958 results about "Analytic model" patented technology

The invention relates to the technical field of computer vision, and discloses a real-time video analysis method based on deep learning. The method comprises the following steps: acquiring a real-time video stream through image acquisition equipment, and performing frame segmentation processing to generate a continuous video frame sequence; and extracting features of the video frame sequence by using a pre-trained convolutional neural network to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into the time sequence analysis model to calculate dynamic relevance, and outputting an inter-frame movement track and object behavior features. And constructing a scene understanding map containing a spatial position and a time evolution relationship according to the above-mentioned data, and carrying out abnormal event detection and generating event marking data based on the map. And performing semantic analysis on the event marking data, determining an abnormal event type and a confidence score, triggering a real-time alarm signal according to a result, and updating a historical event database. In the analysis process, the resource occupancy rate of the system is continuously monitored, the calculation precision is dynamically adjusted, a degradation processing mechanism is started when a preset threshold value is exceeded, and key area analysis is preferentially guaranteed.

The invention is suitable for the technical field of data asset management, and provides a data asset management system based on a block chain and big data analysis, and the system comprises a first terminal which carries out the storage of the ownership information of data assets through a block chain network, and generates a storage data package; generating an evaluation result based on a preset technical index analysis model; writing the hash value of the evaluation result into transaction data of the main chain of the block chain, and generating an evidence storage voucher matched with the block chain transaction; an interaction data sequence is packaged for identity signature, encryption and compression to generate a target code stream, and the target code stream is sent to the second terminal; the second terminal decrypts the encrypted data in the target code stream by using a private key, decompresses the data by using a compression algorithm matched with the first terminal, and restores the data into an interactive data sequence; verifying the access authority and the transaction condition of the evidence storage data packet; checking the anchoring state of the evidence storage voucher on the main chain of the block chain; and the data transaction is completed, the data asset state report is generated, and the data element marketization configuration efficiency is improved.

The invention relates to the technical field of digital engineering, and particularly provides a BIM-based engineering construction dynamic three-dimensional visual management method and system, and the method comprises the steps: collecting construction multi-dimensional data, carrying out the correlation mapping with an initial BIM model component after preprocessing, and forming a structured construction element data set; dynamically reconstructing an initial BIM model based on the data set, generating a three-dimensional dynamic twinborn body, and dynamically displaying information such as superposition progress, quality, resources and environment; a prediction analysis model is called to generate progress prediction, quality early warning and resource optimization suggestions, and a final scheme is determined after visual simulation verification; and updating the data set and the twin according to an execution result, and iteratively optimizing the prediction model. According to the scheme, dynamic integration and visual management of construction total elements are achieved, the timeliness, accuracy and intelligent level of construction management are effectively improved through intelligent prediction and closed-loop optimization, the construction efficiency is improved, and the management cost is reduced.

The invention discloses a data security risk assessment method based on a big data model, and relates to the technical field of data security, and the method comprises the steps: collecting and preprocessing multi-source data, collecting security-related data from network equipment, a server and an application system, carrying out the preprocessing, carrying out the adaptive feature extraction, and carrying out the data security risk assessment. The feature importance is evaluated by calculating the mutual information amount of features and risk tags, a standardized feature vector set is constructed, multi-model collaborative analysis is performed, feature vectors are input into a cascade collaborative network composed of an anomaly detection model, a threat recognition model, a correlation analysis model and a prediction model, and a risk risk is obtained. Through cross-model feature transmission and a bidirectional information feedback mechanism, deep collaborative analysis and multi-model deep fusion decision making are carried out, a weight is calculated according to historical accuracy of each model, a comprehensive risk score is calculated by adopting dynamic gating deep fusion, and a dynamic threshold value is calculated based on a sliding time window. And the risk is divided into three levels of high risk, medium risk and low risk.

The invention discloses a Beidou-based electric power tower attitude change on-line monitoring method, which comprises the following steps: A, setting a tower attitude acquisition system, and respectively acquiring multi-source data of a tower in real time by using the tower attitude acquisition system; b, constructing a meteorological database; c, constructing a tower attitude analysis model; the tower attitude analysis model comprises a data acquisition and preprocessing module, a multi-source data fusion module, a comprehensive analysis module, a situation diagnosis and risk assessment module, an intelligent prediction module, an early warning decision module and a visualization and feedback module; and D, on the basis of the trained constructed tower attitude analysis model, acquiring multi-source data of the tower in real time by using a tower attitude acquisition system, and performing online monitoring on the attitude change of the power tower in combination with a meteorological database. According to the invention, the posture change of the power tower can be obtained more accurately, and further prediction and early warning are carried out on the posture change of the power tower in combination with historical and predicted environmental data.

The invention discloses a human resource intelligent management method and system based on man-post matching, and the method comprises the steps: receiving an unstructured post description text, extracting key information through a natural language processing technology, generating a structured multi-dimensional post portrait, and classifying the structured multi-dimensional post portrait; on the basis of historical recruitment data, predicting the number of future post demands by means of a time sequence analysis model; for the candidate resumes and the target post portraits, keyword correlation scores, depth semantic similarity scores and predictive stability scores are calculated in parallel; according to the post portrait classification application dynamic weight, performing weighted summation on the scores to generate a comprehensive matching score; and sorting the candidates according to the comprehensive matching scores, and outputting a sorted candidate list. According to the method and the system provided by the invention, the man-post matching accuracy and the recruitment efficiency are effectively improved, the core pain points of intelligent recruitment, man-post matching and demand prediction in human resource management are solved, and the method and the system are particularly suitable for the demands of human resource outsourcing and labor dispatching industries for stable service selection.

The invention discloses a Beidou time service synchronous multi-source data fusion monitoring device, system and method, and aims to solve the core problems of low meteorological factor-ground surface settlement causal correlation analysis precision and poor early warning reliability caused by multi-source data time asynchronization due to sensor clock independence. The device directly drives each sensor in the multi-source data acquisition module to perform physical-level synchronous acquisition through a hardware synchronous trigger pulse generated by the Beidou time service module, and ensures that settlement, weather and positioning data have the same timestamp at the source; and time axis calibration and dynamic weight fusion based on real-time data quality are carried out through the preprocessing module. The system integrates the devices, a cloud analysis model and an early warning engine. According to the invention, a closed loop from hardware-level synchronous acquisition, space-time alignment to intelligent analysis is realized, a multi-source data synchronization error is reduced from a second level to a millisecond level, and the accuracy and timeliness of water and soil conservation monitoring are remarkably improved.

The invention discloses a reactor instrument control system equipment fault early warning method and related equipment, and relates to the technical field of reactor operation and maintenance, and the technical scheme is characterized in that the method comprises the following steps: obtaining operation parameters of target equipment of a reactor instrument control system in real time, and synchronously collecting historical operation data and historical fault data of the target equipment at the same time; constructing a machine learning analysis model, and training the machine learning analysis model by taking the historical operation data and the historical fault data as training samples; running parameters collected in real time are input into the trained machine learning analysis model, abnormal features representing the change trend and the deviation degree of the real-time parameters are output, a preset equipment abnormal feature library is matched based on the abnormal features, whether the target equipment has faults or not is judged, and a health degree index representing the health state of the target equipment is calculated; and if the fault judgment result is that the potential fault exists or the health degree index is lower than the preset health threshold value, generating a corresponding fault early warning signal and a health degree evaluation result.

The invention discloses an Internet of Things edge computing task unloading method based on a comprehensive gateway, which belongs to the technical field of computer data processing, and comprises the following steps: acquiring tasks, equipment and network data of Internet of Things equipment, and fusing to generate a unified information view; determining task priorities based on the view by applying a dynamic priority evaluation mechanism, and generating a dynamic priority list; applying the time sequence analysis model to generate short-term prediction data; in combination with the dynamic priority list and the short-term prediction data, multi-objective optimization is performed through a collaborative decision model, and a task unloading strategy is generated; generating feedback data according to the task unloading strategy; and generating an adjustment signal by using the feedback data, and updating the dynamic priority evaluation mechanism and the collaborative decision model. According to the method, the unified information view is generated by fusing the multi-source data, collaborative decision making is carried out in combination with dynamic priority evaluation and short-term prediction data, and the model is continuously optimized by utilizing feedback data, so that self-adaptive, forward-looking and efficient task unloading can be realized.

The invention provides a saline-alkali soil underground water regulation and control method and device, electronic equipment and a storage medium, and relates to the technical field of land governance. Inputting the first historical monitoring information into a pre-constructed prediction model to obtain a predicted value of soil salinity in a future time period; the predicted value of the soil salinity is input into the analysis model, and the target water level of underground water in the future time period is obtained; wherein the target water level of the underground water is used for restraining the target value of the soil salinity to be smaller than a preset salinity threshold value and the target value of the soil humidity to be larger than a preset humidity threshold value; generating a water level adjusting strategy based on the target water level of the underground water, and controlling the opening or closing of the water pump based on the water level adjusting strategy. According to the method, the soil salinity in the future time period is predicted by acquiring the saline-alkali soil monitoring information, so that the target water level of the underground water is obtained, the water level regulation strategy is generated, and the perspectiveness, dynamic nature and stability of underground water level regulation are ensured.

The invention discloses a one-key performance test method, medium and equipment based on JMeter and template configuration, and the method comprises the steps: obtaining a parameterized performance test template from a template configuration center in response to a test instruction, injecting runtime parameters through a template engine, and generating an executable JMeter test plan; parsing the plan to identify a load pressure demand, thereby dynamically scheduling resources from a distributed load injector cluster and distributing a test plan; uniformly triggering test execution on the cluster, and synchronously acquiring a performance data stream from the JMeter and a resource data stream of the tested system; and finally, timestamp alignment and fusion are carried out on the two types of data streams, intelligent diagnosis is carried out on a formed monitoring data set based on a diagnosis rule base or an intelligent analysis model, and a visual test report containing bottleneck positioning, resource association and link tracking analysis results is generated. According to the invention, full-process automation and intelligentization of performance testing are realized, and the testing efficiency is remarkably improved.

The invention relates to the technical field of electric power prediction, discloses a photovoltaic power generation prediction system based on a multi-source analysis model, and aims to solve the problems that an existing photovoltaic power generation prediction technology is insufficient in individual and isomerized node prediction capability, and prediction is limited due to dependence on historical data in a newly-accessed and data-missing node scene. The system comprises a multi-dimensional node feature quantification module, a virtual historical data synthesis module, a power prediction module based on an enhanced data set, a closed-loop deviation traceability and correction module and a compensation strategy execution module oriented to a specific scene. Through adoption of the technical scheme, high-confidence prediction can be provided for blank or data missing nodes on the premise of not depending on historical data of the target node, and the precision, the coverage rate and the dynamic adaptability of distributed photovoltaic prediction are remarkably improved.

The invention relates to a precise health monitoring method based on multi-source data fusion. The method comprises the following steps of 1, collecting multi-source data; step 2, data preprocessing; step 3, multi-source data fusion; 4, health analysis and decision making; step 5, dynamic weight adjustment; and step 6, feedback optimization. The hierarchical fusion strategy is adopted to process heterogeneous data, a machine learning algorithm is combined to establish an individualized health model, physiological state changes are tracked in real time, a targeted intervention scheme is generated, and in terms of technical implementation, the system supports collaborative decision making of various analysis models, including rule-based behavior reasoning, time sequence feature analysis and environment threshold judgment, and the system has the advantages of being simple in structure and convenient to use. The contribution degrees of information of different sources are balanced through an adaptive weight distribution mechanism, time-frequency analysis and dimension reduction technologies are fused in the feature extraction process, the dynamic mode of the health state is effectively captured, cross validation optimization is adopted in model training, and the reliability of an evaluation result is ensured.

The invention discloses a fermentation strain yield prediction method based on artificial intelligence, particularly relates to the field of biological fermentation production, and is used for solving the problems that in the prior art, due to the fact that the yield is difficult to directly map in concentration prediction, monitoring indexes are normal, the final yield deviates from expectation, and reliable strain comparison and production management decision cannot be supported. A historical batch process observation sequence and a material account sequence are aligned to generate a conversion efficiency characterization sequence to form a yield learning sample training yield prediction model, and the conversion efficiency characterization sequence is generated on batches and input into the model to obtain a yield prediction value. And calculating an accounting volume trajectory consistency parameter and a conversion contribution steady-state proportion parameter, inputting the parameters into a coefficient analysis model to obtain a decision credible coefficient, generating a yield risk judgment result for a yield prediction value based on the decision credible coefficient, and realizing a new-batch online prediction record.

A train operation intelligent dispatching system and method are provided, where the system includes: a data acquisition and control module, configured to collect multi-source data affecting train operation; a data processing module, configured to perform data aggregation on the multi-source data, and process the multi-source data to form multi-source data in a unified data format; a multi-disciplinary data resource pool, configured to perform real-time storage and updating of the multi-source data in the unified data format, and store historical multi-source data; an intelligent fault analysis module, having intelligent fault analysis models therein, configured to perform fault analysis based on the multi-source data to obtain fault analysis results of corresponding disciplinary systems, and output fault advance warnings of corresponding disciplinary systems according to the fault analysis results; and a process preplan module, configured to provide preplan handling processes corresponding one-to-one with external multi-disciplinary systems.

The invention discloses a boiler energy efficiency prediction method and system based on sampling flue gas component analysis. The method comprises the following steps: acquiring heat flux density data of a plurality of radial positions, and inverting a temperature distribution profile through a radiation heat flux prediction model; constructing a multi-component concentration layering graph based on the diffusion equation and the component transport equation; constructing a sampling path optimization model by using temperature gradient and concentration fluctuation intensity information, driving a dynamic telescopic sampling probe to arrange a sampling position, and collecting a flue gas sample; the analysis model determines original concentration data, thermal-mass offset correction is executed in combination with temperature distribution, and a concentration correction function is constructed to obtain a corrected concentration value; and fusing boiler operation condition data, and inputting into the energy efficiency prediction model to output a heat efficiency index. According to the method, temperature field inversion driven by heat flux density is constructed, a multi-component concentration layering graph is generated in combination with a diffusion and transport model, and the spatial representativeness of flue gas sampling, the physical authenticity of component data and the accuracy of energy efficiency prediction are remarkably improved.

The invention discloses a power grid dispatching report generation method based on natural language processing, relates to the technical field of power grid dispatching report processing, and aims at solving the problems that a traditional report generation mode is low in efficiency and large in subjective deviation. The method comprises the steps that multi-source data are integrated, the data are preprocessed, and multi-dimensional time sequence data are output; generating an event atlas based on the high-dimensional data abstract and the state recognition result; converting the structured data into basic natural language expression, and filling dynamic content; generating a report text; generating a dynamic visual chart and an image-text report based on the generated report text, the event graph and trend data generated by the time sequence analysis model; an image-text report is adopted, the report is divided into key suggestions and detail descriptions, and highlighted high-risk event prompts are generated. According to the method, the report generation efficiency can be improved, subjective deviation in manual analysis is eliminated, and objectivity and specialty of report content are ensured.

The invention belongs to the technical field of geological analysis, and discloses a ground subsidence analysis method and device, and the method comprises the steps: matching a sensor layout strategy corresponding to a foundation subsidence risk and meteorological data of a monitoring region; acquiring land subsidence data of the monitoring area according to a sensor; performing time-frequency analysis on the land subsidence data to obtain a geoacoustic spectrogram corresponding to the land subsidence data; inputting the geoacoustic spectrogram into a preset neural network, and outputting a settlement semantic vector corresponding to the geoacoustic spectrogram; acquiring and analyzing historical land subsidence data, and constructing a subsidence rule corresponding to the historical land subsidence data; and inputting the settlement semantic vector and the settlement rule into a preset space-time analysis model, and predicting to obtain a future settlement trend of the monitored area. By using the method disclosed by the invention, the problem that all risks in a certain area cannot be checked when geological data is monitored based on local monitoring points can be solved, and the problem that the geological risk analysis efficiency is relatively low can be solved.

The invention discloses a multi-modal sensing edge intelligent monitoring system, which relates to the field of edge intelligent monitoring and comprises a multi-modal data acquisition module for acquiring multi-modal data; the edge intelligent processing module performs space-time registration, feature extraction and feature level fusion on the multi-modal data to generate a multi-modal feature vector; performing joint reasoning on the multi-modal feature vector by adopting an intelligent analysis model, outputting a structured analysis result, and generating a local early warning instruction when the danger level indicated by the structured analysis result exceeds a threshold value; and the central control module carries out early warning information issuing, data archiving and model iteration training according to the structured analysis result. Localized feature level fusion and intelligent analysis are carried out on multi-modal data through an edge intelligent processing module, and low-delay risk identification and early warning are realized. Meanwhile, continuous optimization and global situation awareness of the model are realized through a cloud edge coordination mechanism, and the defects of the traditional architecture in real-time performance, reliability and intelligent level are effectively overcome.

The invention relates to the technical field of environment monitoring, in particular to an environment regulation system and method based on an environment monitoring technology, and the system comprises a region division module which is used for dividing monitoring sub-regions based on the spatial distribution features of a heterogeneous data set, and distributing a corresponding data feature extraction rule for each monitoring sub-region; and the feature extraction module is used for extracting abnormal fluctuation indexes and region association features from the environmental data of each monitoring sub-region based on the data feature extraction rule to obtain a sub-region feature set. According to the method, the parameters of the anomaly analysis model are updated by generating the specific environment optimization strategy and real-time feedback data, and continuous optimization and accurate execution of the regulation and control strategy are ensured; meanwhile, through staged regulation and control and cooperative work of equipment, the environment regulation effect can be improved to the maximum extent.

The invention provides a method, a system, equipment and a medium for analyzing running states of multiple automatic warp tying machines, and belongs to the technical field of automatic warp tying machines. Equipment health, warp tying efficiency, warp process, joint quality, fault early warning and associated loom data are acquired from multiple pieces of equipment in real time; a standard data set is formed through intelligent routing transmission, data cleaning, format conversion and aggregation processing; and adopting a self-adaptive strategy to match an optimal analysis model for various types of data for analysis processing, comparing a result with a multi-machine cooperative operation reference value, marking abnormity, and summarizing state results according to device numbers. Multi-device data full-link closed-loop management is realized, and the monitoring efficiency and accuracy are improved; through cross-device data association verification and multi-dimensional feature fusion, the fault positioning and prediction capability is enhanced; operation state results are fed back to an equipment end in real time, stable operation of equipment is effectively guaranteed, fault risks are reduced, and production efficiency and warp tying quality are improved.

An apparatus and method for fraud-risk assessment. Event data including identification, transactional, geospatial, biometric, and behavioral signals, are received and processed by a scoring module employing one or more analytical models to generate a score. Based at least in part on the score, a security action is selected and a risk-tiered alert is generated. An audit record including the score, rationale, action, timestamps, and associated data characteristics is stored in one or more datastores configured for secure, verifiable, or immutable recordkeeping. The operations may be performed in any order or in parallel. Embodiments include velocity controls for real-time flows and post-event workflows to escalate to external recipients, create or update an investigation case, or supply features for model retraining, while preserving auditability.

The invention discloses a service log abnormity early warning method and system based on an AI intelligent agent, and relates to the field of communication services. Comprising the steps of 1, carrying out log data acquisition and preprocessing, 2, constructing an AI agent model, 3, carrying out intelligent feature extraction: extracting semantic features of logs by utilizing a multi-modal log analysis model, analyzing time sequence features and statistical features of the logs, constructing multi-dimensional feature vector representation, and carrying out intelligent feature extraction; 4, performing abnormal mode identification: identifying an abnormal log mode based on a multi-mode log analysis model, discovering a potential abnormal type, and performing abnormal severity evaluation in combination with historical data, and 5, providing an intelligent early warning decision: making an early warning strategy according to the abnormal type and severity, generating a detailed abnormal analysis report, and performing early warning on the abnormal log mode. And step 6, carrying out model optimization: collecting user feedback and processing results, continuously optimizing model parameters and early warning thresholds, and updating the knowledge graph and the abnormal mode library.

The invention provides an intelligent response method and device for dam safety abnormity based on a large text model, and the method comprises the steps: collecting dam monitoring data in real time through an edge calculation node, and carrying out the abnormity detection based on an LSTM-Attention time sequence prediction model and a multi-physics coupling analysis model; generating a standardized text report containing exception description, time, position and associated parameters; the text report serves as input, intelligent response analysis is conducted through a text large model based on LoRA fine tuning, and emergency disposal suggestions conforming to industry specifications are generated; and performing knowledge base matching verification on the emergency disposal suggestions, screening out a high-confidence-coefficient response scheme based on a cosine similarity threshold, and automatically triggering an emergency response process when a dam break risk coefficient is detected to exceed a preset threshold. According to the method, full-process automation of dam safety monitoring from abnormal detection to emergency response can be realized, the response time is remarkably shortened to the minute level, and the early warning accuracy and decision scientificity are improved.

The invention relates to the technical field of oil and gas field data management, and discloses an oil and gas field interval time sequence data management method based on a swan mongolian system. The method comprises the following steps: acquiring open time sequence data in real time through a distributed data acquisition module of a swan gap system, calculating data point density of a target area and comparing the data point density with a safety threshold; when the density exceeds the limit, evaluating data source interaction by using a statistical analysis model, and outputting a data coupling effect hidden danger state; analyzing and processing the environmental factor data by adopting a time sequence, and generating potential interference degree evaluation; determining whether to trigger an overall data optimization process or not according to the hidden danger state and interference evaluation; and during triggering, individual path complexity evaluation is executed on each data source, a priority sequence is calculated and adjusted, oil and gas field equipment is controlled according to the sequence, and dynamic management of open time sequence data is realized. According to the method, the real-time performance, accuracy and flexibility of data management are improved, and the production requirements of modern oil and gas fields are met.

The invention discloses a crop ralstonia solanacearum disease prediction method based on an integrated learning model, and the method comprises the following steps: S1, collecting a published 16s rRNA gene sequence related to solanaceae crop bacterial wilt, and carrying out the preprocessing of original sequencing data based on an EasyAmplicon standardized process; s2, performing data dimension reduction by using a principal component analysis algorithm, and retaining 95% of variance; s3, performing hyper-parameter search based on 5-fold cross validation and grid search on the Light GBM model, the CatBoost model and the XGBoost model respectively, and selecting three groups of optimal hyper-parameters of each model; s4, constructing a model according to three groups of optimal hyper-parameters of each selected model, performing prediction, analyzing model result difference based on a Pearson correlation coefficient, and retaining Pearson correlation coefficient mean < lt > with other eight model prediction values; a model of 0.8; and S5, inputting the screened model prediction result into a second-layer element learner RF for integrated learning to obtain a final prediction result.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses an anti-fraud method, device and equipment for information verification, and a medium, and the method comprises the steps: obtaining multi-dimensional verification data associated with an information verification request, and carrying out the preprocessing of the multi-dimensional verification data to generate standardized data, inputting the standardized data into a preset data analysis model to obtain a first abnormal value, inputting the first abnormal value and a predefined business strategy corresponding to the information verification request into a business logic processor to generate a second abnormal value, and fusing the first abnormal value and the second abnormal value to obtain a comprehensive abnormal score, and comparing the comprehensive abnormal score with a preset abnormal threshold value, and executing an anti-fraud operation when the comprehensive abnormal score exceeds the threshold value. According to the method, the comprehensive anomaly score is formed by fusing model anomaly and the business strategy result, and judgment is performed based on threshold comparison, so that the defects of single data or a general model in the prior art are effectively overcome, the identification accuracy and real-time performance are improved, and business risk prevention and control are realized.

The invention discloses an electrical load analysis method and system based on multi-source data fusion. The method comprises the steps of obtaining load data and a meteorological data sequence of a target area; carrying out high-fidelity interpolation on the load missing data by adopting a strategy of combining form matching and meteorological verification; adaptively splitting the complete load sequence into a plurality of internally stable load data subsequences based on a preset change rate threshold; calculating a time weighted average value of the meteorological data corresponding to each sub-sequence as target meteorological data; performing two-stage clustering according to the data quantity and the target meteorological data to form a homogeneous load sub-sequence set; a representative sub-sequence with the meteorological closest to the central value is extracted from each set and input into the load analysis model to obtain a reference result; and efficiently deriving the reference result to all other subsequences in the set according to the meteorological difference by using a preset load correction coefficient. According to the method, on the premise of ensuring the analysis precision, the processing efficiency and the result consistency of the mass load data are remarkably improved.

The invention provides an equipment fault diagnosis method and system, equipment and a medium, and the method comprises the steps: obtaining full-stack monitoring data of target equipment, the full-stack monitoring data being from at least two different technology stack levels of the target equipment; abnormal events in the full-stack monitoring data are detected; a propagation path of the abnormal event in a topological graph is analyzed, a starting node of the propagation path is used as a root cause node, and the topological graph is constructed based on the monitoring objects in the target equipment and the dependency relationship between the monitoring objects; and matching the abnormal features of the root cause node with known fault features in an equipment knowledge base to obtain a fault diagnosis result of the target equipment. According to the method, the starting node of the propagation path can be traced to position the root cause, and accurate matching is carried out in combination with the equipment knowledge base, so that deep faults which cannot be identified by a traditional method can be accurately positioned and diagnosed, and the problems of inaccurate root cause positioning and low efficiency caused by incomplete monitoring data and unmatched analysis models are avoided.

The invention relates to the technical field of data processing, in particular to an intelligent technical supervision analysis decision method and system, and the method comprises the steps: obtaining a natural language supervision demand of a user, extracting a supervision target, a supervision dimension and a constraint condition through semantic analysis, and generating a structured supervision demand expression; the method comprises the following steps: generating a multi-source data query instruction set by adopting a model for converting a natural language to a database query statement on the basis of a structured supervision demand expression, obtaining a structured query result, an unstructured document fragment and external dynamic data in combination with a retrieval enhancement architecture, and generating an enhanced decision data set after fusion filtering; constructing a supervision analysis model, inputting the enhanced decision data set, and outputting a preliminary supervision analysis result; and verifying the preliminary supervision analysis result, if yes, outputting a final supervision decision result, and if not, returning to adjust the multi-source data query instruction set, and repeating the subsequent steps. The method can improve the efficiency and reliability of supervision decision, is adaptive to multi-source heterogeneous data, and achieves the dynamic optimization of a whole process.