2222 results about "Data visualization" patented technology

The invention discloses an interactive question intelligent agent system based on a large language model, and relates to the technical field of dialogue interaction systems. Aiming at the technical defects of the traditional BI tool, the technical scheme is as follows: a heterogeneous data management engine realizes unified access and secure access of cross-source data; the NLP semantic recognition engine converts a natural language into structured semantics through multiple steps, drives the hybrid SQL generation engine and provides visual parameters; the generation engine realizes precise generation from semantics to SQL through a two-stage architecture based on metadata and authority rules; and the visual management console is combined with multi-party configuration and parameters to visually build a number-asking agent. The method is used for realizing intelligent conversion from a natural language to a structured query language (SQL) and a data service interface, and automatically generating an interactive data visualization result.

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

The invention relates to the technical field of medical intelligent scheduling, in particular to a patient flow data visualization adjusting system and method, and the system comprises a global dynamic sensing unit, a medical intelligent evolution center, a space-time mirror image twinning module, a multi-dimensional decision optimization matrix, and a risk closed-loop management and control unit. The global dynamic sensing unit gathers data to construct a diagnosis and treatment panorama; the medical intelligent evolution center is used for building a medical process evolution system to form a three-dimensional cognitive architecture; the space-time mirror image twinning module is used for constructing physical virtual space mapping, and forming a diagnosis and treatment resource space-time distribution path based on Internet of Things position data and a digital twinning technology; the multi-dimensional decision optimization matrix extracts patient flow data according to the space-time simulation path and a multi-target model, and generates a resource scheduling scheme; and the risk closed-loop management and control unit is used for evaluating the risk, marking congestion early warning and dynamically optimizing a regulation and control strategy and a threshold value. Therefore, the problems of data isolation, poor resource configuration efficiency, insufficient real-time performance and the like in the prior art are solved.

The embodiment of the invention discloses a multi-agent collaborative data visualization analysis method and device and a medium, and relates to the technical field of visualization analys.The method comprises the steps that a natural language analysis request input by a user is received, task splitting is conducted on the natural language analysis request through a preset task planning agent, and the task splitting result is obtained; generating corresponding task plan information, obtaining real-time operation data of a preset functional agent cluster, carrying out agent distribution on the plurality of sub-tasks based on the real-time operation data and the task plan information, determining a target functional agent corresponding to each sub-task, and sending the target functional agent to a server; the target function agent comprises any one of a data analysis agent and a visual display agent; and executing the corresponding data analysis subtask through the data analysis agent to obtain a data analysis result, performing agent cooperative verification on the data analysis result, and after the verification is passed, generating visual analysis data corresponding to the data analysis result through the visual display agent.

The invention discloses a building waterproof construction quality monitoring and early warning system based on the Internet of Things, relates to the technical field of building engineering, and realizes omnibearing and real-time monitoring and intelligent early warning of a building waterproof construction area through a multi-layer architecture formed by a data acquisition layer, a data transmission layer, a data processing layer and an application layer. The data processing layer analyzes data features from a time sequence and a spatial distribution dimension through normalization, denoising processing and a multi-head attention mechanism in a Transform model, and accurately recognizes the abnormality and risk trend of a weak point in combination with dynamic threshold and trend analysis, and the application layer determines the risk trend of the weak point through multi-dimensional data visualization, historical playback and real-time interaction. And the early warning module is combined with intelligent reasoning to generate dynamic maintenance suggestions, a maintenance scheme is optimized, the construction quality and the maintenance efficiency are improved, the intelligent level of building waterproof monitoring is improved, risk prediction and accurate maintenance are realized, the management cost is reduced, and the system has wide application value.

The invention relates to the technical field of monitoring systems, in particular to an electric power internet of things transformer substation auxiliary equipment monitoring system and method, and the method comprises the steps: collecting the operation data of transformer substation equipment in real time; preprocessing the collected operation data by utilizing edge calculation; analyzing the preprocessed operation data based on a deep learning algorithm, and generating a preliminary fault diagnosis and prediction result; constructing a digital twinborn model of the substation equipment, and performing equipment health assessment and fault prediction by utilizing a preliminary fault diagnosis and prediction result and combining a physical model and historical data of the equipment; and according to the equipment health assessment and fault prediction result, data visualization display is carried out through the monitoring platform, and hierarchical alarm is carried out to inform operation and maintenance personnel. Through the deep learning algorithm and the digital twinborn model, deeper fault analysis and prediction can be provided, and the problem that the equipment is not maintained in time or is excessively maintained is avoided.

The invention discloses a wind resource assessment report generation method based on a large model technology. The method comprises the following steps: processing multi-source data, and constructing a domain knowledge base and a fine tuning database; field adaptive RAG enhancement is carried out, professional literatures, historical cases and industry specifications in a field knowledge base are retrieved by adopting a retrieval enhancement generation technology, and wind resource assessment contents meeting technical standards are generated through a multi-modal fusion word vector technology; performing large model fine tuning, and optimizing the generative large model through a gradient-free optimization algorithm Wind-DFO on the basis of a fine tuning database by adopting a field self-adaptive fine tuning strategy; and automatically generating a report, arranging and matching multiple templates through an AI workflow, embedding a data visualization chart, executing verification of a grammar layer, a logic layer and a compliance layer, and outputting a standardized wind resource assessment report. The accuracy, specialty and efficiency of report generation are remarkably improved, and the problems that a traditional method depends on artificial experience, the data utilization efficiency is low, and standardization is insufficient are solved.

The invention relates to the technical field of intelligent operation inspection of power transmission lines, in particular to an intelligent detection method of a power grid frame and a satellite image processing system.The method comprises the steps that the power grid frame is detected according to collected multi-source monitoring information, information of a plurality of power grid structures is obtained, and a power grid topology model is established; performing connection analysis on the power grid topology model to obtain a power grid topology analysis result, and performing spatial distribution analysis on the power grid frame by collecting coordinate information of the power grid frame and the power grid topology analysis result to obtain power grid coverage data. And performing data visualization on the plurality of pieces of power grid structure information, the power grid topology analysis result and the power grid coverage data to generate a power grid frame detection result. According to the invention, the method effectively solves a problem that the weak edge region is liable to leak detection in the recognition of the power grid frame in a large-range and high-complexity scene, remarkably improves the recognition efficiency and accuracy of the power grid frame, and reduces the cost and time of manual inspection.

The invention discloses a building pedestrian simulation statistics and analysis method, and belongs to the technical field of simulation. According to the invention, a comprehensive pedestrian behavior evaluation and optimization scheme is provided through normal pedestrian circulation simulation and emergency evacuation simulation in combination with statistical analysis, data visualization and an optimization algorithm. The system manages pedestrian density through a KD tree, performs real-time data visualization by using ScottPlot, generates one-dimensional, two-dimensional and three-dimensional charts, and visually displays pedestrian distribution and flow conditions in a building. In an emergency evacuation scene, the system dynamically plans a path through a # imgabs0 # algorithm, and identifies and relieves a potential bottleneck area in combination with a heuristic function of congestion measurement. The simulation system can collect abundant statistical data including evacuation time, people flow distribution, congestion point analysis and the like, and provides a scientific basis for building safety design improvement and emergency plan making.

The invention specifically relates to the technical field of intelligent irrigation, and discloses a meteorological prediction-based irrigation dynamic regulation and control system, which comprises a data acquisition module, a data analysis module, a strategy generation module, a strategy execution module, a feedback regulation module and a man-machine interaction module, the data acquisition module integrates a water quantity acquisition terminal, a crop state acquisition terminal and a meteorological monitoring terminal, acquires multi-source irrigation decision data of a target irrigation farmland in real time, judges a water shortage risk level of the target irrigation farmland through the data analysis module, and generates an irrigation strategy through the strategy generation module; the strategy execution module executes an irrigation strategy through an electrically-driven intelligent water-saving valve, the feedback adjustment module dynamically adjusts the irrigation strategy based on sensor feedback data, and the man-machine interaction module provides a data visual interface, dynamically optimizes the irrigation strategy in combination with real-time meteorological changes and irrigation feedback, divides water shortage risk levels, and provides a water-saving management system. The utilization efficiency of water resources is effectively improved, and the applicability and reliability of the system are improved.

A method generates data analysis templates. A computing device receives, via a graphical user interface, a natural language input, which includes multiple terms. The terms include one or more data fields and / or data values from a data source. The device designates the natural language input as trigger text for a data analysis template. The trigger text includes the multiple terms. A user defines one or more rules for a subset of the terms, each rule specifying respective criteria to match a respective term in the subset. A user specifies multiple data visualizations corresponding to the trigger text. Each of the data visualizations (i) uses a respective one or more data fields, from the data source, corresponding to terms from the trigger text and (ii) has a respective data visualization type. The device generates the data analysis template according to the trigger text, the user definition, and the user specification.

The invention discloses a transformer substation external damage prevention intelligent monitoring method and system based on cloud computing and artificial intelligence, and the method comprises the steps: data collection: collecting environment data and equipment state data of a transformer substation in real time through high-definition cameras and various sensors disposed around the transformer substation; data transmission: transmitting the collected data to a cloud computing platform for storage and processing; intelligent analysis: performing real-time analysis on the data transmitted to the cloud computing platform by using big data analysis and an artificial intelligence algorithm, and identifying potential external damage behaviors; performing early warning and response, triggering an early warning mechanism when an abnormal behavior is detected, and notifying operation and maintenance personnel; and data visualization and report generation: generating an operation and maintenance report, an alarm report and a risk analysis report for decision reference of operation and maintenance personnel and a management layer. Through the real-time monitoring and intelligent identification technology, the system can accurately capture abnormal behaviors inside and outside the transformer substation, effectively avoid external damage events, and significantly reduce safety risks.

The invention discloses a transformer substation intelligent monitoring system and method based on digital spasm, and relates to the technical field of digital spasm, the transformer substation intelligent monitoring system comprises a transformer substation information exchange hub, the transformer substation information exchange hub is in communication connection with an operation parameter monitoring module, a fault early warning module and a data visualization module; the parameter monitoring module collects temperature and current data of a transformer substation, the fault early warning module combines a simulation model and historical and real-time monitoring data, an LSTM model is used for early warning a fault, the data visualization module displays performance, trend and alarm, an LSTM is called to predict a load, and an algorithm is used for optimizing energy consumption, cost and load distribution. Intelligent monitoring of the transformer substation is achieved through the digital spasm technology, the accuracy and the real-time performance of monitoring are improved, the fault early warning precision and the load prediction capacity are improved through the LSTM model, meanwhile, the energy consumption, the cost and the load distribution are optimized through the algorithm, and the operation efficiency and the economic benefits of the transformer substation are effectively improved.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine heterogeneous data visualization processing method combined with artificial intelligence, and the method comprises the steps: collecting multi-source heterogeneous data above and under a coal mine to construct a real-time heterogeneous data matrix; feature categories are divided based on data dynamic relevance, and stable data areas are screened through a dynamic analysis window to generate an optimization matrix; performing pattern recognition on the unstable region to extract an abnormal data cluster, and calculating an abnormal feature factor of a data point; analyzing the feature distribution difference to generate a stable feature index, and calculating an abnormal risk value in combination with the distance, the abnormal feature factor and the stable feature index; and mapping the center coordinate of the stable region to a three-dimensional space of the coal mine, and positioning a key information region in combination with the abnormal risk value. An apparatus includes a memory, a processor, and a computer program. The coal mine heterogeneous data processing efficiency and the safety monitoring accuracy are improved, intelligent positioning of the key information area is achieved, and powerful support is provided for coal mine safety production.

The invention relates to the technical field of smart city data visualization, in particular to a visual smart city display method, which comprises the following steps: S1, collecting traffic flow data, energy consumption data, environment monitoring data and city three-dimensional geographic information data in real time; s2, constructing a reference three-dimensional model with a space coordinate system; s3, mapping the data into a dynamic data layer with a timestamp; s4, superposing the dynamic data layer to the reference three-dimensional model according to space-time coordinates to generate a fused dynamic scene; s5, receiving deduction parameters input by a user, and calculating a corresponding evolution path in real time; and S6, converting the evolution path into a dynamic visualization effect, and outputting the dynamic visualization effect to a display terminal. According to the invention, through integrated processing of multi-source dynamic data fusion, space-time path deduction and visual output, continuous expression and dynamic evolution display of the operation state of the smart city are realized.

The invention provides a high-precision topographic mapping and three-dimensional modeling system based on an unmanned aerial vehicle, and the system comprises an unmanned aerial vehicle cooperation module which is used for deploying a multi-source sensor and a sensor electronic interface on the unmanned aerial vehicle, and building a distributed network of the unmanned aerial vehicle; the real-time dynamic positioning module is used for acquiring unmanned aerial vehicle positioning data and an initial target terrain map; the autonomous control module is used for generating a flight path, performing dynamic adjustment, constructing an autonomous flight control model and outputting a flight control instruction; the data quality enhancement module is used for performing parameter adjustment on the real-time data and performing space and signal level combined processing on the real-time data so as to enhance the data quality; and the terrain model optimization module is used for processing the laser radar point cloud data so as to construct a three-dimensional terrain model post-processing module, and carrying out data visual display and flight suggestion generation. According to the method, the positioning and modeling precision is greatly improved, efficient surveying and mapping are achieved, the single-point fault risk is reduced, and it is guaranteed that tasks can be completed in a high-safety, high-precision and intelligent mode.

The invention relates to the field of playing data visualization, in particular to a playing data visualization method and system. The method comprises the steps that at least one section of pedal data of at least one pedal is obtained, and the pedal data comprises a pedal value and a time stamp of the pedal value; generating at least one section of labeling track according to the pedal data; wherein the labeling track comprises a first simulation track and a second simulation track; correspondingly, the method comprises the steps that when the pedal value is larger than a first pedal threshold value, a first simulation track is generated according to the pedal value, and the first simulation track is displayed in a first labeling form; when the pedal value is larger than a second pedal threshold value and smaller than or equal to the first pedal threshold value, a second simulation track is generated according to the pedal value, and the second simulation track is displayed in a second labeling form; and generating a combined track according to the at least one section of labeled track, and displaying the combined track on the music score. According to the invention, the smoothness and integrity of pedal data display can be improved, and the user experience is optimized.

An agent-based website search interface utilizes a multimodal model to enhance enterprise operations. Data agents collect and process diverse inputs, while an orchestrator manages these agents. The system leverages machine learning models to generate insights and automate decision-making processes. It includes tools for data visualization and validation, ensuring accuracy and reliability. By integrating generative AI, the interface provides advanced search functionalities, improving user experience and operational efficiency. This facilitates seamless interaction to answer context specific questions from complex data, offering a robust solution for enterprise-level search and analysis.

A management server system may obtain a request for image data from a computing device. The request may identify a subset of image data to be downloaded and / or streamed to the computing device. The management server system can provide an indication of a storage destination for the image data to a gateway device and initiate upload of the image data from the gateway device to the storage destination. The management server system may monitor the upload and, based on the monitoring, determine the upload is complete. The management server system can cause display of a user interface that includes a visualization of the image data for download of the image data from the storage destination to the computing device.

The invention relates to the technical field of data visualization, in particular to an earthquake emergency information rapid visualization method and system. The method comprises the following steps: acquiring earthquake monitoring real-time data; based on earthquake monitoring real-time data grid division, earthquake region population density distribution and key facility positions are generated, and an earthquake region sensitivity matrix is generated; dynamically simulating a seismic wave propagation process based on the seismic monitoring real-time data and the seismic region sensitivity matrix, and generating fluctuation propagation time sequence data; and identifying epicenter topography and landform vector data of the earthquake monitoring real-time data, and respectively evaluating topographic relief and geological stability to obtain earthquake topographic relief index data and earthquake geological stability score data. According to the invention, rapid and accurate assessment and visualization of earthquake damage distribution are realized through a data visualization technology, so that the earthquake emergency response efficiency and the disaster rescue decision scientificity are improved.

The invention discloses a bulk cargo supply chain data transmission method and system based on an intelligent transportation box, and relates to the technical field of logistics data transmission, and the method comprises the steps: collecting multi-modal sensing data in real time through a multi-source data collection interface of the intelligent transportation box; the intelligent transportation box edge calculation module performs cleaning, compression aggregation and key event intelligent identification on the data; a transmission strategy is adaptively selected according to the data type and the network state, a key event alarm message is immediately sent, data are locally cached when the network is interrupted, and subsequent transmission is recovered; transmitting the data to a cloud by adopting a secure communication protocol; the cloud receives management data and provides remote configuration, alarm notification and data visualization functions, the system comprises an intelligent transportation box terminal unit and a cloud service platform unit, data transmission reliability and efficiency are improved, bandwidth and cloud cost are reduced, cargo transportation safety is guaranteed, and the system is suitable for bulk cargo cross-regional transportation scenes.

The invention provides a gas data collection and analysis system. The gas data collection and analysis system comprises a data acquisition module, a data transmission module, a data storage module, a data analysis module and an alarm and visualization module. According to the system, gas operation parameters are collected in real time through flow, pressure, temperature and gas concentration sensors, the data accuracy is improved through a correction algorithm, data are transmitted to a cloud or a local server for storage based on the wireless communication technology, and data safety and efficient storage are guaranteed through a distributed architecture. According to the gas data collection and analysis system, gas demand prediction and anomaly detection are achieved through a time sequence prediction model and a clustering algorithm, leakage or abnormal operation conditions are accurately recognized, when gas leakage, flow anomaly or equipment faults are detected, an alarm is automatically triggered, related personnel are remotely notified, meanwhile, a data visualization interface is provided, and the system is convenient to use. And real-time monitoring and trend analysis are supported.

The invention discloses a low-voltage switch cabinet operation data visualization and control system, and relates to the technical field of power equipment intelligent control, and the system comprises a multi-modal sensing module which outputs a multi-dimensional feature vector to an edge unit, and obtains a mechanical looseness level and an arc fault type; the self-adaptive feature engine is used for obtaining an unstructured feature matrix; the dynamic topology reconstruction module is used for obtaining a reconstruction topology and a thermal stress prediction result; the virtual-real mapping visualization engine is used for inputting a real-time topological graph and a fault path and outputting a visual interface containing arc hot spots and cascade early warning; the intelligent decision-making module outputs to the circuit breaker and the energy storage unit; and the man-machine cooperation interface module is used for outputting a tamper-proof log. According to the invention, potential faults can be pre-warned in advance, fault switching can be quickly processed, the operation data of the low-voltage switch cabinet with visual space interaction experience can be provided, and the problems that the potential faults cannot be pre-warned in advance, the fault switching processing is slow, and the space intuition and interactivity are poor in the prior art are solved.

The system develops a set of complete traceability management system based on the block chain technology according to the quality safety and traceability management requirements in the production, processing and circulation processes of traditional Chinese medicinal materials, guarantees the traceability of the whole process from planting to consumption, improves the transparency of a supply chain, guarantees the rights and interests of consumers, and provides precise data support for supervision departments. The system adopts a front and back end separation architecture, a back end is developed based on SpringBoot and interacts with a Fabric block chain network, tamper-proof and verifiable storage of traceability data is realized, and data storage, quality examination and authority management are automatically executed through an intelligent contract; the front end is developed based on a Vue.js framework, smooth user experience is provided, and consumers can inquire complete traceability information of the traditional Chinese medicinal materials by scanning codes. System functions cover full life cycle traceability, block chain evidence storage, intelligent contract management, authority hierarchical control and data visualization, a supervision mechanism is supported to utilize big data analysis to accurately supervise quality problems, and market supervision efficiency is improved. The system guarantees the data security and transparency through the block chain, optimizes the supply chain management, improves the brand value, and assists the digital and standardized development of the traditional Chinese medicinal material industry.

The invention relates to the technical field of data visualization, in particular to a multi-modal fusion anorectal data visualization analysis method and system, and the method comprises the following steps: synchronously collecting an anorectal image and a pressure signal, aligning a timestamp, carrying out the preprocessing, outputting a standardized image and a coded physiological signal, and extracting edge and texture features. After LSTM coding, splicing with image features, and fusing into a joint feature map; generating a multi-scale heat map by the feature pyramid network, and coarsely positioning a lesion; after boundary optimization, U-Net recovers details, and a pixel-level lesion probability graph is output; superposing the Jet color gradation to the original image in a semitransparent manner, and marking a pressure abnormal time period; and performing weighted scoring to generate a fourth-level clinical suggestion. According to the anorectal disease diagnosis method, through multi-modal space-time alignment, multi-scale feature fusion, boundary optimization and a quantitative scoring system, the problems of data splitting, insufficient precision and low efficiency of a traditional method are solved, a high-precision and high-robustness intelligent diagnosis tool is provided for anorectal diseases, and the scientificity and efficiency of clinical decision making are remarkably improved.

The invention discloses an unmanned aerial vehicle edge intelligent analysis terminal based on a single Beidou, and relates to the technical field of satellite navigation and unmanned aerial vehicles, and the terminal comprises an edge calculation unit which is in communication connection with a high-definition image collection module, a high-precision positioning module, a Beidou short message communication module and a ground control station; the high-definition image acquisition module is used for acquiring high-definition image data through a CMOS (Complementary Metal Oxide Semiconductor) sensor; the high-precision positioning module is used for acquiring centimeter-level positioning data in the flight process of the unmanned aerial vehicle through an RTK technology and an INS system; the edge calculation unit is used for carrying out target recognition through a lightweight convolutional neural network in combination with a YOLOv5 target detection algorithm; the Beidou short message communication module is used for realizing short message communication between the unmanned aerial vehicle and a ground control station by using RDSS service; and the ground control station is used for creating a user interface by using Qt, carrying out data visualization through Tableau and adopting a security protocol. According to the scheme, remote control, remote information transmission and high-precision positioning functions in a complex environment of the unmanned aerial vehicle are realized.

The invention discloses an automatic medical risk screening system, which relates to the technical field of medical information and comprises a data acquisition module, an intelligent analysis module, a risk early warning module, an intervention management module, a complaint management module and a data visualization module. According to the medical risk automatic screening system provided by the invention, risk data sources in a hospital HIS system are collected in real time and are preprocessed and intelligently analyzed, so that the system can more accurately identify potential risk patients and provide timely early warning prompts for medical staff, comprehensive management of medical risks is realized through each module, and through PDCA circulation, the medical risk screening efficiency is improved. According to the technical scheme, the system automatically generates disposal suggestions, distributes disposal tasks, evaluates the intervention effect and optimizes early warning rules, a continuously improved management process is formed, and a multi-dimensional interactive analysis interface is also provided, so that medical staff can intuitively understand information such as risk distribution, intervention effect tracking and complaint causes in each inpatient area.

The invention provides a visualization system capable of synchronously displaying multiple types of data according to time. Comprising a distributed service cluster composed of a micro-service, a user management micro-service, a data transmission micro-service, a data storage management micro-service, a data extraction micro-service, a data visualization display micro-service, a data visualization template management micro-service and a resource monitoring micro-service; the micro-services are packaged in a containerized manner and interact by adopting a lightweight communication protocol, and automatic deployment and dynamic expansion are realized through a container arrangement tool; the system is provided with a basic parameter set with customizable micro-service configuration and a graphical configuration interface which are used for cooperatively generating a system operation environment configuration strategy. The user management micro-service establishes a multi-dimensional authority system based on a role access control model, and records an operation log to realize audit tracking; the data transmission micro-service integrates a plurality of heterogeneous data access modes, carries out transmission process verification and carries out secure channel transmission through the service gateway.

The invention relates to the technical field of big data processing, in particular to a data acquisition and analysis system for network big data information analysis, which comprises a data acquisition module, a data preprocessing module, a data storage module, a data analysis module and a data visualization module, wherein the data acquisition module is used for acquiring original data from various data sources in real time through a network interface; the data preprocessing module is used for preprocessing the original data; the data storage module is used for carrying out storage and management by adopting a distributed database; and the data analysis module is used for performing deep analysis by applying a machine learning algorithm and a data mining technology to generate a trend prediction result. According to the method, real-time acquisition, automatic preprocessing, distributed storage and multi-algorithm fusion analysis of the multi-source data are realized, so that the problems of low data quality, poor processing efficiency, model isolation and the like in the prior art are solved, and the precision, efficiency and decision support capability of big data analysis are remarkably improved.

The invention relates to the technical field of data visualization and integration, in particular to a power equipment fault positioning and maintenance decision-making method based on knowledge graph reasoning, which comprises the following steps of: acquiring a structure parameter generation node, constructing a causal path positioning source component and outputting a maintenance link structure. In the invention, the time attribute is introduced through the node set constructed by the structure number and the operation parameter to strengthen the time sequence characteristic of the power equipment state identification, and the logic response sequence between the nodes is sorted through the causal relationship path chain to improve the accuracy of fault chain reasoning. A multi-path screening mechanism effectively avoids the problems of large path hop count and inconsistent response directions, path dominance identification ensures convergence and pertinence of fault positioning, and key components and maintenance suggested nodes are spliced so that a link structure not only has traceability, but also has operability. An ordered closed loop from structure identification, causal analysis and maintenance output of the power equipment is realized, and the fault processing accuracy and the performability of the power equipment are improved.