30 results about "Big data visualization" patented technology

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 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, 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 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 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 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, 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 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.

The invention provides a big data visualization-based meteorological event monitoring data processing method and system, and the method comprises the steps: building a multi-source data input channel for meteorological event monitoring, receiving and integrating meteorological observation data of different monitoring dimensions, carrying out the spatial-temporal feature collaborative coding of the meteorological observation data, and carrying out the spatial-temporal feature collaborative coding of the meteorological observation data; the method comprises the following steps: generating a multi-dimensional meteorological characteristic spectrum with time relevance, constructing a visual rendering model based on the multi-dimensional meteorological characteristic spectrum, generating a meteorological event thermodynamic spectrum containing meteorological element space distribution characteristics, carrying out spatio-temporal evolution mode recognition on the meteorological event thermodynamic spectrum, extracting a development characteristic sequence of a meteorological event, and carrying out data processing on the meteorological event thermodynamic spectrum. And carrying out meteorological event early warning grade judgment based on the development feature sequence, and generating a meteorological early warning instruction containing an early warning grade identifier and an influence region boundary. According to the invention, the accuracy and timeliness of meteorological early warning can be improved, so that the overall effect of meteorological event monitoring data processing is effectively improved, and powerful support is provided for accurate early warning and response of meteorological events.

The application relates to the technical field of big data visualization, and provides a chain quality monitoring information visualization method and system based on meteorological observation engineering. The method comprises the following steps: collecting engineering quality monitoring data of each key quality control node in the implementation of meteorological observation engineering; constructing a whole-process stage quality monitoring model comprising a node quality feature description unit, a quality influence transmission path unit between nodes and a quality control threshold interval unit according to the engineering quality monitoring data; then, combining quality control permission information of each participant in a matrix organization collaborative framework, performing correlation analysis on the related units of the model, and generating a chain collaborative interaction strategy; finally, converting the engineering quality monitoring data into multidimensional dynamic views comprising a quality control node correlation view, a quality influence transmission path view and a quality index change trend view according to the strategy, performing abnormal mode identification on the quality index change trend view, and generating hierarchical early warning information comprising early warning grades and early warning ranges.

The invention discloses an intelligent monitoring method for a power transformation and distribution station based on big data visualization, and relates to the technical field of measurement data visualization, and the method comprises the steps: calculating the offset degree between a space-time fingerprint base template and a real-time behavior fingerprint, generating an offset vector set, obtaining the distribution of the offset vector set in the space through a kernel density algorithm, and carrying out the calculation of the offset vector set. Generating an abnormal density field and a space abnormal density point cloud; setting a density threshold according to the spatial abnormal density point cloud, solving an equipotential plane of an abnormal density field, and generating an abnormal three-dimensional equipotential plane group; and extracting a risk evaluation index of the abnormal three-dimensional equipotential plane group, performing dynamic rendering on the abnormal three-dimensional equipotential plane group, and outputting a dynamic visual picture. According to the method, the space-time fingerprint base template is constructed, the offset vector set is calculated, an abstract offset behavior is converted into a three-dimensional risk form with a space boundary, an expansion trend and a density aggregation feature, and the interpretability, the visual identification degree and the prospective monitoring capability of the abnormal structure of the power transformation and distribution station are improved.

The invention relates to the technical field of educational resource data visualization, in particular to an educational resource big data visualization analysis method and system based on a knowledge graph, and the method comprises the steps: obtaining educational resources, carrying out the knowledge extraction, obtaining triads, and obtaining the word vectors of all entity words in all triads; for the chapters under the same subject, obtaining a correlation coefficient between any two entity words in each paragraph in each chapter, and constructing a weighted undirected graph of each chapter; obtaining an intensity coefficient for relation connection between entity words corresponding to any two nodes in each weighted undirected graph; performing relation connection processing on the entity words in each chapter in the knowledge graph construction process; and obtaining a consistency coefficient and an alignment confidence coefficient between any two entity words in different chapters, and performing entity alignment processing on the entity words in different chapters in a knowledge graph construction process. The invention aims to improve the integrity and accuracy of the knowledge graph.

The application relates to the technical field of Internet of Things data processing and artificial intelligence application, in particular to a smart campus AIOT big data visual analysis method, which comprises the following steps: collecting heterogeneous behavior data; performing Z-Score standardization processing on the heterogeneous behavior data to generate dimensionless standard scores; constructing a behavior state matrix; generating compliance entropy; calculating an AI intervention confidence score; comparing the AI intervention confidence score with a preset dynamic decision threshold; if the AI intervention confidence score is higher than the dynamic decision threshold, performing AI precise induction intervention; otherwise, performing space mapping calculation on the compliance entropy to generate an administrative intervention resource allocation amount; calculating the difference between the number of false positives and the system expected false positives; using the updated data entropy penalty factor for AI intervention confidence score calculation at the next time step; and the application solves the problem of high false positive rate caused by the lack of logical verification in the prior art, and ensures the robustness and reliability of early warning decision.

This invention discloses a big data-based evaluation method for marine land use in territorial spatial planning, relating to the field of territorial spatial planning technology. It collects multi-dimensional planning data, including current marine area use, territorial spatial planning, marine functional zoning, and ecological environment data. This data is then integrated using GIS technology to construct a database of marine land use for projects. Big data visualization tools are used to overlay and display the multi-dimensional planning data, presenting spatial layout relationships and identifying potential conflict areas. By integrating multi-dimensional data from current marine area use, territorial spatial planning, marine functional zoning, and ecological environment data, and utilizing big data technology for refined evaluation, this invention provides a more comprehensive reflection of the impact of marine land use projects on territorial space compared to traditional methods. It avoids biases caused by limited data and experience-based judgments, thereby improving the accuracy of planning evaluation and ensuring that marine land use projects comply with the detailed requirements of territorial spatial planning.

This invention discloses a method and system for visual analysis of big data in enterprise operations, specifically relating to the field of enterprise operation data processing and visualization analysis technology. It includes acquiring target action records, edge fact records, and cloud fact records. Each edge and cloud fact record includes a business object identifier, a fact item identifier, a fact value, an observation time, and a reception time. By extracting the edge-seen facts and cloud-seen facts at the time the target action occurred, backfilled facts formed before the action occurred but received by the cloud after the action occurred are identified. Based on these backfilled facts, the cloud-time records are corrected and visually evaluated.

The application relates to the technical field of big data interaction visualization, and discloses a big data visualization interaction display method and system, which first carries out sparse projection on high-dimensional data and constructs a dynamic grid index; then captures an interaction behavior to generate a dynamic intention vector; combines a global / local projection matrix to generate a hybrid projection result; identifies an attention area based on the hybrid projection and dynamically adjusts a privacy protection strength; recommends a projection parameter through multi-view association rendering and a reinforcement learning strategy; finally, iteratively optimizes an intention model and a recommended strategy by using user feedback; and the application further constructs a closed-loop system comprising data preprocessing, intention perception, dynamic projection, dynamic aggregation, visualization rendering and feedback iteration modules. The intention-driven hybrid projection and dynamic privacy control balance the privacy-utility contradiction, the reinforcement learning is combined to recommend cross-scene parameters, a bidirectional feedback closed loop is constructed to realize system continuous optimization, and the interaction adaptability and long-term evolution capability are improved.

The invention provides a big data visual display optimization control method, front-end equipment and a storage medium, and relates to the field of front-end data display. The method comprises the following steps: if a request is not a first request, integrating request incremental data and front-end cache data into new cache data; if the request is the first-time request, forming new cache data by full-amount request data; if the new cache data is not greater than the maximum data display quantity and is displayed for the first time, displaying based on the new cache data; if the new cache data is not greater than the maximum data display quantity and is not displayed for the first time, generating an incremental image based on the incremental data, splicing the incremental image with the original image and then displaying the spliced image; and if the new cache data is greater than the maximum data display quantity, performing point sampling on the new cache data and displaying the new cache data. According to the method, the requested data are placed in the front-end cache region, subsequent data requests only need to request incremental data, the display mode is dynamically selected by combining the data size and whether the data are displayed for the first time or not, and the data processing efficiency and the display efficiency are improved.

The application relates to the technical field of big data visualization, in particular to a big data playback system and method based on a data layering algorithm, which comprises an eight-channel data decomposition module, a Zarr multi-layer data pyramid construction and storage module, an overview drawing module, a window point budget real-time calculation module, a selected layer block reading module, a visual secondary thinning module and a front-end rendering drawing module. The application takes the Min-Max method as an algorithm basis, designs a bucket sampling data structure based on the Zarr format, and performs data pyramid layering on 8-channel 100-GB-level data. Based on the data layering structure, the application designs 100-GB-level data rapid playback visualization software in view of the display requirement of 8-channel parallel acquisition data. The application has the characteristics of self-adaptive layering data reading and can be widely applied to the scene of massive data rapid playback and interactive analysis required in the fire gun continuous shooting state monitoring or other industrial test processes.

This invention discloses a big data visualization system for marine equipment, specifically relating to the field of visualization technology. It involves collecting underwater environmental monitoring data, equipment operating status data, and location information data from marine equipment; analyzing the abnormal change characteristics of the underwater environmental monitoring data and the spatial distribution characteristics of the location information data to generate environmental anomaly characteristic data and location spatial characteristic data; analyzing the operating trend characteristics and historical fault mode characteristics of the equipment operating status data to obtain equipment status trend data and historical fault characteristic data; outputting a marine environment visualization layer through spatial coupling analysis; outputting an equipment operation prediction visualization layer through equipment status prediction analysis; and merging the marine environment visualization layer and the equipment operation prediction visualization layer to form a unified multi-dimensional visualization interface; thus achieving a comprehensive and intuitive display of marine equipment operating data and environmental data.

The application relates to the technical field of big data analysis and visualization, in particular to a big data visualization analysis display method, which comprises the following steps: constructing and maintaining a unified knowledge graph for fusing and semantically representing multi-source heterogeneous data; based on the knowledge graph, a predefined visualization description language is used to generate a visualization configuration specification defining a plurality of views and linkage rules between the views; in response to a user's interactive operation on any view, other views affected according to the linkage rules are determined, and information is distributed to related views based on triggered data state changes to realize synchronous rendering update; the user's interactive history is collected and analyzed to construct and update a user portrait reflecting the user's visualization preferences; during the interactive process, based on the user portrait, a recommendation model is used to adjust the linkage rules or the display form of the views in real time. The application realizes intelligent linkage and personalized adaptive optimization of multiple views, and improves the efficiency and experience of the visualization analysis.

The invention relates to the technical field of Internet of Things data processing and artificial intelligence application, in particular to a smart campus AIOT big data visualization analysis method, which comprises the steps of collecting heterogeneous behavior data; performing Z-Score standardization processing on the heterogeneous behavior data to generate a dimensionless standard score; constructing a behavior state matrix; generating a compliance entropy; calculating an AI intervention confidence score; comparing the AI intervention confidence score with a preset dynamic decision threshold; if the AI intervention confidence score is higher than a dynamic decision threshold value, AI precise induction intervention is executed; otherwise, executing space mapping calculation on the compliance entropy to generate an administrative intervention resource allocation quantity; calculating a difference value between the false alarm number feedback and the expected false alarm number of the system; the updated data entropy penalty factor is used for AI intervention confidence score calculation of the next time step; according to the invention, the problem of high false alarm rate caused by lack of logic verification in the prior art is solved, and the robustness and reliability of early warning decision are ensured.

This application relates to the field of educational resource data visualization technology, specifically to a method and system for big data visualization and analysis of educational resources based on knowledge graphs. The method includes: acquiring educational resources and extracting knowledge to obtain triples; obtaining word vectors for each entity word in each triple; for chapters within the same subject, obtaining the correlation coefficient between any two entity words in each paragraph of each chapter, and constructing a weighted undirected graph for each chapter; obtaining the strength coefficient of the relationship connection between entity words corresponding to any two nodes in each weighted undirected graph; performing relationship connection processing on entity words within each chapter during the knowledge graph construction process; obtaining the consistency coefficient and alignment confidence between any two entity words in different chapters, and performing entity alignment processing on entity words in different chapters during the knowledge graph construction process. This application aims to improve the completeness and accuracy of knowledge graphs.

The invention relates to the technical field of constructional engineering informatization management, and particularly discloses a visual management and control system and method for constructional engineering operation and maintenance big data. According to the system, full-dimension operation and maintenance data is acquired through a multi-source heterogeneous data acquisition module, after the full-dimension operation and maintenance data is processed by a data fusion and feature engineering module, a dynamic three-dimensional thermodynamic diagram and an efficiency instrument panel are generated by a space-comfort dynamic visualization engine to be presented, and an intelligent self-adaptive management and control decision module based on deep reinforcement learning is utilized to realize real-time management and control. And generating an optimization regulation and control instruction for the environment and energy system. According to the invention, transparent insight and self-adaptive precise management and control of the operation state of the building are realized, and the space resource allocation efficiency and the energy utilization efficiency are improved.

The invention belongs to the technical field of computer graphics and big data visualization, and discloses a dynamic hierarchical rendering method and system for super-large-scale atlas data and application. The rendering method comprises a structure entropy-based dynamic graph layering mechanism, a super-cluster node-subgraph rendering architecture and a polymorphic coordination layout calculation strategy. According to the method, the challenges existing in the conventional means for rendering the large-scale data volume atlas are solved, that is, browser memory overflow caused by direct rendering of million-level nodes at a time and repeated rendering resource waste caused by lack of a cache mechanism are solved, and a scheme is provided for large-scale data visual rendering.