594 results about "Data modeling" patented technology

The invention relates to the technical field of building construction, in particular to a building construction safety intelligent early warning system based on multi-sensor fusion and deep learning. Comprising a multi-source sensing unit; an intelligent fusion unit; a depth analysis unit; and a dynamic response unit. According to the method, through a mixed deep learning model, personnel-equipment-environment space association in a 1m * 1m * 0.5 m space grid is extracted through an improved U-Net network, and a space risk association map is output; modeling data of 10 sampling periods by using a bidirectional LSTM network, and outputting a short-term prediction value; and carrying out weighted fusion through an attention mechanism to form a risk feature vector, and removing invalid anomalies in cooperation with parameter anomaly judgment and cross validation. And then a risk grade evaluation module introduces multiple coefficients to calculate a risk grade index, and a grid diffusion range is delimited according to grades, so that real-time identification, quantitative evaluation and range pre-judgment of construction safety risks are realized, and the problem that risk identification evaluation lacks scenarized accuracy and comprehensiveness is solved.

The invention relates to the field of wastewater treatment, and provides an industrial wastewater membrane process intelligent optimization method and system based on multi-dimensional data modeling. The method comprises the following steps: acquiring wastewater quality parameters of a target scene and a production line equipment state in real time through a multi-dimensional sensor network, and constructing a digital twinborn model of a wastewater treatment system in the target scene; the membrane surface pollution type of each node in the wastewater treatment system is identified through a digital twin model, and the inner membrane pollution evolution condition in the future preset duration is predicted; a multi-strategy evolutionary reinforcement learning algorithm is adopted, the wastewater quality parameters, the production line equipment state and the membrane pollution prediction result are combined, multi-target dynamic optimization is conducted on a wastewater treatment system through a multi-agent collaborative decision-making algorithm, an optimization strategy is generated based on the multi-target dynamic optimization result, and membrane process operation parameters and production control parameters are dynamically adjusted. According to the method, real-time intelligent adjustment of membrane process operation can be realized, the stability of membrane process operation is guaranteed, and the treatment efficiency of membrane process operation is improved.

The invention discloses a hardware fault real-time detection method and system based on cooperation of a CPU (Central Processing Unit) and a BMC (Baseboard Management Controller), and the method comprises the following steps: respectively collecting high-frequency state data and tendency indexes by establishing a communication mechanism between the CPU and the BMC; the processor uses a CUSUM algorithm to carry out abrupt change analysis on the periodically collected operation state to generate an abrupt change event; and the management controller uses an EWMA algorithm to model the trend data, and extracts abnormal changes. And the system performs fusion analysis on the mutation event and the trend anomaly to form a fusion anomaly vector. The risk assessment module calculates a risk score based on a preset rule, determines a fault level, positions a target component, and outputs a processing strategy. And triggering a response action according to the strategy and recording an execution state. And the fusion data and the response record are input into the adaptive module together for dynamically adjusting CUSUM and EWMA parameters, so that adaptive updating of the algorithm is realized, and finally a fault detection signal is generated.

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 the technical field of electric energy meter metering, and discloses an electric energy meter metering anomaly analysis method and system, and the method comprises the steps: collecting data, such as voltage waveforms, to generate a metering feature vector set, and constructing an anomaly detection rule base; setting a scene parameter type set, and establishing an abnormal association judgment model; dynamically correcting the threshold value by combining the model, and generating an optimized error threshold value set and an abnormal triggering condition set; and updating a metering analysis strategy to generate an abnormal judgment scheme, and correcting data verification sequential logic. The system comprises a data acquisition module, an abnormal rule base construction module, a scene parameter configuration module, a correlation model training module, a dynamic threshold optimization module, a strategy updating module and a time sequence correction module. Through multi-dimensional data modeling, scene-based threshold configuration and dynamic time sequence calibration, the accuracy and adaptability of electric energy meter measurement anomaly detection are improved, and the method is suitable for measurement anomaly analysis of diversified power consumption scenes in a smart power grid.

The invention relates to the field of post data modeling, in particular to an enterprise cross-domain data modeling system and method based on data twinning, and the method comprises the following steps: obtaining an enterprise cross-modal real-time data flow, carrying out key service node identification and service load processing peak evaluation, and constructing a post workload peak datum line; the method comprises the following steps: collecting enterprise multi-post performance information, performing growth trend evolution analysis, performing dynamic post skill perception, and generating a skill assessment map of multiple posts; project processing logs are collected, organization communication association mining is carried out, organization topology association evolution is carried out, and an organization topology interaction visual network is constructed; carrying out multi-attribute evolution evaluation based on a post workload peak reference line and the skill evaluation map, carrying out accurate digital modeling on an organization topology interactive visual network, and constructing an adaptive synchronous optimization twin model; the enterprise operation efficiency is improved, and the post requirement is accurately recognized.

The invention discloses a wind field data modeling method and system for a small and medium-sized unmanned aerial vehicle wind resistance test, and the method comprises the steps: collecting the topographic data, wind speed data and fan data of a test wind field, and determining a preset region and an unmeasured region according to the preset trajectory of an unmanned aerial vehicle based on the test wind field; the method comprises the following steps: acquiring boundary conditions of an unmeasured area and disturbance grid wake flow through fluid simulation based on topographic data and a preset area, performing wind speed vector decomposition on a grid according to a preset wind field boundary condition, and acquiring a preset wind field model according to a disturbance time sequence, an unmanned aerial vehicle maneuvering weight and the disturbance grid wake flow, and obtaining a predicted wind field of the unmeasured area through Gaussian process regression according to the boundary condition of the unmeasured area based on a preset wind field model, and coupling the predicted wind field through a grid time sequence by using different unmanned aerial vehicle preset trajectories to obtain a target wind field model. According to the method, through space-time collaborative interpolation and fluid simulation of Gaussian process regression, the inlet boundary condition better conforms to the fluid mechanics law, and the physical consistency and reliability of the target wind field are improved.

The invention relates to the technical field of geographic space intelligence, and discloses a territorial space purpose control intelligent analysis system, which comprises the following modules: a multi-source data acquisition module, which is based on satellite remote sensing and an IoT sensor, plans vector data, adopts a spatio-temporal data fusion algorithm, and integrates territorial, ecological and economic field heterogeneous data through a distributed crawler technology; generating a territorial space total element data set; the multi-source data acquisition module comprises a remote sensing acquisition sub-module, an Internet of Things access sub-module and a planning data analysis sub-module. Through a distributed data crawling and real-time stream fusion technology and spatio-temporal data modeling, rapid integration of multi-source heterogeneous information is realized, low-efficiency delay of traditional manual acquisition is eliminated, high-precision deformation monitoring and land use change identification are synchronously completed, the violation behavior discovery timeliness is remarkably improved, and the method is suitable for large-scale popularization and application. The three-dimensional space analysis algorithm accurately quantifies the above-ground and underground space element interaction relation, and the engineering conflict risk is effectively avoided.

The invention relates to the technical field of atomic clock control, and discloses a punctuality type atomic clock control method based on a microwave frequency band. The method comprises the following steps: firstly, acquiring microwave frequency band signal data and atomic state data, and constructing a time calculation model in combination with historical operation data to output a theoretical time value; performing multi-dimensional difference analysis on the theoretical time value and the actual measurement time value to generate a time difference coefficient matrix; inputting the matrix into a spatial propagation analysis network, and combining atomic clock component parameters and environmental parameters to generate a time anomaly probability distribution diagram; and finally, configuring microwave control parameters according to the distribution diagram, including starting a high-frequency monitoring mode for a high-probability abnormal region and applying a disturbance test to adjacent frequency bands. According to the method, through multi-dimensional data modeling, difference analysis and dynamic parameter configuration, the accuracy and adaptability of atomic clock control are improved, and the operation stability of the atomic clock in a complex environment can be enhanced.

The invention discloses an automatic data modeling and optimizing system and method fusing a knowledge graph and ChatBI, and relates to the technical field of data analysis. In order to solve the problems of high interaction threshold and insufficient analysis depth of a traditional BI tool, the scheme adopted by the invention comprises a data layer which has the capabilities of multi-source data access, domain knowledge graph construction, intelligent mapping recommendation, federal calculation and data mild governance, and realizes data integration and semantic unification; the analysis layer realizes accurate conversion from a natural language to an SQL and semantic reasoning of a knowledge graph through graph vectorization, multi-model cooperation, natural language understanding, intelligent SQL generation and graph dynamic updating, and supports efficient data query and analysis; and the application layer has the functions of natural language interaction, visual recommendation and generation, root cause analysis and intelligent early warning, and provides a visual interaction interface and data display service for a user. According to the invention, intelligent data analysis and visualization based on natural language interaction can be realized.

The invention relates to the technical field of slope catastrophe intelligent early warning and data modeling, and particularly discloses a strip mine high and steep slope multi-source monitoring and early warning method based on a graph neural network and Transform, and the method comprises the following steps: S01, carrying out the data preprocessing and disturbance variable construction of monitoring data; s02, constructing a heterogeneous space diagram structure by taking the monitoring points as nodes and taking geography, lithology and dynamic response relationships as edges; s03, constructing a space-time end-to-end multilayer coding framework based on the graph attention network and the integrated deep neural structure; s04, on the basis of graph coding and time sequence output, introducing a disturbance variable embedding mechanism, and designing a joint attention fusion structure; and S05, generating a deformation trend prediction value of the slope in a future period of time and performing corresponding risk grade judgment. The invention aims to solve the key technical problem of weak adaptability and interpretability of an early warning system.

The invention provides a dynamic risk prediction and optimization decision-making method and system for a deep foundation pit construction process. A deep foundation pit numerical simulation model is established, and the model is used for simulating soil mechanical behaviors and supporting system response parameters in the construction process; acquiring multi-dimensional real-time monitoring data of the foundation pit, comparing the monitoring data with a numerical simulation result, and calibrating parameters of the deep foundation pit numerical simulation model according to a comparison result; performing time series data modeling analysis on the real-time monitoring data by using a Transform algorithm, capturing possible risk modes in the construction process, and identifying potential risk signals in the data; on the basis of Transform algorithm analysis and numerical simulation feedback results, the foundation pit construction risk state is predicted in real time, and a real-time risk prediction result is obtained; and according to the prediction result, dynamically adjusting the construction scheme to complete decision optimization. According to the method, the risk management and control capability of the deep foundation pit construction process is remarkably improved, and the construction safety and efficiency are greatly improved.

The invention provides a machined part logistics transportation path planning management system, and relates to the technical field of data processing, and the system is used for reconstructing the spatial relationship of path nodes, splitting and combining the arrangement sequence and time sequence characteristics of the path nodes, and storing the path nodes in a database; calculating a dynamic quantitative index of a preset transportation path between a time dimension and a resource occupation dimension to obtain a time sequence tensor factor, identifying an offset track of a machined part between the preset transportation path and an actual transportation path, extracting transportation behavior characteristics between path nodes, calculating a time sequence disturbance degree of transportation path offset to a scheduling plan, and obtaining a scheduling result; the method comprises the steps of obtaining a scheduling influence coefficient, correcting a preset transportation path, adjusting the passing sequence and priority of path nodes, synchronously updating scheduling information, and carrying out linkage matching on path change information and a workshop plan to obtain linkage scheduling data. According to the invention, the whole-course data modeling and dynamic scheduling management of the transportation process of the workpieces are realized.

The invention relates to the technical field of silver paste preparation, in particular to a silver paste conductivity data modeling and formula optimization system based on machine learning, which comprises a data acquisition and storage module, a preprocessing module, a characteristic influence analysis module, a formula optimization module, a simulation verification module and the like. The method comprises the following steps: acquiring original data of a silver paste formula, preprocessing, calculating influence coefficients of all components on target performance by utilizing a machine learning model, and identifying high and low influence components; the formula optimization module is combined with component content constraints and adopts a multi-objective optimization algorithm to generate candidate formulas; and the simulation verification module verifies the performance of the formula through sintering simulation and process adaptation, and feeds back optimization. According to the invention, the full-process intelligentization of the silver paste formula from data processing to optimization verification is realized, the conductivity and other performances of the silver paste are accurately improved, the research and development cost is reduced, the period is shortened, the suitability of the formula process is enhanced, and the research and development and industrial upgrading of the silver paste are promoted.

The invention discloses a risk knowledge graph construction and intelligent early warning method based on artificial intelligence, and the method comprises the following steps: collecting and preprocessing multi-source risk related data, and generating a composite input data set; constructing an enhanced TabPFN structure and introducing a risk logic rule constraint matrix to generate a risk prediction result set; mapping the risk prediction result set to a knowledge graph entity node and relation set to generate a risk knowledge graph structure; the method comprises the following steps: introducing Sheaf Neural Networks into a risk knowledge graph structure, and generating a consistency parameter set; the knowledge graph structure is updated, and risk knowledge graph enhanced representation is generated; and performing reasoning in combination with the risk prediction result set and the consistency parameter set, and outputting an intelligent early warning result set. According to the method, table data modeling and graph consistency propagation are fused, and the risk prediction accuracy and the early warning reliability are improved.

The invention provides an edge-collaborative point cloud data modeling and building design collaborative management method and system, electronic equipment and a storage medium, and relates to the technical field of edge computation.The method comprises the steps that city multi-source point cloud data is collected through a mobile scanning device, and time-space reference synchronization is conducted through an edge node dynamic transmission channel to generate a unified data set; detecting terrain contour features based on the environment sudden change index, triggering to re-collect updated data and generating scene constraint parameters; density self-adaptive compression is executed, a compression data set with adjustable partition precision is generated in combination with vegetation and temporary building distribution, and spatial topological features are extracted; inputting a building rule base to start distributed collaborative optimization to generate a design model; the model boundary is compared with the topographic change, the space conflict is solved through geometric structure adjustment, the closed-loop cooperative management adaptive to the environment contour is realized, the closed-loop cooperative control of the building design and the actual topographic data can be realized, and the space conflict is effectively avoided.

A data modeling and analytics platform augments and annotates content captured from a user's online interactions and other documents. The data modeling and analytics platform is performed within a machine learning and artificial intelligence-based processing environment that enables observability, explainability, and data analytics for dynamic information discovery over time within a user library that includes files representing the online interactions and documents containing information of user interest.

The invention discloses a sparse coding and graph space-time attention fused coagulant addition prediction method, and relates to the technical field of time sequence data modeling prediction. According to the method, through the combination of a Transform encoding-decoding architecture and a sparse self-attention and top-k sparse strategy, the missing data complementation precision is effectively improved, and high-quality and missing-free input data is provided for subsequent prediction modeling; in the aspect of spatial feature expression, node association of multiple subsystems of a water production system is captured by means of a graph attention network, and by dynamically calculating association weights among nodes and aggregating neighbor features, the information entropy of spatial features is improved, and the expression ability of the spatial features to a multi-node coupling relationship is greatly enhanced; in the aspect of time sequence prediction performance, a time attention mechanism can dynamically focus on key time sequence nodes, the utilization rate of key time sequence features is further improved, and the accuracy of coagulant adding prediction is improved.

The invention discloses a three-normal-form automatic modeling method and system, electronic equipment and a storage medium, and the method comprises the steps: constructing a structured domain knowledge base, and injecting three-normal-form design experience for an RAG framework; business semantics are clarified in real time in combination with user demand input and multi-round dialogue interaction of model questions; a double-stage AI recommendation engine is adopted to match user requirements and knowledge base semantic vectors, a high-precision model change scheme is generated, automatic normal form optimization is conducted on a recommendation scheme, DDL scripts, ER diagrams, data migration schemes and the like conforming to the normal form are dynamically output, integration is conducted in a sandbox environment, and finally closed-loop automatic modeling of'requirement-design-verification 'is achieved. The method is used for solving the problems that traditional data modeling is low in efficiency and high in error rate and cost rate, the problem that model knowledge updating needs full-amount or increment fine tuning, and the cost is high, and the problem that business semantic understanding is insufficient in three-normal-form modeling is solved.

The invention relates to the technical field of informatization system development, in particular to a natural language-based complex data model generation method, which comprises the following steps of receiving business requirement input in a natural language form, preprocessing and normalizing input contents, processing ambiguity and incompleteness of requirements through a multi-round dialogue complementation mechanism, and generating a complex data model. Obtaining a complete and clear business demand description; and carrying out deep semantic analysis on the normalized business requirements by adopting a large language model. Aiming at the pain points that an existing data modeling technology is high in threshold, low in efficiency, difficult in quality guarantee, weak in integration adaptation and the like, the method has the remarkable advantage of multiple dimensions, non-technical background personnel can directly input service requirements through natural language interaction and deep semantic analysis on the premise of reducing the technical threshold, database knowledge and SQL specifications do not need to be elaborated, and the method is suitable for large-scale popularization and application. The cognitive gap of business and technology is spanned, the dependence on professional design talents is reduced, and the learning cycle of green hands is shortened.

The invention relates to the field of constructional engineering, and discloses a dynamic risk prediction system, which generates space-time alignment input through multi-source data fusion, adopts tensor field modeling to embed contract constraint to construct a risk dynamic model, and solves and outputs a continuous risk field through a partial differential equation; a propagation path is analyzed in combination with asymmetric causal analysis, model parameters are adjusted in real time through a dynamic optimization algorithm, and closed-loop optimization of a risk field is achieved; and finally, through four-dimensional thermodynamic diagram interaction early warning and resource intelligent scheduling, a whole-process closed-loop system of data modeling-causal analysis-dynamic optimization-visual management and control is formed. According to the method, dynamic optimization of risk field parameters is realized through adjoint equation back propagation, and the modeling precision of a complex scene is improved; a four-dimensional space-time thermodynamic diagram rendering technology is innovated to solve the problem of fragmentation of multi-modal information expression, and risk disposal response is accelerated; key task resource supply is guaranteed by combining video memory preemption and containerization scheduling strategies, and the system stability bottleneck in a high-load scene is overcome.

The invention discloses a perception driving grouting self-adaptive control method and system for a complex stratum, and relates to the technical field of grouting control, and the method comprises the following steps: constructing a three-dimensional grouting response model to simulate the diffusion process of grout; collecting multi-source information data, and inputting the processed multi-source information data into the trained grouting parameter prediction model to obtain a grouting parameter prediction result; grouting is conducted on the to-be-grouted area according to the grouting parameter prediction result, grouting parameters are monitored in real time in the grouting process, and the grouting parameters are compared with the predicted grouting parameter prediction result so as to adjust the subsequent grouting process; inputting data acquired in the whole grouting process into an evolution modeling module, and performing data modeling and three-dimensional reconstruction in the grouting process to obtain a visual whole grouting process; the self-adaptive control of the grouting process is realized by utilizing the sensing data obtained by cooperating the grouting simulation model and the grouting prediction model with the stratum sensing node.

The invention relates to the technical field of risk identification, and provides a risk identification system and a risk identification method. According to the risk identification system, by introducing the scene perception module, the data alignment module and the multi-modal analysis module, automatic acquisition and fusion of multi-source heterogeneous data, including images, videos, voices, sensor data, operation information and the like, of a construction site are realized, and the comprehensive perception ability of the system to the site state is effectively improved. Through a data alignment mechanism guided by structured data, multi-dimensional alignment of time, space and task semantics can be realized based on a historical schedule, B I M parameters, a construction plan and the like, the problem that multi-source data is difficult to fuse in a traditional method is solved, and the accuracy and consistency of data modeling are improved. A pre-trained multi-modal risk identification model is adopted, the cross-modal analysis capability is achieved, images, texts and behavior data of a construction site can be comprehensively understood, and then risk identification is accurately carried out.

The invention relates to the technical field of video behavior recognition, and discloses a video abnormal behavior recognition method fusing spatio-temporal characteristics. The method comprises the following steps: a video data modeling step: modeling according to a current video frame sequence and preset behavior characteristics to obtain a video behavior model; an experience pool forming step of dividing a plurality of experience layers according to historical identification result differences and record confidence by means of historical video abnormal behavior records to form a multi-layer experience pool; a strategy determination step of determining an intelligent identification strategy of each stage based on a video behavior model target behavior state, and screening a multilayer experience pool according to record confidence and a target matching degree to obtain an experience identification strategy; a strategy adjustment step: dynamically adjusting the two strategies by using a dual-channel mechanism to adapt to a real-time video environment, and determining a target identification strategy; and a behavior state acquisition step of analyzing the video frame sequence according to an identification instruction to acquire an actual behavior state, the identification instruction being generated based on the target identification strategy and the current video frame sequence.

The invention relates to the technical field of food safety data processing and intelligent early warning, and particularly discloses a safety early warning method and system based on food safety inspection data. The method comprises the following steps: acquiring multi-source food safety inspection data, performing structured preprocessing, and constructing a unified feature vector space; extracting space-time correlation characteristics to generate a dynamic risk factor sequence; inputting the sequence into an interpretable fusion model for score calculation, and generating a risk score matrix; fusing the product information to generate an attribute labeling vector, constructing a food safety knowledge graph and carrying out causal reasoning; and generating early warning information and a risk tracing path based on a reasoning result, and carrying out reverse correction and model updating on the risk factors. According to the method, multi-source data modeling, causal relationship reasoning and a model self-learning mechanism are fused, dynamic identification and closed-loop early warning of food risks are realized, and the method has high adaptability and interpretability.

The invention provides a multi-modal data joint query analysis method and system supporting natural language interaction, and relates to the technical field of data query analysis. Historical operation data, audio data and text data of a user on an intelligent search platform supporting natural language interaction are collected; modeling the historical operation data to obtain a user preference vector, performing voice recognition and text standardization processing on the audio data to obtain standard query data, and integrating the standard query data and the text data into context information; an attention mechanism-based algorithm is used for semantic understanding and intention recognition to obtain an intention feature vector, and the intention feature vector is fused with a user preference vector to obtain a classification result; and finally, based on the result, querying in a preset multi-modal database through a collaborative filtering algorithm to obtain a joint query result, so that personalized accurate query of the multi-modal data under natural language interaction can be realized, and the result fits the intention and long-term preference of the user.

The invention discloses a multi-source unmixing analysis method and system for a geological source-sink system based on a dzmix inverse Monte Carlo model, relates to the technical field of tectonic sedimentology and data modeling crossing, and collects and arranges zircon U-Pb chronological data of a target horizon mixed sample in a research area and zircon U-Pb chronological data of a basin peripheral material source area. Establishing a multi-source area big data chronology database of the research area; and establishing standard stratum characteristics in combination with regional sedimentary characteristics and petrology characteristics. According to the method, the contribution proportion of the multi-source substances in the geological source-sink system is quantitatively recovered, the defects that a traditional source-sink analysis method depends on priori end member selection and is easily influenced by personal errors are overcome, the inverse Monte Carlo model is used for carrying out iterative simulation for multiple times, the simulation effect is gradually optimized, the accuracy and reliability of a de-mixing result are ensured, and the method is suitable for large-scale popularization and application. And a more scientific and accurate technical means is provided for geological research and resource exploration.

A method of training a machine learning model for reconstructing video or audio data based on neuroimaging data of a subject is provided. The method includes: training a neuroimaging data encoder based on neuroimaging data from a neuroimaging training dataset for generating neuroimaging data embeddings; and training a diffusion model based on the neuroimaging data embeddings generated by the neuroimaging data encoder as conditions on the diffusion model. In this regard, the diffusion model is trained to reconstruct video or audio data based on neuroimaging data embeddings of neuroimaging data of a subject obtained in response to a visual or audio stimulus. The neuroimaging data encoder includes a masked autoencoder. The above-mentioned training the neuroimaging data encoder includes training an encoder of the masked autoencoder based on the neuroimaging training dataset using unsupervised learning with masked data modeling for generating the neuroimaging data embeddings. The unsupcrviscd learning with masked data modeling includes generating neuroimaging data embeddings from neuroimaging data from the neuroimaging training dataset, masking a portion of the neuroimaging data embeddings into masked neuroimaging data embeddings and training the masked autoencoder to recover the masked neuroimaging data embeddings. There is also provided a method of using the machine learning model trained for reconstructing video or audio data based on neuroimaging data of a subject.

The invention relates to the technical field of wind power plant equipment monitoring, and discloses a wind power plant equipment state online monitoring system. The system comprises a real-time acquisition module, a multi-modal fusion module, a dynamic segmentation module, a preliminary evaluation module, a deep analysis module and a maintenance trigger module. The real-time acquisition module acquires a multi-source sensor data stream, and the multi-modal fusion module performs adaptive weighted fusion on the multi-source sensor data stream to generate a comprehensive signal feature set. The dynamic segmentation module segments a feature set according to a load rate to generate a running state feature sequence, and the preliminary evaluation module performs health degree scoring by using a deep belief network. The deep analysis module performs modeling on abnormal time period data through a space-time diagram neural network to generate an equipment state label, and the maintenance triggering module triggers a real-time maintenance strategy instruction according to the equipment state label. The system can accurately monitor the equipment state of the wind power plant in real time, improve the fault diagnosis accuracy, realize intelligent maintenance decision, and improve the operation reliability and economy of the wind power plant.

The invention relates to a wall recognition modeling method based on a two-dimensional CAD drawing, belongs to the technical field of data modeling, and solves the problem that a closed single-line wall cannot be automatically and accurately generated from the two-dimensional CAD drawing in the prior art. Comprising the following steps: extracting a straight line segment set of all walls from a two-dimensional CAD drawing; calculating a wall continuity index value of the straight line segment set according to a first matching condition, and obtaining a segment grouping set by adopting a corresponding wall line grouping method; based on the line segment grouping set, multiple wall body center lines are generated through fitting according to the distance and the line segment direction; the method comprises the steps of identifying suspended endpoints in a plurality of wall body center lines, obtaining corresponding target intersection points according to other wall body center lines and endpoints in a region adjacent to each suspended endpoint, adjusting the corresponding suspended endpoints according to the target intersection points, and cutting and reconstructing the wall body center lines where the target intersection points are located to obtain optimized wall body center lines. And then a single-line wall body is constructed. The closed single-line wall is automatically and accurately generated.