1664 results about "Data compression" patented technology

An AI-enhanced distributed system for neural network-based data compression leverages reinforcement learning optimization and privacy-preserving computation across edge and central computing devices to autonomously optimize efficiency and quality. The system includes a lightweight compression subsystem at edge devices that applies privacy-preserving preprocessing and partially compresses input data before securely transmitting it to central computing devices. A reinforcement learning agent continuously monitors system performance and automatically optimizes compression parameters, model selection, and task allocation based on multi-objective rewards. The central compression subsystem processes data using AI-optimized parameters and temporal modeling components. The system incorporates hardware detection capabilities that automatically select optimal compression models based on available processing resources and implements homomorphic encryption for computation on encrypted data while coordinating federated learning across distributed devices. This AI-enhanced distributed approach improves bandwidth efficiency, energy consumption, and adaptability while ensuring data privacy and security.

The invention relates to the technical field of equipment monitoring, in particular to an equipment corrosion evaluation and life prediction method and application, and the method comprises the following steps: deploying a sensor network in an easily-corroded area of coal chemical equipment, and collecting multi-dimensional data; carrying out abnormal value elimination, data compression, time synchronization and space-time alignment preprocessing on the collected multi-source data; image features are extracted through a convolutional neural network, processed data are analyzed through an LSTM-attention model, and a fuzzy comprehensive evaluation matrix is established to evaluate the corrosion level; a physical model based on the Faraday electrolysis law and a data driving model based on the Transform network are constructed, and the residual life is predicted through Bayesian network fusion output and Monte Carlo simulation. Through fusion of multi-source data and an intelligent algorithm, accurate evaluation of the corrosion state of the equipment and accurate prediction of the residual life are realized, and safe and efficient operation of the coal chemical equipment is guaranteed.

The invention discloses a time sequence data management method of an edge computing gateway, which relates to the technical field of edge computing and industrial Internet of Things, and comprises the following steps of: respectively recording a communication bandwidth occupancy rate, a buffer area residual rate and a scheduling thread occupancy rate of the edge computing gateway in a preset fixed time period; and constructing a resource use original data matrix covering all time points in the fixed time period. According to the method, by periodically monitoring the resource use state and fusing the high-priority task scheduling performance, the scheduling resource abnormal occupancy index is dynamically generated, and intelligent sensing and scheduling optimization of the edge computing gateway on the resource pressure are achieved. When the abnormal index is increased, the system automatically triggers buffer area redistribution and low-optimal task data compression, data writing and scheduling real-time performance of key tasks are guaranteed preferentially, the problems of task starvation and data loss are effectively avoided, and the stability and the response capability of the system in a high-pressure environment are improved.

The invention provides a real-time deep sea subsurface buoy monitoring system, which belongs to the technical field of deep sea measurement, and comprises a control chip, a multi-parameter sensor array, a data storage device and a power supply, high-precision monitoring is realized through the following steps: recording ocean data acquired by a multi-parameter sensor; correcting the sound wave propagation model and calculating the relative position of the subsurface buoy; constructing a three-dimensional data matrix and realizing data compression; identifying abnormal data by using a matrix disorder index; processing the time sequence data by adopting a sliding window weighted average method and eliminating jump; carrying out distributed preprocessing on the multi-source heterogeneous data and carrying out parallel processing by applying a matrix partitioning technology; accumulated errors are corrected by combining historical data and applying a Kalman filtering algorithm, and the problems of environment interference and sensor drift are solved through a deep sea environment disturbance compensation network model and a self-adaptive weighted error optimization function.

The invention relates to the technical field of information processing, in particular to a hard mountain side slope digital twinborn monitoring method and system based on a cloud platform. The method comprises the following steps: obtaining real-time monitoring data of a slope in a hard mountain area, and carrying out data preprocessing to obtain standardized slope dynamic monitoring data; performing data compression and feature extraction on the standardized slope dynamic monitoring data, and transmitting a feature extraction result to a cloud platform to obtain cloud slope key information data; the method comprises the following steps: constructing a digital twinborn model by utilizing cloud side slope key information data based on a cloud platform to obtain a preliminary side slope digital twinborn model; through construction and optimization of the digital twinborn model, stability evaluation and risk early warning, the precision and real-time performance of slope monitoring in the hard mountainous area are remarkably improved, and a scientific basis and an executable long-term scheme are provided for slope treatment.

The invention provides an edge fusion diagnosis system and method for multi-source heterogeneous power transmission monitoring data, and belongs to the technical field of power transmission line monitoring. The multi-source heterogeneous power transmission monitoring data edge fusion diagnosis system comprises a multi-source heterogeneous data access module, a data preprocessing and standardization module, an edge computing node, an edge node collaborative diagnosis module, a data compression and feature extraction module and a cloud fusion analysis platform. The method comprises the following steps: accessing multi-source heterogeneous data, preprocessing and standardizing, locally diagnosing by an edge computing node, triggering multi-node collaborative diagnosis when the credibility is insufficient, and uploading key feature data to a cloud for global analysis and model optimization. According to the method, comprehensive integration and efficient processing of multi-source data are realized, the real-time performance, accuracy and comprehensiveness of power transmission monitoring diagnosis are improved, and the efficient monitoring requirement of a complex power transmission network can be met.

The invention relates to the technical field of carbon footprint dynamics, in particular to a railway traffic carbon footprint dynamic tracking system and method based on a block chain and edge calculation. The data acquisition unit is used for acquiring train operation state, environment parameters and traction energy consumption data; the edge calculation unit fuses the multi-source data acquired by the data acquisition unit through adaptive Kalman filtering to obtain standardized data, converts a train operation state into carbon emission based on an instantaneous power model, and introduces a dynamic emission factor to calculate and obtain accumulated carbon emission; and the block chain network unit is used for compressing the accumulated carbon emission data and then generating a Merkle tree root hash upper chain, and storing the carbon emission data to a block chain. Incremental uplink and rapid verification of the data are realized, the on-chain data processing efficiency and the query traceability are improved, and the problems of high storage pressure and low verification efficiency of a traditional on-chain evidence storage mode in a high-frequency track data writing scene are solved.

The invention discloses an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

A system and method for cross-domain knowledge transfer in federated compression networks. The system enables efficient lossless data compression across diverse data types by intelligently sharing compression strategies between domains. A cross-domain knowledge transfer system identifies relationships between different data domains, adapts compression parameters accordingly, and optimizes learning processes to maximize knowledge reuse. The architecture may include a knowledge repository for storing domain features and compression patterns, domain mapping components that identify similarities, and transfer learning optimization that enables efficient adaptation with minimal examples. This approach significantly accelerates model training for new domains while improving compression performance. Applications include satellite telemetry systems where efficient compression is critical for transmitting large information sets between distant locations. The system may employ probability prediction driven arithmetic coding paired with long short-term memory networks, enhanced by cross-domain knowledge sharing that adapts successful compression strategies from one domain to another while preserving domain-specific optimization.

The invention relates to the technical field of edge computing, in particular to an intelligent parking unattended vehicle access management system and method based on edge computing. The method comprises the following steps: a vehicle detection positioning unit obtains multi-modal data based on a multi-modal sensor array module, and realizes real-time positioning of a vehicle and real-time updating of a parking space state through a Kalman filtering algorithm in combination with a QBCN information interference variable; the license plate recognition unit completes license plate feature extraction and recognition on the basis of a multispectral imaging technology and a lightweight CRNN model under the condition of abnormal illumination. The edge calculation decision unit combines a real-time rule engine and a space-time anomaly detection algorithm, optimizes a resource allocation strategy, and realizes low-delay decision and localization control; the payment authentication unit adopts a block chain intelligent contract, dynamic rate calculation, multi-factor identity authentication and abnormal payment fusing protection; and the edge cloud collaboration unit realizes model differential updating, cross-domain task scheduling and energy efficiency optimization through federated learning and space-time data compression technologies.

The invention discloses a double-rocker-arm derrick monitoring system based on multi-sensor data fusion, and particularly relates to the field of monitoring, which comprises a sensor module, a data preprocessing module, a data fusion analysis module, a state evaluation module and a visual terminal module, parameters of the double-rocker-arm derrick are monitored in real time through the sensor array, multi-source data synchronization and feature extraction are achieved in combination with the data preprocessing module, multi-modal data fusion is conducted through the improved D-S evidence theory, and the structure health index, the risk level and the fault probability are output; the state evaluation module generates three-level early warning signals based on a dynamic threshold model, historical data is compressed and stored and supports cloud synchronization, and the visual terminal renders a three-dimensional digital twinborn model through Unity 3D, dynamically displays postures, deformation fields and environmental factors, provides an interactive evaluation report and AR early warning annotation, and realizes whole-process real-time monitoring and intelligent decision support.

The invention discloses an ultra-weak light detection system and method based on an SPAD array and time correlation photon counting, and belongs to the technical field of ultra-weak light detection. A multi-channel SPAD array detection unit; a time-to-digital conversion module; the photon arrival time analysis unit comprises a TCSPC model, a noise modeling and suppression module and a self-adaptive convolution filter; a data compression and fitting module; a spatial denoising filter; an output unit; and a master control processing unit. Based on a multi-channel SPAD array detection unit, a time-to-digital conversion module, a TCSPC model, a noise modeling and suppression module, a self-adaptive convolution filter and the like, high-sensitivity imaging and quantitative detection of materials (such as black silicon) with extremely low reflectivity are realized. The system has the advantages of being good in real-time performance, high in time resolution, high in signal-to-noise ratio, high in on-chip processing capacity, high in integration and the like, and meanwhile the bottleneck of a traditional TCSPC method for large-scale photon data processing is solved.

The invention relates to the technical field of data compression, and particularly discloses a method for realizing data compression in an RAID card and an RAID control chip, and the method comprises the following steps: S1, receiving a plurality of IO writing commands issued by a host, and obtaining the logic block address and length of each IO; s2, executing a merging operation on the plurality of IOs to generate a merged IO block; s3, performing a data compression operation on the merged IO block through a data compression engine to obtain compressed data; s4, establishing a mapping relation from a logic block address to a physical block address of the compressed data, and storing the mapping relation in a logic-to-physical mapping table; s5, the compressed data are written into a solid state disk through an RAID engine, and meanwhile, logic-to-physical mapping table metadata are stored persistently; s6, sending a recovery command or a zero-writing command to the solid state disk recovery space for the address space which is not used after compression; the problem that an existing SSD compression scheme is high in power consumption is solved, reliability is better, original user data content is closer, and the obtained compression effect is better.

The invention provides a data compression method and device, and relates to the technical field of data processing and data compression. The method comprises the following steps: acquiring to-be-compressed data, and acquiring a data category of the to-be-compressed data; determining a plurality of candidate compression strategies corresponding to the to-be-compressed data based on the data category; the importance degree of the to-be-compressed data is obtained, and the equipment state parameter of the current equipment is obtained; determining a target compression strategy and a compression level corresponding to the target compression strategy from the candidate compression strategies based on the equipment state parameters and the importance; and executing the target compression strategy on the to-be-compressed data according to the compression level to obtain compressed data. According to the method, the target compression strategy can be dynamically determined according to the data type, the data importance and the equipment state, high flexibility and adaptability are achieved, the data storage and processing efficiency is improved, good balance between the data quality and the storage space is ensured, and the method adapts to modern changeable equipment environments and data requirements.

The invention discloses an intelligent elevator host bearing fault diagnosis system based on AI and fault physical fusion, and belongs to the technical field of elevator diagnosis. Comprising an edge end which is connected with a cloud end; when fault diagnosis is carried out, the edge end firstly collects a high-frequency signal through a sensor, sequentially executes signal preprocessing, feature extraction, anomaly detection and data compression, and finally sends compressed feature data to the cloud end; after the cloud end receives and verifies the integrity of the data, the feature data is stored in a time series database, deep analysis is executed through a data analysis engine, a lightweight model is optimized based on historical data, an analysis result is displayed through a remote monitoring platform, and an alarm notification is sent when abnormity is detected; meanwhile, the cloud end issues the optimized lightweight model to the edge end to form a continuously optimized closed loop. Through the feature extraction and data compression technology of the edge end, the data volume needing to be transmitted is greatly reduced, and the network bandwidth requirement and the transmission delay are remarkably reduced.

The invention discloses a multi-source information fusion-based dynamic cooperative regulation and control method for a transmitting-end power grid energy storage station, and relates to the field of power grid energy storage regulation and control. The method comprises the following steps: compressing global heterogeneous data, and extracting a low-dimensional critical feature flow; constructing a dynamic coupling potential energy field, and quantifying gradient intensity to capture recessive instability; generating a layered fractal regulation strategy, resolving conflicts and outputting an instruction and a residual error; constructing a topological phase change trigger network, and triggering regulation and control through spectrum gap jump; causal tracing and model evolution are carried out, and parameters are optimized to form a closed loop. According to the invention, through fractal cooperation, topological response and evolution closed loop, regulation and control collaboration, instability response speed and system adaptability are improved, and safe and stable operation of a power grid is guaranteed.

The invention relates to the technical field of data recording, in particular to a data recording system for an automatic driving vehicle, and the system comprises a multi-source signal synchronization module, a behavior stage recognition module, a track abnormity elimination module, a dynamic compression adjustment module and a segmented index marking module. According to the method, a unified time alignment mechanism is constructed in a signal synchronization processing link, a strict corresponding relation between acquisition sequences is ensured, key time sequence characteristics of driving behavior switching are extracted by adopting a multi-behavior parameter comprehensive judgment method, segmented expression of a complex driving process is realized, and the accuracy of driving behavior switching is improved. According to track anomaly identification, through collaborative screening of inertial data and a positioning track, abnormal fragments which do not conform to physical rules are effectively eliminated, data compression dynamically sets interval length according to a parameter combined fluctuation trend, the fidelity of a key period is remarkably improved, non-key data redundancy is inhibited, and after a segmented index structure is established, the data compression efficiency is improved. And the data retrieval process is clearer.

A correlation-aware adaptive codebook compaction system for multi-modal data compression that preserves cross-modal relationships while providing enhanced reconstruction quality. The system analyzes temporal and spatial relationships between different data modalities to generate correlation maps that guide compression decisions. A virtual management layer performs stream characterization and adaptive routing, while a processing pipeline implements primary codebook compression with mismatch handling for novel data blocks. High-entropy data segments receive pre-compression processing before codebook compression. Sequential registration data is processed through matrix factorization and dedicated matrix codebooks. The system continuously monitors data distribution characteristics and automatically retrains codebooks when drift thresholds are exceeded. A neural upsampling subsystem uses correlation information to guide cross-modal enhancement processes through modality-specific networks and attention mechanisms. The unified output includes compressed data streams, correlation maps, synchronization metadata, neural model parameters, and updated codebooks, enabling synchronized reconstruction with preserved cross-modal relationships and enhanced quality through correlation-guided neural upsampling.

The invention provides an unstructured data compression method and device, equipment and a storage medium, and the method comprises the steps: carrying out the feature extraction and clustering processing of a to-be-compressed unstructured data set, and obtaining a data segment clustering group; performing semantic region segmentation processing on each data fragment in the data fragment clustering group to obtain a semantic important region and a semantic non-important region; performing differential coding processing on the semantic non-important regions in the same clustering group to obtain differential coding data; and performing high-quality compression processing on the semantic important regions in the same clustering group, and performing merging processing on the compressed important semantic data and the difference coding data to obtain an unstructured data compression result. According to the method, semantic region segmentation and differential compression processing are performed on the unstructured data, so that the data compression efficiency can be effectively improved on the premise of ensuring the quality of important information, and an efficient solution is provided for storage and transmission of large-scale unstructured data.

A federated system and method for data compression optimization in distributed device networks. The system comprises multiple edge devices that analyze local data patterns to generate device characteristic profiles while performing local compression optimization and maintaining data privacy. Edge devices contribute to collaborative learning by generating privacy-preserved updates without transmitting raw data. A central coordination system aggregates encrypted contributions using secure multi-party computation protocols, identifies device groups based on data pattern similarities, and generates optimized compression parameters for each group. The system coordinates collaborative training of data reconstruction models across device groups and deploys group-optimized reconstruction capabilities. Device grouping is performed by calculating similarity scores between device characteristic profiles and clustering devices with scores above predetermined thresholds. The system dynamically adapts compression and reconstruction parameters through federated learning while preserving individual device data privacy, enabling efficient data compression and near-lossless recovery across heterogeneous Internet-of-Things networks.

The invention discloses a method for aggregating ocean buoy data, which comprises the following steps of: establishing a space-time correlation model according to the spatial distribution density of buoy clusters and the dynamic characteristics of an ocean environment, and adaptively dividing buoy groups with a data compression cooperative relationship; performing a data-related compression operation within the buoy group; constructing a transmission priority dynamic sorting engine, and calculating a dynamic weight coefficient; performing hierarchical packaging processing on the differential coding data, distributing enhanced forward error correction coding resources for high-priority data, and reserving redundant transmission time slots; the method comprises the following steps: receiving a hierarchical data packet through a low-orbit satellite and executing on-satellite preprocessing, reconstructing an original data stream by using a distributed decoder, and meanwhile, fusing multi-dimensional space-time related data to generate an aggregated data product; and finally, the aggregated data product is transmitted to a ground data center through a satellite downlink. According to the invention, uplink data volume is reduced, disaster monitoring timeliness is improved, ground station processing load is reduced, and system environment adaptability is enhanced.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. A method performed by a terminal in a wireless communication system is provided. The method includes identifying whether a packet data convergence protocol (PDCP) entity is suspended in case that the PDCP entity re-establishment is requested from an upper layer, identifying whether an indicator is configured for indicating that an uplink data compression (UDC) is continuously used and a PDCP service data unit (SDU) is compressed, for an acknowledged mode (AM) data radio bearer (DRB) whose the PDCP entity is not suspended, and submitting the PDCP SDU, in case that the indicator is configured for indicating that the UDC is continuously used and the PDCP SDU is compressed.

The invention provides a sea area digital twinning method and system based on buoy and station data fusion, belongs to the related technical field of sea area digital twinning, and establishes a data quality evaluation mechanism and a similarity data compression mechanism by constructing a multi-source heterogeneous ocean data acquisition system. Intelligent fusion processing of buoy data and station data is achieved, and the problem that in the prior art, the data fusion precision is low is solved; through the self-organizing growth network structure of the ocean dynamic response enhancement model, neuron connection can be automatically adjusted according to the spatial-temporal variation degree of training data, an optimal topological structure is formed, and the defect that a traditional fixed parameter model cannot adaptively process a complex ocean environment is effectively overcome; by establishing a dynamic parameter adjustment mechanism and a multi-algorithm adaptive switching strategy, the most suitable prediction algorithm is automatically selected according to the correlation characteristics of the ocean data sequence, and high-precision digital reproduction of the complex ocean environment is realized.

The invention discloses an ultra-low illumination-oriented SPAD active imaging data compression method, which comprises the following steps of: performing wavelet decomposition on histogram data of a single pixel in a time-frequency domain based on wavelet transform to obtain low-frequency data of each pixel after wavelet decomposition; by taking each pixel as a center, performing non-maximum suppression and data enhancement on the low-frequency data after the wavelet decomposition of the current pixel by using adjacent pixels to obtain processed compressed data; customizing different Gaussian kernel parameters for each pixel according to the possibility that each pixel is located at the boundary, and performing Gaussian filtering on the processed compressed data of each pixel to obtain filtered data; and performing depth estimation on the filtered data to obtain a final depth image. According to the method, the depth reconstruction performance of the laser pulse can be improved by utilizing the multi-resolution characteristic of wavelet transform, the space-time correlation of signal photons and the smoothness of Gaussian filtering.

The invention provides a dam safety monitoring system lightweight construction method and device, and the method comprises the steps: deploying a protocol conversion gateway, carrying out the protocol analysis and data standardization processing of an existing heterogeneous sensor, generating data in a unified format, and supporting the network disconnection caching and automatic supplementary transmission; deploying a data processing module on a local server or an industrial personal computer based on an edge computing architecture, performing feature extraction and multi-scale fusion analysis on acquired data, and performing data compression by adopting an improved revolving door algorithm; constructing a lightweight anomaly detection model, training by using historical data to generate a normal mode template library, calculating a dynamic time warping distance between new data and a template in real time, and triggering early warning when the distance of a plurality of continuous cycles exceeds a preset threshold value; the sampling frequency of the sensor is dynamically adjusted according to the real-time data analysis result, key parameters are collected in a high-frequency mode, non-key parameters are collected in a low-frequency or intermittent mode according to the change rate prediction result, and therefore the system resource use efficiency is optimized.

The invention belongs to the technical field of data analysis and processing, and particularly relates to an equipment state monitoring method based on a time sequence synchronous compression mechanism, and the method comprises the steps: 1, a monitoring terminal collects vibration type, acoustic type and acceleration type signals on a unified time baseline of Beidou time service signals, and registers a second sequence number and a frame sequence number in a unified fragment index table; 2, inputting an original time sequence data packet, constructing a phase grid frame by a phase locking ring according to a reference phase rail, extracting road-level flag event bits frame by frame, and fusing the road-level flag event bits into frame-level flag event bits; and step 3, outputting an equipment state label according to a mutual exclusion priority rule only on the basis of the synchronous compression time sequence packet and the coupling mark, the mode instruction and the abnormal fragment mark in the unified fragment index table in the identification window. The method is obviously superior to the prior art in the aspects of time sequence alignment precision, pattern recognition certainty, data compression efficiency and health state judgment reliability.

The techniques disclosed herein provide a system for condensing large log files to enable interoperability with transformer-based large language models in automated failure analysis. In various contexts such as enterprise cloud services, log files can be large oftentimes containing millions of lexical units that can incur significant processing time and resource consumption. As such, the disclosed techniques provide a system for producing a directional knowledge graph representing a condensed form of a log file for automated failure analysis. Firstly, the log file is preprocessed to filter information that is irrelevant to the failure that previously occurred. The filtered lexical units are then used to generate a directional knowledge graph comprising a plurality of vertices which correspond to individual lexical units, and which are connected by edges representing the semantic connection between the lexical units. In this way, the directional knowledge graph significantly reduces processing times and improves resource consumption.

A system and method for latent geodesic traversal across multi-axis hyperspaces for real-time video reconstruction and augmentation. Spatiotemporal video data are compressed into navigable latent representations using hierarchical and Lorentzian autoencoders that preserve geometric and temporal structure. A geodesic traversal engine computes paths across spatial, temporal, spectral, and semantic axes, guided by symbolic anchors and spatiotemporal routing protocols. A correlation network restores fine detail, while an augmentation generator synthesizes additional or counterfactual content to enable infinite zoom, continuous multi-scale exploration, and temporally coherent augmentation. A strategy caching system preserves successful traversal patterns for reuse, supporting persistent learning and adaptive real-time performance.

The invention discloses a multi-dimensional time sequence data compression and rapid retrieval method and system, and relates to the technical field of large data compression retrieval. The multi-dimensional time sequence data compression and rapid retrieval method comprises the following steps: S1, collecting and preprocessing multi-source data in a monitoring abstract video stream, and constructing a standardized time sequence behavior data set; s2, analyzing behavior characteristics of frame segments in the sliding window, and dynamically adjusting anchor point labeling and compression strategies; s3, evaluating the coverage integrity of anchor point information in a compression section, and driving generation of an index path; s4, comprehensively evaluating the path behavior association strength and dynamically adjusting a loading decision; and S5, verifying the matching integrity of the behavior anchor point field and the jump pointer, and guaranteeing the localizability and jump stability of the event in the compression structure. The problems that in an intelligent monitoring scheme, video abstract compression is not bound with behavior semantics, a fine-grained positioning mechanism based on behavior labels is lacked, and key action segments cannot be directly positioned during user playback are solved.

The invention discloses a three-dimensional terrain rendering method and device based on mass terrain data, and belongs to the technical field of three-dimensional terrain rendering, and the method comprises the steps: S1, obtaining to-be-rendered terrain data, and carrying out the layering processing of the terrain data; s2, obtaining a three-dimensional topographic data set after hierarchical processing according to the rendering range of the rendering engine; s3, registering and rendering an engine data format conversion drive, and converting the three-dimensional topographic data in the three-dimensional topographic data set into 16-bit binary data; and S4, converting the 16-bit binary data into grids, converting the three-dimensional topographic data into Cartesian coordinates by referring to geographic coordinates, calculating the position and height of each node, and encoding grid data corresponding to a region needing to be rendered into a proper rendering format for rendering. The method is simple in operation, does not need complex data conversion, is high in expansibility, optimizes the aspects of data caching, data transmission, data compression and the like, further improves the rendering efficiency, and improves the smoothness of map interaction.