1210 results about "Analysis models" patented technology

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

The invention relates to the technical field of special equipment intelligent monitoring and maintenance, in particular to a special equipment monitoring and maintenance method and system based on multi-dimensional data fusion, and the method comprises the steps: collecting and processing multi-source heterogeneous sensor data; constructing a dynamic digital twinborn model based on sensor data and a cross-domain term mapping rule; associating the sensor data with the digital twinborn model to establish a lightweight analysis model, performing health state simulation analysis on the edge equipment, and generating an analysis result; the data quality of the edge device is monitored through the analysis model, when the data quality is lower than a threshold value, a co-simulation process is started, a simulation analysis task is transferred to the cloud digital twin platform, and the edge device continues to conduct simulation analysis based on the analysis model; and constructing a simulation design decision rule base, and generating a maintenance suggestion. Through the multi-dimensional data fusion and edge cloud co-simulation architecture, the accuracy, real-time performance and reliability of monitoring and maintenance of the special equipment are improved.

The invention relates to the technical field of three-dimensional model adjustment, in particular to a three-dimensional model adjustment method and system and a medium. The method comprises the following steps: obtaining a multi-angle image of an original model, carrying out multi-view normalization on the multi-angle image, generating a normalized view image set, extracting feature points of the original model, carrying out parallax correction on the feature points, reconstructing a simulation three-dimensional model, collecting basic purpose data of the model, carrying out ideal demand mapping through the data, and carrying out ideal demand mapping. The method comprises the following steps: determining an ideal three-dimensional model structure, carrying out core region segmentation on a reconstruction model according to basic purpose data to obtain key region model slices, carrying out highlight region comparison with the ideal model structure, analyzing model differences, determining a structure adjustment amplitude interval according to a comparison result, and carrying out cyclic fine adjustment correction on the key region model slices to obtain a three-dimensional model. And the three-dimensional model is consistent with the ideal three-dimensional model in structure, so that the optimized three-dimensional model is generated. According to the invention, efficient and accurate three-dimensional model adjustment and optimization are realized.

The invention discloses a fault early warning method and system based on an AI large model, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the denoising and standardization processing, and obtaining fusion data; inputting into a feature extraction model, and outputting a feature vector set; identifying the dynamic operation mode based on a K-means algorithm to obtain an operation mode baseline; inputting a feature sequence model, and outputting a precursor feature sequence; calculating an abnormal score according to the precursor feature sequence, marking as abnormal if the score is greater than or equal to a threshold value, otherwise, marking as normal, and obtaining an abnormal detection result; evaluating a risk level according to a detection result; inputting the risk level into a fault analysis model to obtain fault cause distribution; determining optimized operation mode parameters according to the fault cause distribution; and performing deviation analysis on the data and the optimized parameters, and if a deviation value is greater than a threshold value, triggering an early warning signal. The method can solve the problem of insufficient recognition capability in a scene with variable fault types.

The invention relates to a higher education personalized hierarchical teaching method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-dimensional learning data, carrying out the time-space alignment processing, and generating a synchronous multi-dimensional data set; performing spatial-temporal feature fusion and time sequence modeling on the data set by using a deep neural network, and constructing a dynamic student portrait; analyzing knowledge mastery degree features in the portrait through a semantic analysis model, and generating a personalized resource recommendation sequence in combination with the knowledge graph; based on the sequence and the portrait, planning a personalized learning path by using a path reasoning algorithm; and carrying out teaching hierarchy binding on the personalized resource recommendation sequence and the learning path to form a hierarchical teaching scheme. Accurate teaching support is provided for individual differences of students, and the teaching effect and learning experience are effectively improved.

The invention provides a user behavior data mining method and system applied to digital enterprise management, and the method comprises the steps: collecting the multi-dimensional behavior data of a target user in a business operation interface, carrying out the multi-modal data analysis of the multi-dimensional behavior data, generating a behavior track feature set with time sequence relevance, and carrying out the mining of the behavior track feature set; training an adaptive time sequence analysis model based on the behavior trajectory feature set, capturing a long and short term dependency relationship in a user behavior mode by the time sequence analysis model through a dynamic window division strategy, generating a potential loss risk prediction index, and constructing an interaction process parameter matrix according to the potential loss risk prediction index; and calling the optimized interaction process parameter matrix to drive a service operation interface to reconstruct, generating an interaction interface adaptive to the current user behavior mode, and iteratively updating the time sequence analysis model through an incremental feedback mechanism in a preset verification period. According to the invention, the comprehensiveness and accuracy of user behavior pattern mining can be improved.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method, device, equipment and medium based on a context agreement, comprising: receiving a business demand event, extracting a demand text, and disassembling the demand text to generate a plurality of subtasks; obtaining a data source context according to the sub-tasks and task subject information, generating a corresponding data extraction path and an analysis model strategy, connecting a target data source through an access strategy, collecting original data, preprocessing the original data, and dividing the original data into training data and verification data; and training the model based on the analysis model strategy and generating a reasoning result, and completing verification output through a result verification model. According to the method, task disassembly, data access and model generation processes are driven through a model context protocol, and a knowledge graph reasoning and verification mechanism is combined, so that a closed-loop automatic analysis path is formed, and the processing efficiency and intelligent adaptation capability of cross-domain data tasks are effectively improved.

The invention provides an urban inland inundation risk management method and system based on multi-model coupling, and the method comprises the steps: constructing a digital twin holographic base, constructing an urban rainfall flood analysis model through the deep coupling of a ponding recognition intelligent model and a physical mechanism drive type rainfall flood simulation engine, and carrying out the construction of an urban inland inundation risk management model. A high-precision waterlogging risk thermodynamic diagram and a dynamic forecast threshold value are generated, and the risk situation is broadcasted in real time through a voice interaction module; constructing a rainstorm-runoff-pipe network three-dimensional coupling simulation system by means of a digital twin holographic base, updating a deduction result at a minute-level frequency, and transmitting rainfall space-time distribution data to an urban rainfall flood analysis model in real time; and establishing a multi-target collaborative decision engine, inputting the accumulated water diffusion rate to the multi-target collaborative decision engine, and generating a pump station scheduling scheme, a traffic control strategy and a material delivery path. The system comprises a model construction module, a data analysis module and a collaborative decision module. According to the invention, a complete urban inland inundation risk management system is constructed.

The invention discloses a grouting method and system for enhancing strip mine slope stability, and belongs to the technical field of slope protection. The grouting method comprises the steps that early-stage data are pre-collected, and an analysis model is established; grouting is designed according to a model analysis result; a pressure-resistant grouting pipe is installed and connected with a grouting pump; grouting parameters are adjusted in real time according to feedback of the sensor; after grouting, all data in the grouting holes are analyzed; outputting a slurry permeation three-dimensional cloud picture, a stress change curve and a stability evaluation index; and storing all monitoring data and model parameters. A cyclic self-adaptive system is formed, it is ensured that the grouting process is efficient and accurate, the overall stability is improved for heterogeneous rock mass, dependence on a static geological report is reduced, and therefore the problem that grouting parameters depend on engineering experience or conservative of the static report is avoided.

The invention relates to the technical field of data processing, in particular to a backtracking analysis model construction method based on an attack chain, which comprises the following steps that: a kernel layer security agent acquires process, file and network behavior characteristics in a hardware isolation environment, and generates an event tuple; the tensor network pipeline performs three-dimensional decoupling mapping on the tuple into a behavior fingerprint vector, an orthogonalization noise feature and an asymmetric adjacent tensor, and compresses the behavior fingerprint vector, the orthogonalization noise feature and the asymmetric adjacent tensor into a space-time topology tensor block; the reinforcement learning controller constructs a directed acyclic graph based on the tensor blocks, calculates connectivity loss and outputs an event risk score; the dynamic routing engine constructs a decision tree model according to the risk mark, the burst frequency and the correlation entropy, and implements three-level shunting and a multiple simulation system to generate an anti-interference index; and when the deviation between the physical trajectory and the digital model exceeds the tolerance, the closed-loop feedback weight coefficient updates the loss function parameter and adjusts the channel resource weight. And the problem of threat discovery delay caused by attack chain breakage under massive events is solved.

The invention relates to an immersive VR psychological detection system and method based on multi-modal AI. The system comprises a data acquisition and processing module which is used for acquiring a multi-modal data set of a user in a virtual reality scene based on unified clock synchronization, performing time-space alignment and noise reduction standardization processing on the multi-modal data set, and extracting key biological characteristics. And the correlation model construction module performs space-time correlation mapping through a spatial transformation network, constructs a three-dimensional space attention model, and generates a real-time fluctuation curve after inputting the key biological characteristics into the trained model. And the state report generation module identifies a real-time fluctuation curve by using a time sequence analysis model, performs backtracking analysis in combination with the psychological state conversion node and a multi-modal cross validation result, and finally generates a three-dimensional interactive report. By adopting the method, multi-modal data fusion can be realized, the dynamic change of the psychological state of the user can be effectively captured, the psychological state of the user can be comprehensively and deeply analyzed, and a scientific basis is provided for psychological health assessment and intervention.

The invention relates to the technical field of artificial intelligence, and discloses a financial report analysis method, and the method comprises the steps: carrying out the data preprocessing of historical financial association data and historical business operation data, and obtaining to-be-analyzed historical data; performing multi-order feature engineering processing on the to-be-analyzed historical data to obtain historical feature vector data; constructing an initial financial analysis large model, and training the initial financial analysis large model by using the historical feature vector data to obtain a trained financial analysis large model; accessing the trained large financial analysis model into a target system, and triggering the large financial analysis model to operate; and driving the large financial analysis model to perform multi-dimensional semantic analysis and quantitative reasoning on the to-be-analyzed financial report data to obtain visual financial report analysis result data. The method can be applied to internal financial statements of enterprises with businesses of science and technology finance, medical health, old-age care and the like, and the financial statement analysis efficiency and comprehensiveness can be improved through the multi-modal data fusion and knowledge enhancement large model architecture technology.

The invention relates to a bank marketing model construction method and system based on machine learning, and the method comprises the following steps: constructing a customer multi-dimensional feature engineering system which is used for integrating customer multi-dimensional features, and extracting customer behavior period features through employing a time sequence feature coding technology; meanwhile, a graph neural network is adopted to construct customer social influence features; constructing a dynamic customer value analysis model integrated with XGBoost and establishing a dynamic attenuation function of customer life cycle value; establishing a marketing response prediction rate model of a hybrid model architecture combining LightGBM and Transform, and embedding an adversarial training sample generation mechanism; a marketing strategy generation model is constructed, the marketing strategy generation model is based on a multi-objective optimization function considering the marketing response rate, the marketing cost and the customer satisfaction, and a marketing strategy is generated through a marketing path planning algorithm of Monte Carlo tree search and the multi-objective optimization function.

The invention provides a digital twin water conservancy hyper-fusion method for coupling multi-source data and an all-in-one machine, and relates to the technical field of intelligent water conservancy. By constructing a unified water conservancy spatio-temporal data link protocol, efficient fusion of multi-source heterogeneous data of hydrology, meteorology, engineering operation and the like is realized, and the problem of water conservancy system data islands is solved; a coupling distributed hydrological model and a hydrodynamic model are established, and real-time accurate simulation of the watershed hydrological process and the river channel hydrodynamic process is achieved; a multi-target dynamic weight reinforcement learning algorithm is adopted to optimize a water conservancy scheduling scheme, model parameters are dynamically corrected in real time through a time sequence error regression analysis model, and closed-loop adaptive optimization is achieved; according to the method, closed-loop cooperation of water conservancy data fusion, real-time accurate simulation, intelligent scheduling optimization and dynamic parameter self-correction is realized, and the real-time performance and accuracy of water conservancy intelligent decision making are remarkably improved.

The embodiment of the invention relates to the technical field of data modeling analysis, and discloses a tunnel rock stratum large deformation analysis method and system based on three-dimensional modeling, and the method comprises the steps: collecting initial three-dimensional point cloud data through three-dimensional laser scanning; constructing an initial three-dimensional geologic model containing spatial morphological characteristics and a rock stratum interface geometric topological relation through space coordinate matching and curved surface reconstruction; finite element unstructured mesh generation is carried out on the model, and a rock stratum mechanical analysis model is established in combination with physical and mechanical parameters; inputting construction parameters and process information, activating unit nodes in sequence, and solving a force balance equation to simulate stress distribution; identifying a potential deformation area through iterative calculation, and adjusting boundary conditions to predict a deformation trend; the three-dimensional space distribution cloud atlas, the time evolution curve and the position marks are fused to generate multi-view deformation early warning information, the accuracy and the visualization degree of tunnel rock stratum deformation analysis are improved, and support is provided for construction safety.

The invention relates to an enterprise multi-source data intelligent association analysis method based on artificial intelligence and a large model, and the method comprises the steps: introducing time sequence dynamic analysis, a business rule base and statistical correlation test, carrying out the multi-dimensional and automatic cross verification and consistency test of an association pair outputted by a semantic association engine, and carrying out the analysis of the association pair. Screening out a high-confidence correlation set conforming to the business logic, the time sequence evolution rule and the statistical significance; and packaging to form a reusable business insight analysis model based on the enterprise data knowledge graph, receiving a business query request by the model, automatically generating a deep analysis report for business process optimization and potential risk early warning through graph reasoning, path discovery or an abnormal sub-graph detection algorithm, and pushing a result to a decision support system.

The invention discloses a traffic operation and maintenance fault intelligent scheduling method and system based on an AI large model, and belongs to the technical field of traffic control. The method comprises the following steps: collecting a vehicle driving track GPS coordinate set, a traffic flow density matrix, a vehicle-mounted camera monitoring image frame sequence and a fault vehicle owner speed anomaly detection result in real time; constructing a traffic operation state analysis model, and outputting real-time traffic operation state characteristics; generating a traffic fault probability distribution curved surface in a future time window; generating a comprehensive fault positioning confidence coefficient matrix; and planning an optimal maintenance resource path according to the pheromone updating rule, and updating the optimal maintenance resource path to the visual scheduling platform in real time. According to the method, space-time diagram convolutional network dynamic modeling is constructed according to multi-source data, so that the limitation of a space blind area of a single data source is broken through, the fault positioning speed is improved, and the problems of incomplete coverage and low positioning speed in the prior art are solved.

The invention belongs to the field of data processing, and discloses a social media comprehensive public opinion analysis method and related device.The method comprises the steps that feature vectors of all cross-modal public opinion data are obtained through a pre-trained cross-modal large model, and public opinion events are clustered through an unsupervised clustering method according to the feature vectors; utilizing a pre-training sentiment analysis model and a pre-training topic analysis model to respectively obtain sentiment scores and topic sensitivity scores of the public opinion events in the public opinion event clusters; according to the emotion score and the topic sensitivity score of each public opinion event in each public opinion event cluster, respectively obtaining an emotion score index score and a topic sensitivity index score of each public opinion event cluster, and obtaining a comprehensive public opinion analysis score of each public opinion event cluster in combination with the exposure index score, the interactivity index score and the propagation intensity index score; and obtaining a public opinion evaluation result of each public opinion event cluster according to the comprehensive public opinion analysis score of each public opinion event cluster, thereby realizing comprehensive perception and accurate study and judgment of the public opinion situation.

The invention discloses a submarine pipeline state early warning method and system based on scour-vibration coupling sensing, and the method comprises the steps: obtaining the historical marine environment information of a target submarine pipeline region, laying a monitoring sensor array according to the historical marine environment information, collecting multi-source monitoring data, and carrying out the preprocessing of the multi-source monitoring data, thereby forming a multi-source monitoring data set. And constructing a multi-physical field coupling analysis model, inputting the data set into the model to analyze the response condition of the pipeline in the current seabed environment, and obtaining pipeline response simulation information. And analyzing resonance influence caused by environmental scouring in different sections, generating resonance derivative characteristics, and constructing pipeline multi-modal fusion characteristics in combination with monitoring data. Predicting the future response state of the pipeline through the submarine pipeline state analysis model, and carrying out risk early warning prompt; meanwhile, the fatigue life of each section is analyzed based on resonance derivative characteristics, fatigue-prone sections are identified, and maintenance suggestions are generated and pushed. Therefore, the accuracy and timeliness of sensing the state of the submarine pipeline are improved, and the early warning intelligence of the submarine pipeline is effectively improved.

The invention discloses a local legislation compliance intelligent detection system and method based on deep semantic analysis and a multi-modal legal knowledge graph, and relates to the field of computer technology and law crossing technology, the method comprises the following steps: constructing a legal knowledge graph; designing a multi-dimensional rule sub-library with a dynamic weight adjustment mechanism; a large language model based on Deepseek is utilized to construct legal provisions to perform a deep semantic analysis model, so that the intelligence of generation of triads of laws and regulations in the national field is realized, and the field adaptability of triad generation is improved; executing multi-dimensional conflict detection based on the knowledge graph, a conflict detection rule base and a semantic analysis result; and a multi-dimensional law conflict report is automatically generated. According to the method, a legal knowledge graph is constructed, multi-source legal data is integrated, a multi-dimensional rule sub-library is combined, a Deepseek-based special model is used for carrying out deep semantic analysis on legal provisions, and explicit and implicit conflict judgment is carried out on contradictory point locations and contexts.

The embodiment of the invention provides a fine tuning method and device for a large language model, equipment and a computer readable storage medium. According to the method, the performance of a to-be-fine-tuned large language model is tested, a sample set of prediction errors of the large language model is collected, the samples of the prediction errors are classified on the basis of real categories and error prediction categories of the samples of the prediction errors, namely, the prediction errors of the large language model are classified, and the classification accuracy of the prediction errors of the large language model is improved. Then, a trained pre-training language model is utilized to analyze reasons for generation of the prediction errors, and similar error samples with the same error condition are generated based on the reasons, so that targeted fine adjustment is carried out on the model, and the over-fitting phenomenon is eliminated. According to the method, the performance of the model on different types of errors can be better analyzed, and the overfitting problem of the model in a specific scene can be deeply known and solved, so that the performance of the model in each vertical field is remarkably improved.

The invention discloses a data governance agent system and an implementation method, and relates to the field of data processing, and the system comprises a data analysis module, a data storage module, a user response module, a quality monitoring and feedback module and a field large language model module. The data analysis module performs unified analysis modeling on structured and unstructured data; the data storage module adopts a graph database and a vector database to store knowledge data; the user response module supports natural language interaction and intelligent question and answer; the quality monitoring and feedback module optimizes data quality through closed-loop feedback; and the field large language model module constructs an optimization model adaptive to the vertical field. Through multi-modal analysis, knowledge graph construction, double-library collaborative storage and model dynamic optimization, a data barrier is broken through, manual dependence is reduced, the data treatment efficiency, quality and intelligent level are improved, and the method is suitable for specialized data treatment requirements of industries such as manufacturing and finance.

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

The invention relates to the technical field of equipment management, in particular to an equipment full-life-cycle digital twinborn decision-making system enabling a large model. Comprising a digital twin modeling unit; a large model enabling analysis unit, wherein the large model enabling analysis unit adopts a hydroelectric equipment multi-modal causal constraint analysis model; a whole-process closed-loop management and control unit; and an intelligent decision output unit. According to the method, the multi-modal causal constraint analysis model adaptive to the working condition of the hydroelectric equipment is constructed, and a causal chain verification backtracking mechanism is introduced, so that the problem that the reasoning result lacks logic verification is effectively solved, the logic consistency of a fault reasoning conclusion is guaranteed, and the reliability of decision output is improved; through a scene adaptation mode of'pre-training + fine tuning 'of a large model, multi-modal feature fusion processing and deep linkage of a workflow engine and a digital twinborn body, full-life-cycle management requirements of equipment are fully covered, and the refinement and intelligence level of hydroelectric equipment management is further improved.

The invention provides an intelligent customer service dynamic intention recognition system based on a semantic analysis model, and the system collects the multi-dimensional data of a user through a data collection module, constructs a user portrait through a feature extraction module, extracts the portrait features, carries out the semantic analysis of multiple rounds of historical dialogue data, and extracts preference features. And constructing a dynamic user-entity association graph to extract GNN node features. Multi-modal data of a user is analyzed through an emotion recognition module, emotion features are recognized, portrait features, preference features and emotion features are fused and analyzed based on an MLP model to obtain a final emotion state, and the portrait features, the preference features, GNN node features and the emotion features are fused through an intention recognition module to obtain a final emotion state. According to the method, dynamic intention analysis is carried out on the basis of the Transform sequence-to-sequence model, the current intention of the user is recognized, the recognition accuracy is high, the dynamically changing intention is adjusted in real time, and more accurate and targeted answers or services are provided for the user.

The invention belongs to the technical field of simulation analysis, and particularly relates to a medium-high voltage switch cabinet internal humidity inversion calculation method based on simulation analysis, which comprises the following steps: establishing a geometric model of a switch cabinet, importing the geometric model into finite element simulation software to obtain a simulation analysis model, adding each physical field and computational domain and boundary conditions thereof, and calculating the internal humidity of a medium-high voltage switch cabinet. All the physical fields are coupled, and mesh generation is carried out; performing numerical solution on the simulation analysis model by using a transient solver to obtain a temperature and humidity distribution condition in the switch cabinet; the simulation analysis model is optimized, a simulation data set is constructed, and a PINN is trained; based on the environment temperature and humidity and the operation current collected in the actual operation of the switch cabinet, the trained PINN is used for prediction, and the steady-state relative humidity of the current transformer is obtained. According to the invention, based on the limited boundary monitoring data, efficient and accurate reconstruction of the humidity field of the key area in the switch cabinet is realized, and a feasible path is provided for equipment state sensing and condensation risk early warning.

The invention relates to the technical field of tunnel engineering safety, in particular to a tunnel fire multi-stage damage collaborative prediction method and system, and the method comprises the steps: constructing a data simulation fusion model, and carrying out the processing of fire and tunnel structure parameter data, and obtaining a multi-working-condition parameter database; constructing a heat conduction equation, a fire plume and wall surface heat exchange relational expression, a boundary layer correction model and a comprehensive damage evaluation model, and analyzing the equation, the relational expression and the model by utilizing a multi-physical field coupling unified boundary condition to obtain a multi-physical boundary constraint condition; obtaining a multi-physical constraint loss function based on a network architecture and a multi-physical boundary constraint condition, and further obtaining a comprehensive constraint loss function of a temperature field-stress field-damage index; constructing a PINN-LSTM network architecture and a damage evolution analysis model based on the fire ending initial state coding vector, the fire course feature vector and the cooling process feature vector; and performing linkage analysis on the function, the frame and the model to predict and monitor the transient damage and the long-term creep damage of the tunnel.

The invention discloses a wind generating set fault prediction method and system based on edge computing, and the method comprises the steps: collecting the vibration, temperature, rotating speed and other parameters of a wind generating set in real time through a data collection module deployed by edge computing equipment, carrying out the preliminary screening and packaging through a built-in data processing unit, and synchronizing to a constructed digital twinborn model; establishing an edge calculation fault analysis model based on model data, identifying potential fault features, comparing a physical unit operation state with a digital twin model simulation state parameter by parameter, performing preliminary diagnosis in combination with the fault features, evaluating a unit health state according to a fault mechanism model, and outputting a fault prediction result and part information. The system is correspondingly provided with six units including a data acquisition interaction unit, a data processing unit and a model construction updating unit, and all the units work cooperatively, so that accurate prediction and health management of faults of the wind generating set are realized.

The invention discloses a Solidworks model-based machining program generation method and system, and belongs to the technical field of computer-aided manufacturing, and the method comprises the steps: S1, analyzing the modeling features of each part in a Solidworks model, carrying out the matching analysis of the modeling feature sketch contour and sketch plane normal vector with a feature library, obtaining a machining feature category, and carrying out the recognition of the machining feature category; performing feature grouping according to the processing feature category and the processing direction; s2, identifying and extracting the part size and tolerance marked in the sketch; s3, analyzing a part machining process based on the machining feature category, the part size and the tolerance; s4, the blank type, machining allowance distribution, machining steps and a clamping mode are determined; s5, the three-dimensional space coordinates of the machining contour are converted into machine tool machining coordinates, and machining information is sequenced; s6, the machining path of the part machining technology is calculated; and S7, according to the machining path, combining with an instruction supported by a specific machine tool, performing post-processing to generate a G code.

Disclosed in the present invention are a model training method and apparatus based on federated learning, and a device and a storage medium. The method comprises: training a local model on the basis of power data, so as to generate a fault analysis model used for fault analysis of a power device; and uploading local model parameters to a cloud server, such that the cloud server aggregates the local model parameters, then updates a corresponding global model, and issues updated global model parameters to edge nodes, wherein when aggregating the local model parameters, the cloud server marks the edge node corresponding to an abnormal local model parameter as a suspected abnormal node, monitors the marked suspected abnormal node, and removes, when it is detected that the suspected abnormal node is abnormal, the local model parameters uploaded by the suspected abnormal node. By means of the present invention, it can be ensured that the performance of a fault analysis model obtained by training a local model is not degraded.