96 results about "Data complexity" patented technology

The invention discloses an intelligent management system and method for knowledge graph quality evaluation and self-repairing, belongs to the technical field of knowledge graphs, and aims to solve the problems that in traditional knowledge graph management, manual auditing efficiency is low, an effective automatic repairing means is lacked, and data complexity and real-time changes are difficult to deal with. The system firstly collects multi-source heterogeneous data in a target field, cleans the data through a deep learning noise recognition model, extracts entities and relationships by using a natural language processing technology, and adds metadata to convert the entities and relationships into graph structure data; then, a graph framework is defined based on the ontology, entity semantic alignment is achieved in combination with a graph neural network, and a knowledge graph is constructed by complementing implicit relations with the help of a pre-training language model. Then, the quality of the atlas is quantitatively evaluated through a four-layer quality evaluation system, meanwhile, a repair scheme is generated based on vulnerability feature extraction, knowledge base matching and decision fusion, and intelligent self-repair is achieved; the map can be monitored in real time and evaluated regularly, a repair strategy and a knowledge base are optimized through reinforcement learning, it is ensured that the map is kept accurate and time-efficient for a long time, and the practical value is improved.

The embodiment of the invention discloses a multi-modal large model optimization method and device for a complex task and a medium, and relates to the technical field of multi-modal large models.The method comprises the steps that multi-modal input data are received, modal specific feature extraction is carried out on the multi-modal input data, and initial feature representation of each modal is generated, the multi-modal input data comprises image data, text data and audio data; generating a dynamic parameter adjustment strategy based on a pre-acquired task type and input data complexity, and adjusting calculation parameters of a multi-modal encoder in the multi-modal large model according to the dynamic parameter adjustment strategy to determine a corresponding optimization modal processing sub-module; and performing cross-modal fusion by optimizing the modal processing sub-module and the initial feature expression to generate a fused multi-modal feature expression, and performing semantic analysis and reasoning on the multi-modal feature expression by using a pre-trained multi-modal large model to generate a task output result.

The invention discloses an artificial intelligence large model reasoning acceleration method and system based on a GPU and an NPU, and relates to the technical field of large models. The invention discloses an artificial intelligence large model reasoning acceleration system based on a GPU (Graphics Processing Unit) and an NPU (Network Processing Unit). The system comprises a reasoning information shunting module and a reasoning data acceleration module, according to the method, by combining input data features, model structure dynamic control and heterogeneous computing resource collaborative scheduling, highly-adaptive large model reasoning optimization is realized, accurate matching of computing resources and data complexity is realized, and the overall reasoning throughput and response speed are remarkably improved; a dynamic scheduling mechanism based on a strategy output model is adopted, GPU / NPU calculation tasks are reasonably distributed, resource idling and congestion are avoided, the hardware utilization rate is improved, and the method is particularly suitable for multi-task concurrent and high-frame-rate video scenes.

The invention relates to a ship user behavior self-learning recommendation system based on a large model, and relates to the technical field of ship informatization. According to the system, through collection and fusion of multi-source heterogeneous ship user behavior data, a large-scale pre-training language model (large model) is utilized to carry out deep understanding and semantic mining on massive ship field text information and user behavior sequences, and a ship field knowledge graph or semantic vector space is constructed. The large model can identify and predict potential demands, behavior patterns and preference changes of ship users, and generates highly personalized, accurate and prospective ship service, product, route or information recommendations in combination with real-time operation data and external environment factors. Besides, a user feedback self-learning mechanism is introduced into the system, recommendation strategies and model parameters are continuously optimized according to interaction behaviors and explicit evaluation of the users in modes of reinforcement learning or continuous learning and the like, and intelligent iteration of the system and continuous improvement of the recommendation effect are achieved. According to the method, the challenges of a traditional recommendation system in the aspects of data complexity, semantic gaps and dynamic demand adaptability in the ship field are effectively solved, and the ship operation efficiency and the user satisfaction degree are remarkably improved.

The invention relates to the technical field of smart park data monitoring, and discloses an artificial intelligence smart park multi-source data monitoring and analysis method. The method comprises the following steps: constructing a park operation state basic model; when the model is identified to be abnormal, data dynamic change characteristics are extracted by utilizing a spatio-temporal data fusion technology, and an abnormal event mode in a specific region is identified by combining deep belief network deep analysis; calculating data complexity by using permutation entropy, optimizing and analyzing an abnormal propagation path in combination with an A star algorithm, and evaluating a diffusion range and an influence path; recognizing a comprehensive risk area, analyzing the environment and equipment state characteristics of the area through a multispectral imaging technology, and evaluating the operation abnormity in combination with real-time multi-source data; and performing accurate intervention on the comprehensive risk area based on the abnormal condition. According to the method, effective integration and analysis of multi-source data of the smart park are realized, abnormity is accurately identified, risks are mastered, a scientific means is provided for park management, and stable and efficient operation of the park is guaranteed.

The invention provides a significant wave height prediction method and device and electronic equipment, and relates to the technical field of sea wave data processing. The method comprises the following steps: acquiring an effective wave height data set for wave height prediction; determining model input data based on the correlation; carrying out two-layer decomposition on the effective wave height; wherein the primary decomposition comprises empirical mode decomposition based on a self-adaptive noise complete set and K-means clustering of mode components; the secondary decomposition comprises the step of optimizing variational mode decomposition of parameters based on a crown porcupine optimization algorithm; inputting model input data into a bidirectional long-short-term memory network based on an attention mechanism for training; the method comprises the steps of firstly screening data with relatively high correlation with the significant wave height as input data to reduce the calculation amount, then performing two-layer decomposition on the significant wave height to reduce the data complexity, and finally fully extracting data features through a bidirectional long-short-term memory network based on an attention mechanism to comprehensively predict the data so as to improve the prediction accuracy of the significant wave height. And the accuracy of predicting the significant wave height is improved.

The invention provides a financial risk prediction method based on a multi-objective ensemble learning algorithm, and aims to solve the problems of multi-risk type comprehensive evaluation, data complexity and nonlinear relation processing, dynamic change adaptability and the like in risk prediction in the financial field. According to the method, a financial time sequence is divided into a plurality of time blocks through a space-time block data preprocessing technology, and a heterogeneous model library is independently trained, so that the adaptability and prediction precision of a model are improved. Meanwhile, an NSGA-II multi-objective optimization algorithm is combined with Gibbs distribution to dynamically distribute weights, the prediction precision and the engineering practicability are balanced, and dynamic optimization of economic indexes and engineering indexes is achieved. In addition, a time decay and mixed attention mechanism is designed, multi-model prediction results are fused, market dynamic changes are further captured, and the accuracy and real-time performance of risk prediction are improved. According to the method, the precision and the real-time response capability of financial risk prediction are effectively improved.

The invention belongs to the technical field of data security, and particularly relates to a non-intrusive power load identification method and system based on an intelligent fusion terminal, and the method comprises the steps: collecting power load data, and dividing an analysis window based on the power load data; calculating a volatility index corresponding to the analysis window based on the data in the analysis window so as to represent the data complexity in the analysis window; determining the number of iterations for encryption based on the volatility index; and executing a compression algorithm on the data in the analysis window to generate compressed data, and executing multiple rounds of encryption perturbations determined by the number of iterations on the compressed data to generate a dynamically encrypted compressed data stream. According to the method, the privacy protection intensity can be adaptively adjusted according to the dynamic complexity of the data, and the privacy leakage risk caused by the fact that the static encryption intensity cannot adapt to the data dynamics in the prior art is solved.

The invention provides a method and device for analyzing a video and electronic equipment. The method comprises the following steps: acquiring video image data and a video analysis task; performing image sparse processing on the video image data by using a target detection model according to the video analysis task to generate sparse image data; performing feature extraction on the sparse image data by using a visual embedding model to generate visual embedding features; according to a multi-modal cue word template corresponding to the video analysis task, constructing multi-modal dialogue data based on the visual embedding features; and processing the multi-modal dialogue data by utilizing a visual language model to generate an analysis result corresponding to the video analysis task. The analysis result of the visual language model on the multi-modal dialogue data is the result of the corresponding video analysis task, and the data volume and the data complexity needing to be processed in the multi-modal feature extraction process are reduced through sparsification, so that the processing efficiency is improved, and the processing efficiency of the video analysis task is improved by utilizing the visual language model.

The invention discloses a PFMCGAN-DNN-based unbalanced network flow data anomaly detection method, and the method comprises the steps: obtaining an unbalanced network flow data set with marked data categories, segmenting the unbalanced network flow data set to form a training set, a verification set and a test set, and carrying out the preprocessing of the training set, the verification set and the test set; building an improved generative adversarial network model, namely a PFMCGAN model, training the model by using training set data subjected to feature selection, calculating a loss function and updating model parameters through reverse gradient propagation, after training is completed, generating new data by using the PFMCGAN model, and fusing the new data with original training set data to obtain a balanced training set; building a DNN classification model, training the DNN model by using the balanced training set, calculating a loss function and updating parameters; obtaining a classification threshold by using the verification set; and evaluating model performance, inputting test set data into the model to obtain a prediction category of each piece of input data, and calculating a classification evaluation index. According to the method, the feature extraction method and the deep learning model are combined, the data complexity of the model is reduced, and the accuracy of unbalanced data network traffic anomaly detection is improved.

The invention belongs to the technical field of industrial equipment anomaly detection, discloses an unsupervised equipment anomaly detection method oriented to high-dimensional time series data, and aims to solve the problem that a traditional anomaly detection model is difficult to effectively learn and train due to scarcity of abnormal samples and too high multivariate time series data dimensionality in an actual industrial scene. The one-dimensional time sequence data is learned through a plurality of convolutional auto-encoder networks by using an integrated learning strategy, so that the data complexity of model processing is effectively reduced, an attention extrusion and excitation mechanism is fused, the attention of the model on a specific channel is enhanced, the perception ability of the model is improved, and the interference of redundant information is reduced. And finally, integrating the hidden feature information and the reconstruction difference information of the time sequence data of each dimension, and calculating a new reconstruction error as an abnormal score of the sample. According to the method, the high-dimensional time series data-oriented unsupervised equipment anomaly detection precision can be effectively improved.

The invention discloses a multi-modal data processing method and system based on an attention mechanism, and relates to the technical field of deep learning, and the method comprises the steps: collecting a multi-modal data set, carrying out the multi-scale time sequence calibration through dynamic time warping, and obtaining a time sequence alignment data stream; performing cross-modal semantic association on the time sequence alignment data stream to form a multi-modal feature vector; performing sparse processing on the multi-modal feature vector by using a multi-head self-attention mechanism to generate potential sparse representation; and carrying out coarse graining analysis and fluctuation mode capture on the potential sparse representation, generating a feature sequence length and a variance descriptor, and carrying out spectral entropy calculation to obtain a data complexity score. According to the method, cross-modal semantic association is performed by using canonical correlation analysis, and meanwhile, differential processing is performed on samples with different complexities through the hierarchical adaptive processing model, so that dynamic matching of computing resources is realized, and the resource utilization rate of multi-modal data processing is remarkably improved.

The invention relates to a deep learning cloud particle target detection method based on a dynamic detection head. According to the method, firstly, complexity evaluation is carried out on a cloud particle image data set, the weighted information entropy, the weighted texture complexity and the weighted local standard deviation of each image are calculated, and the comprehensive complexity is calculated based on the complexity indexes. Then, according to the overall complexity grade of the data set and the image data complexity, a detection head selection standard is formulated, and a proper dynamic detection head combination is selected according to the standard; and finally, performing target detection on the cloud particle image data set by using the selected dynamic detection head combination. Through the method, the most suitable detection head combination can be adaptively selected, the target detection precision and efficiency are effectively improved, and the method is particularly suitable for a large-scale cloud particle image data set with complexity difference.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an AI-based file data content accurate and deep analysis and interpretation method, which comprises the following steps: acquiring multi-format file data and file meta-information, constructing an AI analysis network, extracting text semantic vectors, image visual features, table structure information and document layout features, and constructing a multi-dimensional semantic map. According to a user query intention, semantic extension is performed in combination with a domain knowledge base, an enhanced semantic description vector is generated through a graph attention mechanism, semantic reasoning and relation mining are performed by adopting an improved knowledge distillation Transform model, and a deep analysis conclusion is generated through a multi-hop reasoning model in combination with file data complexity, information density and user requirements. And generating a personalized interpretation report in combination with a user role and a task scene, and outputting an analysis result through a visual interface. Therefore, the problems of poor understanding ability, poor file adaptability and the like in the prior art are solved.

The invention provides a water supply pipeline leakage detection method and device, computer equipment and a medium, and belongs to the field of pipeline leakage detection.The method comprises the steps that sample operation information of a pipeline and environment information of the environment where the pipeline is located are obtained; mapping the sample operation information and the environment information into a unified vector space by using a knowledge embedding technology, and generating a sample one-dimensional feature vector; constructing a random forest model RF, and initializing the position and speed of a dragonfly population of a dragonfly algorithm DA; optimizing RF parameters through DA iteration; and training the optimized RF model, obtaining a current one-dimensional feature vector of the pipeline, and inputting the current one-dimensional feature vector into the trained RF to obtain a classification result of the leakage degree of the water supply pipeline. Therefore, the data volume is simplified, redundant feature interference is eliminated, the data complexity is effectively reduced, the data processing efficiency and accuracy are improved, and the RF is optimized through the DA, so that the algorithm is easier to deploy.

The invention provides a map rendering method and device, equipment and a storage medium, and the method comprises the steps: obtaining multiple real-time data of equipment where a client is located in the operation process of the client, and the real-time data comprises map request data; for each performance dimension, at least one sub-score of the performance dimension is calculated according to the real-time data, and the performance dimension comprises the network condition, the equipment performance and the data complexity; obtaining a plurality of existing rendering strategies, wherein each rendering strategy comprises a rendering mode and a performance score calculation mode; for each rendering mode, calculating the adaptability score of the rendering mode according to the calculation mode of the performance score of each performance dimension and the sub-score of the performance dimension, and taking the rendering mode with the highest adaptability score as a target rendering mode; according to the method, switching is carried out on the basis of the current rendering mode and the target rendering mode to achieve map rendering, and the map rendering system has the environment self-adaptive capacity by dynamically sensing data of multiple performance dimensions.

The invention provides an exploration data anomaly detection method and device based on artificial intelligence, relates to the technical field of data processing, and thoroughly gets rid of the constraint of a fixed frequency band on an unsteady logging signal by enabling a frequency band range to be dynamically adjusted along with data complexity through adaptive frequency band division driven by information entropy. Furthermore, high-frequency dynamic features are reserved through normalized logging values, global information is supplemented through window statistics, cooperative information of associated channels is effectively utilized in combination with cross-channel correlation, feature fusion is performed by using frequency band energy vectors, and high-frequency abnormal features are still kept in a remarkable identification degree after fusion. And the suppression by low-frequency backgrounds or statistical characteristics is avoided. In conclusion, the model can accurately distinguish the easily confused exceptions, and the exception recognition precision is improved.

This invention discloses a method and system for fitting safety monitoring data based on adaptive hybrid basis functions, belonging to the field of computer-aided data analysis technology. The method achieves fully automatic high-precision curve fitting through a six-layer adaptive architecture: it automatically analyzes the trend complexity, periodicity, noise level, and nonlinearity of the data using multi-dimensional feature recognition technology; it calculates the data complexity score based on the feature analysis results and intelligently selects basis function combinations from an extended basis function library containing 32 sub-functions across 8 categories; it constructs a dynamic weighted hybrid basis function model, achieving adaptive adjustment of model parameters through time-varying weight functions and coupling correction terms; and it employs a multi-model dynamic fusion mechanism, integrating multiple candidate models based on six-dimensional confidence evaluation. This invention innovatively introduces adaptive regularization technology and a hierarchical optimization strategy, effectively balancing fitting accuracy and generalization ability, and possesses advantages such as full automation, strong robustness, and complete uncertainty quantification.

This invention belongs to the field of scene recognition technology and discloses a sequential multimodal scene recognition method based on a state-space model. It involves jointly encoding laser point clouds and visual images from a trajectory to form multimodal sequence data, which is then processed into unique global descriptors through a global descriptor encoding network. The global descriptors of the two trajectories serve as the map and the query, respectively. During the query process, a nearest neighbor search algorithm is used to find the most similar data in the map, completing scene recognition. This invention proposes to fuse and encode laser point cloud and image data, increasing data dimensionality and compressing data complexity. Global descriptors are obtained through a single-frame module and a sequence module based on a state-space model. The cross-scan design in the single-frame module improves computational efficiency, while the sequential full-combination representation strategy in the sequence module solves the problem of position recognition for trajectory changes within the scene. This invention features high computational efficiency, high accuracy, and robustness.

The application relates to the technical field of digestive tract detection, and discloses a digestive tract detection system based on artificial intelligence, which comprises a data acquisition unit, a preprocessing unit, a feature extraction unit, an abnormality detection unit and a result display unit. The application focuses on endoscopic images, accurately preprocesses the images into grayscale images and normalizes the grayscale images, reduces data complexity, highlights textures, and is beneficial to subsequent analysis; the application effectively extracts texture features, quantitatively presents subtle changes, accurately identifies lesions, scientifically judges abnormalities according to thresholds and displays the abnormalities, and realizes automatic and standardized detection. Meanwhile, physiological indexes are introduced, multidimensional data is integrated, and the function of the digestive tract is comprehensively reflected; the indexes are finely processed, individual differences and dynamic changes are considered, abnormal fluctuations are captured, and diseases are early warned; the application accurately detects abnormalities, cooperatively displays image and index results, and helps medical staff comprehensively judge. The application comprehensively and deeply detects, improves the accuracy of early screening, efficiently comprehensively diagnoses, has wide clinical applicability, promotes intelligent and accurate diagnosis and treatment, and the like.

The invention discloses a multi-modal database-oriented adaptive index structure selection method, which comprises the following steps of: analyzing statistical characteristics such as data dimensions, variances, sparseness and distance distribution of each modal, calculating hidden dimensions of the modals, and describing effective data complexity of the modals; based on a preset index adaptation rule, automatically mapping each mode to an optimal index type in the candidate index structure set, and completing automatic construction of a local index and binding of the local index and a global routing structure according to the optimal index type; on the basis, the multi-modal query can be automatically routed to the corresponding index to execute retrieval according to the index mapping relation. According to the method, an index structure self-adaptive selection mechanism without manual configuration is realized, the index maintenance cost of the multi-modal database can be remarkably reduced, and the query efficiency and expandability in a complex retrieval scene are improved.

The invention discloses an electric power project budget calculation method and system, and relates to the technical field of data analysis. Acquiring historical data of the target area and preprocessing to obtain target historical data; performing feature extraction on the target historical data to obtain feature data, obtaining spatial data of the target region, and performing construction according to the feature data and the spatial data to obtain a target database; updating the preset model according to the target database to obtain a target model, obtaining an electric power project task, and inputting the electric power project task into the target model to obtain a final project scheme; and if the final project scheme completes the electric power project task, determining an electric power project budget. Obtaining target area load data, performing preprocessing and feature extraction, constructing a target database in combination with the spatial data, and updating and optimizing the preset model; the final project scheme is obtained after the electric power project task is input into the model, the electric power project budget is determined, the data complexity is reduced, and the calculation efficiency and precision are improved.

The invention specifically discloses a vehicle-pile-network multi-space-time regulation potential quantification method based on a space-time diagram, and relates to the technical field of smart power grids. The method comprises the following steps: S1, based on a Monte Carlo simulation method, establishing a city-level vehicle-pile-network interactive operation base line under constraint conditions; s2, potential index parameters are calculated and adjusted, key factors influencing potential parameters are screened by means of Pearson's correlation coefficients, and data complexity is reduced; and S3, constructing a multi-space-time adjustment potential quantitative evaluation model based on the space-time diagram neural network STGNN, and realizing adjustment potential prediction in combination with an online learning framework. The method can sense the adjustment potential of the vehicle-pile-network system in all directions, improves the operation digitization level and scheduling flexibility of the power grid, enhances the operation stability of the power grid, and provides technical support for the construction of a novel power system.

Embodiments of the present specification provide a model training method, device, program product and storage medium, the method comprising: obtaining, according to at least one preset category, an initial data set corresponding to the at least one preset category respectively; if it is determined that the initial data set does not satisfy a preset balance condition, adjusting the initial data set so that the adjusted data set satisfies the balance condition; wherein the balance condition comprises one or more of the following: a condition that the data amount of the data set is balanced in different languages, a condition that the data amount of the data set is balanced in different lengths, or a condition that the data amount of the data set is balanced in different data complexities; determining at least one training task of a preset model, and training the preset model based on the adjusted data set of the preset category corresponding to the training task.

The application provides a method and system for checking the cross-modal information consistency of a maintenance plan based on a large model, and relates to the technical field of big data analysis. The method comprises the following steps: identifying a target maintenance plan through a power large model, determining key maintenance content, an unstructured proportion, and an attachment type complexity; obtaining a text data complexity and a business risk index based on key maintenance content analysis, as a first checking difficulty coefficient and a second checking difficulty coefficient; determining a third checking difficulty coefficient according to the unstructured proportion and the attachment type complexity evaluation; taking the expected review time limit of the target maintenance plan as a constraint, formulating an adaptive checking depth and an adaptive checking breadth based on the first checking difficulty coefficient, the second checking difficulty coefficient, and the third checking difficulty coefficient; and performing cross-modal information consistency checking on the target maintenance plan according to the adaptive checking depth and the adaptive checking breadth. The dynamic allocation of resources for checking the cross-modal information consistency of a maintenance plan is achieved.

The invention discloses an adaptive parking page display method and device, equipment and a storage medium, and the method comprises the steps: responding to a user operation, and transmitting a parking state query request to a remote control platform; receiving a feedback result of the remote control platform on the parking state query request; determining a target display page and a page data template based on the parking function type identifier, the parking execution stage identifier and the map identifier; and determining a corresponding data transmission strategy according to the current communication state and the data complexity identifier, and loading and displaying the target display page based on the data transmission strategy and the page data template. The target display page and the page data template are determined based on the parking function type identifier, the parking execution stage identifier and the map identifier, and then the corresponding data transmission strategy is determined according to the current communication state and the data complexity identifier. The page matching accuracy and the real-time performance and adaptability of data transmission and information display are improved.

A data management method of a data lake comprises the following steps: S1: collecting data information and uploading to an initial data pool module to classify a complex data system, and then delivering the classified data to a data pool processing module; S2: the different types of data pool processing modules comprise an analog signal data pool processing module, an application program data pool processing module and a text data processing module, each module processes data information of its own type to realize unified data management; S3: a thread pool occupancy rate model is constructed, and data containing a removal mark and an archiving mark are managed and scheduled to an archiving data pool module. Through the above scheme, the internal rules of numerous data are found and corresponding storage is realized, and the technical problem that current data complexity is not easy to arrange is solved.

The application relates to the technical field of artificial intelligence computer systems, and particularly discloses a medical data management method and system based on artificial intelligence, which comprises the following steps: collecting a medical data set and obtaining attribute parameters thereof; analyzing a data complexity index; simultaneously considering performance parameters of a management device to which the medical data set belongs; comprehensively evaluating device performance; using artificial intelligence technology to perform encryption processing on the medical data set according to performance evaluation factors, and marking the medical data set as an encrypted medical data set, thereby enhancing the security of the medical data, ensuring privacy protection of the data in the transmission and storage process, simultaneously collecting encryption parameters of the encrypted medical data set, evaluating storage complexity, intelligently judging whether the encrypted data set needs to be compressed and stored according to the storage complexity, the method effectively balances data security, privacy protection and storage efficiency, reduces possible risk abnormalities in the storage process, improves the intelligent level and the security level of medical data management, and realizes efficient and safe storage of medical data.

The application provides an exploration data anomaly detection method and device based on artificial intelligence, relates to the technical field of data processing, and through self-adaptive frequency band division driven by information entropy, lets the frequency band range dynamically adjust with the data complexity, and completely gets rid of the restraint of the fixed frequency band on the non-steady-state logging signal. Further, the high-frequency dynamic characteristics are reserved through normalized logging values, the global information is supplemented through window statistics, the synergistic information of the associated channels is effectively utilized in combination with the cross-channel correlation, and the feature fusion is carried out by using the frequency band energy vector, so that the high-frequency abnormal characteristics still maintain the significant recognition degree after fusion and are not suppressed by the low-frequency background or statistical characteristics. In summary, the model can accurately distinguish the confusing abnormalities and improve the abnormal recognition accuracy.

The application provides a STEP model dynamic rendering method and system based on componentization processing, and the method comprises the following steps: when a local update request for a STEP model component is detected, a target component is first determined, then an independent component relationship table obtained by pre-extracting independent components, attributes and topological relationships is used to determine an influence domain in combination with update operation information. Subsequently, the new components in the influence domain are subjected to grid re-triangulation and lightweight processing, and a component-grid mapping table recording the mapping relationship between the component ID and the vertex mapping range and the like is updated according to the processing result. Finally, an incremental update package is generated based on the updated mapping table, and GPU rendering is performed by using the incremental update package. According to the scheme, the influence domain is accurately positioned through componentization processing, the data processing amount is reduced, the grid is lightweighted to reduce the data complexity, the incremental update package avoids full data transmission and rendering update, and the rendering efficiency and performance are effectively improved.