23 results about "Cause effect analysis" patented technology

The invention discloses a network traffic anomaly detection and identification method based on artificial intelligence, and the method comprises the following steps: obtaining original network traffic data, carrying out the preprocessing, and carrying out the fusion construction of a multi-layer traffic language field tensor; behavior semantic modeling and path structure coding are carried out; constructing a behavior event sequence tensor, inputting the behavior event sequence tensor into the improved RetNet model, and outputting a behavior drift response vector; performing deviation analysis on the behavior drift response vector and the target behavior intention trajectory tensor, and constructing an abnormal evolution graph of abnormal growth and propagation; performing type classification and causal analysis according to the abnormal evolution graph, and outputting an abnormal type label and a causal path set; constructing a minimum disturbance sample, and performing comparative reasoning to generate an abnormal credibility change interval and an anti-robust score; and updating the abnormal knowledge graph and executing incremental updating. According to the invention, high-precision, interpretable and robust anomaly detection and classification analysis in a complex network environment can be realized.

The application discloses a memory abnormality early warning method and device based on data mining and a medium, relates to the technical field of memory abnormality early warning, and comprises the following steps: performing semantic element mapping on memory running semantic data, and outputting a semantic state time sequence vector; performing cross-domain time sequence alignment and associated feature extraction on the semantic state time sequence vector and memory performance index data, and outputting memory associated rules; performing cause-effect analysis on the memory performance index data, constructing a graph structure on a cause-effect edge set through a PC algorithm, and outputting a memory index cause-effect graph; performing memory behavior deduction on real-time memory running semantic data according to the memory associated rules, outputting memory abnormality early warning data, performing cause-effect tracing and report integration on the memory abnormality early warning data through the memory index cause-effect graph, and outputting a memory abnormality early warning report. The application realizes interpretable early warning and root cause positioning of memory abnormality through the cooperation of semantic state time sequence vector construction and memory index cause-effect graph generation.

The application discloses a kind of electric cable winding device fault early warning methods, it is related to fault early warning and processing technology.The method includes: collecting electric cable winding device state information, the cause and effect analysis is carried out to electric cable winding device state information, and constructs follow-up collaborative link;Based on follow-up collaborative link acquisition historical data, and construct qualified stage fingerprint set;Based on follow-up collaborative link acquisition real-time data, obtain current stage fingerprint;Build electric cable winding device fault judgment model, use qualified stage fingerprint set to train, input current stage fingerprint into trained electric cable winding device fault judgment model, output fault judgment result;Based on fault judgment result and follow-up collaborative link, generate potential failure chain, carry out double threshold determination to potential failure chain, obtain electric cable winding device fault early warning result.The application generates electric cable winding device fault early warning result by graph attention network, realizes the fault early warning and processing of electric cable winding device.

The invention relates to an operation and maintenance fault positioning method, an operation and maintenance fault positioning system and a storage medium. The operation and maintenance fault positioning method comprises the following steps: obtaining operation and maintenance summary data of different operation and maintenance fields, and compressing the operation and maintenance summary data into structured summary data; inputting the structured abstract data into a large language model inference engine for fault causal analysis to obtain initial verification information output by the large language model inference engine; sending the initial verification information to a policy scheduling agent node, so that the policy scheduling agent node sends a verification instruction to a target verification agent node corresponding to the initial verification information based on the initial verification information; and according to a verification result fed back by the target verification agent node, determining a target confidence coefficient of the initial verification information, and when the target confidence coefficient is greater than or equal to a target threshold value, generating a fault root cause report based on the initial verification information. The fault positioning accuracy can be improved, and the fault positioning timeliness is improved.

The application discloses a kind of heterogeneous data processing method and system of energy big data, the method includes: obtaining different types of energy data and inputting pre-trained directed association graph model, obtain cross-type dynamic contribution flow network node forest;Obtain cross-type linkage report and build basic directional positioning dynamic correction chain through report analysis positioning and cause-effect analysis;The cross-type dynamic contribution flow network node forest and basic directional positioning dynamic correction chain are input into pre-trained positioning correction path model, and real-time positioning error correction adjustment is carried out on cross-type linkage report, and updated and adjusted cross-type linkage report is obtained.The application realizes cross-type linkage analysis and report real-time error correction of energy data, improves the accuracy and efficiency of energy data processing.

This invention discloses a method and system for graded detection of aspirating smoke fires based on wireless networking, comprising the following steps: deploying nodes within the target monitoring area and extracting air samples from monitoring sub-areas; performing baseline smoke concentration learning, short-term disturbance removal, and continuous response segmentation on the original sampling sequence; extracting segment features, constructing local smoke response vectors for nodes, and determining local response states; transmitting data and performing time-series alignment on the uploaded data to form a node collaborative response matrix; determining the causal relationship, direction, and intensity of response transmission between monitoring sub-areas, and constructing a smoke propagation chain; performing regional aggregation and fire grade determination to generate graded detection results for the target abnormal area; and transmitting data to the nodes and outputting regionalized fire alarm information. This invention, based on wireless networking and Granger causality analysis, achieves multi-node collaborative graded detection, possessing advantages such as high positioning accuracy and strong anti-interference capability.

The application discloses a power control method, system and device based on security posture perception and active response. The method comprises the following steps: calculating a first global security posture assessment result and a dynamic response threshold based on intermediate results sent by each local twin, generating a response strategy template set when the comparison result of the two is greater than or equal to, combining an introduced response strategy parameter set to generate a first response strategy, executing the first response strategy when the first global security posture assessment result is not less than a preset execution threshold, and generating and executing a second response strategy when the first global security posture assessment result is less than the preset execution threshold; and calculating based on an updated twin system state of each local twin, and the calculation result is used for updating the response strategy parameter set. The application realizes abnormality detection, cause analysis, risk assessment and active response by deploying a global twin and a plurality of local twins, so that the perception accuracy, response speed and overall security resilience to network attacks and composite threats are improved.

The invention discloses a bid evaluation result clustering analysis method based on reinforcement learning, and relates to the technical field of data processing and bidding analysis, and the method comprises the specific steps: firstly collecting multi-source heterogeneous bid evaluation data and carrying out the standardization processing through relying on an Internet data service platform; inputting a reinforcement learning clustering model, outputting a result and intermediate data and storing the result and the intermediate data; carrying out abnormity verification, and if the standard is not reached, triggering causal analysis to generate a correction instruction; performing causal intervention and iteration on the model according to an instruction; and finally, sorting up standard results to form a report, pushing a final result, and visually displaying evidence storage data and a causal map. According to the method, multi-source heterogeneous bid evaluation data are integrated and standardized, a reinforcement learning clustering model is constructed, and distributed storage and multi-dimensional verification are combined; through causal intervention closed-loop iterative optimization, dynamic clustering is realized, results and the like are visually displayed, accuracy and transparency are improved, and bid evaluation analysis is promoted to develop towards the direction of intelligence and the like.

The application discloses an industrial process supervision management system based on multi-modal data processing, comprising a data acquisition module (100), a data fusion module (200), an anomaly detection module (300), a decision optimization module (400) and a visual interaction center (500), the data acquisition module (100) transmits multi-source sensing data to the data fusion module (200) in real time through an industrial Ethernet, the data fusion module (200) forms a multi-modal feature vector and sends the multi-modal feature vector to the anomaly detection module (300), the anomaly detection module (300) presets a threshold value and sends a joint alarm signal to the decision optimization module (400) and the visual interaction center (500). The application has the beneficial effect of realizing breakthroughs in detection speed, positioning accuracy and decision reliability through multi-modal data fusion and dynamic modeling, effectively solving technical bottlenecks such as timing misalignment, single-source misjudgment and insufficient cause-effect analysis existing in traditional industrial monitoring systems.

The invention provides an intelligent ultrasonic body fat management system and method based on full-link data fusion, and relates to the technical field of digital health management. According to the system, an ultrasonic intervention module collects physical field coupling data and subcutaneous fat thickness data by utilizing a physical field sensing and in-situ measurement technology; the physiological monitoring module acquires whole body metabolic behavior data by using a physical sign tracking technology; the fusion decision module constructs a full-link fusion data set by using a space-time alignment technology, and identifies a dominant factor through a causal analysis algorithm to generate a feedback control instruction; and the execution response module analyzes the instruction by utilizing a self-adaptive control technology and generates physical field modulation output and interaction strategy window output. Through multi-dimensional data fusion and attribution closed loop, precise management from treatment perception and behavior tracking to software and hardware cooperative regulation is realized, and the problems of process black box and data island in the prior art are solved.

The application discloses a kind of vehicle sensor data collection collation method and system for fault detection, specifically relates to vehicle fault prediction and health management technical field, for solving the problem that the accuracy and timeliness of existing data collection method are restricted due to lack of real-time quality evaluation and abnormal traceability capability;It is through obtaining the multi-channel sensor data stream of vehicle and real-time running state parameter, using the pre-built vehicle health state space model to generate the state correlation quality vector reflecting the data deviation from normal mode, then the abnormal data stream is analyzed in time-frequency domain energy and the current modal parameter of vehicle system to generate modal cooperative deviation parameter, the cause-effect analysis is judged in combination with both to quality anomaly, and according to the determination result, the collection strategy, preprocessing mode or transmission priority of corresponding sensor data stream is dynamically adjusted to be realized, the intelligibility and effectiveness of data collection are improved, and high-quality data basis is provided for accurate fault prediction.

The invention provides a method for monitoring and early warning implementation conditions of basic public service standards of key people with one old and one small based on an index system. The method belongs to the technical field of public service management, and comprises the following steps: firstly, constructing an index system covering core dimensions such as medical treatment, old-age care, education, young support and social security, determining a weight through an analytic hierarchy process and fuzzy consistency adjustment, formulating a standardized data acquisition template, and acquiring and verifying multi-channel data. And dynamically evaluating the implementation level of the public service standard of each region by using a weighted scoring model and an improved TOPSIS algorithm in combination with a time sequence. According to the system, real-time automatic early warning of service glide risks is realized through trend modeling and anomaly detection, and targeted rectification suggestions are provided by adopting hierarchical notification and a causal analysis mechanism. All early warning and rectification results are returned to a system database, self-learning and continuous optimization of an index system and an evaluation model are supported, and scientific decision and closed-loop management of public service governance are realized.

The invention discloses an industrial operation data visualization display method and system based on an artificial intelligence platform, and relates to the technical field of industrial equipment operation data visualization. Multi-dimensional operation data of industrial equipment are collected and associated identifiers are marked, and a part physical association relation table and a global unique identifier are established; binding the data and the global unique identifier, converting the causal analysis conclusion of the artificial intelligence platform into a global unique identifier association chain, generating a numerical label and a thermodynamic diagram based on the binding relationship and the global unique identifier association chain, and performing highlight labeling and global unique identifier association chain display when the fault data reaches the standard. According to the method, the relevance and intuition of industrial equipment operation data visualization are improved, a user is helped to quickly grasp the equipment operation state and the abnormal cause, and the actual requirements of industrial operation and maintenance are met.

The application relates to the technical field of construction safety detection, in particular to an abnormal behavior detection system and method. The system comprises a multi-modal perception layer, an edge processing layer, a behavior cognition reasoning engine, a dynamic risk assessment and early warning module and a visual management platform connected in sequence. The multi-modal perception layer collects visual, Internet of Things and environmental data; the edge processing layer performs real-time target detection, behavior preliminary classification and space-time registration; the behavior cognition reasoning engine realizes behavior understanding and intention prediction by constructing a space-time graph and fusing rule reasoning and a deep learning model, and integrates a cause-and-effect analysis submodule to perform root cause tracing; the risk assessment module dynamically calculates a risk level according to a reasoning result and triggers early warning, and deduces risk evolution by using a risk transmission simulator; and the visual management platform realizes three-dimensional display and emergency linkage, and continuously optimizes a model by feedback data driving. The application realizes real-time and accurate identification and closed-loop risk control of abnormal behaviors on a construction site.

The invention discloses a mining equipment fault causal analysis method based on a knowledge graph, equipment and a medium, and relates to the technical field of operation and maintenance management, and the method comprises the steps: collecting real-time sensor data flow, historical operation data, maintenance cost records, production plan data and unstructured historical maintenance reports of gold mine key equipment; constructing an equipment fault causal knowledge graph; according to a causal analysis path packaged in the operation and maintenance decision script, directionally collecting refined data of gold mine key equipment, performing logic consistency verification, and determining a fault root cause and a causal chain influencing a production plan; and generating a decision scheme based on the fault root cause and the causal chain influencing the production plan. According to the method, the equipment fault causal knowledge graph is constructed, so that early-stage accurate early warning and predictive maintenance decision support of the gold mine key equipment hidden fault are realized, and the equipment reliability management level is improved.

This invention relates to the field of CNC machining equipment monitoring technology, and discloses a CNC machining center monitoring and fault prediction system based on multi-sensor fusion. The system includes: a data acquisition module for acquiring data from lubricating oil circuit dielectric constant sensors, spindle temperature sensor array data, and tool tip three-dimensional position deviation data; a data synchronization module for synchronizing multi-source sensor data to a unified time reference; a feature extraction module for extracting high-frequency pulse disturbance features of the dielectric constant and position-temperature correlation parameters; a causal analysis module for constructing a multi-physical quantity causal topology graph and identifying the dominant causal path; a risk assessment module for calculating a lubrication contamination risk score; and an early warning decision module for generating targeted maintenance instructions. This invention solves the technical problem of existing technologies being unable to distinguish between lubrication contamination and anomalies caused by external heat sources.

The invention relates to the technical field of chemical informatics, in particular to a free implementation analysis knowledge base construction method based on an agent and workflow, and the method comprises the following steps: firstly, constructing a free implementation knowledge graph by using chemical structure data and patent text data, and establishing an association relationship among molecular nodes, claim nodes and target nodes in the graph; markush templates are generated on the basis of claims with structural formulas in the knowledge graph, free implementation distance vectors are calculated for each molecule and each Markush template, and the Markush templates and the distance vectors are written into the knowledge graph; on this basis, a target analysis workflow is constructed, and a candidate molecule generation agent and a free implementation assessment agent are driven to carry out free implementation risk assessment and recommendation on candidate molecules; and further establishing a causal analysis model by using a workflow log, a patent review result and an infringement judgment result, and calibrating an aggregation mode of a distance vector and a risk assessment rule to realize continuous optimization and traceable management of free implementation analysis.

The invention discloses a game SDK automatic packaging and efficiency improving method, system and equipment, and the method specifically comprises the steps: sending a packaging task request to a packaging service platform constructed based on a Knative Serving framework, and carrying out the expansion and contraction of a packer container instance in a Kubernetes cluster according to the load condition of a current task queue; in the resource-ready packer container instance, executing packing commands of the Android platform and the iOS platform in parallel, and extracting a code change log and a construction parameter in a packing process to generate packing data; inputting the packaged data of this time and historical packaged data into a cause and effect analysis engine based on a DoWhy framework, and outputting an optimal configuration recommendation file; and taking the historical packaging data and the optimal configuration recommendation file as training data, training to generate a packaging parameter optimization model, and re-executing the packaging task in combination with the packaging data of this time. According to the method, the packaging efficiency and quality are improved, and a more efficient and intelligent solution is provided for mobile game development.

The application discloses a kind of present network system operation and maintenance method, device, equipment, medium and program product, it is related to artificial intelligence technical field.The method comprises: using root cause positioning large language model, target topology dependency graph, target index triplet and historical operation and maintenance knowledge base are carried out abnormal clustering and cause-effect analysis, obtain the candidate root cause failure information of target present network system;Using operation and maintenance decision-making large language model, the candidate root cause failure information of target present network system, historical fault operation and maintenance process, the historical effectiveness of historical fault operation and maintenance process, target operation and maintenance constraint information and reference optimization target are carried out operation and maintenance decision-making, and output target executable decision sequence;According to target executable decision sequence, generate target operation and maintenance task sequence of target present network system, and according to target operation and maintenance task sequence, target present network system is operated and maintained.The technical scheme of the embodiment of the application realizes the comprehensive operation and maintenance of present network system, improves the accuracy of fault location and the effectiveness of system operation and maintenance.

The invention relates to the technical field of equipment state monitoring and fault diagnosis, and discloses an electromagnetic valve multi-parameter fusion fault diagnosis model which comprises the steps that multi-physical field signals such as driving voltage, coil current, an external leakage magnetic field and valve body vibration are synchronously collected; based on a signal physical connotation, three core energy conversion links of electromagnetic conversion, magnetic force conversion and force feedback are defined; and the stability of the energy conversion process is quantified in real time by calculating the collaborative entropy of each link. On the basis, a real-time entropy increasing sequence is constructed and compared with a health benchmark, and sensitive fault detection is achieved. When a fault occurs, the system performs causal analysis on the entropy increase sequence of each link by using the transfer entropy, constructs a weighted directed fault causal relationship graph, and realizes accurate positioning of a fault initial entropy source and clear tracing of a fault propagation path by analyzing the weighted out-degree and in-degree difference of each node in the graph. According to the invention, tracing from phenomena to roots is realized, so that the diagnosis accuracy is improved.

This invention discloses an intelligent prediction and early warning system for intravenous infusion interruption. The system synchronously acquires weight, pressure, and optical multi-source sensor signals through a data acquisition module. A feature processing module performs joint time-frequency domain analysis to construct a feature vector representing the infusion status. An intelligent analysis module matches the feature vector with an abnormal pattern library to predict the abnormality type and remaining time. A causal analysis unit, combined with a directed graph model, performs root cause tracing and verification. An early warning execution module generates tiered early warning information based on the prediction results and root causes and intelligently pushes it to the corresponding terminals. The system also has the ability to dynamically optimize the abnormal pattern library based on actual treatment feedback. This invention achieves early and accurate prediction and intelligent root cause diagnosis of intravenous infusion interruption risks, significantly reducing false alarm and false negative rates, and improving the initiative and efficiency of clinical intravenous infusion safety management.

The invention discloses an ICU (Intensive Care Unit) multi-organ function failure risk pre-judgment, prevention and control system, and relates to the technical field of medical information technology and critical illness guardianship. By constructing a cross-organ time sequence causal intensity tracking module, accurate identification and prediction of a multi-organ failure risk conduction path and timeliness are realized; for time sequence data of liver, kidney, heart and blood coagulation key organ function indexes, a causal analysis algorithm based on transfer entropy is adopted, causal influence intensity and effect delay time between any two indexes are continuously calculated in a sliding time window, when a system recognizes that a CIS value from an organ X to an organ Y exceeds a threshold value, it is judged that a risk conduction path is activated, and if the CIS value exceeds the threshold value, it is judged that the risk conduction path is activated. And the predicted time span of risk conduction is quantified by ELT, so that the problem that a risk conduction mechanism cannot be revealed is solved, and a clinician can immediately start a targeted target organ protection prevention and control package according to the risk conduction mechanism, so that active intervention and accurate blocking of a specific risk chain are realized.

The present application relates to metal product surface spraying process parameter acquisition and defect root cause analysis technology, aiming at solving the causality analysis and root cause determination problem of defects caused by multi-parameter disturbance in spraying process. By deploying multiple types of sensors, 12-dimensional core process parameters are collected in real time, signal conditioning, synchronization and data encapsulation are used to form high-fidelity time sequence flow data. Based on parameter change, disturbance fingerprint is extracted, combined with principal component analysis and dynamic time warping to realize disturbance type clustering. Using lightweight granger causality analysis, the parameter subset is limited to efficiently capture local causal paths, and the causality path stability entropy of process sensitive weight adjustment is proposed by combining the direction consistency ratio and the response delay standard deviation, which is used for root cause variable sorting and explanation. The scheme improves the accuracy and decision interpretability of spraying defect causality tracing, effectively enhances the process optimization and intelligent quality control level of production line.