155 results about "Root cause diagnosis" patented technology

The present application relates to, but is not limited to, the technical field of communications. Provided are a fault diagnosis method, an electronic device, and a storage medium. The fault diagnosis method comprises: constructing an operation and maintenance knowledge graph based on a fault mode; acquiring root cause alarms of a fault, and by means of the operation and maintenance knowledge graph, determining target fault modes corresponding to the root cause alarms; performing root cause diagnosis on the basis of the target fault modes, so as to obtain fault root causes corresponding to the root cause alarms; and then, performing fusion processing on the fault root causes corresponding to the root cause alarms, so as to obtain a target fault root cause. In the solutions in the embodiments of the present application, an operation and maintenance knowledge graph based on a fault mode is constructed, and target fault modes corresponding to root cause alarms of a fault can be determined using the operation and maintenance knowledge graph, and root cause diagnosis is then performed on the basis of the target fault modes, so as to obtain fault root causes. On the basis of the solutions in the embodiments of the present application, the fault diagnosis efficiency and the automation of intelligent operation and maintenance can be greatly improved.

The invention discloses a defect detection method and system based on joint distribution optimization and structural knowledge guidance. The method comprises the following steps: analyzing an equipment structured document to construct an equipment physical topological graph containing a physical connection relationship; processing the topological graph by adopting a graph convolutional network to generate a structured feature vector, and extracting a sensing feature vector of the multi-modal sensing data; constructing a knowledge-enhanced attention model, and guiding a perception feature vector to perform cross-modal fusion by using a structured feature vector as a key and a value; and training the model through a combined optimization target containing joint distribution divergence loss and Lipschitz stability constraint so as to ensure the consistency and physical authenticity of the multi-modal features in the shared semantic space. According to the method, the defect detection accuracy can be remarkably improved, and the root cause diagnosis report with physical interpretability is generated.

The invention discloses a multi-source information fusion equipment health diagnosis management system, and relates to the technical field of equipment management. Comprising an information acquisition module, a feature perception module, a processing fusion module, an equipment modeling module, an evolution prediction module, a root cause diagnosis module, a cross-domain inspection module, a learning sharing module, a state evaluation module, a collaborative decision-making module, a chain account book module and a visual decision-making module. According to the method, the perspectiveness and the sensitivity of anomaly detection are remarkably improved, noise interference and sampling deviation are reduced, fused data are more stable and have higher physical consistency, differential diagnosis and individual-level prediction are supported, the interpretability of diagnosis and the decision reliability are improved, false alarm and missing alarm are avoided, the early warning reliability is improved, and the method is suitable for popularization and application. And scientific, transparent and traceable health management is realized.

The invention relates to an intelligent operation and maintenance method and apparatus based on a knowledge graph and a large model, and an electronic device. The method comprises the steps of collecting multi-source runtime data of a Kubernetes cluster; constructing a knowledge graph with time dimension based on the resource change event, recording termination time in response to graph relationship failure, recording starting time in response to a newly added relationship and not setting the termination time, and associating the entity with the performance index and the log data; responding to the diagnosis request, scheduling a specialized agent by a coordination agent through multi-agent cooperation to retrieve associated information from multi-source data, and iteratively integrating to generate a structured context; and inputting the generated structured context information into a large model reasoning service to output a fault root cause diagnosis and solution. The technical problems that information dispersion and relevance are weak, root cause positioning is difficult and time-consuming, comprehensive context sensing ability is lacked and expert experience is excessively relied on are solved.

The invention discloses a video monitoring equipment fault real-time detection, root cause diagnosis and closed loop processing method and system. According to the method, by collecting multi-dimensional operation data of equipment, a dynamic weighted health degree model is constructed to realize early-stage accurate discovery of a fault; performing intelligent alarm grading by combining time sequence prediction and health degree change; realizing automatic root cause diagnosis by utilizing topological correlation analysis, log semantic analysis and case similarity matching; and closed loop processing and model self-optimization are realized through a work order system. According to the method, the problems of lagging fault discovery, difficulty in positioning, slow repair and disjunction in assessment in the prior art are effectively solved, and the operation and maintenance efficiency and the service quality are remarkably improved. The abstract drawing is Figure 1.

The invention provides an event root cause intelligent diagnosis method and system for financial service operation risks, and the method comprises the steps: firstly collecting an operation risk event data set which is generated by an operation risk event in a financial service system and contains various information, and then carrying out the feature extraction of the operation risk event data set; the method comprises the following steps: generating an event feature set containing multi-aspect features, constructing a dynamic event association network based on the event feature set, determining nodes and influence intensity parameters, performing causal path weight iterative calculation on the network by using a preset root cause inference rule set, and positioning a root cause node set; and finally, according to the root cause node set, generating a root cause diagnosis result containing the root cause type identifier, the influence path description and the correlation feature contribution degree, thereby improving the accuracy and efficiency of financial service operation risk root cause diagnosis.

The invention relates to the technical field of electronic equipment detection, and discloses an FPC connector quality detection method and system based on multi-modal fusion, and the method comprises the steps: obtaining an original multi-modal data set containing electrical stress, thermal stress and mechanical stress, and carrying out the filtering and time sequence alignment preprocessing, and obtaining a standardized multi-physical field sequence; constructing a three-dimensional space field intensity distribution diagram according to the sequence, and identifying field intensity anomalies by adopting density clustering so as to accurately position potential defects; learning a time sequence evolution mode based on the multi-moment observation value of the defect position to predict the future diffusion trend of the defect; and integrating the diffusion trend, the real-time field intensity and the environment variable to carry out comprehensive modeling and evaluation, and generating a dynamically updated and final quality detection report. According to the method, root cause diagnosis of connector defects, accurate three-dimensional space fault positioning and accurate prediction of future evolution trends can be realized, and the detection precision and predictability are remarkably improved.

The invention discloses an artificial intelligence abnormity early warning diagnosis method for industrial equipment operation state monitoring, and belongs to the technical field of industrial equipment intelligent monitoring, and the method comprises the steps: collecting a multi-source heterogeneous signal of industrial equipment, and carrying out the time-frequency dual-domain feature extraction; constructing a multi-scale time window based on the time-frequency features, and executing self-supervised contrast learning by injecting a preset abnormal mode to obtain cross-scale contrast feature representation; constructing a dynamic adjacency matrix according to the comparison features, extracting space-time correlation features through a graph attention network, and determining an abnormal score based on joint evaluation; according to the method, a deep coupling closed-loop cooperative system is formed, multi-dimensional state characterization, adaptive anomaly detection and root cause diagnosis are realized, and the problems of single data source and lack of fault analysis capability in the prior art are effectively solved.

The invention discloses an intelligent automobile interpretable abnormity diagnosis method and system, and relates to the technical field of intelligent traffic. The method comprises the steps of collecting multi-dimensional sensor data based on an intelligent automobile test platform, and constructing a directed causal graph and a causal adjacency matrix which are used for describing a causal relationship between the sensor data; designing a causal constrained graph attention mechanism based on the causal adjacency matrix, and constructing a causal constraint enhanced graph attention anomaly diagnosis model; and based on the directed causal graph and the graph attention anomaly diagnosis model, constructing a hierarchical anomaly diagnosis strategy integrating a feature reconstruction error, a variable causal relationship and a graph attention network weight, positioning an anomaly root cause and identifying a propagation path of the anomaly in the sensor network. According to the invention, the problems of false correlation and lack of exception explanation ability of graph attention network learning in the prior art can be overcome, and reliable exception detection and root cause diagnosis of intelligent automobile multi-sensor data are realized.

The invention provides a vehicle fault root cause diagnosis method, equipment, equipment and a medium, and the method comprises the steps: obtaining a vehicle target fault phenomenon, determining a to-be-detected fault event set based on a preset fault logic relation, constructing an initial scoring matrix, enabling a row vector to correspond to a fault event, enabling a column vector to correspond to the state feature parameters of a plurality of evaluation dimensions, and carrying out the detection of the fault event set; executing the detection task of the highest comprehensive score fault event, obtaining feedback data, updating the initial score matrix parameters based on the feedback data to obtain an updated score matrix, and calculating the comprehensive score of each event based on the updated score matrix again. Iteratively executing the detection task corresponding to the updated highest score event to identify whether the detection event is a fault root cause or not until the fault root cause of the target fault phenomenon is determined; according to the method, through a dynamic priority scheduling mechanism, real-time feedback data is fused in multi-dimensional evaluation, a high-value diagnosis task is executed preferentially, the resource consumption of traditional traversal diagnosis is remarkably reduced, and the fault positioning efficiency is effectively improved.

The invention relates to the technical field of cloud computing, and discloses a log intelligent aggregation and threat traceability analysis method and system for a multi-cloud environment, and the method comprises the following steps: collecting original logs of the multi-cloud environment, injecting a unified service identifier and a cloud platform label, analyzing a heterogeneous log format into structured data, and storing the structured data in a database; sorting and complementing missing log entries according to timestamps; constructing a service dependency graph, quantifying the calling relation weight between services, fusing log semantic features and statistical features, and generating a feature matrix after standardization; according to the method, standardized aggregation of multi-cloud logs is realized through unified service identifiers, the problem of heterogeneous log processing is solved, and the accuracy of complex threat identification and the propagation path verification capability are remarkably improved based on a feature fusion and cascade integration detection mechanism of a service dependency graph; the cross-cloud attack path is accurately positioned in combination with tracing analysis of the service identifier, and root cause diagnosis and security protection in a multi-cloud environment are effectively supported.

The invention provides an EAM equipment full life cycle management method and system, and relates to the technical field of equipment life cycle management. According to the invention, a full life cycle management system integrating multi-source data fusion, physical constraint driven intelligent prediction and root cause diagnosis, environment working condition adaptive maintenance strategy matching, multi-physics field simulation evaluation and multi-target optimization scheduling is constructed, so that accurate perception of states of key parts of equipment and life prediction are realized; the accuracy of fault diagnosis and the adaptability of a maintenance scheme are improved, and maintenance resource configuration and inventory management are optimized. The system has a dynamic adaptive optimization capability, and can continuously improve a maintenance strategy and a prediction model according to actual operation feedback, so that the reliability and the maintenance efficiency of equipment operation are remarkably improved, the overall maintenance cost is reduced, and the high-standard requirement of intelligent equipment management in a complex industrial environment is met.

The invention discloses a predictive maintenance algorithm for transmission equipment, and relates to the technical field of maintenance of the transmission equipment, in particular to a roller conveying system applied to flexible materials, which is characterized by comprising the following steps: S1, arranging position triggers at key transition nodes of a roller conveyor for acquiring timestamps of the materials passing through detection points in real time, generating a unique number and a corresponding time sequence relationship for each material; and S2, standard material passing time is calculated based on the theoretical beat period, the difference between the actual time and the standard period is calculated, and a beat offset set is constructed to reflect the conveying beat deviation of the materials between the adjacent detection points. According to the method, early warning and intelligent regulation and control of flexible material conveying rhythm imbalance are achieved through rhythm offset modeling, abnormal aggregation recognition and root cause diagnosis, compression bandwidth and driving time delay are dynamically adjusted when the imbalance risk occurs, the accumulation risk is effectively relieved, and the system stability, the automation level and the operation efficiency are improved.

The invention belongs to the technical field of aircraft fault diagnosis, and particularly relates to an aircraft fuel control system fault path mining and root cause diagnosis method. The method comprises the following steps: providing a fault propagation-oriented fault root cause diagnostic graph model to realize more interpretable fault source positioning, and backtracking from an alarm node to a fault source based on propagation mechanism information; constructing a variable propagation graph of the aircraft fuel control system in combination with correlation coefficient analysis and a Lingam causal algorithm, and then performing correction based on a system simulation structure in a manual inspection mode so as to establish an interpretable data structure; introducing an edge propagation quantization factor and a path scoring criterion integrating nodes, edges and path lengths to measure the possibility of candidate fault propagation paths; and learning an optimal threshold set of each node in the propagation graph structure by adopting a particle swarm optimization algorithm. The method supports path-level transparent backtracking, improves the accuracy and flexibility of the model, and can excavate a plurality of fault propagation paths.

The invention belongs to the field of equipment evaluation, particularly relates to an intelligent evaluation method and system for the operation quality of a flight simulator, and aims to solve the problems of single evaluation dimension, poor real-time performance, low intelligent degree and lack of quantitative standards in the prior art. The method comprises the steps that operation data such as fault time, duration and available time are collected in real time through a sensor network; calculating core indexes such as fault frequency per unit time, accumulated duration and malignant fault rate; dynamically weighting and fusing the indexes to generate a single-machine health index; a correlation model is constructed based on fault space-time correlation between devices, and cluster health scores are output in combination with single machine indexes; when the malignant fault exceeds a threshold value, driving causal inference by utilizing a knowledge graph containing entity causal relationships of fault types, equipment parts and the like, and outputting a root cause diagnosis and maintenance scheme; and acquiring intervened data to update atlas parameters and models to form closed-loop optimization. Multi-dimensional real-time monitoring and diagnosis closed loop of the operation state are realized, and the maintenance efficiency is improved.

The invention relates to a data AI analysis management method and system based on a production element association map. The method comprises the steps that a multi-source heterogeneous production data flow covering the whole industrial production cycle is obtained; constructing a whole-process production element association map through an entity recognition and relationship extraction technology; actual operation indexes of the production nodes are calculated in real time and compared with a reference threshold value, and potential production bottleneck nodes are accurately recognized; upstream associated node data are backtracked, historical data in the same period are combined to be input into the bottleneck root cause diagnosis model, and core influence factors are accurately obtained; generating an optimal scheduling strategy according to the core influence factors and issuing the optimal scheduling strategy to a production execution system; according to the scheme, the multi-source heterogeneous data of the whole period of industrial production can be effectively analyzed and managed, accurate regulation and control of the production process are achieved, and the production efficiency and the product quality are improved.

The invention provides a dam safety monitoring system abnormity diagnosis method, system and device and a medium, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the steps: evaluating a diagnosis object to which measuring point data of an abnormity label belongs through a knowledge graph engine, generating an abnormity alarm grouping list through employing a diagnosis object-monitoring abnormity format, and carrying out the abnormity alarm grouping list; searching an abnormal phenomenon entity matched with the alarm description in the knowledge graph through Cypher; and positioning matched abnormal phenomenon nodes in the Bayesian network, extracting associated abnormal reason nodes by using directed edges, obtaining a prior probability and conditional probability table of the abnormal reason nodes, performing probability reasoning, and outputting an abnormal reason diagnosis list. Through multi-source data fusion and knowledge graph driven intelligent reasoning and Bayesian network probabilistic reasoning, scientific and operable root cause diagnosis and disposal suggestions are provided, and the reliability and intelligent level of a dam monitoring system are enhanced.

The invention discloses a health monitoring method and device for industrial equipment. The method comprises the following steps: acquiring a target multi-modal feature vector set; performing time sequence alignment and feature fusion processing on the target multi-modal feature vector set to obtain a target fusion feature vector sequence; inputting the target fusion feature vector sequence into a target equipment health state prediction model for processing to obtain a target equipment collaborative analysis result; obtaining health state prediction data of the target equipment according to the collaborative analysis result of the target equipment; generating a target equipment fault root cause diagnosis report, a target equipment maintenance suggestion and a target risk assessment report; and performing optimization processing on the target equipment health state prediction model according to the target equipment actual operation data, the target equipment fault root cause diagnosis report, the target equipment maintenance suggestion and the target risk assessment report. According to the invention, the fault detection rate of industrial equipment can be improved, the false alarm rate can be reduced, and organic combination of millisecond-level abnormal response and deep historical analysis is realized.

The invention relates to an operation and maintenance alarm root cause positioning method and system fusing a knowledge graph and a large model, and belongs to the technical field of data processing and intelligent operation and maintaining.The method comprises the steps that original alarm data are obtained from a plurality of heterogeneous monitoring sources based on alarm data query service of time period parameters and subjected to cleaning and structural processing, and the original alarm data are obtained; obtaining alarm data to be analyzed; performing unsupervised clustering analysis to realize alarm automatic classification and noise filtering to obtain classified alarms and category labels; extracting corresponding object topological relation information from a pre-constructed knowledge graph; and inputting the classified alarms, category labels and object topological relation information into a large language model, guiding the large language model to perform semantic reasoning through a preset cue word project, and generating a structured diagnosis report containing root cause diagnosis, an influence range and repair suggestions. According to the method, the alarm storm is inhibited, the fault root cause is quickly, accurately and interpretably positioned, and the operation and maintenance efficiency and the system stability are improved.

The invention provides a fault diagnosis method, electronic equipment and a storage medium, and relates to but is not limited to the technical field of communication, and the fault diagnosis method comprises the steps: constructing an operation and maintenance knowledge graph based on a fault mode; obtaining a root cause alarm of a fault, and determining a target fault mode corresponding to the root cause alarm through the operation and maintenance knowledge graph; performing root cause diagnosis according to the target fault mode to obtain a fault root cause corresponding to a root cause alarm; and carrying out fusion processing on the fault root causes corresponding to the root cause alarms to obtain a target fault root cause. According to the scheme provided by the embodiment of the invention, the operation and maintenance knowledge graph based on the fault mode is constructed, the target fault mode corresponding to the root cause alarm of the fault can be determined by using the operation and maintenance knowledge graph, and then root cause diagnosis is performed according to the target fault mode to obtain the fault root cause. According to the scheme of the embodiment of the invention, the fault diagnosis efficiency and the automation of intelligent operation and maintenance can be greatly improved.

The invention relates to a multi-protocol compatible ultrasonic radar test method and system. According to the method, protocol configuration is automatically obtained by scanning a radar identification code, communication connection is established, a standardized test process is controlled and executed based on a parameter file to collect response data, and a reconstruction error of the test data is calculated in real time by using an LSTM auto-encoder model to realize anomaly detection and feature extraction. According to the method, a fault source is accurately positioned by combining causal reasoning of a fault knowledge graph, and a diagnosis report containing a solution is finally generated, so that full-process automatic closed loop from protocol adaptive configuration, intelligent anomaly recognition to root cause diagnosis is realized, the efficiency of multi-protocol radar testing and the accuracy of fault diagnosis are remarkably improved, and the fault diagnosis efficiency is improved. The technical defects that a traditional method depends on artificial experience, efficiency is low, and diagnosis results are one-sided are effectively overcome.

The invention discloses a test failure root cause diagnosis method, which relates to the technical field of electric digital data processing, and is characterized in that after a test failure signal is captured, comprehensive analysis is carried out in combination with multi-source log information of a test case and test environment configuration data, so that the influence of environment configuration on a test is fully considered, and information in a log is fully mined; according to the method, a more accurate diagnosis result is obtained, and a corresponding solution is matched according to a test failure root cause to solve the problem of test failure, so that the problems of low efficiency and high misjudgment probability of manual analysis, neglect of the influence of a test environment on the test failure, difficulty in identifying implicit connection between texts in a log and difficulty in testing failure in the prior art can be solved. Therefore, the technical problem that the obtained analysis result is difficult to be used for accurate root cause diagnosis is solved, the diagnosis efficiency of test failure root causes is improved, the diagnosis precision is improved, and the overall test period is shortened.

The invention discloses a remote fault diagnosis method and system for a mixing plant, and the method comprises the steps: constructing a digital twin body, obtaining a multi-modal sensing signal of each operation part, and forming a multi-modal joint feature vector representing a multi-source operation state; importing the feature vector into a digital twinborn body for physical constraint correction, and establishing a time sequence causal network by taking a key component of the mixing plant as a node according to the corrected feature vector; acquiring real-time monitoring data of a target mixing plant, acquiring abnormal probability distribution through residual modeling, and performing root cause inference in combination with a time sequence causal network to generate root cause diagnosis information; the operation situation data of the associated mixing plant is gathered based on the cloud platform, and cross-site early fault diagnosis and early warning are realized by using a federal learning mechanism; and constructing a mixing station cluster network, performing operation health evaluation on each station, performing station adaptation analysis in combination with a current mixing task, and generating mixing station selection recommendation information so as to improve the operation and maintenance efficiency and the production stability.

The invention discloses a positioning end PPP-RTK real-time precision product monitoring method and device, and belongs to the technical field of satellite navigation and positioning. The method comprises the steps that a PPP-RTK positioning result and a post precision coordinate reference value are acquired in real time, ENU direction positioning deviation is calculated, three types of parameters including satellite geometric configuration, product precision and data timeliness are fused to generate a credibility index C, and an alarm threshold value is dynamically adjusted according to the credibility index C; establishing a positioning precision database to support rapid query and analysis; making a multi-level alarm rule for positioning continuity and precision overrun, and presetting a diagnosis item mapping relation; and after the alarm is triggered, data recording, root cause diagnosis and information output are automatically executed, so that full-process automation from abnormal sensing to root cause definite diagnosis is realized. According to the invention, the reliability, the stability and the operation and maintenance efficiency of the PPP-RTK positioning service are remarkably improved through an evaluation-prediction-diagnosis three-layer intelligent closed-loop system.

A method may obtain a failure propagation model, wherein the failure propagation model comprises: a model representation of a plurality of hardware components, a set of hardware failure probabilities; and a logic that maps signals to the hardware components. A method may receive an alert signal from the aircraft. A method may map the alert signal to the plurality of hardware components. A method may perform a root-cause diagnosis of the alert signal that has been mapped to the plurality of hardware components comprising: determining via the failure propagation model, one or more combinations of hardware failures associated with the alert signal; and determining a probability of occurrence associated with the combinations of hardware failures. A method may display a report that includes combinations of hardware failures associated with the alert signal and the probability of occurrence associated with the combinations of hardware failures.

The invention relates to an AI-driven intelligent work reporting method, system and device and a medium. The method comprises the steps of collecting original operation data of production equipment and performing anomaly detection to generate a standardized anomaly event stream; carrying out unified representation on the abnormal event and the production context thereof, and generating a space-time aligned feature sequence; calculating causal intensity between abnormal events based on a preset model and constructing a dynamic time sequence causal graph; back diffusion iteration is carried out by simulating abnormal influence to trace root cause nodes, and a root cause score sorting vector is generated; positioning a key root cause and tracing a propagation path of the key root cause, analyzing backlog data for root cause types such as network interruption, and performing compensation work reporting; and finally, optimizing causal intensity model parameters by utilizing artificial feedback. The method overcomes the defects that in the prior art, abnormal events are analyzed in an isolated mode, and causes and effects cannot be correlated, intelligent root cause diagnosis and work report data self-correction of a complex abnormal chain can be achieved, and therefore production data reliability and operation and maintenance efficiency are improved.

The invention relates to the technical field of data analysis, in particular to a data and knowledge combined driven intelligent decision-making method and system.The method includes the steps that multi-source heterogeneous data and knowledge base concepts are unified into a structured graph structure through multi-dimensional feature extraction and multi-head attention fusion context prompt, and a structured semantic basis is provided for intelligent decision-making; by combining data-driven mining and knowledge prior constraints, the accuracy and robustness of causal reasoning are improved, and the reliability of root cause diagnosis is ensured; a root cause path is searched and enumerated in a reverse breadth-first manner by taking an abnormal entity as a starting point, so that rapid and traceable abnormality positioning is realized, and a user is assisted to focus a high-probability root cause; through modular assembly and full-link element traceability, while the analysis threshold is reduced, decision full-link transparency is realized, and the auditing requirement of a high-risk scene is met; by automatically converting natural language requirements into executable scripts, the decision-making efficiency of non-technical users is improved.

The invention discloses an automatic fault detection and diagnosis system and method based on an artificial intelligence optimization operation system and a function library, and belongs to the technical field of intelligent building and industrial equipment operation and maintenance. The system comprises a data acquisition and preprocessing layer, a fault detection layer, a fault diagnosis layer, an intelligent recommendation layer and a visual display layer. The method comprises the following steps: collecting equipment data in real time and preprocessing; performing anomaly detection and performance trend analysis in combination with statistical process control and a machine learning algorithm; performing root cause diagnosis by using a Bayesian reasoning model; classifying faults according to an energy performance type, a controllability type and a thermal comfort correlation type, and evaluating priorities through a multi-dimensional weighted scoring model; and intelligently generating a solution and updating the knowledge base. According to the method, whole-process automation from early warning, diagnosis, classification to recommendation is achieved, the equipment reliability, the energy efficiency and the operation and maintenance intelligent level are improved, and the method is suitable for application scenes such as artificial intelligence middleware and computer visual and auditory software.

The invention relates to the technical field of vehicle energy consumption analysis, and discloses a vehicle energy consumption closed-loop analysis method based on big data, which comprises the following steps: data acquisition and scene recognition, clustering and reference calculation, anomaly recognition, and hierarchical root cause diagnosis on vehicles with abnormal energy consumption, comprising macroscopic state comparison and microcosmic operation parameter comparison, and if no suitable vehicles with normal energy consumption in the same track exist, self historical comparison analysis is carried out; and improvement and tracking verification: based on the diagnosis result, generating improvement measures and implementing the improvement measures, and performing multi-dimensional effect tracking verification on the vehicle after the improvement measures are implemented. The invention further discloses a vehicle energy consumption closed-loop analysis system based on the big data. According to the vehicle energy consumption closed-loop analysis method and system based on the big data, the problems of reference static state, low diagnosis efficiency and flow splitting in the prior art are effectively solved.

The invention provides an injection molding quality problem traceability analysis method, which comprises the following steps of: acquiring a surface image and process parameters of a molded product, extracting defect characteristics through methods of image enhancement, edge detection, region segmentation and the like, and fusing the defect characteristics with normalized process parameter data to obtain an injection molding quality problem traceability analysis result; constructing a multi-modal knowledge graph containing entity nodes and causal relationship edges; further introducing implicit causal edges and path diversity scores to realize dynamic sorting and root cause identification of defect causal paths; the system supports knowledge increment updating and scoring model reinforcement learning under actual production feedback, so that the root cause diagnosis accuracy and the system adaptive capacity are continuously optimized, and the automation, accuracy and knowledge evolution efficiency of injection molding defect analysis in a complex scene are improved.