1348 results about "Large scale data" patented technology

The invention relates to the technical field of real-time data processing, in particular to a distributed real-time data stream processing system and method, and the system comprises a state mapping modeling module, a load dynamic calculation module, a node task adjustment module and a resource scheduling execution module. According to the method, the dynamic identification and classification of the flow state are realized by analyzing the data flow change sequence and identifying the frequency and trend of state switching, the monitoring and understanding capability of real-time data is improved, the node resource pressure is accurately quantified, the predictability of load balancing is enhanced in the resource occupation and load range analysis, and the load balancing efficiency is improved. A scientific basis is provided for resource scheduling, task priority ranking and dynamic task adjustment are combined, the node computing resource utilization rate and the task processing response speed are optimized, flexible configuration of internal storage and computing resources is achieved, peak load pressure is relieved, and data processing efficiency and system stability are improved. And the adaptability and the reliability of processing large-scale data streams by the system are improved.

The invention discloses an RAG enhanced Text-to-SQL (Structured Query Language) query method and system for a large-scale database environment. The method comprises the following steps: constructing a vector database; based on the original query of the user, generating query enhancement description aiming at Schema recall and query enhancement description aiming at SQL (Structured Query Language) by utilizing LLM (Logistics Language Model); carrying out Schema recall and historical question and answer pair recall operations in a double-way parallel manner by utilizing an RAG technology and relying on the constructed vector database; based on query enhancement description, recalled Schema and historical question and answer pairs, generating an SQL query statement by using LLM in combination with RAG, and realizing Text-to-SQL conversion; the generated SQL query statement is subjected to post-processing optimization through a database interface, the post-processing optimization comprises grammar verification, performance optimization and error correction, the SQL query statement is combined for execution and feedback, efficient, accurate and reliable natural language query is achieved, and continuous optimization can be conducted through user feedback.

The invention discloses a large and small model-based data anomaly detection method, system and device and a medium, belongs to the technical field of data processing and artificial intelligence, and aims to solve the technical problem of how to combine the rapid screening capability of a small model and the deep analysis capability of a large model to realize efficient and collaborative anomaly detection and cleaning. According to the technical scheme, an improved Isolation Forest small model is used for rapid screening, potential anomalies in large-scale data are rapidly detected through a dynamic threshold adjustment mechanism, adaptive feature weight distribution and multi-scale division optimization, an anomaly score is generated, and suspected abnormal data are preliminarily screened; secondly, performing fine detection on suspected abnormal data by utilizing a large model, and accurately identifying an abnormal type and generating a high-reliability abnormal result through deep semantic embedding analysis, abnormal feature clustering and domain rule verification; and finally detected abnormal data are classified and labeled, and the data characteristics are combined to process the abnormal data by adopting a deletion, correction or marking cleaning strategy.

The invention provides a remote sensing image super-resolution method based on a diffusion model and a multi-modal large language model, and relates to the technical field of image processing. According to the method, a pre-trained stable diffusion model is adopted, the model is trained on a large-scale data set, the stable and detailed super-resolution effect can be achieved, and the convergence speed is higher. According to the method, a multi-mode large language model is used for enhancing the semantic comprehension ability of the model, and detailed content description and texture description are provided. The invention also introduces an additional classifier which provides global information and a simple signal about the LR. The prior information is adaptively integrated into a diffusion model through a cross attention module. And finally, through combination of the strong generation capability of the stable diffusion model and the semantic understanding capability of the MLLM, the method provided by the invention is excellent in the aspect of remote sensing image reconstruction, and excellent performance is realized in the aspect of quality of a plurality of images on different data sets.

The invention provides a medical image segmentation method, which comprises the following steps: acquiring PET indirect development original image data, and preprocessing the image, including denoising, contrast enhancement and gray level normalization, to obtain a preprocessed PET image data set; designing segmentation middleware, integrating a plurality of segmentation algorithms including threshold segmentation, region growth and level set methods, and selecting proper segmentation algorithms for different types of lesion regions according to feature vector analysis results; pET image data with labels are generated through the simulation technology, a training data set is constructed in combination with real case data, the sample size is expanded through the data enhancement technology, and a large-scale data set used for algorithm training is generated; and the segmentation result of the U-Net is refined by using the segmentation middleware, the lesion area edge is focused on, and the lesion boundary is accurately positioned through an edge detection algorithm to obtain a fine segmentation result.

The invention relates to an intelligent semantic analysis knowledge graph system for multi-source heterogeneous data. The system comprises a data preprocessing module used for preprocessing to-be-processed multi-source heterogeneous data to obtain format-standardized text data; the semantic analysis module is used for performing natural language processing on the text data to obtain words, and then performing vocabulary layer, syntactic layer and semantic layer analysis on the text data and the words to obtain structured data; the structured data comprises a triple consisting of entities, relationships and events and a network diagram of the entities and the relationships; the model structure mapping module is used for mapping the structured data into a predefined structure of a knowledge graph to obtain structured graph data; and the knowledge graph module analyzes entities and relationships in the graph data, performs reasoning to obtain potential relationships in the graph data by utilizing a logic rule and a machine learning algorithm, realizes cross-data-source and cross-document deep semantic association, and improves the efficiency of large-scale data processing and knowledge management.

The invention discloses a data center operation and maintenance fault prediction system and method based on deep learning. The system comprises a multi-source heterogeneous data acquisition module, a data preprocessing module, a deep learning prediction model module and the like. The method comprises the following steps: acquiring multi-dimensional operation data of a data center through full-quantity acquisition of multi-source data, and inputting a CNN-LSTM-Attention hybrid model to realize fault prediction after preprocessing and feature enhancement; fault grades are divided in combination with fault grading, early warning is pushed in multiple channels, a coping strategy is intelligently generated, the effect is verified in a closed loop mode, and finally the model is iteratively optimized. According to the scheme, the fault prediction precision and real-time performance are improved, the operation and maintenance response time is shortened, the service interruption risk caused by faults is reduced, and the method is suitable for efficient operation and maintenance of large-scale data centers.

The invention discloses a magneto-rheological damper modeling method based on a Gaussian process regression model. The magneto-rheological damper modeling method comprises the following steps: step 1, acquiring and preprocessing data based on a mechanical property test of a magneto-rheological damper; 2, determining Gaussian process regression prior distribution of the magnetorheological damper; 3, calculating a posteriori mean value and a posteriori covariance of the Gaussian process regression model; 4, evaluating the Gaussian process regression model of the magnetorheological damper; 5, performing hyper-parameter optimization calculation; and step 6, final evaluation and application of the Gaussian process regression model of the magnetorheological damper. On the premise of ensuring the model precision, the problems of insufficient computing resources, increased training time cost and the like possibly caused when a non-parameterized model represented by a neural network model faces a large-scale data set are solved; meanwhile, for the problem of model overfitting possibly caused by noise interference in training data, a kernel function with a noise interference suppression effect is provided so as to improve the modeling precision of the model.

The invention relates to a cloud side-end cooperative processing system based on a network computing technology. A cloud computing platform is used for storage of large-scale data and training of a deep learning model; the edge computing node module is used for receiving the data stream from the end side equipment and processing the data stream in real time; the end side equipment is used for collecting environmental data and primarily processing the environmental data, and the end side equipment comprises one or more of an unmanned aerial vehicle, an unmanned vehicle and an unmanned ship; the cloud edge-end cooperation module is used for efficient communication and calculation cooperation among the cloud, the edge and the end-side equipment; and the task scheduling and resource allocation module is used for scheduling and allocating calculation tasks in real time according to current task requirements, resource states and network conditions. According to the invention, the overall operation efficiency and real-time performance of the cloud side-end cooperative processing system can be improved, and the defects of the existing system in the aspects of processing capability and task scheduling are overcome.

The present invention relates to the technical field of blockchains. Disclosed are a smart-contract vulnerability detection method and system based on a hypergraph and multi-teacher distillation. Specifically, the smart-contract vulnerability detection method based on a hypergraph and multi-teacher distillation comprises: using a vulnerability detection open-source tool to perform vulnerability detection and analysis on source codes of a smart contract, and marking labels for the source codes of the smart contract; then, using a hypergraph to dynamically capture a deeper and richer relationship between the source codes, key functions and key variables in the smart contract, and on the basis of the hypergraph, training a plurality of vulnerability detection teacher models having excellent performance; and finally, using the plurality of vulnerability detection teacher models to perform knowledge distillation, so as to obtain a vulnerability detection student model, such that the vulnerability detection student model can also obtain knowledge without exposure to large-scale data, and thus directly applies the knowledge to vulnerability detection of new data. The smart-contract vulnerability detection method based on a hypergraph and multi-teacher distillation in the present invention can effectively detect various types of smart-contract vulnerabilities, and can effectively reduce the occupancy of storage space and improve the running speed.

The invention relates to the technical field of computer storage, and discloses a computer memory data processing system, which comprises the following modules: an intelligent cache prefetching module, which is used for predicting a future memory access address based on deep learning and reinforcement learning technologies and loading data into a cache in advance; the heterogeneous storage management module is used for dynamically classifying the data based on the data popularity value and realizing migration of the data among different storage media; and the storage consistency guarantee module is used for managing the data consistency among different storage layers through the improved MES I protocol. Cache prefetching is optimized by introducing deep learning and reinforcement learning technologies, and by predicting a future memory access address in real time and loading data in advance, the system effectively reduces the cache missing rate, improves the cache hit rate, remarkably reduces the memory access delay, and improves the memory access efficiency. And the system can keep more efficient data access performance in a large-scale data processing scene.

The invention discloses a visual time sequence feature network implementation method based on multi-module feature fusion. The system comprises a visual time sequence feature encoder VTFE, a multi-scale self-attention fusion module MSAF, an efficient feature reconstruction engine EFRE and an adaptive feature refinement processor AFRP. The VTFE module fuses a convolutional neural network and an extended long-short-term memory network, and realizes high-precision capture of spatial-temporal features. The MSAF module processes characteristic frequency domain information through fast Fourier transform, and the recognition capability of complex boundaries and noise is enhanced. The EFRE module optimizes feature reconstruction through depth separable convolution and jump connection to maintain global consistency. The AFRP module integrates multi-level features and improves the detail expression ability through deep convolution and an attention mechanism. The method is excellent in performance on a large-scale OCT data set, the segmentation precision of the retina edema and the recognition capability of a fine structure are remarkably improved, and an efficient and non-invasive auxiliary tool is provided for early diagnosis of ophthalmic diseases.

The invention relates to a traffic anomaly detection and network security reinforcement method for a full data center, and the method comprises the steps: achieving the precise recognition and active blocking of an attack chain through the real-time collection of full network traffic, the construction of a multi-dimensional time sequence model based on historical attack chain features, and the combination of a dynamic judgment threshold value and spatial and temporal distribution feature analysis. The method specifically comprises the steps of analyzing traffic in real time to generate an attack chain feature sequence; performing clustering and correlation analysis based on the multi-dimensional time sequence model; dynamically adjusting the monitoring density to focus the high-risk area; and generating a threat blocking instruction and executing network isolation. Aiming at the problems of high attack chain omission ratio, resource contention conflict and insufficient dynamic defense capability caused by dependence on a static rule and a fixed threshold value in the traditional scheme, the method solves the problem of weak perception capability of the traditional technology on a complex attack chain, remarkably reduces the omission rate and the error blocking rate, guarantees the service continuity through dynamic resource scheduling, and improves the service performance of the system. The method is suitable for real-time security protection of a large-scale data center.

The invention belongs to the field of computer graphics, and discloses a fast parallel multi-scale smooth symbol distance curved surface reconstruction method and system, and the method comprises the steps: carrying out the local fitting of point cloud data through employing a moving least square method, and calculating the Gaussian curvature of a point cloud; constructing a curvature adaptive octree based on Gaussian curvature, wherein the octree is used for dynamically adjusting local resolution and capturing geometric details to improve curved surface reconstruction precision; global fitting is carried out through an implicit function, and a zero contour surface is obtained through an octree contour surface extraction algorithm; and multiple threads are introduced in the process of iteratively solving the linear system to carry out parallel computing. The adaptive octree based on Gaussian curvature can effectively capture complex geometric details, and the algorithm realizes high-precision curved surface reconstruction under different scales. In addition, the local and overall calculation efficiency is remarkably improved through parallel calculation, and the high efficiency of the algorithm in large-scale data processing is ensured. The method provided by the invention provides an efficient and accurate solution for complex curved surface reconstruction.

The invention discloses a cross-modal image text retrieval method based on deep learning, and provides a novel cross-modal feature extraction and alignment framework, efficient cross-modal retrieval is performed by learning semantic representation of an image and a text in a common feature space, the framework comprises a plurality of modules, and in a feature extraction stage, the cross-modal feature extraction and alignment framework is used for extracting and aligning the images and the texts. BERT Tokenizer and BERT Embedding are adopted for text coding, and the image feature extraction is combined with a Faster R-CNN (Convolutional Neural Network) model and a ResNet-101 model, so that the dimensional consistency of image features and text features is ensured. In the feature alignment stage, fine-grained semantic alignment of image and text features is realized through an I2T Attention module and a T2I Attention module, and the cross-modal matching precision is remarkably improved. In the correlation scoring stage, a correlation scoring matrix is generated by calculating the similarity score between the image features and the text features, so that the accuracy of cross-modal matching is improved. And further optimizing the feature alignment effect through normalization and attention redistribution. In an optimization stage, the system innovatively adopts a shared semantic and sorting loss strategy, integrates an OpenCLIP model framework, efficiently mines a semantic relationship between an image and a text from unlabeled large-scale data through comparative learning, and shows strong transfer learning ability and inference precision.

The invention relates to the technical field of data processing, in particular to an artificial intelligence data aggregation method based on big data, and the method comprises the following steps: collecting traffic monitoring original data, removing repeated data and correcting wrong data through data cleaning and preprocessing, and generating preliminary cleaning data; dynamic index adjustment is applied to the preliminarily cleaned data, the depth and branches of an index tree are dynamically adjusted according to the access frequency and types of the data items, and optimized index data are obtained. In the dynamic index adjusting process, the index tree structure is dynamically optimized according to the data access frequency and type, the retrieval efficiency of high-frequency data is improved, and the storage overhead of low-frequency data is reduced. The hierarchical data compression performs hierarchical processing on the data, the use of storage space is greatly reduced through a mode of combining lightweight compression and an advanced compression algorithm, and a foundation is laid for large-scale data processing.

The invention provides a target tracking method and system based on a grouped attention feature extraction network, and the method comprises the steps: obtaining a template and search features based on a template and a search image through employing the grouped attention feature extraction network; learning local areas of the template target image features and the search image features, and aggregating to obtain target information; splicing the template with the search features to obtain the input of the self-attention encoder in the asynchronous interaction stage; the fused features are combined with a prediction head for calculation, a classification regression result is obtained, and a target is predicted according to the classification regression result; training the target tracking model in combination with the large-scale data set to obtain a trained target tracking model, and performing accurate target tracking on the target by using the trained target tracking model; according to the multi-scale feature extraction network based on Transform, sequential local and global information interaction in the windows and between the windows is promoted, and the optimal balance between performance and speed is achieved.

According to the OpenCSG large model intelligent data annotation system provided by the invention, by virtue of an automatic annotation technology driven by a large model, the requirement of manual annotation is greatly reduced, and the data annotation efficiency is remarkably improved. The system can generate a high-precision labeling result by using the intelligent labeling module and the automatic quality control module, and ensures the consistency and accuracy of the labeling result through an automatic auditing process. No matter sentiment analysis, entity recognition or complex text classification, the system can efficiently adapt to various types of labeling tasks, and different data processing requirements are met. Through modular design, the system supports flexible expansion, and can meet various data labeling requirements from small-scale test projects to large-scale commercial projects. According to the overall architecture, all the function modules are closely coordinated in the system, so that efficient operation and high-quality output are always kept in a large-scale data annotation project.

The invention discloses a distillation SAE and dynamic integration converter steelmaking carbon temperature soft measurement method, which comprises the following steps of: training a teacher model on a large-scale data set of a conventional production working condition, and compressing the model by adopting a knowledge distillation technology; then, a small amount of data from unconventional production working conditions is used for fine tuning to obtain a plurality of expert-type student stacking auto-encoder models SAE; in the prediction stage, a to-be-tested sample is mapped to respective feature space through a plurality of SAEs, and the posterior probability of the sample is calculated in each feature space based on a Gaussian distribution model; and finally, respectively inputting a to-be-measured sample into a plurality of corresponding regression devices to obtain a predicted value, and carrying out weighted fusion on the output of each regression device according to a posterior probability to realize dynamic integration selection of the soft measurement model. The method can effectively adapt to complex data distribution of multi-working-condition changes in converter steelmaking, the carbon temperature prediction precision under the unconventional working condition is improved, and the method has high robustness and engineering practicability.

The invention relates to the technical field of data processing, in particular to a computer feature extraction processing system and method fusing multi-source heterogeneous data, and the system comprises a structure correlation mapping module, a density mutation recognition module, a disturbance path analysis module, a path logic matching module and a risk logic output module. According to the method, by optimizing a data processing mode, important features in data can be accurately recognized, manual intervention and experience dependence are reduced, large-scale data analysis and processing efficiency is improved, the accuracy of a data model is enhanced, and by deeply analyzing the fluctuation amplitude and the change trend of the data, potential associated areas and abrupt change grids can be accurately positioned; key disturbance information is effectively identified, path logic matching can reveal a risk behavior mode in data evolution, potential early warning events are marked in time, the efficient processing capacity of the model for a complex data mode is ensured, and particularly, the problems of low efficiency and insufficient precision in a traditional scheme are solved in the face of large-scale data.

The invention provides a report generation and optimization method and system based on an offline data warehouse. The system comprises a data extraction layer, a data loading layer, a data cleaning layer, a data integration layer, a model data layer, a report mart layer, a report generation layer and a report optimization layer. The method comprises the following steps: S1, extracting data; s2, data cleaning; s3, integrating the data; s4, ensuring data quality; s5, storing model data; s6, a report market is constructed; and step S7, report generation. A layered architecture is adopted, independent operation of each module of the system is ensured, and expansion and maintenance are facilitated. Data extraction, cleaning and integration processes are simplified by introducing an automatic tool, and redundant calculation is reduced by optimizing SQL and solidifying logic. Bottom logic is optimized for large-scale data volume and complex query, and report generation performance is improved. And monitoring and management functions of the whole life cycle of the data flow are provided, and data quality and report accuracy are ensured.

The invention discloses an information management method based on a big data processing technology, and the method comprises the steps: S1, data collection and pre-processing optimization: S1.1, multi-source data cleaning and pre-processing: automatically detecting and correcting errors, missing and abnormal values in data through an integrated intelligent data cleaning technology, such as a machine learning algorithm (such as abnormal detection and missing value filling); the data quality is ensured, data standardization and format unification tools are adopted, heterogeneous data from different sources are converted into a unified format, and follow-up processing is facilitated. The problems of data privacy, quality, real-time performance, complexity and compliance in a traditional big data method are solved by enhancing data quality management, improving data security, optimizing storage and processing architecture and introducing modes such as real-time processing and intelligent analysis. Meanwhile, the expandability and the fault-tolerant capability of the system ensure the high efficiency and the stability of the system in a large-scale data environment, and a more intelligent, safer and more efficient information management solution is provided for enterprises.

The present invention relates to a system for implementing large-scale FIFO data processing based on DDR, comprises ingress asynchronous clock domain FIFO; burst read and write data block chain table, for writing data block chaining table and reading data block chaining table by setting of data bit width and data block length; DDR4 AXI interface for interacting with the flow control state machine and the burst read and write chain table; egress asynchronous clock domain FIFO; flow control state machine, used for data flow control to monitor writes and reads of data streams to the ingress asynchronous clock domain FIFO, DDR4 AXI interface and egress asynchronous clock domain FIFO. The present invention also relates to a method for implementing DDR-based large-scale FIFO data processing using DDR. By adopting the system, method, device, processor and its computer-readable storage medium for implementing large-scale FIFO data processing based on DDR of the present invention, the time utilization rate is improved, the data acquisition efficiency is effectively improved, and the FIFO storage depth is increased to ensure continuous data acquisition for a longer period of time.

The invention relates to the technical field of data security, in particular to an unstructured video data automatic classification method, which comprises the following steps: S1, video data preprocessing: standardizing video data to a uniform format and size, and carrying out noise processing; s2, video data feature extraction and multi-mode feature fusion: feature extraction is performed on the preprocessed video data from three modes of audio, video and text, time sequence processing is performed, and multi-mode feature fusion is performed; s3, video data same-category matching: classifying the fused multi-modal features to complete video classification; according to the invention, multi-modal information in a video can be captured more accurately, and automatic processing of video data is realized; high processing efficiency and classification accuracy are kept in large-scale data, meanwhile, the degree of automation is improved, the necessity of manual participation is reduced, and the overall process is optimized.

A large language model generation method based on retrieval enhancement belongs to the field of computers and comprises the following steps: retrieving similar document abstracts according to input questions, converting the input questions and the document abstracts into points in a high-dimensional vector space, retrieving related documents and converting the related documents into a structured knowledge graph, generating sub-questions and extracting key information from the sub-questions, then, calling a large language model to carry out few-sample learning so as to generate associative cue words; generating a query command in combination with the associative cue word and the sub-question; obtaining sub-knowledge graph information by executing a query command, converting the sub-knowledge graph information into a text, and compressing and converting the text; and inputting the converted text and the question into a GPT-4 large language model to generate a natural language answer. According to the method, multi-level information and complex data in complex questions can be processed more accurately, the accuracy, efficiency and quality in large-scale data set and complex document processing are improved, accurate and coherent answers are provided, and the operation cost and resource consumption are reduced.

The invention discloses a macro server system, an application method, electronic equipment and a storage medium, and relates to the technical field of computers, physical devices (equipment components) of the macro server system are modularly designed to be combined into modules with different functions, and the physical devices support hot plugging, so that the physical devices can be conveniently and quickly combined into modules with different functions. Meanwhile, a plurality of physical devices are connected through a high-speed communication network to form a distributed module cluster, so that a large number of computing resources, management resources and network resources can be packaged into an independent server environment, and the limitation of a current server in the aspects of processor performance, bandwidth, physical structure, hardware quantity, firmware resources and the like is solved; according to the technical scheme, the macro server system capable of supporting large-scale data processing, high-concurrency access and complex operation is constructed, so that the technical problem of how to improve the performance and expandability of the server can be solved, and the technical effect of constructing the macro server system capable of supporting large-scale data processing, high-concurrency access and complex operation is achieved.

The invention provides a medical image classification unsupervised domain self-adaption and multi-granularity alignment system and a medical image classification unsupervised domain self-adaption and multi-granularity alignment method. According to the method, a coarse-to-fine progressive feature optimization strategy is provided, that is, a fine-grained feature encoder is initialized by using the training weight of a coarse-grained feature encoder, and alignment optimization of fine-grained features is guided while original semantic information is reserved. Specifically, a staged and layer-by-layer unfreezing fine adjustment method is adopted, and the adaptive capacity of fine-grained features is enhanced on the premise that the invariance of a coarse-grained domain is not damaged. According to the strategy, the generalization and convergence efficiency of the model on target domain data is remarkably improved. And experimental verification is carried out on a plurality of cross-domain medical image classification data sets. Results show that no matter on a small-scale data set or a large-scale data set, the method provided by the invention can obtain performance obviously superior to that of an existing method, and generalization ability and robustness are stronger.

The invention provides a data processing method and system based on cloud computing, belongs to the technical field of data processing, and realizes high concurrent processing and automatic load balancing by dynamically scheduling computing power through elastic resource allocation and a distributed computing framework and combining a storage and computing separation framework. The system supports cross-node disaster recovery backup and multi-layer encryption, and data security is guaranteed; and an on-demand payment mode is adopted, so that the hardware input cost is reduced, the resource utilization rate is improved, and the large-scale data processing efficiency and the system expandability are remarkably improved.

The present application relates to the technical field of large-scale data processing, and particularly relates to a parallel computing method and system suitable for large-scale data processing. The system comprises a task management unit, a distributed load balancing module, an elastic expansion architecture, an intelligent communication optimization module, and a resource monitoring unit, wherein the task management unit divides large-scale data into a plurality of sub-tasks by means of a task decomposer, and distributes the sub-tasks to computing nodes by means of a task scheduler and a priority distributor; the distributed load balancing module achieves global load balancing by means of a load sensing unit, a dynamic adjustment unit and a balance optimization unit; the elastic expansion architecture dynamically adjusts system resources by means of a node manager, a resource pool controller and an expansion decision-making device; the intelligent communication optimization module optimizes inter-node communication by means of a communication path planning unit, a bandwidth distribution unit and a delay compensation unit; and the resource monitoring unit monitors the system performance in real time by means of a performance collector, a state analyzer and an anomaly detector.

The invention discloses an air-ground cooperative vehicle networking distributed digital twin service credible sharing method, which comprises the following steps of: firstly, according to an air-ground cooperative traffic data type, performing hierarchical packaging and storage on operation data of a vehicle networking distributed digital twin system; secondly, an authoritative random equilibrium consensus algorithm is used, and a distributed edge digital twinning system of the air-ground cooperative Internet of Vehicles carries out consensus on blocks generated by data and uploads the blocks to a block chain; and finally, requesting a twinning service from the remote digital twinning system at the edge digital twinning system, and verifying the service record through a directed acyclic graph block chain and an interstellar file system, thereby realizing the trusted sharing of the service among the edge digital twinning systems in the air-ground collaborative internet-of-vehicles distributed digital twinning system. According to the method, the authenticity and tamper-proof capability of the operation data of the air-ground cooperative Internet of Vehicles distributed digital twin system in cross-node sharing are ensured, the access efficiency of large-scale data is improved, and technical support is provided for cooperation of the Internet of Vehicles and the unmanned aerial vehicle.