734 results about "Big data processing" patented technology

The invention discloses a marketing content generation method and system based on big data, and relates to the technical field of big data processing and artificial intelligence, and the system comprises a multi-source data collection and fusion module which is used for receiving original multi-source data from different behavior sources, content sources and business scene sources, and performing structured processing, time relationship coordination and feature fusion on the original multi-source data to generate a fused portrait data set for describing the relationship between the user and the product. In the invention, through the multi-source data acquisition and fusion module, deep integration of heterogeneous multi-source data and unified representation of multi-modal features are realized, a high-quality data basis is provided for subsequent knowledge graph construction and content generation, and the problems of isolated island and insufficient feature utilization of traditional marketing data are solved; through a ternary knowledge graph construction module, information such as users, products, scenes and the like is constructed into a structured knowledge graph, and the semantic understanding ability and relevance of content generation are greatly enhanced.

The invention provides a data retrieval method, system and device, and relates to the technical field of big data processing, the method comprises the following steps: obtaining a search text of a user; performing data permission assembly according to the search text to obtain a user permission set of the user; according to the user permission set, generating a retrieval filtering condition of the search text; according to the retrieval filtering condition, performing multi-source data search in combination with the search text to obtain an initial retrieval result corresponding to the search text; and performing unauthorized rejection operation on the initial retrieval result to obtain a final retrieval result of the search text. According to the method, the range is defined through permission assembly, the condition focusing target is filtered, the efficiency is improved through pre-matching, and the precision is guaranteed through post-removing, so that the retrieval data volume is compressed to improve the efficiency, and the matching pertinence is enhanced to improve the precision.

The invention relates to the technical field of big data processing and analysis, in particular to a commodity traceability management method and system based on big data. The method comprises the steps of determining a data range and association logic of each type of events by constructing a semantic event system of a commodity life cycle, performing data acquisition, preprocessing and credibility evaluation at edge nodes and generating an evidence header, and further converting data into multilayer verifiable commodity fingerprints of an L1 edge abstract, L2 event level fusion and L3 full link fusion, the method comprises the following steps: realizing feature fusion and dynamic incremental updating, then performing distributed notarization and multi-party verification on fingerprints through a federated anchor point mechanism to ensure that a full link is traceable and cannot be tampered, and finally supporting full link traceability query and anomaly tracking based on a unique commodity identifier. And closed-loop traceable management is realized through visual display and multi-role authority management. According to the invention, the reliability, interpretability and cross-organization trust of commodity traceability are effectively improved.

The invention relates to the technical field of big data processing, in particular to a hydrological information forecast data collection automatic processing method, which comprises the steps of multi-source data automatic acquisition and access, data quality intelligent diagnosis processing and abnormity early warning, multi-index comprehensive drought evaluation model construction and drought trend intelligent prediction. GIS spatial analysis and visual display are carried out, and drought evolution is dynamically reflected; and user-defined configuration and adaptive learning are carried out. The equipment state can be displayed, and related personnel can be prompted to maintain the observation station in time; existing hydrological data of the hydrological bureau are fully applied, rainfall, river flow, soil moisture content and other single items are normatively determined to determine the drought level, and meanwhile, according to multiple indexes, comprehensive drought judgment is performed according to the proportion, and based on the GIS technology, an interpolation method and a proximity association method are utilized to determine a Xinjiang whole administrative division plane drought map; and according to the analysis data, automatically sorting out soil moisture content brief report materials.

The invention discloses an abnormal data processing method and system for a virtual game, and relates to the technical field of big data processing. Receiving an interaction data frame sequence uploaded by the terminal equipment, and analyzing to obtain an input equipment sampling point set and a displacement coordinate vector; performing feature extraction on the sampling point set of the input equipment to obtain a high-dimensional feature vector; mapping the high-dimensional feature vector to a preset behavior feature space, calculating a multi-dimensional statistical distance of the high-dimensional feature vector in the behavior feature space, and generating a first abnormal confidence coefficient; calling space mapping data corresponding to the virtual scene; performing discretization ray stepping detection in the space mapping data based on the displacement coordinate vector to generate a second abnormal confidence coefficient; performing fusion calculation on the first abnormal confidence coefficient and the second abnormal confidence coefficient based on a dynamic weighting algorithm to obtain a comprehensive abnormal score; and judging abnormal data through the comprehensive abnormal score, and executing a state rollback operation for the virtual object.

The invention relates to the technical field of Internet of Vehicles and big data processing, and discloses an Internet of Vehicles user perception method and system based on multi-modal data fusion and dynamic portray.The method comprises the following steps that a vehicle terminal collects operation, interaction and environment data and sends the data to a cloud; the cloud access module cleans and vectorizes data; the real-time calculation module generates a fused context feature by using sliding window aggregation and spatial association; the offline module outputs long-term preference and attribute correction characteristics; the dynamic portrait module combines the above characteristics, and constructs a user portrait comprising basic attributes, real-time states and intention labels by using a rule discrimination and deep learning double-track architecture and an adaptive weighting algorithm; and generating and issuing a control instruction according to the matching rule. According to the invention, through spatio-temporal data alignment, static attribute dynamic correction and intention real-time prediction, the problems of portrait lag and prediction delay are solved, and the accuracy and active perception ability of the Internet of Vehicles service are improved.

The invention discloses a big data measurement asset supply and demand matching and inventory optimization method, and relates to the technical field of big data processing and supply chain management. The method comprises the following steps: segmenting supply chain asset data through a hash function according to multi-dimensional attributes to generate a data fragment set, and constructing a global index tree according to the data fragment set; and monitoring the load state of the leaf nodes of the global index tree in real time, and migrating and verifying data during unbalance. And querying the global index tree, positioning fragment data associated with the query request from the uniformly distributed leaf nodes, generating a preliminary matching asset set, calculating the matching degree of each asset and the query request, and generating a resource allocation result. And updating the global index tree, and generating the latest data view representation. And adjusting the attribute weight coefficient of each dimension in the hash function, and generating optimized storage layout configuration. Irrelevant data are filtered through a neighbor algorithm, and a final matching result set is generated. Balanced storage, efficient query and accurate matching of asset data are realized, and the overall response speed and the resource utilization rate are improved.

The invention relates to a GPU-based adaptive compressed data direct query method, system and device and a medium, and the method comprises the steps: carrying out the data compression of input column data through employing an adaptive compression strategy, and obtaining a block-level structure suitable for GPU storage and calculation; loading the compressed data into a GPU memory, and performing Tile-level memory management by taking Tile as a basic scheduling unit; and according to the received query statement, executing direct query of the compressed data on the Tile level through the GPU, and outputting a query result. By designing a Tile-level direct query framework, a hardware-aware memory management and control flow coordination mechanism and a self-adaptive compression strategy, high-performance query execution is realized without decompression, and the computing potential of the GPU is fully released. The method can be widely applied to the technical field of big data processing.

The invention provides an intelligent road management and maintenance system and method based on multi-model fusion, and relates to the technical field of road maintenance and intelligent traffic. The method comprises the following steps: acquiring road condition data and positioning data through a multi-source data acquisition module; the disease recognition module is combined with a neural network model to analyze the multi-modal data, so that the disease detection and recognition precision is improved; the big data processing early warning module mines a disease rule through a big data model, and realizes disease early warning and predictive maintenance; the digital twin modeling module is used for constructing a road digital twin model and realizing real-time monitoring and simulation analysis; and the decision scheduling module realizes automatic maintenance scheduling based on the output of the modules. Through multi-model deep fusion, the problems of high manual dependence, low precision, lack of predictability and scheduling lag in traditional road management and maintenance are solved, the road maintenance efficiency, quality and intelligent level are remarkably improved, and the maintenance cost is reduced.

The invention relates to the technical field of big data processing and artificial intelligence, and discloses an intelligent statistical method and system for innovation and entrepreneurship data, and the method comprises the steps: obtaining heterogeneous data through a multi-modal data access module, and constructing an initial time sequence semantic hypergraph; measuring a semantic drift vector and an innovative trajectory curvature of the entity through a semantic evolution calculation module; calculating an interaction entropy weight of a hyperedge by combining a historical co-occurrence probability and a time decay factor through an entropy weight measurement module; executing hyperedge splitting and fusion operation based on an interaction entropy gradient according to the curvature and the weight through a topology dynamic reconstruction module, and generating a reconstructed time sequence semantic hypergraph; and finally, searching an effective hyperedge in the reconstructed atlas by utilizing a statistical analysis service module and calculating an innovative momentum index. Through time sequence semantic hypergraph evolution and an interaction entropy weight mechanism, data noise caused by conventional operation is effectively eliminated, a cross-dimension substantive innovation chain is identified and aggregated, and the objectivity of innovation evaluation and the interpretability of a result are improved.

The invention relates to a lithofacies intelligent pattern classification method based on a conditional diffusion probability model, and belongs to the technical field of deep learning, pattern recognition and big data processing. Comprising the following steps: step (1), collecting and preprocessing big data; step (2), constructing a conditional diffusion probability model; step (3), model training and parameter optimization; step (4), generating a category balance sample; (5) enhancing data quality evaluation; and (6) training and verifying the pattern classification model. According to the method, shale lithofacies data enhancement and intelligent mode classification based on the conditional diffusion probability model and the deep learning technology are realized, the lithofacies identification problem under the condition of training set category imbalance is effectively solved, and the minority class lithofacies identification precision and the overall classification performance of the model are remarkably improved; and a reliable technical method is provided for marine shale oil and gas reservoir evaluation and sweet spot prediction.

The invention belongs to the field of big data processing, and particularly relates to an event analysis method, device and equipment and a storage medium, target event information is obtained, the target event information is converted into event feature vectors, the event feature vectors are input into multiple analysis models respectively, and the analysis models output corresponding event types and initial risk levels respectively. Performing similarity matching on the event feature vector and a historical event feature vector of a historical event in a preset knowledge base, screening one or more pieces of historical event information of which the similarity score is greater than a preset threshold, and determining the credibility of each analysis model based on the historical event information and the event type, performing weighted fusion on the initial analysis result based on the credibility of each analysis model, and determining a target risk level corresponding to the target event information; according to the method, by combining multi-model analysis, historical data similarity matching and credibility weighted fusion, the accuracy of risk level judgment of the target event is improved.

The invention belongs to the technical field of big data processing and artificial intelligence, and particularly relates to a distributed efficient processing and intelligent analysis service system and method for mass big data. Comprising a distributed storage management layer, an intelligent metadata index layer, a task semantic analysis layer, an affinity perception scheduler, a multi-mode execution engine and a credible feedback control unit. High-affinity matching of tasks and data is realized by fusing data distribution perception, task semantic understanding and resource collaborative scheduling. According to the scheme, remote data pulling can be remarkably reduced, the scheduling efficiency and the resource utilization rate are improved, and collaborative efficient execution of multi-modal tasks such as streaming, graph calculation and AI reasoning is supported.

The invention discloses an extreme short and temporary rainfall forecasting method and system based on wavelet domain decoupling and multi-course learning, and belongs to the technical field of meteorological big data processing and artificial intelligence deep learning. According to the invention, a meteorological image sequence is decomposed into a low-frequency approximate component and a high-frequency detail component through frequency domain decoupling, a double-branch deep neural network is adopted to extract features and predict corresponding frequency domain coefficients, and a rainfall prediction map is output through fusion of a spectral domain reconstruction and refining module. In addition, a multi-task course learning strategy is introduced during model optimization, and the loss weight is dynamically adjusted. The method is characterized in that modeling is moved to a wavelet domain to explicitly protect high-frequency details, local severe convection is accurately captured in combination with a double-branch architecture and a feature pyramid, the problem of traditional prediction fuzziness is effectively solved, the prediction definition and the extreme rainfall early-warning capacity are remarkably improved, high-fidelity and high-precision minute-level short-temporary rainfall prediction is achieved, and the prediction efficiency is improved. And reliable technical support is provided for meteorological disaster prevention and reduction.

The invention discloses a software development cloud computing system with a big data processing module, which relates to the technical field of computer systems and data processing, and comprises a development input acquisition module used for receiving source code data, construction script data, dependency declaration data, configuration file data and development environment description data, and respectively executing integrity verification, structure analysis, dependency analysis, parameter extraction and environmental element analysis processing to form a standardized development input data set. According to the method, unified feature abstracting and aggregation are carried out on source codes, construction scripts, dependency declarations, configuration parameters and development environment elements, version fingerprint data capable of stably representing the software version state is formed, the strict degree of analysis, verification and influence analysis is remarkably enhanced in a high-difference version scene, and the software version state can be stably represented. Therefore, the problems of misjudgment, missed judgment or resource waste caused by adoption of a fixed threshold value or manual parameter configuration in the prior art are avoided.

The invention provides a self-adaptive data consistency maintenance method for an electric power information system based on incremental updating, and relates to the technical field of big data processing, and the method comprises an initialization step, a detection step and a feedback step. In the initialization step, a network including regional power data nodes and a distributed account book is constructed, and full power data is stored; in the detection step, power data changes are confirmed, key attributes are used for marking data, automatic conflict detection is carried out through a distributed consensus algorithm, solutions for coping with conflicts are dynamically selected, and then an incremental updating mechanism is adopted for synchronously changing the data; in the feedback step, a monitoring system with a closed-loop feedback mechanism is established, the data consistency state is automatically tracked when the electric power data is changed, and abnormity is recognized. Compared with a traditional data synchronization method which needs to synchronize full data, the method provided by the invention only synchronizes the changed data fragments through an incremental updating mechanism, thereby ensuring the consistency of the data, remarkably improving the data processing efficiency and saving the network bandwidth.

The present invention belongs to the technical field of big data processing, and relates to a big data-based highway vehicle profile generation method and apparatus, and a medium. The method comprises: preprocessing structured data in high-speed traffic data of a vehicle, to obtain preprocessed target data, the high-speed traffic data comprising transaction data, gantry license plate identification data, and vehicle model identification data; on the basis of the target data, extracting a target feature of the vehicle, target features comprising a shape feature, a traveling feature, a driving feature, a consumption feature, and a fee evasion feature; on the basis of the target feature, constructing a profile model of the vehicle by means of HiveSql; and on the basis of the profile model, generating a vehicle profile. The big data-based highway vehicle profile generation method provided by the present invention constructs a profile model of a vehicle by using an extracted target feature by means of HiveSql, and the constructed profile model can quickly and accurately generate a vehicle profile.

The invention belongs to the field of big data processing, and particularly relates to an abnormal behavior detection method and device, equipment and a storage medium, and the method comprises the steps: obtaining an image sequence of a target region, extracting feature regions of multiple objects in multiple frames of continuous images contained in the image sequence, determining the target feature trajectory data of each object, obtaining the spatial motion data of the target region, and obtaining the spatial motion data of the target region; the spatial motion data is sensing data generated when an object performs physical motion in a target area, aligning the target feature trajectory data and the spatial motion data according to a timestamp, generating time-synchronized multi-modal time sequence data, inputting the multi-modal time sequence data into a pre-trained abnormal behavior detection model, and outputting a detection result, the detection result is whether an abnormal behavior exists or not and the target object with the abnormal behavior; according to the method, accurate and synchronous abnormal behavior and corresponding target object detection is realized by fusing the image and sensing multi-modal data, and the detection accuracy and reliability are improved.

The invention discloses a medical insurance intelligent auditing method and system based on a multi-modal large model, and relates to the technical field of big data processing, and the method comprises the steps: recognizing a material name and a personal name, and auditing the compliance of a material needed for business handling according to the integrity and correctness of the material; identifying first expense detail data according to the expense detail list material and the expense identification cue word; matching the first expense detail data with a medical insurance catalog vector database to obtain a closest catalog entity, and combining the closest catalog entity with the first expense detail data to form second expense detail data; obtaining medicine knowledge from the medicine specification, matching the medicine knowledge with the medical insurance directory vector database, and associating the directory code of the optimal directory entity into the corresponding medicine knowledge; and for each record of the second expense detail data, performing expense rationality auditing according to the corresponding intelligent auditing rule and medicine knowledge. Content acquisition, medical project matching and intelligent auditing of the electronic material are realized, and the auditing speed and accuracy are improved.

The invention discloses a film covering process chain quality tracing method and system based on a knowledge graph, and relates to the technical field of quality tracing big data processing. According to the specific implementation scheme, the method comprises the following steps: carrying out coating dynamic process parameter curve feature analysis to determine whether to adopt time sequence curve feature optimization and coating timestamp consistency calibration, carrying out coating map time sequence causal judgment to determine whether to trigger map time sequence constraint supplementation and association relationship complementation, carrying out coating process quality tracing reasoning, and carrying out coating process quality tracing reasoning. And after the reasoning is finished, carrying out sequential logic qualification verification on the coating traceability path, if the verification is qualified, outputting a final coating quality defect traceability result, and if the verification is not qualified, sending an unqualified prompt of the coating quality defect traceability result, so that the problem of low accuracy of the coating process chain quality traceability result can be solved.

The invention relates to the technical field of computer big data processing and retrieval, and discloses a data multivariate storage and full-text retrieval system which is characterized in that an isomorphic hash intake module is used for truncating an input data stream into logic data blocks, extracting lexical elements to generate isomorphic hash packets and distributing the isomorphic hash packets to a bottom layer for storage; the state monitoring module maintains heat potential energy and queries fingerprints, updates the state according to scanning feedback, and sends out a trigger signal when the heat exceeds a dimension rising threshold value; the differential projection module responds to the signal to read the isomorphic hash packet, and constructs a differential inverted index by calculating the query fingerprint and the intersection of the query fingerprint; and the cascade retrieval module executes memory search when the index is mounted, and scans the logic data block and feeds back a result to the state monitoring module if the index is not hit. According to the method, the heat potential energy of the data block is tracked through the state monitoring module, index construction is triggered only when query accumulation reaches the dimension raising threshold value, and cold start resource consumption of the system is obviously reduced.

The invention discloses a landslide detection deep learning method fusing geometry, context and frequency domain multi-source features, and belongs to the technical field of geological disaster recognition and landslide remote sensing information processing. The problem that a traditional landslide detection method in the prior art is low in recognition precision is solved. The method comprises the following steps: constructing a landslide detection model based on a GCF-Net architecture, and inputting a to-be-detected image into the landslide detection model; capturing feature information of different scales in the to-be-detected image through a ghost pyramid feature fusion module; an output characteristic pattern is input to a decoder through a GCFmodule; and an EdgeECI module in the decoder is used to process the output feature map of the GCFI module, and a final output feature map after fusion is obtained. The landslide detection accuracy is effectively improved, and the method can be applied to geological disaster monitoring and early warning, mountain traffic and infrastructure safety, water conservancy and energy engineering operation monitoring, remote sensing big data processing platforms and other scenes.

The invention relates to the technical field of computers and big data processing, and discloses an electric quantity data processing method based on industrial big data and an electric energy metering system.The method comprises the steps that an intelligent electric energy metering terminal collects and encrypts original electric quantity data; uploading to an alliance chain network for distributed hash evidence storage; the data compliance is verified through zero-knowledge proof without leaking an original value; dynamic noise disturbance is applied to the transaction data in combination with differential privacy; and the intelligent contract automatically completes authority verification and settlement. The system comprises an intelligent metering terminal, an alliance chain network, a zero-knowledge proof verification unit, a differential privacy disturbance unit and a data transaction settlement module. By fusing the alliance chain, the zero-knowledge proof and the differential privacy technology, tampering-free, traceable, strong privacy protection and compliant transaction of the electric quantity data are realized.

The invention relates to the technical field of electric power Internet of Things, in particular to a time-space hierarchical data compression method and system for multi-source heterogeneous data on a power distribution network side, and the method comprises the following steps: S1, carrying out the classification and lightweight compression processing of original data, and outputting intermediate data with a priority identifier; s2, based on the intermediate data obtained in the step S1, a gateway layer carries out topological grouping according to the electrical distance, and then carries out time alignment and weighted fusion on the data in the group to generate a fusion data packet; and S3, based on the fusion data packet obtained in the step S2, the cloud platform performs panoramic insight and value evaluation, generates and dynamically issues an optimization strategy instruction, and the strategy instruction is dynamically issued back to the edge layer and the gateway layer through a control channel. According to the method, the real-time advantage of edge calculation and the big data processing capability of a cloud platform can be fully exerted, modules of all levels work cooperatively, a set of complete power distribution network multi-source heterogeneous data processing solution is formed, and the overall efficiency of the system can be effectively improved.

The invention relates to the technical field of agricultural production resource big data processing, and discloses an agricultural production resource configuration optimization and management system based on deep learning, which realizes deep fusion of macroscopic remote sensing and microscopic sensing data by constructing a space-time heterogeneous graph neural network, effectively solves the problem of mismatching of space-time scales of multi-source agricultural data, and improves the accuracy of agricultural production resource configuration optimization and management. By using the domain adversarial training and meta-learning technology, the system can extract environment-independent general crop characteristics from source domain historical data, and by combining physical mechanism constraints, the rapid self-adaption of the model can be completed in a target new farm only by using a very small number of samples, so that the data acquisition cost and the model deployment period are remarkably reduced, and the model deployment efficiency is improved. In addition, a physically guided loss function ensures that a prediction result accords with a biological law, and accurate on-demand distribution of water and fertilizer resources is realized in combination with a reverse resource mapping algorithm, so that agricultural production resources are greatly saved while the crop yield is increased, and the robustness and generalization ability of an intelligent system in a complex and changeable environment are enhanced.

The invention relates to a diagnosis and treatment decision path generation method and device, a storage medium and electronic equipment, and relates to the technical field of medical big data processing, and the method comprises the steps: obtaining original multi-modal medical data associated with a target patient, and carrying out the alignment of the original multi-modal medical data based on a preset cross-modal semantic alignment model, the aligned multi-modal medical data is obtained; target user features of the target patient are determined according to the aligned multi-modal medical data, and structured feature nodes associated with the target patient are constructed according to the target user features; based on a preset disease diagnosis and treatment rule and the structured feature node, generating an interpretable candidate diagnosis and treatment decision path associated with the to-be-detected disease of the target patient; and inputting the interpretable candidate diagnosis and treatment decision path into a preset path prediction model to obtain an interpretable target diagnosis and treatment decision path associated with the to-be-detected disease of the target patient. The accuracy of the target diagnosis and treatment decision path is improved.

The invention provides a dairy product quality monitoring method and system based on big data, and relates to the technical field of big data processing. The method comprises the following steps: acquiring historical batch quality monitoring data of dairy products of the same type, extracting quality feature information based on production batches, and forming historical batch quality feature information corresponding to different production batches; performing range control clustering analysis based on content coincidence on the historical batch quality feature information corresponding to different batches to form batch content range control clustering data; and obtaining quality sampling data of the current batch of dairy products, and performing quality monitoring analysis by combining the batch content range control clustering data to form a real-time dairy product batch quality monitoring result. According to the method, more reasonable and accurate milk powder quality monitoring is realized through reasonable milk powder batch component content analysis, and the milk powder production quality is fully and effectively guaranteed.

The invention provides an alignment method for territorial space planning multi-modal data, and belongs to the technical field of big data processing.The alignment method comprises the steps that a quadtree structure is adopted to segment a remote sensing image, a three-dimensional index is established, a graph segmentation algorithm is utilized to divide vector data space subgraphs, a space-time weight matrix fusing space weight, time weight and credibility weight is constructed, and the three-dimensional index is established; inputting the preprocessed multi-modal data into a multi-modal semantic fusion model comprising a residual neural network, a bidirectional encoder and a graph convolutional network to extract a unified semantic representation vector, identifying conflict data by calculating semantic similarity, constructing a rule priority directed graph, and calculating a rule importance score by adopting a random walk algorithm; and executing the conflict resolution rule according to the priority and outputting the aligned data set. The technical problem that efficient alignment of the multi-source heterogeneous territorial space planning data on the semantic level is difficult to realize is solved.

The invention relates to the technical field of line loss assessment, in particular to a power system line loss assessment method and system based on big data processing, and the method comprises the following steps: collecting voltage, current and phase angle signals of a power supply section, carrying out the frequency domain transformation, extracting the amplitude and phase of a frequency component, and generating a frequency spectrum feature set; energy spectrum characteristics and a migration sequence are calculated to analyze spectrum form stability, a risk section is identified by combining a power flow direction and an energy migration included angle difference, and a line loss assessment result is generated by combining a gravity center drift trend and an energy change sequence. Energy spectrum features and migration sequences are extracted to describe power grid energy distribution changes, an included angle difference between a power flow direction vector and an energy migration direction is calculated to screen an abnormal section, an energy drift trend is analyzed in combination with a frequency spectrum gravity center track, line loss real-time quantification and area positioning are achieved, and abnormal loss identification precision and operation evaluation stability and reliability are improved.

The invention relates to the technical field of space-time big data processing, and discloses a Web-end-oriented space-time big data efficient rendering optimization method and system, a storage medium and electronic equipment, and the method carries out unified and intelligent management on the whole rendering process by introducing a central rendering task scheduler. Space-time big data fragments are split into fine-grained vector tile rendering tasks, a dynamic rendering priority score is calculated for each task, and the vector tile rendering tasks with high priorities are sequentially taken out from the head of a dynamic priority queue according to a current available rendering resource threshold value of a Web end. The method comprises the following steps: acquiring a plurality of rendering tasks, distributing the rendering tasks to a plurality of parallel execution rendering task execution units for real-time rendering processing, outputting a final spatio-temporal data visual image, and dynamically adjusting a task execution sequence and resource allocation in combination with real-time system monitoring, thereby preferentially ensuring that contents most concerned by a user are quickly rendered under limited Web end resources, and improving the user experience. And an efficient and smooth visual effect is realized.