63 results about "Big data visualization" patented technology

The invention relates to the technical field of data visualization, in particular to a smart city big data visualization management method, which comprises the following steps of: based on sensing node and monitoring point position data streams, reconstructing time and space labels, identifying abrupt changes and combining the abrupt changes into candidate blocks, classifying positions and events to generate a structure label set, analyzing interaction frequency to extract a hot spot region, and extracting a hot spot region; and sequencing layer numbers to generate a rendering queue, and identifying a high-frequency interaction area to output a focusing block. According to the method, the loading efficiency is improved by reconstructing a time sequence and a space label, extracting continuous mutation data segments and combining the continuous mutation data segments into a visual area, classifying positions and event types to uniformly map layers, enhancing label definition and space coherence, recognizing a hot spot area in combination with interaction frequency and connection density, and rearranging layer element numbers to optimize a rendering sequence; the attention area is positioned according to user clicking, hovering and rolling paths, interface response intelligence and personalized expression are enhanced, and city data visualization accuracy and interaction adaptation capability are comprehensively improved.

The invention relates to the technical field of data processing, in particular to a warehouse goods allocation layout optimization method and system based on big data visualization analysis, and the method comprises the steps: obtaining an access probability thermodynamic diagram of goods in recent periods of a warehouse and the same-batch warehouse-out category data, and according to a high-correlation goods combination determined based on the same batch of ex-warehouse category data, goods layout in a plurality of high-frequency turn-back routes determined based on the access probability thermodynamic diagram is adjusted, and after adjustment is completed, based on the smoothness degree of each arrangement sequence, the target sequence of goods placement in each high-frequency turn-back route is screened, and the target sequence of goods placement in each high-frequency turn-back route is obtained. And the optimization degree of each high-frequency turn-back route is calculated based on the smoothness degree of the target sequence and the length of the high-frequency turn-back route, so that the warehouse goods allocation layout is adjusted based on the target sequence of the high-frequency turn-back route with the relatively high optimization degree. According to the invention, the pick-up efficiency of a pick-up person in a warehouse can be improved.

The invention discloses an industrial smart energy multi-energy evaluation visualization system and method, and relates to the technical field of energy big data visualization crossover, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data, and generating space-time aligned high-dimensional energy data through a space-time hash coding method; based on the three-dimensional manifold coordinate model, converting equipment energy efficiency parameters into an equivalent energy mass field, and through an energy field reconstruction and geometric optimization method, generating a curved manifold space and identifying an optimal transmission path and a distortion strength cloud picture; and based on the distortion strength cloud picture, generating a thermodynamic color gradation through a thermodynamic color gradation mapping algorithm, rendering three-dimensional gradient field distribution of the equivalent energy mass field by adopting a ray tracing method, and generating a visual energy evaluation interface. According to the method, space and time hash codes are generated through a space-time hash coding method, the equipment-grid mapping relation is established in combination with GeoHash coding, and accurate space-time alignment of multi-source heterogeneous data is achieved.

The invention provides a multi-data-source online interface service system, which belongs to the technical field of data services and comprises a data source management module, an interface configuration module, a dynamic parameter processing module, a data caching and synchronizing module, a visual integration module and a security management module. A user can connect various data sources through custom configuration, support data extraction in a full scale acquisition or dynamic SQL query mode, and support parameterized requests to realize data dynamic screening. And after the interface service is modified, the returned data can be synchronized to the large visual screen in real time, so that the instantaneity of data display is ensured. Through the method and the device, the problems of single data source, rigid configuration, update delay and the like in the traditional interface service are solved, the flexibility and the response efficiency of the data service are remarkably improved, and efficient technical support is provided for a big data visualization scene.

The invention relates to the technical field of big data early warning, and discloses an agricultural big data visual analysis early warning method and device, and the method comprises the steps: continuously collecting multi-source agricultural data, carrying out the feature extraction of an agricultural data set, generating project data features, and forming multi-dimensional agricultural analysis features based on the incidence relation between data projects, the method comprises the steps of performing multi-mode data visualization display to construct an agricultural data visualization analysis platform, obtaining agricultural alarm information, marking alarm nodes on the visualization platform, tracing and analyzing forward early warning features based on the alarm nodes, performing future early warning analysis according to the early warning features to generate an early warning report, and performing early warning analysis according to the early warning report. According to the method, accurate prediction and management of agricultural risks are realized through integration and feature extraction of multi-source data in combination with forward and future early warning analysis, the decision-making ability of a user is enhanced through data visualization, and the problem of relatively low reliability of agricultural risk early warning in the prior art is solved.

The invention discloses a big data visualization method and system for an intelligent construction site, and relates to the technical field of intelligent construction site management, and the method comprises the steps: carrying out the standardization processing of construction site data through a data fusion module by employing a federated learning technology and a distributed calculation architecture, and calculating a data fusion efficiency index Df; the data compatibility is optimized by combining an adaptive data cleaning algorithm and a heterogeneous data matching technology, the problem of data islands is solved, and the information sharing efficiency is improved; the environment monitoring module calculates and evaluates an environment risk index Er, and combines BIM, GIS and AR technologies to realize visual early warning of a risk area, so that the accuracy and timeliness of environment monitoring and safety early warning are improved; and the intelligent scheduling module is combined with the construction progress deviation rate Sp, adopts a quantum computing optimization algorithm to intelligently distribute personnel, machinery and materials, and dynamically corrects a scheduling scheme through an adaptive adjustment mechanism, so that intelligent scheduling of construction site resources is realized, and the construction efficiency and the resource utilization rate are remarkably improved.

The invention relates to the technical field of Internet of Things application, in particular to an intelligent security management and control system and method based on energy consumption big data analysis, and the system comprises a TCP communication module, a data analysis processing module, an early warning module and a big data display module. Collecting a data packet in a TCP communication process in real time; the collected data is subjected to preprocessing of data cleaning, duplicate removal and formatting, the processed data is deeply analyzed, and key features are extracted; setting an early warning rule and a threshold value based on a data analysis result, monitoring TCP communication data in real time, and sending detected abnormal information to an upper computer; the big data visualization platform generates a chart of TCP communication data, a data analysis result and early warning information, and independently displays an abnormal information analysis result, so that normal data and abnormal data can be visually displayed through the big data visualization platform, and a manager can quickly and completely know the energy consumption condition.

The invention discloses an industrial big data visual management system based on big data analysis, and relates to the technical field of industrial data management, and the system comprises a data collection module, a data analysis module, a visual early warning module and a strategy optimization module. The data analysis module integrates the three-dimensional data to form a single-main-body full-process and single-element full-access double-track chain, the single-main-body full-process and single-element full-access double-track chain is fused to obtain a behavior track chain, and abnormal nodes are identified through double verification; the visual early warning module visually presents data in a layered manner through a statistical layer and a risk layer, and triggers real-time early warning; the strategy optimization module extracts risk scenes based on the visual data, and adopts different optimization strategies respectively. According to the method, complete tracing of the operation track, accurate identification of abnormal nodes and dynamic optimization of strategies are realized, and the industrial data security control and operation and maintenance efficiency is improved.

The invention relates to a data processing technology, and discloses a big data visualization downsampling method, computer equipment and a storage medium, and the method comprises the following steps: initializing chart setting, and creating a sub-thread running independent of a main thread; the main thread divides original data into blocks, converts the original data into binary data and performs zero copying on the binary data to the sub-thread, and the sub-thread generates view blocks according to the received data blocks and caches the view blocks; the sub-thread combines the cached view blocks into a view memory, performs downsampling processing on the view memory, and returns a downsampling result to the main thread in a streaming manner to complete chart rendering; and when the rendering chart is triggered by zooming operation, the main thread notifies the sub-thread of the corresponding data interval, so that the sub-thread performs binary search in the view memory, performs downsampling processing again based on a search result, and returns a downsampling result to the main thread in a streaming manner to update chart display. According to the method, efficient rendering and smooth interaction of big data visualization are achieved, and the response speed and stability of the system are improved.

The invention belongs to the field of testing, and particularly relates to a large-scale test data visualization method. The invention relates to a large-scale test data visualization method, which comprises the following steps of: carrying out longitudinal huge channel big data visualization, in test unit data, when the number of sampling channels is 8 or less and software design is carried out, constructing a plurality of drawing objects, and configuring one drawing object for each channel; when the number of the channels is increased to 8-16, displaying data of each channel in a drawing object in a layered manner; and when the number of the data channels of the test unit is increased again, the huge data channels are displayed by adopting a channel paging method, and the user switches the current drawing display channel through a page up-and-down turning button. According to the method, through a multi-graph-multi-curve mode, the problem of data visualization of a longitudinal huge channel level is solved; by means of a multi-layer point extraction mode, the problem of data visualization of a transverse timeout long level is solved, and GB-TB level large-scale data visualization can be achieved.

The application discloses a kind of wisdom energy multi-energy evaluation visualization system and method for industry, it is related to energy big data visualization cross technical field, including, acquisition multi-source heterogeneous data and pre-processing, by space-time hash coding method, generate high-dimensional energy data of space-time alignment;Based on three-dimensional manifold coordinate model, the equipment energy efficiency parameter is converted into equivalent energy quality field, by energy field reconstruction and geometric optimization method, generate curved manifold space and identify optimal transmission path and distortion intensity cloud chart;Based on distortion intensity cloud chart, generate thermal color scale by thermal color scale mapping algorithm, and the three-dimensional gradient field distribution of equivalent energy quality field is rendered using ray tracing method, generate visual energy evaluation interface.The application generates space and time hash code by space-time hash coding method, establishes equipment-grid mapping relationship in combination with GeoHash coding, realizes the accurate space-time alignment of multi-source heterogeneous data.

The application belongs to the technical field of computer graphics and big data visualization, and discloses a dynamic hierarchical rendering method, system and application for super-large graph data.The rendering method comprises a dynamic graph layering mechanism based on structural entropy, a super-aggregated node-subgraph rendering architecture and a polymorphic coordination layout calculation strategy.The application solves the challenges existing in rendering large-scale data volume graphs by conventional means, i.e., browser memory overflow caused by direct rendering of million-level nodes at a time and resource waste caused by repeated rendering due to the absence of a cache mechanism, and provides a solution for large-scale data visualization rendering.

The invention relates to the technical field of big data visualization, in particular to a glutinous rice central processing workshop collaboration system for chain stores, which improves convenience and safety. The data source collection unit is used for collecting and storing various types of data sources, and the data source arrangement unit is used for carrying out uniform format processing on the collected data sources, generating a uniform format data source data packet and sending the uniform format data source data packet; the storage module is used for receiving the data view sent by the data view unit and storing the data view; the firewall unit is used for discovering and processing security risks and data transmission during operation of the computer network; the central control unit, the data source acquisition unit, the data source arrangement unit, the data view unit, the storage module, the firewall unit, the encryption unit, the login analysis unit and the IP locking unit are connected and are used for controlling each unit to work normally through the main control computer.

The invention relates to the technical field of big data visualization, and provides a chained quality monitoring information visualization method and system based on meteorological observation engineering. The method comprises the following steps: acquiring project quality monitoring data of each key quality control node in meteorological observation project implementation, and constructing a whole-process stage quality monitoring model comprising a node quality feature description unit, an inter-node quality influence transmission path unit and a quality control threshold interval unit according to the project quality monitoring data; performing association analysis on related units of the model in combination with quality control permission information of each participant in the matrix type organization collaborative architecture to generate a chain type collaborative interaction strategy; and finally, converting the engineering quality monitoring data into a multi-dimensional dynamic view containing a quality control node association relationship view, a quality influence transmission path view and a quality index change trend view according to the strategy, and performing abnormal mode identification on the quality index change trend view. And generating graded early warning information containing the early warning grade and the early warning range.

The invention provides an intelligent evaluation method and system for a film and television virtual production scheme based on big data visualization. The method comprises the following steps: generating an initial video based on demand information; when a user side carries out circling interaction on any initial image frame forming the initial video, carrying out image interception on the initial image frame based on a circling area corresponding to the circling interaction to obtain a target sub-image; an image modification strategy is determined based on an obtained modification mode corresponding to the user side, a modified sub-image corresponding to the target sub-image is generated based on the image modification strategy, and the modification mode comprises manual modification and automatic modification; and carrying out association modification on each modification association frame in the initial video based on the modification sub-image to obtain an updated initial video, and carrying out image comparison on the modification sub-image and the target sub-image to obtain a modification evaluation value corresponding to the modification sub-image. According to the invention, the adaptability of diversified modification requirements is at least improved.

The application relates to the technical field of surveying and mapping geographic information auxiliary planning decision, and discloses a surveying and mapping big data visualization analysis and management method and system for planning decision, which comprises the following steps: obtaining a scanning image of a historical surveying and mapping archive, establishing a mapping table of symbol styles and geographic element types in the scanning image by using a legend analysis algorithm, giving each vector polygon an element type attribute according to the mapping table, and generating historical archive vectorization data; extracting attribute feature sets of geographic elements for each matching record in archive-decision matching data sets, verifying and refining candidate causal rules, and generating an archive-decision causal knowledge base; obtaining surveying and mapping data of a current planning area to be decided, extracting geographic condition attribute feature vectors thereof, searching for historical decision types and historical cases corresponding to matching rules, and generating a planning experience inheritance recommendation report. The application realizes knowledge-based inheritance and reuse of historical planning experience.

The invention provides a vector geographic data efficient visualization method and system based on multi-scale optimization and containerization architecture, and belongs to the field of spatial big data visualization and cloud native GIS application. According to the method, the filling rate of the enclosing rectangles and the number of the vertexes are calculated for the original geographic element data, so that automatic classification of regular elements and irregular elements is realized; and respectively constructing two sets of multi-scale expression systems, and storing four types of multi-scale data tables in a spatial database. A containerized micro-service architecture is adopted, and dependence management and cooperative operation are automatically completed through a container arrangement tool. The vector slicing service dynamically selects a corresponding scale expression according to the scaling level. Through cooperative work of a database trigger, a monitoring service and a cache service, the system can calculate an affected slice number range and execute accurate cache deletion when element attributes are updated, and real-time and on-demand slice updating is realized. The method has the beneficial effects that the visualization performance and updating efficiency of massive vector elements are improved.

The invention discloses an intelligent old-age care management method, device and equipment based on the Internet of Things, and the method comprises the steps: collecting user data of an activity region in real time through Internet of Things equipment, and carrying out the user classification of call types through a clustering algorithm according to the number of demands; selecting service items by calling according to health requirements of the classified users, performing data analysis on user requests according to types of the service items, acquiring service providing information corresponding to calling levels, and then running corresponding pension service response intervention; the user performs data feedback on the service quality according to the call type; the feedback result of the activity area and the user data are displayed in real time through a big data visualization platform; before collecting the user data of the activity area, the endowment informatization management platform checks the entering qualification, and enters the endowment informatization management platform after passing the qualification check; and performing entering operation on the user passing the qualification audit. And intelligent and refined behavior analysis and abnormal behavior management of old-age care are realized.

The invention provides an establishment method of an efficient biological carbon sequestration enzyme database, electronic equipment and a computer readable storage medium. The method comprises the following steps: S1, constructing a carbon sequestration enzyme information source data set; s2, sorting the source data set, and performing classification, redundancy elimination and information improvement to obtain a first data set; s3, calculating the atom economy of each reaction formula of the first data set, and obtaining a second data set based on a theoretical atom economy calculation formula; s4, creating a Web application program; s5, establishing a biological carbon sequestration enzyme database screening mechanism, and screening out a target carbon sequestration enzyme and a carbon sequestration reaction with high atom economy through system screening conditions; and S6, establishing an efficient biological carbon sequestration enzyme database, and visually displaying related information of the carbon sequestration enzyme through a big data visualization technology. The problem that carbon sequestration enzyme related information in an existing enzyme database is much and miscellaneous is solved, and reasonable utilization and conversion of one-carbon resources are accelerated.

The invention relates to the technical field of big data interaction visualization, and discloses a big data visualization interaction display method and system.The method comprises the steps that firstly, sparse projection is conducted on high-dimensional data, and a dynamic grid index is constructed; capturing an interaction behavior to generate a dynamic intention vector; generating a mixed projection result in combination with the global / local projection matrix; recognizing a region of interest based on mixed projection and dynamically adjusting privacy protection intensity; recommending projection parameters through a multi-view association rendering and reinforcement learning strategy; and finally, iteratively optimizing the intention model and the recommendation strategy by utilizing user feedback. The invention further constructs a closed-loop system comprising data preprocessing, intention perception, dynamic projection, dynamic aggregation, visual rendering and feedback iteration modules. The privacy-utility contradiction is balanced through intention-driven hybrid projection and dynamic privacy regulation, cross-scene parameters are recommended in combination with reinforcement learning, a two-way feedback closed loop is constructed to realize continuous optimization of the system, and the interaction adaptability and the long-term evolution capability are improved.

The application discloses a kind of based on big data visualization's transformer substation intelligent monitoring method, it is related to measurement data visualization technical field, including, the offset degree between the space-time fingerprint base template and real-time behavior fingerprint is calculated, generates offset vector set, obtains the distribution of offset vector set in space by kernel density algorithm, generates abnormal density field and space abnormal density point cloud;According to space abnormal density point cloud setting density threshold and solving the equipotential surface of abnormal density field, generate abnormal three-dimensional equipotential surface group;Extract risk evaluation index of abnormal three-dimensional equipotential surface group and carry out dynamic rendering to abnormal three-dimensional equipotential surface group, output dynamic visualization picture.The application constructs space-time fingerprint base template and calculates offset vector set, and abstract offset behavior is converted into three-dimensional risk form with space boundary, expansion trend and density aggregation characteristics, improves the explainability of transformer substation abnormal structure, visual identification and forward-looking monitoring capability.

The invention belongs to the technical field of big data, and particularly relates to a big data visualization system which comprises a data acquisition module, a data storage module, a data processing module, a data visualization module, a user interaction module, an authority management module, a system monitoring module and an alarm and notification module. The operation of the visualization system comprises the following steps: step 1, data acquisition: determining data sources including a database, an API (Application Program Interface) and a sensor, acquiring data from each data source by writing a data acquisition script or using a data integration tool, cleaning the acquired data, and removing repeated, wrong and incomplete data; and 2, data storage: storing the cleaned data in a database or a data warehouse, and backing up the data regularly. According to the method, the data processing and analysis efficiency can be remarkably improved, the data quality and safety are improved, real-time decision making and service optimization are supported, and the stability and reliability of the system are enhanced.

The present invention relates to the technical field of data processing. More specifically, the present invention relates to a method and system for optimizing the layout of warehouse storage locations based on big data visualization analysis. The method includes: obtaining a heat map of the access probability of goods in the warehouse in the recent period and the data of the same batch of outbound product categories, and based on the highly correlated goods combinations determined from the data of the same batch of outbound product categories, adjusting the goods layout in multiple high-frequency return routes determined based on the heat map of the access probability. After the adjustment is completed, based on the smoothness of each discharge order, screening the target order of the goods placement in each high-frequency return route, and based on the smoothness of the target order and the length of the high-frequency return route, calculating the optimization degree of each high-frequency return route, so as to adjust the layout of the warehouse storage locations based on the target order of the high-frequency return route with a larger preference degree. The present invention can improve the picking efficiency of pickers in the warehouse.

The embodiment of the invention provides an underground pipeline big data visualization system, and the system comprises an underground pipeline data collection module which is used for collecting original data needed for forming an underground pipeline digital map; the data processing module is used for storing the data and performing spatial conversion processing on the original data to obtain available data with consistent spatial geolocation reference and unified data logic; the underground pipeline digital map modeling module is used for carrying out grid division on a geographic area; carrying out partition modeling based on available data in each grid, and generating a subregional underground pipeline digital map; the big data visualization module is used for receiving the underground pipeline digital maps of the sub-regions and combining the underground pipeline digital maps to form a complete underground pipeline digital map; and the data is displayed at the visualization terminal, so that the visualization of the underground pipeline is realized. The underground pipeline data can be displayed more visually, a user can obtain information more simply and conveniently, and user experience is improved.

The invention belongs to the technical field of smart water affairs, and particularly relates to a big data visualization processing method and system applied to smart water affairs. The invention aims to solve the problems that data are disordered and difficult to process in the existing integrated water affair pipeline management and data interference results in error reporting when the data is abnormal. The system comprises a monitoring module, a data processing module, a recording module, a water affair system scheduling processing training module, a central scheduling general control module and a mobile terminal module. The monitoring module collects data according to preset partitions, the data processing module cleans and classifies the data in a partition mode, the water affair system dispatching processing training module generates partition fault processing suggestions through deep learning, the central dispatching master control module issues instructions and visually displays the data in a partition mode, and the mobile terminal module feeds back maintenance data. The method is realized through partition acquisition preprocessing, partition model training analysis, scheduling visualization and feedback optimization. The data processing difficulty is reduced, errors are reduced, and the fault processing efficiency and the intelligent water affair management level are improved.

The invention relates to the technical field of big data visualization, in particular to a visual management method and system based on a large model and an AR live-action map, and the method comprises the steps: carrying out the splicing, recognition and marking of multi-dimensional image data through a visual large model, obtaining a plurality of processing results, generating an initial AR live-action map, and displaying the initial AR live-action map through a visual terminal; and analyzing the preset instruction by using the natural language processing large model to obtain a target analysis result, adjusting at least one element in the initial AR live-action map according to the target analysis result to obtain a target AR live-action map, and updating display of the visual terminal to realize visual management. According to the method and the device, comprehensive and visual display of the target area is realized, the monitoring picture of the target area can be quickly updated and displayed in real time, the monitoring content can be flexibly adjusted according to the management requirement, and the management efficiency is improved.

The invention relates to the technical field of big data, and provides an unmanned aerial vehicle cloud data analysis method, device and equipment and a medium, which can adopt a data batch export mechanism to collect unmanned aerial vehicle cloud data and meet the requirement of rapid collection of mass cloud data of an unmanned aerial vehicle. By adopting a Spark elastic distributed data set mode, the data processing efficiency can be further improved based on the cluster parallel processing capability; a plurality of analysis dimensions of small-granularity big data sets are constructed based on a Spark calculation engine, second-level granularity data set splitting is achieved, and the fine requirement for real-time monitoring of the flight state of the unmanned aerial vehicle is met; parallel analysis of a Spark calculation engine is combined with a rapid query function of Cassandra, so that the analysis efficiency is further improved; aggregation calculation is carried out on the multi-dimensional feature data, so that interference of redundant data on decision making is avoided; and a big data visualization analysis tool is used for carrying out personalized visual display on a multi-dimensional analysis result, so that related personnel can be assisted to make decisions in time.

The invention relates to a data media interaction system based on big data visualization, which belongs to the technical field of data processing, and comprises a multi-source heterogeneous data acquisition module configured to acquire structured and unstructured data in real time through a distributed crawler and an API interface; the dynamic preprocessing module comprises a streaming data cleaning engine and a cache management unit and is used for performing real-time data denoising, format conversion and cache optimization; the intelligent analysis engine is integrated with a dynamic modeling unit based on machine learning and an incidence relation self-optimization mechanism; the multi-modal interaction module has a multi-channel input analysis capability of voice, gesture and eye movement tracking; the self-adaptive visual rendering module comprises a GPU accelerated real-time rendering engine and a visual element dynamic recombination unit; and the resource intelligent scheduling module is deployed with a heterogeneous computing resource dynamic allocation algorithm and a load prediction model. According to the invention, multiple data can be collected, and the data can be integrated.

The application relates to the field of educational technology and data mining, and discloses a course learning behavior big data visual analysis method and system. The method first extracts semantic features of heterogeneous teaching resources to construct an initial content index graph and calculate the theoretical semantic distance between nodes; combines the cleaning and weighted aggregation results of multi-dimensional interactive logs, quantifies user behavior work, and obtains group average behavior work; then establishes a path learning impedance model and performs impedance numerical calculation; establishes a force-oriented mapping model based on the impedance, performs energy minimization iteration simulation to obtain the topological tension of the connection edge; according to the comparison result of the topological tension and the dynamic tearing threshold, automatically performs virtual node injection and physical writing operation at the graph database level on the logical fault area; and finally performs dynamic flow visualization rendering based on the reconstructed topological structure. The application can explicitly show the implicit learning impedance as topological tension, and realizes automatic discovery of course logic defects and structural optimization.

The invention relates to the technical field of smart city ecological big data visualization, and particularly discloses a full-dimension big data visualization supervision system for green-top plants. The problem that an existing green top supervision system cannot recognize cross-dimension coupling abnormity is mainly solved. A traditional method depends on a single threshold value alarm, and combined risks such as high illumination and low humidity are difficult to find. According to the method, real-time topological projection is carried out on 50 + dimension plant data by adopting an incremental UMAP algorithm and parameterized t-SNE through a dynamic high-dimensional data dimension reduction engine, and a global health situation map is generated; through a multi-view collaborative analysis system, a parallel coordinate axis and a radial propagation graph are utilized to reveal hidden association between dimensions; constructing an abnormal conduction chain in combination with a graph neural network, and positioning a key inducement combination; and finally, accurate execution and verification of the maintenance scheme are realized through an AR auxiliary decision-making module. According to the invention, the conversion from passive alarm to active early warning is realized, and the accuracy and timeliness of green plant anomaly identification are improved.