465 results about "Relational model" patented technology

The invention discloses a distributed power supply access-oriented power distribution network fault distance measurement method and system, and relates to the technical field of power systems, and the method comprises the steps: collecting the electrical parameters and operation states of distributed power supply access nodes in a power distribution network in real time, building a dynamic manifold model based on an ecological niche theory, and carrying out the calculation of the dynamic manifold model; adaptively adjusting manifold learning neighborhood parameters according to power fluctuation data included in the electrical parameters, and updating node ecological niches to reconstruct a dynamic manifold model; based on the reconstructed dynamic manifold model, fault features are extracted from three scales of a current harmonic component, a feed line inter-harmonic propagation path and whole network voltage influence, and a three-dimensional feature vector is generated through fusion of a graph correlation algorithm; based on the expanded fault sample library and the power fluctuation data, constructing a fault transfer relation model to predict a ground fault and a short circuit risk area; a fault source is modeled by adopting a topological neural network, and a fault point distance measurement value is output through state prediction and strategy deduction.

The invention relates to an intelligent maintenance decision method and system based on a knowledge graph, and relates to the technical field of intelligent operation and maintenance and decision support and information, and the method comprises the steps: obtaining the operation state data and historical maintenance data of industrial equipment; constructing an industrial knowledge graph and generating an incidence relation model; an optimal maintenance path is obtained based on the analysis of the relationship between the fault type and the maintenance path; optimizing a maintenance task allocation scheme through the optimal maintenance path, the incidence relation model and the maintenance resource constraint condition; carrying out maintenance time window dynamic adjustment analysis to obtain an optimal maintenance strategy; and finally, performing multi-objective optimization iterative analysis to obtain a final maintenance decision. According to the scheme, various factors can be comprehensively considered, and the scientificity and the accuracy of maintenance decision making of the industrial equipment are improved.

The invention discloses a substation switching misoperation risk intelligent control method and system, and relates to the technical field of power system safety production, and the method comprises the steps: collecting multi-dimensional real-time risk factor data, building a dynamic probability dependency relationship model based on the risk factor data, quantifying the correlation influence between different dimensions, and updating the model parameters, feature information of a current operation scene is converted into a standardized vector, similarity matching is carried out on the standardized vector and a scene template, an adaptive basic strategy is screened, the basic strategy is dynamically adjusted according to a real-time risk assessment result, and a scenarized risk management and control strategy is generated; and implementing a scenarized risk management and control strategy, collecting multi-dimensional feedback data in an execution process, generating a new generation of optimization strategy based on the feedback data, and updating the strategy library. The risk management and control strategy is generated and adjusted according to the actual situation, the strategy is continuously optimized, the safety and reliability of switching operation of the transformer substation are improved, accidents caused by misoperation are reduced, and stable operation of a power grid is guaranteed.

The invention relates to the field of industrial automation control, and discloses an intelligent control system and method based on multi-parameter coupling analysis and predictive maintenance. According to the method, flow, temperature and water quality parameters are collected in real time through a sensor network, and after data cleaning and standardization processing, a parameter coupling relation model is constructed; an LSTM neural network is adopted to predict a system energy consumption trend, and a time sequence regression model is combined to analyze equipment performance attenuation characteristics; and when the predicted value exceeds a threshold value, dynamically adjusting operation parameters and optimizing load distribution to form closed-loop control. Accurate energy-saving control under complex working conditions is achieved, energy consumption can be reduced by 8%-12% through tests, the equipment maintenance cost is reduced by 15%-20%, and the system operation efficiency is remarkably improved.

The invention relates to the field of waterproof sockets, and discloses a multi-scene adaptive intelligent waterproof socket remote regulation and control system and method, and the method comprises the steps: obtaining multi-source sensing data of a socket deployment environment, and constructing a multi-scene adaptive input data set in combination with a parameter fusion strategy and a signal interference compensation mechanism; environment feature recognition is carried out on the multi-scene adaptive input data set, and typical application modes in different scenes are extracted by using a socket state tag extraction algorithm; based on the state mapping relation model, a remote regulation and control strategy optimization algorithm is adopted to dynamically analyze the operation state of the socket, and the matching degree of the current operation mode and the environment scene is identified; an intelligent judgment mechanism is introduced, pre-execution verification is carried out on the multi-scene regulation and control suggestion set, and potential control conflicts are recognized in combination with historical operation data and real-time control instructions; and performing instruction issuing management on the remote control behavior of the socket based on the optimized execution strategy. The invention has the advantage of improving the universality of the intelligent waterproof socket.

The invention discloses an intelligent diagnosis and maintenance system for power equipment, and relates to the technical field of power equipment detection, and the system comprises the following components: an array microphone module which is composed of a plurality of microphones, is used for accurately calculating the spacing, is arranged in a three-dimensional or plane matrix manner, and is used for collecting sound signals generated in the operation process of the power equipment; according to the invention, the array microphone module is combined with a beam forming technology, the sound signals are collected efficiently, a voiceprint topological map is constructed, sound source distribution and energy conduction paths of all parts of the equipment are represented in a visual form, and the sound source distribution and energy conduction paths of all the parts of the equipment are displayed in a visual manner. Meanwhile, voiceprint features and vibration modal features are extracted through a feature extraction module, and a joint inversion module is used for performing joint analysis on the two features based on a mapping relation model and a machine learning algorithm and in combination with a modal database, so that the damage degree of the mechanical part of the power equipment is accurately inverted.

The invention discloses a high-voltage direct current transmission converter thyristor fault identification method based on reliability evaluation. The method comprises the following steps: constructing a stress-degradation-life mapping relation model through a stepped temperature-voltage cooperative stress test; establishing a three-stage Wiener degradation model, and predicting a critical failure point in combination with a dynamic threshold jump mechanism; an FPGA + CPU heterogeneous computing platform is adopted to execute an EM-MCMC joint estimation algorithm in parallel, and parameter updating is accelerated through assembly line MCMC sampling; designing a dynamic correction factor optimization reliability index based on the buffer layer parameters; constructing a dual-channel LSTM network to extract degradation features and generate a dynamic failure threshold value; a converter topology is reconstructed through a graph neural network, and fault positioning and damage grading are realized in combination with a three-level early warning mechanism. According to the method, fault identification is carried out through combination of multi-stress coupling modeling and a dynamic threshold value, the real-time performance is improved through a heterogeneous computing architecture, the false alarm rate is reduced through combination of a dynamic correction and topology reconstruction technology, and accurate identification and operation and maintenance decision support of the thyristor fault are realized.

The invention discloses a substation operation and maintenance task risk digital assessment method and system, and belongs to the technical field of task risk digital assessment, and the method comprises the steps: carrying out the hidden risk dominant modeling based on the structural data of a data lake, generating a personnel and equipment dynamic coupling risk coefficient, and carrying out the hidden risk dominant modeling; the space-time diagram neural network captures a personnel-equipment-environment coupling relationship by fusing spatial topology and time sequence features, the model can be associated with a historical fault mode of adjacent equipment, potential risks are identified in advance, and the causal model can identify the potential risks in advance by calculating causal strength between nodes and revealing hidden risk driving factors. The attention weight mechanism dynamically adjusts the fusion proportion of the space and time features, the method adapts to the complex scene of the transformer substation, the dynamic coupling risk coefficient is combined with the frequency and similarity of the historical accident library, the risk weight is quantified, and the probability of artificial misjudgment is reduced.

The invention belongs to the technical field of information, and discloses an IT performance index early warning method based on time sequence analysis, and the method comprises the steps: S1, inputting historical data; s2, risk detection; and S3, outputting early warning. Through fusion of statistical learning and machine learning technologies, accurate identification and early warning of three scenes of slow degradation of IT performance indexes, mode switching and capacity bottleneck are realized. The method specifically aims to improve the accuracy of performance index trend identification through a variable-point enhanced segmentation regression algorithm, and help an I T manager to more accurately evaluate the influence and trend of performance changes. Designing a cycle-adaptive double-sample hypothesis testing mechanism, and detecting and identifying switching of different load modes, especially significant changes caused by special or emergency events; and constructing a multivariable autoregressive VAR model, establishing a relation model of performance indexes and capacity indexes, evaluating capacity bottleneck conditions, and performing early warning in time.

The invention relates to the technical field of software and hardware compatibility testing, in particular to a domestic software and hardware compatibility intelligent evaluation system and method, and the system comprises a compatibility analysis engine, a compatibility detection task execution engine, a software behavior analysis module, a fault positioning module, a knowledge graph module, a performance optimization suggestion module and a visual interface. The software behavior analysis module constructs a high-dimensional feature space of a software running state through deep learning, and extracts abnormal features based on a topological space mapping technology; the fault positioning module constructs a dynamic causal network based on a probability graph model, and identifies a root node through a chaos sensitivity analysis algorithm; the knowledge graph module constructs a software and hardware dependency relationship model and provides priori knowledge for fault location; and the performance optimization suggestion module generates an optimization scheme based on the root positioning result, so that the functions of automatically executing a compatibility test, accurately positioning the root of the compatibility problem and providing targeted optimization suggestions are realized, and the adaptation efficiency of domestic software and hardware is greatly improved.

The invention discloses a recirculating ball type electric power steering system fault self-checking method and system and a readable medium, and relates to the technical field of vehicles, and the method comprises the steps: storing a signal transmission path and each function node, determining the type of an influence factor which influences the instruction execution accuracy of the function node, and determining the instruction execution accuracy of the function node; analyzing and acquiring the relationship between the fluctuation amplitude of the data signal at each function node and the numerical value of the influence factor, storing the relationship as a correction model, and storing a first relationship model for reflecting the data association relationship among the function nodes on the same signal transmission path; acquiring real-time data and source data of various impact factors, and generating theoretical response data according to the first relation model, the real-time impact factor data and the correction model; the actual response data is collected, the deviation amplitude between the actual response data and the theoretical response data is calculated, whether the function node breaks down or has a potential fault is judged according to the deviation amplitude, early warning is output, and the fault detection accuracy and the potential fault pre-judgment capability are greatly improved through the technical scheme.

The invention provides a pollutant traceability method, system and device based on multiple media and multiple links and a medium, and belongs to the technical field of target area pollution abatement, and the method comprises the steps: preprocessing pollutant monitoring data to obtain a standardized data set; the pollutant monitoring data comprises data of pollutants in a plurality of links and a plurality of media in a target area, and the standardized data set comprises a plurality of standardized data; based on the standardized data set, main characteristic components representing pollutant distribution and evolution rules are extracted; constructing a transfer coupling relation model based on the main characteristic components, and obtaining a transfer path and a coupling strength coefficient of the pollutants according to the transfer coupling relation model; based on the transmission path and the coupling strength coefficient, constructing a causal model and carrying out joint verification; and when the verification is passed, outputting a pollution traceability result. According to the invention, the scientificity, the accuracy and the decision support reliability of mine pollution traceability are obviously improved.

The invention provides a construction method and system of a settlement landscape evolution database based on multivariate spatial-temporal characteristics. According to the method, historical literatures, aerial photography, satellite images and landscape data are integrated to form a multivariate data set; constructing a dynamically updated database by using MySQL, and realizing data source associated storage by using a relational model; establishing a settlement landscape state machine model, and extracting spatio-temporal data to identify typical states and conversion conditions; constructing a state transition matrix, and analyzing the influence of social economic and environmental changes on evolution; the database is updated through field investigation, and a visual tool is developed to display the evolution process. According to the method, full-process dynamic analysis of settlement landscape evolution is realized, and efficient and accurate support is provided for decision making.

The invention provides a power grid mountain fire prediction method based on causal driving and a space-time diagram convolutional network, and belongs to the technical field of mountain fire prediction. A dynamic feature encoder and a static feature encoder are designed to extract high-dimensional spatial-temporal features of meteorological time sequence information, geographic space environment and power transmission line distribution multi-source heterogeneous data, a causal discovery algorithm is adopted to construct a dynamically evolved causal graph topology, a real causal driven relationship between variables is identified, a causal intensity matrix is decoupled into positive and negative adjacent matrixes, and the dynamic evolved multi-source heterogeneous data is obtained. And designing a causal constrained graph convolution module to aggregate and propagate high-order information, and finally realizing accurate prediction of the power grid forest fire. According to the method, the PCMCI causal discovery algorithm is introduced, so that the real causal driven relationship among multivariate time sequence factors is effectively identified; a causal GCN layer in the CSTGCN model extracts spatial dependence features by using an adjacent matrix constrained by a causal structure, the CSTGCN model embeds causal structure information into a spatio-temporal feature learning framework, and higher prediction precision and generalization performance are achieved.

The invention discloses a pipeline topological relation construction method based on a graph neural network. The method comprises the following steps: collecting and preprocessing original spatial data of a pipeline system; utilizing feature extraction to generate a node feature matrix and an edge feature matrix; constructing a pipeline diagram structure; index alignment and size verification are executed, and a pipeline diagram input matrix is generated; inputting to an improved EvolveGCN model, and introducing a graph attention control evolution mechanism to obtain a time sequence embedding feature matrix of each pipeline node object; generating a predicted pipeline topology connection set; carrying out topological consistency verification to obtain a final pipeline topological relation structure; a pipeline topological relation model file is formed, automatic modeling and optimization of a pipeline system are achieved, and the recognition precision and modeling efficiency of a pipeline topological structure are improved.

Techniques for an email-security detection system to analyze incoming emails for Business Email Comprise (BEC) attacks of targeted new email users in an enterprise based on peer models of email recipients in an enterprise and the conversation history of recipients. A method is disclosed that includes analyzing an incoming email and identifying one or more recipients of the incoming email in an enterprise network; analyzing contextual information in the incoming email for the email intent, and associating the email with a target recipient; applying a relational model associated with peers of the target recipient for detecting whether the incoming email is a business email compromise (BEC) attack directed to the target recipient; applying a relationship model of other recipients of the incoming email for detecting whether the BEC attack is associated with the target recipient; and determining, whether the incoming email is a BEC attack.

Methods, systems, apparatuses, and computer program products are described. A system may obtain a set of metadata associated with multiple entities of a datastore, the multiple entities including at least two different data types. The system may augment, using a generative artificial intelligence (AI) model, the set of metadata resulting in a set of augmented metadata for the multiple entities of the datastore. The system may generate, using the set of augmented metadata, a set of entity objects for the multiple entities of the datastore and in accordance with a reference schema. The system may identify, using the generative AI model and the set of entity objects as inputs into the generative AI model, first relationships between two or more first entities of the multiple entities of the datastore. Based on the identified relationships and a reference schema relationship model, the system may generate a schema relationship model.

The invention relates to the technical field of medical data processing, and provides a medical image data processing method based on deep learning, which comprises the following steps of: acquiring data according to an acquisition template, extracting a pulse time sequence by self-adaptive threshold peak detection, calculating an instantaneous phase according to linear interpolation, calculating a statistical magnitude, comparing a quantitative index with a preset threshold value, and calculating a pulse time sequence according to the statistical magnitude. Judging a steady state by combining a peak loss rate and an abrupt change detection rule, and calculating phase consistency between channels for verification; the method comprises the following steps of: splitting acquired data according to a concept entity to form a data relation model, implementing rapid global rigid estimation and applying affine transformation, estimating a pixel-level displacement field by adopting a pyramid dense optical flow network, applying the displacement field to an original pixel, and performing time domain fusion by taking optical flow confidence and a registration residual error as weights; and cutting the short-time image stabilization sequence after registration compensation, and outputting a pixel-level risk thermodynamic diagram, a candidate focus list and each output confidence interval by taking a hybrid network of a convolution front end and a space-time Transform backbone as a prediction model.

The invention discloses a chemical single line diagram material identification method, device and equipment and a storage medium, and the method comprises the steps: building a corresponding relation model of a serial number and a line spacing proportion value in a chemical single line diagram material table, and detecting a to-be-identified table according to the corresponding relation model; when it is detected that the serial numbers of the serial number columns of the to-be-recognized table are continuous and the line spacing is within a preset proportion range, corresponding multi-line information is merged; according to the method, the table auxiliary line of the to-be-recognized table is obtained, AI recognition is performed on the to-be-recognized table according to the table auxiliary line, and the recognition result is obtained, so that various material information in the chemical single line diagram can be accurately recognized, and compared with a traditional method, manual intervention is reduced, and the workload and error risk of manual processing are reduced; the method provides more efficient and accurate support for material information management in the chemical engineering design and construction process, has important practical application value and wide market prospect, and improves the speed and efficiency of chemical single line diagram material identification.

The invention discloses a plain small watershed nitrogen and phosphorus pollutant collaborative source analysis method, which belongs to the technical field of environmental pollution traceability, and comprises the following steps: 1, collecting multi-source basic data in a target plain small watershed, carrying out unified coordinate system, format and precision preprocessing, and constructing a watershed basic database; and 2, constructing a response relation model of various pollution sources and potential pollution loads on a GIS platform based on a drainage basin basic database, qualitatively identifying main pollution sources through spatial visualization analysis, and delineating key risk areas. According to the method, macroscopic spatial analysis, microchemical tracing and mathematical model calculation are organically fused, high-precision spatial data, hydrochemical and isotope analysis, multi-model quantitative analysis and uncertainty quantification are integrated, and high-precision and high-reliability cooperative source analysis of nitrogen and phosphorus pollutants is achieved.

The invention relates to a closed-loop production quality control method for a silicon wafer polishing solution of modified silica sol, which comprises the following steps: M1, in the production process of the polishing solution, data information of different batches of materials produced by the polishing solution, data information of processing technological parameters of the materials, data information of detection parameters of products and data information of detection parameters of waste discharge are collected, and a technological parameter and processing quality relation model fusing chaotic mapping and self-adaptive feedback regulation factors is constructed. And predicting the machining process parameters of the polishing solution to obtain data information of the predicted machining process parameters of the polishing solution. The production process of the polishing solution can be accurately controlled and adjusted, so that the production quality of the polishing solution is improved, manual participation is not needed in the whole process, manual errors are reduced, the production cost of the polishing solution is reduced, and the production efficiency of the polishing solution is improved.

The invention discloses a tunnel precise drilling and blasting design method integrating multi-source intelligence and closed-loop optimization. The method comprises the steps that geological parameters are collected through geological detection equipment; based on fracture mechanics and rock mass damage theories, constructing a quantitative relation model reflecting joint network distribution, stress wave propagation characteristics and a rock mass crushing effect coupling mechanism; inputting the geological parameters into a pre-constructed multi-dimensional knowledge base, integrating historical engineering data association rules by the multi-dimensional knowledge base by using a graph database, and extracting geological features; based on a deep learning model, analyzing the mapping relation between the geological features and the blasting parameters, and generating a preliminary blasting scheme including the blast hole distance, the charging structure and the detonating mode; according to the method, a closed-loop optimization system of geologic features-blasting parameters-effect evaluation is constructed, so that dynamic intelligent decision-making and construction self-adaptive regulation and control of tunnel drilling and blasting design are realized, and the contour forming precision and the blasting efficiency are remarkably improved.

The invention discloses a multi-target distinguishing method based on track spatio-temporal characteristics and dynamic similarity measurement, which comprises the following steps: acquiring original track data, preprocessing the original track data, and generating a plurality of continuous track segments; extracting spatio-temporal features and statistical features, and constructing a similarity matrix; performing hierarchical track clustering analysis on the similarity matrix, wherein the result of the hierarchical track clustering analysis is n clusters divided according to the similarity matrix; loading the relation model, executing conflict resolution and target identification, and calculating track association confidence; and generating a track set based on the track association confidence. According to the technical scheme, multi-target echo signals in a scene that a radar seeker tracks dense targets can be distinguished, and high-accuracy association and high-precision low-delay processing of target tracks are realized.

The invention discloses a human and house data comprehensive treatment method, system and equipment based on a multi-level address model, and a medium, belongs to the technical field of data treatment, and aims to solve the technical problem of how to effectively integrate human and house data sources of multiple parties and improve the accuracy, consistency and availability of human and house data. The defects that in the prior art, people and house data are scattered, address description is not standard, the incidence relation is inaccurate, data quality is uneven and a unified identification system is lacked are overcome. According to the technical scheme, the method comprises the steps that a five-level core data table comprising a community table, a building table, a house table, a people and house relation table and a personnel information table is constructed; the method comprises the following steps: accessing original human and house data from a plurality of data sources, performing intelligent analysis and standardization processing on address information, and obtaining data after standardization processing; establishing a house unique identification system and a human-house multi-dimensional relation model based on the standardized data; evaluating and repairing the data quality; and standardized human and house data service is provided for the outside through the data service interface.

The invention provides a medical quality dynamic evaluation method and system based on machine learning. The method comprises the following steps: acquiring a cross-mechanism desensitization data set, constructing a medical quality causal diagram, generating an anti-fact intervention scene by using Do calculation, and outputting an interpretable causal relationship model; constructing a federated learning optimization framework, aggregating causal relationship model parameters of all medical institutions, introducing a doctor crowdsourcing labeling mechanism to correct a result, and generating a global medical quality dynamic evaluation model; and constructing a surgical digital twin model, mapping dynamic data, and generating a dynamic quality score. Through a federated learning framework, the limitation of single mechanism data is broken through, parameters are optimized, and the performance is improved; causal graph construction and anti-fact intervention scene generation are combined, causal logic is mined, and the result value is enhanced; mapping dynamic data based on a digital twinborn model to realize accurate dynamic scoring; a doctor crowdsourcing labeling mechanism is introduced, a closed-loop optimization system is formed, and finally a high-precision medical quality dynamic evaluation result is output.

The invention discloses a vacuum degree condition evaluation method and system of a vacuum arc extinguish chamber, and belongs to the technical field of vacuum circuit breakers. The evaluation method combines RF and an XGBoost algorithm to realize heterogeneous integration, adds a GAN discriminator to perform physical constraint enhancement, and constructs a universal relation model of X-ray intensity and vacuum degree; the feature importance of RF can guide the feature selection or weighting of XGBoost and enhance feature interaction, the confrontation mechanism of GAN can coordinate data fitting and physical constraints, the physical rationality and data authenticity of model prediction are enhanced by means of confrontation training, and a discriminator is used for dual verification of a generated result. The model is ensured to conform to experimental data distribution and theoretical physical law constraints at the same time, and the result credibility is remarkably improved. According to the evaluation method, accurate fitting of the stable attenuation trend in the high vacuum stage and robustness prediction of fluctuation data in the critical vacuum stage can be achieved, and a new thought is provided for vacuum degree condition evaluation.

The invention provides an auxiliary rock core geological logging method based on drilling parameters, and relates to the technical field of geological investigation, the method is applied to a logging control terminal, and the method mainly comprises the following steps: obtaining regional typical rock core samples and five types of drilling parameter signals, classifying and extracting static characteristics of the rock core samples, and carrying out parallel noise reduction analysis on the parameter signals; obtaining a matching relationship between the rock core and the parameter signal, and constructing a lithologic parameter bidirectional association reference library; on the basis of the matching relation of the lithologic parameter bidirectional association reference library, the model is embedded into an incremental learning module to update the weight, and a subsection lithologic pre-judgment result is output; and calling a preset catalog template to load a pre-judgment result, generating a catalog report after man-machine interaction recheck and correction, collecting correction data, reversely transmitting the correction data back to the reference library to update a matching relationship, and triggering incremental learning of the model to complete a closed loop. The technical problems that in traditional rock core logging, regional lithology adaptation is poor, parameter interference is large, a model is not dynamically optimized, the deep operation risk is high, and efficiency is low are effectively solved.

The present invention relates to a production expert system-based iron ore blending optimization method. The method comprises: firstly, performing data enhancement on historical ore blending and beneficiation data by means of an improved generative adversarial network; then, establishing a case library, and by means of a case retrieval method, establishing a relational model between blended ore properties and ore concentrate grades on the basis of the historical ore blending and beneficiation data and expert experience; then, on the basis of material balance, establishing a blended ore property prediction mechanism model to realize blended ore property estimation after ore blending; and finally, by taking an optimal ore beneficiation concentrate grade and a maximum difficult-to-beneficiate ore use amount as optimization objectives, determining constraints on the basis of actual working conditions, obtaining a set of ore blending proportion non-inferior solutions by means of a multi-objective grey wolf algorithm, and deciding an optimal solution by means of a TOPSIS algorithm. The present invention has the following advantages: while ensuring the quality of ore beneficiation products, reasonable ore blending proportions are calculated, the use amount of difficult-to-beneficiate ores is increased, the sustainable mining capacity of mines is improved while ensuring the mining and beneficiation benefits, and the invention has universal applicability.

The invention discloses an industrial Internet of Things sensor fault monitoring method, system and device and a medium, and relates to the technical field of sensor fault monitoring. The invention provides an industrial Internet of Things sensor fault monitoring method, which comprises the steps of obtaining a target sensor and at least one associated sensor associated with the target sensor, and constructing a relation model based on the target sensor and the at least one associated sensor; according to the relation model and the actual reading sequence of the at least one associated sensor in the preset time period, obtaining at least one predicted value sequence of the target sensor in the preset time period, and calculating a fault feature sequence between the at least one predicted value sequence of the target sensor and the actual value sequence of the target sensor; and obtaining the relationship health degree of the target sensor according to each fault feature sequence, if at least one relationship health degree is lower than a preset threshold, generating a relationship abnormality report, and sending the relationship abnormality report to the target user.

The invention belongs to the cross technical field of artificial intelligence and cognitive evaluation, and discloses a multi-task automatic question matching method and system. The core method comprises the steps of collecting real-time behavior data of multi-task operation of a user, and extracting standardized features through cleaning; constructing a cognitive task feature map and establishing a task relation model through node embedding; an individual thread capacity threshold value is dynamically calculated based on the behavior data, and personalized resource allocation parameters are generated; injecting the capacity parameters into the map to predict cognitive conflict intensity indexes; screening security task combinations according to conflict intensity, and performing time axis arrangement and difficulty balance to generate a test instruction set; and executing the instruction set and establishing a feedback link to realize data closed-loop updating. The cognitive resource adaptation limitation of traditional static matching is broken through, through the dynamic capacity modeling and conflict prediction technology, accurate generation of a multi-task combination in a high-risk occupational complex scene is achieved, and the work performance prediction validity and the system self-adaptive capacity are greatly improved.