a dynamic risk matrix and an AI prediction model library, equipment remaining service life prediction, process tiny anomaly capture and accident consequence simulation processing are realized; meanwhile, a root cause analysis module is used for associating low-level alarm and traversing a knowledge graph to automatically infer a fault root, physical equipment and a virtual model are bound through an entity-model mapping engine, the model state is dynamically updated in combination with a real-time rendering technology, and technological parameter optimization, accident deduction and scheme verification can be completed in an analogue simulation engine; and the management intuition and the decision-making scientificity are effectively improved.

The invention relates to the technical field of image processing, in particular to a picking ingot coating defect online detection method and system based on machine vision, and the method comprises the steps: obtaining a multi-view image, carrying out the feature extraction of an image data stream after preprocessing, and carrying out the matching or clustering with a preset defect dictionary; the method comprises the following steps: preliminarily identifying a potential defect area, triggering refined multi-angle image acquisition, applying a multi-task CNN model fused with a polarization perception convolution kernel, jointly optimizing pixel-level segmentation loss and boundary prediction loss, outputting pixel-level semantic segmentation and an accurate boundary of the defect area, and calculating a reconstruction error to carry out defect classification. And finally, the system also has the functions of defect root cause analysis and process flow adjustment, so that defect tracing and production optimization are realized. According to the system, advanced visual technology, deep learning and multi-modal information fusion are integrated, and online, accurate and automatic detection of picking ingot coating defects is realized.

The invention discloses a root cause analysis method based on an IT operation and maintenance system, and the method comprises the steps: obtaining the physical position data and real-time environment data of IT equipment, carrying out the matrix construction through a dual topological relation based on the physical position data and the real-time environment data, and obtaining an IT equipment spatial topological matrix containing environmental impact factors. And obtaining operation state data of the IT equipment, performing spatio-temporal conjoint analysis and dimension reduction based on the operation state data and the IT equipment spatial topology matrix to obtain IT equipment dynamic prediction data, and obtaining an IT equipment state dynamic prediction curve according to the IT equipment dynamic prediction data. If the IT equipment state dynamic prediction curve monitors abnormity, a fault propagation path is generated through a reinforcement learning algorithm, and IT equipment fault root causes are determined in combination with a multivariate fault knowledge graph and a causal graph model. According to the method, the problem of early warning lag of the IT operation and maintenance system can be solved, and the problem that root cause positioning has high dependence on artificial experience is solved.

The invention discloses a large model-knowledge graph driven data blood relationship traceability reasoning method and system. According to the method, through a closed-loop framework of KG-driven dynamic decomposition, small-model lightweight reasoning and visual credibility verification, the defects are systematically solved, and an interpretable, low-cost and easy-to-expand data consanguinity depth traceability scheme is provided for scenes with high credibility requirements such as data governance, compliance auditing and root cause analysis. According to the system, aiming at complex problem requirements, after a complex problem is input, the problems of no interpretation, low reliability and the like in data blood relationship traceability are solved through two modules, namely problem reasoning and verification presentation. The modular design not only conforms to the technical decoupling principle, but also facilitates independent optimization, and is suitable for medical treatment, scientific research and other scenes with high requirements for interpretability.

The invention relates to the field of vehicle-mounted equipment optimization, in particular to a vehicle-mounted equipment optimization method and system based on firmware updating. The method comprises the following steps: carrying out running state full monitoring on a vehicle-mounted firmware module, carrying out multi-behavior state sensing modeling, and constructing a firmware behavior state map; performing dynamic change trend analysis on the firmware behavior state graph, performing performance attenuation situation modeling, and constructing a performance attenuation situation curve; based on the performance attenuation situation curve, code-level program root cause analysis is carried out, firmware optimization target quantification is carried out, and firmware optimization demand data is generated; based on the firmware optimization demand data, multiple optimization direction decisions are made, multi-index optimization solution is carried out, and a firmware optimization task list is constructed; and performing firmware configuration update parameter simulation based on the firmware optimization task list to obtain a configuration update parameter scheme. According to the method, the optimal progressive equipment updating is realized, and the reliability and deployment safety of the vehicle-mounted equipment are improved.

The invention belongs to the technical field of root cause analysis in intelligent operation and maintenance, and discloses a method for mining fault propagation weight parameters, and the method comprises the following specific steps: S1, supervising data root cause probability initialization, S2, supervising data topology perception labeling, S4, unsupervised data space-time slicing, S5, multi-modal root cause reasoning, S6, parameter increment fusion and S7, online adaptive optimization. Precise modeling and dynamic adaptation are achieved through multi-stage collaborative optimization, and the root cause positioning capacity of a complex system is remarkably improved: a supervised and unsupervised data dual-drive strategy is adopted, a root cause probability baseline is constructed by utilizing work order history, and alarm streams are processed in combination with time-space slice standardization to form a structured knowledge base; the method comprises the following steps: quantifying a transition probability in a CMDB dependency relationship through topology perception annotation and a four-dimensional observation matrix, and constructing a feature matrix containing TP / FP counting to support accurate calculation of a probabilistic graph model;

The invention provides a fault root cause analysis method and device for a full link, and relates to the technical field of data processing.The method comprises the steps that after abnormal events are obtained from operation data of multiple department platforms, the abnormal events are matched with fault factors in a preset knowledge base; determining a fault factor corresponding to each abnormal event and instantiation information of the fault factor to form an associated event set; then, according to an association map in the knowledge base and a static factor corresponding to the fault factor, performing multi-dimensional contribution degree quantification on each fault factor in the association event set to obtain a fault contribution degree of the fault factor; and finally, updating the association map based on the abnormal event, the association event set and the fault contribution degree of each fault factor, analyzing the association map through a root cause analysis engine, and determining a fault evidence chain and a fault root cause. Therefore, the accuracy of fault root cause analysis is improved.

The mold injection control method comprises the steps that three-dimensional point clouds of a current injection molding part and an adjacent previous injection molding part are obtained, the global difference degree between the current injection molding part and the adjacent previous injection molding part is calculated through overall registration, if the global difference degree exceeds a threshold value, a difference mask is generated and covers a real-time image of a cavity, a difference image is formed, and the difference image is subjected to injection molding. And performing hybrid clustering on the difference image and a known defect type database to obtain candidate defect types and membership degrees, calculating posterior probability contribution degrees of the sensing nodes to the candidate defect types by taking the membership degrees as input and combining a Bayesian reasoning algorithm, if the contribution degree of any sensing node exceeds a threshold value, outputting a defect root cause report, and if the contribution degree of any sensing node exceeds the threshold value, outputting a defect root cause report. And a machine table or a mold execution mechanism is driven to carry out correction. According to the invention, the problems of low detection efficiency, large subjective deviation and lagging root cause judgment due to the fact that automobile part defect detection and root cause analysis depend on manual visual detection or single-dimensional automatic detection are solved.

The invention discloses a power grid overhaul cost factor identification method and system based on multi-dimensional feature analysis, and belongs to the field of power system analysis and management, and the method comprises the steps: obtaining multi-source data and historical cost data of a power grid overhaul project, and carrying out the preprocessing of the cost data; the method comprises the following steps: constructing a multi-dimensional entity and an associated knowledge graph, carrying out dynamic weight calculation on influence factors through a graph neural network, establishing a factor quantification model, modeling a risk conduction path, identifying high-order association among key influence factors, establishing a cost reference library and a compliance verification rule system, and detecting abnormal cost items in project budget. And generating root cause analysis and optimization suggestions. The method breaks through the limitation of a traditional static analysis model, significantly improves the accuracy and evaluation efficiency of cost recognition, reduces the cost waste caused by human deviation, provides intelligent decision support for an overhaul project under a novel power system, and assists the lean management of assets in the whole life cycle.

An electromechanical equipment knowledge graph link prediction method fused with a large language model belongs to the field of electromechanical equipment and knowledge graphs, and comprises the following steps: step 1, constructing an electromechanical equipment knowledge graph by combining a fine tuning large language model and a prompt project; step 2, designing an encoder with a multilayer graph attention network for the constructed EKG, and realizing embedded representation updating of the knowledge graph; and a third step, combining a decoder to decode the knowledge graph embedding obtained by coding, and executing a link prediction task. According to the method, effective extraction of the triple information of the high-quality electromechanical equipment is realized, and a data basis is provided for fault root cause analysis and fault prediction of the electromechanical equipment; the representation capability of the graph is enhanced, the context sensing capability of the knowledge graph is enhanced, and the adaptability and prediction performance of the model in the field of electromechanical equipment are improved.

A real-time anomaly detection framework for embedded BI systems (100) using LLMs, consisting of: (a) a data collection and pre-processing module configured to collect, cleanse, normalise and encrypt structured and unstructured data from multiple enterprise data sources in real time; b) a contextual embedding and feature extraction module configured to transform processed data into semantically rich, domain-aware embeddings that capture temporal, correlational, and contextual features; c) an LLM-based anomaly detection engine configured to analyze said embeddings using fine-tuned large language models to identify anomalies based on semantic inference, historical trends, and adaptive learning; d) a real-time stream processing and low-latency pipeline configured to perform high-throughput parallel in-memory computations to deliver anomaly detection results with minimal latency; (e) a root cause analysis and interpretability module configured to identify probable causes for detected anomalies and generate human-readable explanations, confidence scores and causal narratives; (f) an alerting and visualisation module configured to deliver real-time notifications via embedded BI dashboards, messaging platforms and graphical representations such as heat maps and trend charts; and g) a continuous learning and feedback optimization module configured to refine the accuracy of anomaly detection by incorporating user feedback, updated historical data, and evolving business rules.

The invention relates to the technical field of intelligent operation and maintenance, in particular to a fault insight and self-healing method and system based on chain control. According to the chain control-based fault insight and self-healing method, indexes, logs and tracking data of a micro-service environment are dynamically collected and fused; the method comprises the following steps: dynamically constructing and visualizing a service dependent topology, realizing dynamic topology construction and intelligent visual rendering, performing intelligent root cause analysis and predictive diagnosis based on multi-dimensional data, realizing self-adaptive early warning, generating an intelligent decision, performing closed-loop learning optimization based on an execution result, and realizing automatic fault repair and continuous optimization. According to the fault insight and self-healing method and system based on chain control, a predictive early warning mechanism is established, automatic fault self-healing is realized, and through intelligent and automatic means, manual intervention is reduced, the operation and maintenance cost is reduced, the average repair time is shortened, the service continuity is guaranteed, and the system availability is improved.

The invention discloses an AI-based digital project performance evaluation data processing method and system, and relates to the technical field of artificial intelligence and digital project management, and the method comprises a multi-source data collection module which is used for obtaining data; the entity alignment module is used for establishing an association relationship among cross-system data entities; the dynamic index generation module is used for dynamically adjusting the evaluation index weight by utilizing a reinforcement learning framework; the efficiency prediction module is used for modeling a task dependency relationship according to the time sequence diagram convolutional network and outputting a delay risk probability; and the visual interface is used for displaying the data. According to the method, cross-system entity alignment, dynamic index weight adjustment, task delay risk prediction and root cause analysis are realized through AI technologies such as BERT semantic matching and a graph neural network, visual data display and interaction functions are provided by virtue of a visual interface, the problems of data dispersion, index static state, risk lag and the like in traditional project evaluation are solved, and the project evaluation efficiency is improved. And the decision support capability of project management is improved.