Internet-based three-dimensional data sharing method and system

Abstract

The present invention relates to the field of data sharing technology, and in particular to an Internet-based three-dimensional data sharing method and system. The method comprises the following steps: obtaining a multi-source three-dimensional data set; performing data preprocessing on the multi-source three-dimensional data set to generate a standard multi-source three-dimensional data set; performing preliminary virtualization of spatial data on the standard multi-source three-dimensional data set to generate a preliminary virtual space unit set; performing virtual space global optimization on the preliminary virtual space unit set to generate a spatial virtual data matrix; performing local feature partitioning on the spatial virtual data matrix to generate virtual space partition area data; and performing local mapping of three-dimensional data on the standard multi-source three-dimensional data set using the virtual space partition area data to generate a three-dimensional data local feature mapping matrix. The present invention improves the efficiency and security of data sharing by optimizing data preprocessing, global alignment, intelligent cache management, and secure sharing.

Description

Technical Field

[0001] The present invention relates to the technical field of data sharing, and in particular to an Internet-based three-dimensional data sharing method and system. Background Art

[0002] Advances in internet technology, particularly the widespread adoption of broadband internet, have made large-scale data transmission more feasible. The introduction of distributed computing and storage technologies has further facilitated remote access and sharing of 3D data, promoting the development of collaborative applications. In recent years, with the rapid development of cloud computing and big data, 3D data sharing systems have begun adopting cloud platforms for data storage and processing. Cloud computing provides elastic computing resources and storage space, making it possible to process large amounts of 3D data. Furthermore, advances in data encryption and compression technologies have improved data security and transmission efficiency. The application of dynamic decoding and real-time recovery technologies has enhanced the real-time and interactive nature of data in various environments. However, current feature mapping and global registration of 3D data often suffer from insufficient registration accuracy and high computational complexity. Furthermore, data caching and load prediction often lack intelligent and dynamic adjustment capabilities, resulting in low caching efficiency and, in turn, low data sharing efficiency and security. Summary of the Invention

[0003] Based on this, it is necessary to provide an Internet-based three-dimensional data sharing method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a three-dimensional data sharing method based on the Internet is provided, the method comprising the following steps:

[0005] Step S1: Acquire a multi-source three-dimensional dataset; perform data preprocessing on the multi-source three-dimensional dataset to generate a standard multi-source three-dimensional dataset; perform preliminary spatial data virtualization on the standard multi-source three-dimensional dataset to generate a preliminary virtual space unit set; perform virtual space global optimization on the preliminary virtual space unit set to generate a spatial virtual data matrix;

[0006] Step S2: performing local feature partitioning on the spatial virtual data matrix to generate virtual space partition area data; performing local three-dimensional data mapping on the standard multi-source three-dimensional data set using the virtual space partition area data to generate a three-dimensional data local feature mapping matrix; performing global registration and mapping on the standard multi-source three-dimensional data set using the three-dimensional data local feature mapping matrix to generate a three-dimensional space registration model;

[0007] Step S3: Based on the 3D spatial registration model, data cache allocation is performed on the standard multi-source 3D dataset to generate a synchronized distributed cache dataset; time series load prediction is performed on the synchronized distributed cache dataset to obtain 3D storage data call load prediction data; based on the 3D storage data call load prediction data, stage cache data sharing is performed on the synchronized distributed cache dataset to generate a stage 3D cache data sharing strategy;

[0008] Step S4: Share the synchronized distributed cache data set according to the stage three-dimensional cache data sharing strategy to generate three-dimensional shared data; encrypt the shared data packet of the three-dimensional shared data to generate a three-dimensional shared encrypted compressed data packet; dynamically decode and restore the three-dimensional shared encrypted compressed data packet in real time to generate dynamically decoded three-dimensional data; upload the dynamically decoded three-dimensional data to the Internet for collaborative operation and shared display, thereby generating an interactive shared three-dimensional data set to perform three-dimensional data sharing operations.

[0009] This invention collects 3D data from various sources to ensure comprehensiveness and coverage. This provides a wealth of raw data for subsequent processing. Data cleaning, denoising, and standardization are performed on multi-source data to address data quality issues and improve data consistency and reliability. This step ensures the accuracy and effectiveness of the data in subsequent analysis. Data is converted into standardized virtual spatial units, enabling spatial unification and normalization, laying the foundation for further analysis and optimization. Global optimization of the virtual space ensures effective representation of the structure and relationships of spatial data. This optimization improves the analysis accuracy and processing efficiency of spatial data. The virtual space is divided into multiple regions to facilitate local feature analysis and processing. This partitioning method enables more accurate data processing and reduces computational complexity. Local mapping enables detailed 3D data features, improving the sophistication of data processing and the accuracy of local analysis. Global registration of different data sources is achieved, ensuring spatial unification and consistency of 3D data. This improves the accuracy and consistency of integrated data. Optimized caching strategies ensure efficient data storage and access within the distributed system, improving overall system performance. Predict data access load trends, help formulate reasonable cache strategies, optimize the allocation of system resources, and improve the response speed and efficiency of the system. Formulate data sharing strategies based on load prediction results, improve the management efficiency and sharing effect of cached data, and optimize the load handling capacity of the system. Distribute data according to the sharing strategy to improve the efficiency and adaptability of data sharing, so that data can be effectively accessed and used. Encrypt and compress data to ensure the security and privacy protection of data during transmission, prevent data leakage and unauthorized access. Implement dynamic decoding and real-time recovery of data to ensure that data can be recovered and used in a timely and accurate manner, and improve the real-time and efficiency of data processing. Upload the processed data to the Internet to support online collaboration and shared display, improve the interactivity and visualization of data, and promote data application and collaborative operation. Therefore, the present invention improves the efficiency and security of data sharing by optimizing data preprocessing, global alignment, intelligent cache management and secure sharing.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Acquire a multi-source three-dimensional data set;

[0012] Step S12: performing data preprocessing on the multi-source three-dimensional dataset to generate a standard multi-source three-dimensional dataset, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization;

[0013] Step S13: extracting spatial characteristics of the standard multi-source 3D dataset to obtain 3D spatial characteristic data; performing preliminary spatial data virtualization on the standard multi-source 3D dataset based on the 3D spatial characteristic data to generate a preliminary virtual space unit set;

[0014] Step S14: performing multi-dimensional feature extraction on the preliminary virtual space unit set to obtain a multi-dimensional feature space matrix; performing virtual space global optimization on the preliminary virtual space unit set according to the multi-dimensional feature space matrix to generate a spatial virtual data matrix.

[0015] The present invention ensures the integrity and accuracy of the data and reduces the interference of data noise on the analysis results through data cleaning, denoising and missing value filling in step S12. Step S13 generates accurate three-dimensional spatial characteristic data by extracting the spatial characteristics of the standard multi-source three-dimensional data set, laying the foundation for subsequent virtualization and optimization operations. Step S13 also realizes the preliminary virtualization of three-dimensional data, providing a clear spatial unit basis for further multi-dimensional feature extraction and global optimization of virtual space. Step S14 obtains a multi-dimensional feature space matrix through multi-dimensional feature extraction, which makes the global optimization of virtual space more scientific and reasonable, thereby improving the accuracy of the spatial virtual data matrix finally generated. The final virtual space global optimization ensures the comprehensive coordination and overall consistency of the virtual space units, making the generated spatial virtual data matrix more efficient and practical in practical applications.

[0016] Preferably, step S2 includes the following steps:

[0017] Step S21: performing local feature partitioning on the spatial virtual data matrix to generate virtual space partition area data; performing local three-dimensional data mapping on the standard multi-source three-dimensional data set using the virtual space partition area data to generate a three-dimensional data local feature mapping matrix;

[0018] Step S22: extracting cross-region data from the standard multi-source three-dimensional data set to obtain three-dimensional cross-region data; performing multi-scale registration on the three-dimensional cross-region data using the three-dimensional data local feature mapping matrix to generate a multi-scale registration matrix;

[0019] Step S23: performing nonlinear global optimization on the multi-scale registration matrix to generate a global mapping network;

[0020] Step S24: using a global mapping network to perform global registration and mapping on the local feature mapping matrix of the three-dimensional data, thereby generating a three-dimensional space registration model.

[0021] The present invention realizes the refined management of virtual space by partitioning the spatial virtual data matrix with local features, and accurately maps the three-dimensional data locally through the local feature mapping matrix, laying the foundation for subsequent cross-region data processing. Cross-region data extraction is carried out on the basis of the standard multi-source three-dimensional data set, and the three-dimensional cross-region data is finely matched and calibrated using multi-scale registration technology. The generated multi-scale registration matrix ensures the coherence and consistency between the data. A global mapping network is generated by performing nonlinear global optimization on the multi-scale registration matrix. This process improves the global consistency of the data, making the overall mapping of the three-dimensional space more coordinated and accurate. The three-dimensional data is globally registered and mapped using the global mapping network, and the three-dimensional space registration model finally generated has the characteristics of high precision and high reliability, which can better adapt to the needs of complex spatial applications. Through a series of refined partitioning, mapping and optimization steps, not only the local and global consistency of the three-dimensional data is improved, but also a strong technical support is provided for the three-dimensional space registration model finally generated, so that it can show higher precision and stability in practical applications.

[0022] Preferably, step S23 includes the following steps:

[0023] Step S231: performing a local nonlinear distortion analysis on the multi-scale registration matrix to generate registration matrix distortion data; performing a nonlinear distortion correction on the multi-scale registration matrix based on the registration matrix distortion data to generate a local correction matrix;

[0024] Step S232: performing global nonlinear mapping coordination on the local correction matrix to generate a global coordination matrix;

[0025] Step S233: Using the global coordination matrix, perform nonlinear global mapping optimization on the multi-scale registration matrix to generate a global mapping network.

[0026] The present invention identifies potential distortion and distortion problems by performing local nonlinear distortion analysis on the multi-scale registration matrix. Subsequently, nonlinear distortion correction is performed based on the distorted data, and the generated local correction matrix effectively corrects the local distortion in the data and improves the local accuracy of the data. Global nonlinear mapping coordination is performed on the basis of the local correction matrix, and the generated global coordination matrix ensures the uniformity and coherence of the data in the global scope, and reduces the mapping conflicts between different local areas. The multi-scale registration matrix is optimized for nonlinear global mapping using the global coordination matrix, and the global mapping network finally generated further improves the mapping accuracy and consistency of the three-dimensional data in the global scope, and ensures stability and reliability in complex application scenarios. Step S23 not only significantly improves the accuracy of three-dimensional data mapping through in-depth analysis and correction of nonlinear distortion problems, and coordination and optimization of global mapping, but also ensures the efficient generation and application stability of the global mapping network, so that it exhibits excellent performance in complex spatial scenes.

[0027] Preferably, step S3 includes the following steps:

[0028] Step S31: Based on the 3D space registration model, intelligent edge computing node technology is used to allocate node caches for the standard multi-source 3D data set to generate initial edge cache data blocks;

[0029] Step S32: performing distributed cache coordination on the initial edge cache data block to generate three-dimensional cache coordination data; performing data synchronization on the three-dimensional cache coordination data to generate a synchronized distributed cache data set;

[0030] Step S33: performing resource call load balancing detection on the synchronized distributed cache data set to generate three-dimensional storage data load balancing detection data; performing time series load prediction on the three-dimensional storage data load balancing detection data to obtain three-dimensional storage data call load prediction data;

[0031] Step S34: dividing the synchronized distributed cache data set into three-dimensional data call types to generate three-dimensional data call types, where the three-dimensional data call types include read-only call, analysis call, and rendering call;

[0032] Step S35: performing phase cache data sharing on the synchronous distributed cache data set based on the three-dimensional storage data call load prediction data and the three-dimensional data call type, thereby generating a phase three-dimensional cache data sharing strategy.

[0033] The present invention utilizes intelligent edge computing node technology to allocate node caches for standard multi-source three-dimensional datasets. The generated initial edge cache data blocks can effectively reduce data access latency and improve local data access efficiency. Through distributed cache coordination and data synchronization, the generated synchronized distributed cache dataset ensures data consistency between cache nodes, reduces data redundancy and synchronization conflicts, and improves the overall data coordination of the system. Through resource call load balancing detection and time-series load prediction, the system load is deeply analyzed. The generated three-dimensional storage data call load prediction data provides a reliable basis for subsequent data management and call optimization, ensuring the stability of the system under high load conditions. The synchronized distributed cache dataset is divided into three types of three-dimensional data calls: read-only call, analysis call, and rendering call, making data calls more targeted and effectively improving data processing efficiency in different application scenarios. Based on the load prediction data and call type, a phased three-dimensional cache data sharing strategy is formulated. This strategy can dynamically adjust the cache data sharing method under different time periods and load conditions, improving the utilization efficiency of cache resources and ensuring the efficient operation of the system.

[0034] Preferably, step S35 includes the following steps:

[0035] Step S351: Obtaining total system load data; calculating call load rates for read-only calls, analysis calls, and rendering calls based on the three-dimensional storage data call load prediction data to obtain read-only call load rate prediction data, analysis call load rate prediction data, and rendering call load rate prediction data;

[0036] Step S352: performing a remaining load analysis on the read-only call load rate prediction data, the analysis call load rate prediction data, and the rendering call load rate prediction data based on the total system load data, and generating read-only call load rate difference data, analysis call load rate difference data, and rendering call load rate difference data;

[0037] Step S353: Based on the read-only call load rate difference data, the analysis call load rate difference data, and the rendering call load rate difference data, call processing priority data is divided into read-only calls, analysis calls, and rendering calls.

[0038] Step S354: Conflict detection is performed on read-only calls, analysis calls, and rendering calls according to the call processing priority data to generate call conflict detection data; emergency call priority adjustment is performed on the call processing priority data based on the call conflict detection data, thereby generating a stage three-dimensional cache data sharing strategy.

[0039] The present invention calculates the load rates of read-only calls, analysis calls, and rendering calls, and calls load prediction data based on three-dimensional storage data. The generated load rate prediction data provides an accurate basis for the system's dynamic load management, thereby improving the system's ability to predict future loads. The remaining load is analyzed based on the system's total load data, and the generated load rate difference data can identify the load differences between different call types. This helps to reasonably allocate system resources when resources are tight, and avoid performance bottlenecks caused by insufficient resources for a certain type of call. Based on the load rate difference data, read-only calls, analysis calls, and rendering calls are prioritized, and the generated call processing priority data ensures that the system can give priority to critical tasks when resources are limited, thereby improving the system's processing efficiency and response speed. Through call conflict detection and emergency call priority adjustment, the generated call conflict detection data can effectively identify and resolve conflicts between calls, thereby avoiding system bottlenecks caused by resource competition. At the same time, emergency call priority adjustment ensures that critical tasks can be given priority in emergency situations, further improving the flexibility and reliability of the system. Step S35, through the comprehensive application of load rate calculation, difference analysis, priority division and conflict detection, not only improves the management and optimization level of three-dimensional data calls, but also ensures the stable operation and efficient response of the system in complex scenarios, providing strong technical support for the implementation of the three-dimensional cache data sharing strategy in this stage.

[0040] Preferably, step S354 includes the following steps:

[0041] Step S3541: performing call request frequency analysis on read-only calls, analysis calls, and rendering calls to generate call request frequency data; performing urgency priority classification on the read-only calls, analysis calls, and rendering calls based on the call request frequency data to generate call urgency priority data;

[0042] Step S3542: performing priority call conflict detection on the read-only call, the analysis call, and the rendering call according to the call processing priority data and the call urgency priority data, and generating call conflict detection data;

[0043] Step S3543: Conflict monitoring is performed on the call conflict detection data. When the call conflict detection data is detected, the call processing priority data is urgently adjusted based on the call conflict detection data, thereby generating a stage three-dimensional cache data sharing strategy.

[0044] The present invention analyzes the request frequency of read-only calls, analysis calls, and rendering calls, and the generated call request frequency data can reflect the usage intensity of different call types in the system, which helps the system to give priority to frequently called tasks and improve overall processing efficiency. The urgency of the calls is prioritized according to the call request frequency data, and the generated call urgency priority data can ensure that high-frequency and important call tasks are given priority, optimizing the allocation strategy of system resources. By performing conflict detection based on the comprehensive call processing priority data and the call urgency priority data, the generated call conflict detection data can identify potential resource competition problems in the system, provide a basis for subsequent conflict resolution, and avoid system delays or failures caused by conflicts. By monitoring the call conflict detection data in real time and adjusting the emergency call priority when a conflict is detected, it is ensured that critical tasks can still be executed first under resource constraints, reducing the negative impact of call conflicts on system performance and improving the flexibility and emergency handling capabilities of the system. Step S354 not only improves the system's response speed and resource utilization efficiency through comprehensive analysis and dynamic adjustment of call request frequency, urgency and conflict situations, but also ensures priority processing of critical tasks under high load conditions, greatly reduces the impact of call conflicts on system performance, and enhances the stability and reliability of the system.

[0045] Preferably, step S4 includes the following steps:

[0046] Step S41: Sharing the synchronized distributed cache data set according to the stage 3D cache data sharing strategy to generate 3D shared data;

[0047] Step S42: compressing the three-dimensional shared data to generate three-dimensional shared compressed data; encrypting the three-dimensional shared compressed data packet to generate a three-dimensional shared encrypted compressed data packet;

[0048] Step S43: dynamically decoding and recovering the three-dimensional shared encrypted compressed data packet in real time to generate dynamically decoded three-dimensional data;

[0049] Step S44: Upload the dynamically decoded three-dimensional data to the Internet for collaborative operation and shared display, thereby generating an interactive shared three-dimensional data set to perform a three-dimensional data sharing operation.

[0050] The present invention shares data of a synchronized distributed cache data set based on a stage-based three-dimensional cache data sharing strategy. The generated three-dimensional shared data can be efficiently transmitted between multiple nodes, ensuring timely sharing and use of data and optimizing the utilization of data resources. The three-dimensional shared data is compressed and encrypted, and the generated three-dimensional shared encrypted compressed data packet not only reduces the bandwidth requirement for data transmission, but also improves the security of the data, prevents sensitive data from being stolen or tampered with during transmission, and ensures the integrity and confidentiality of the data. By dynamically decoding the three-dimensional shared encrypted compressed data packet, the generated dynamically decoded three-dimensional data is restored in real time to ensure that the original data can be quickly restored at the receiving end, ensuring the real-time and reliability of data transmission, and providing a data basis for subsequent collaborative operations and displays. The dynamically decoded three-dimensional data is uploaded to the Internet for collaborative operations and shared displays. The generated interactive shared three-dimensional data set can interact and display in real time between different users, improving the collaborative efficiency of three-dimensional data in the Internet environment, optimizing the user experience, and ensuring the smooth execution of three-dimensional data sharing operations. Step S4 not only improves the transmission efficiency and security of three-dimensional data through the comprehensive processing of data sharing, compression encryption, dynamic decoding and Internet display, but also ensures efficient collaboration and display of data in the Internet environment, greatly enhancing the execution effect of three-dimensional data sharing operations and user interaction experience.

[0051] Preferably, step S43 includes the following steps:

[0052] Step S431: extracting data header information from the three-dimensional shared encrypted compressed data packet to obtain three-dimensional shared parsed data header information and encryption algorithm identifier;

[0053] Step S432: performing distributed decryption on the three-dimensional shared parsed data header information using the encryption algorithm identifier to generate a three-dimensional shared decrypted data set;

[0054] Step S433: dynamically decompressing the three-dimensional shared decrypted data set to generate a dynamically decompressed three-dimensional data set;

[0055] Step S434: performing three-dimensional rendering on the dynamically decompressed three-dimensional data set to generate dynamically decoded three-dimensional data.

[0056] The present invention extracts the header information of the three-dimensional shared encrypted compressed data packet, and the obtained three-dimensional shared parsed data header information and encryption algorithm identification provide necessary references for subsequent distributed decryption, ensuring the accuracy of the decryption process and the consistency of the data. The parsed data header information is distributedly decrypted through the encryption algorithm identification, and the generated three-dimensional shared decrypted data set can quickly restore the encrypted data content, improve the efficiency of the decryption process, and ensure the secure transmission and access of the data. The three-dimensional shared decrypted data set is dynamically decompressed, and the generated dynamically decompressed three-dimensional data set restores the original structure of the data, ensuring that the data maintains its integrity and availability after decryption, and providing a basis for further processing of the three-dimensional data. By performing three-dimensional rendering on the dynamically decompressed three-dimensional data set, the generated dynamically decoded three-dimensional data can be presented in a visual form, making the content of the data more intuitive and easy to understand, providing users with a real-time interactive experience and display effect.

[0057] In this specification, an Internet-based 3D data sharing system is provided for executing the above-mentioned Internet-based 3D data sharing method. The Internet-based 3D data sharing system includes:

[0058] The data space virtualization module is used to obtain a multi-source three-dimensional data set; perform data preprocessing on the multi-source three-dimensional data set to generate a standard multi-source three-dimensional data set; perform preliminary spatial data virtualization on the standard multi-source three-dimensional data set to generate a preliminary virtual space unit set; perform virtual space global optimization on the preliminary virtual space unit set to generate a spatial virtual data matrix;

[0059] A global mapping module is used to perform local feature partitioning on the spatial virtual data matrix to generate virtual space partition area data; use the virtual space partition area data to perform local 3D data mapping on the standard multi-source 3D data set to generate a 3D data local feature mapping matrix; and use the 3D data local feature mapping matrix to perform global registration and mapping on the standard multi-source 3D data set to generate a 3D space registration model.

[0060] The cache call module is used to allocate data cache for standard multi-source 3D datasets based on the 3D spatial registration model to generate a synchronized distributed cache dataset; perform time series load prediction on the synchronized distributed cache dataset to obtain 3D storage data call load prediction data; perform stage cache data sharing on the synchronized distributed cache dataset based on the 3D storage data call load prediction data, thereby generating a stage 3D cache data sharing strategy;

[0061] The three-dimensional data sharing module is used to share data of the synchronized distributed cache data set according to the stage three-dimensional cache data sharing strategy to generate three-dimensional shared data; encrypt the shared data packet of the three-dimensional shared data to generate a three-dimensional shared encrypted compressed data packet; dynamically decode and restore the three-dimensional shared encrypted compressed data packet in real time to generate dynamically decoded three-dimensional data; upload the dynamically decoded three-dimensional data to the Internet for collaborative operation and shared display, thereby generating an interactive shared three-dimensional data set to perform three-dimensional data sharing operations.

[0062] The beneficial effect of the present invention is that through data preprocessing, preliminary spatial data virtualization, and global optimization in the data space virtualization module, multi-source three-dimensional datasets can be integrated into a standardized dataset and optimized into a spatial virtual data matrix. This process ensures data consistency, accuracy, and availability, providing a reliable foundation for subsequent analysis and processing. The global mapping module generates a three-dimensional spatial registration model through local feature partitioning and global registration mapping. This enables three-dimensional data from different sources to be effectively aligned and integrated, improving the spatial consistency and accuracy of the data and providing accurate spatial information for comprehensive analysis. The data cache allocation and load prediction in the cache call module ensure efficient data storage and use. By implementing a staged cache data sharing strategy, cache usage can be optimized, improving the system's storage efficiency and response speed, and reducing data access latency. The three-dimensional data sharing module achieves secure, reliable, and efficient data transmission through data sharing, encryption compression, dynamic decoding, and internet upload. The generated interactive shared three-dimensional dataset not only ensures data security during transmission but also provides real-time collaborative operation and display functions, enhancing the user's operating experience and data visualization. In summary, this process, through the systematic integration of multi-source data, spatial registration, data cache optimization, and secure data sharing and real-time display, not only improves data processing efficiency and accuracy, but also enhances the system's responsiveness and user experience, ensuring the successful execution of 3D data sharing operations. Therefore, this invention improves the efficiency and security of data sharing by optimizing data preprocessing, global registration, intelligent cache management, and secure sharing. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic diagram of a process flow of a three-dimensional data sharing method based on the Internet;

[0064] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0065] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0066] Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0067] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0068] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0069] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0070] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0071] To achieve this, please refer to Figures 1 to 4 , a three-dimensional data sharing method based on the Internet, the method comprising the following steps:

[0072] Step S1: Acquire a multi-source three-dimensional dataset; perform data preprocessing on the multi-source three-dimensional dataset to generate a standard multi-source three-dimensional dataset; perform preliminary spatial data virtualization on the standard multi-source three-dimensional dataset to generate a preliminary virtual space unit set; perform virtual space global optimization on the preliminary virtual space unit set to generate a spatial virtual data matrix;

[0073] Step S2: performing local feature partitioning on the spatial virtual data matrix to generate virtual space partition area data; performing local three-dimensional data mapping on the standard multi-source three-dimensional data set using the virtual space partition area data to generate a three-dimensional data local feature mapping matrix; performing global registration and mapping on the standard multi-source three-dimensional data set using the three-dimensional data local feature mapping matrix to generate a three-dimensional space registration model;

[0074] Step S3: Based on the 3D spatial registration model, data cache allocation is performed on the standard multi-source 3D dataset to generate a synchronized distributed cache dataset; time series load prediction is performed on the synchronized distributed cache dataset to obtain 3D storage data call load prediction data; based on the 3D storage data call load prediction data, stage cache data sharing is performed on the synchronized distributed cache dataset to generate a stage 3D cache data sharing strategy;

[0075] Step S4: Share the synchronized distributed cache data set according to the stage three-dimensional cache data sharing strategy to generate three-dimensional shared data; encrypt the shared data packet of the three-dimensional shared data to generate a three-dimensional shared encrypted compressed data packet; dynamically decode and restore the three-dimensional shared encrypted compressed data packet in real time to generate dynamically decoded three-dimensional data; upload the dynamically decoded three-dimensional data to the Internet for collaborative operation and shared display, thereby generating an interactive shared three-dimensional data set to perform three-dimensional data sharing operations.

[0076] This invention collects 3D data from various sources to ensure comprehensiveness and coverage. This provides a wealth of raw data for subsequent processing. Data cleaning, denoising, and standardization are performed on multi-source data to address data quality issues and improve data consistency and reliability. This step ensures the accuracy and effectiveness of the data in subsequent analysis. Data is converted into standardized virtual spatial units, enabling spatial unification and normalization, laying the foundation for further analysis and optimization. Global optimization of the virtual space ensures effective representation of the structure and relationships of spatial data. This optimization improves the analysis accuracy and processing efficiency of spatial data. The virtual space is divided into multiple regions to facilitate local feature analysis and processing. This partitioning method enables more accurate data processing and reduces computational complexity. Local mapping enables detailed 3D data features, improving the sophistication of data processing and the accuracy of local analysis. Global registration of different data sources is achieved, ensuring spatial unification and consistency of 3D data. This improves the accuracy and consistency of integrated data. Optimized caching strategies ensure efficient data storage and access within the distributed system, improving overall system performance. Predict data access load trends, help formulate reasonable cache strategies, optimize the allocation of system resources, and improve the response speed and efficiency of the system. Formulate data sharing strategies based on load prediction results, improve the management efficiency and sharing effect of cached data, and optimize the load handling capacity of the system. Distribute data according to the sharing strategy to improve the efficiency and adaptability of data sharing, so that data can be effectively accessed and used. Encrypt and compress data to ensure the security and privacy protection of data during transmission, prevent data leakage and unauthorized access. Implement dynamic decoding and real-time recovery of data to ensure that data can be recovered and used in a timely and accurate manner, and improve the real-time and efficiency of data processing. Upload the processed data to the Internet to support online collaboration and shared display, improve the interactivity and visualization of data, and promote data application and collaborative operation. Therefore, the present invention improves the efficiency and security of data sharing by optimizing data preprocessing, global alignment, intelligent cache management and secure sharing.

[0077] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a three-dimensional data sharing method based on the Internet of the present invention. In this example, the three-dimensional data sharing method based on the Internet includes the following steps:

[0078] Step S1: Acquire a multi-source three-dimensional dataset; perform data preprocessing on the multi-source three-dimensional dataset to generate a standard multi-source three-dimensional dataset; perform preliminary spatial data virtualization on the standard multi-source three-dimensional dataset to generate a preliminary virtual space unit set; perform virtual space global optimization on the preliminary virtual space unit set to generate a spatial virtual data matrix;

[0079] In an embodiment of the present invention, raw three-dimensional data is obtained from various three-dimensional data sources (such as laser scanners, photogrammetry, sensors, etc.). This data includes point cloud data, mesh data, depth images, etc. The data from different sources are integrated to ensure the consistency of the data in the spatial coordinate system. The data needs to be preliminarily aligned and registered. A denoising algorithm (such as mean filtering, filter smoothing, etc.) is applied to remove noise points or outliers in the three-dimensional data. Missing data values are filled or incomplete data areas are repaired. Interpolation algorithms or reconstruction techniques are used to fill missing three-dimensional data parts. All three-dimensional data are converted to a unified coordinate system to ensure that data from different data sources are comparable within the same spatial range. The units of all data are ensured to be consistent, for example, all data are converted to the same length unit (such as meters, millimeters, etc.). The pre-processed multi-source three-dimensional data are integrated into a standardized data set. This includes ensuring data format consistency and coordinate system uniformity. Spatial characteristics, such as geometric features, texture information, and spatial relationships, are extracted from the standard multi-source three-dimensional data set. The extracted spatial characteristics are used to preliminarily virtualize the data. A preliminary set of virtual space units is constructed, which represents basic data structures or objects in space. Create a dataset containing preliminary virtual space units. These units can be voxels, grid cells, or other data structures that represent basic building blocks in space. Apply a global optimization algorithm (such as global least squares, global registration algorithm, etc.) to optimize the preliminary virtual space unit set. The optimization goal is to improve the spatial consistency and accuracy of the data. Adjust the configuration and parameters of the virtual space units to ensure the accuracy and consistency of the data in space. Convert the optimized virtual space unit set into a spatial virtual data matrix. This matrix represents the distribution and structure of the data in the virtual space and is suitable for further analysis and application.

[0080] Step S2: performing local feature partitioning on the spatial virtual data matrix to generate virtual space partition area data; performing local three-dimensional data mapping on the standard multi-source three-dimensional data set using the virtual space partition area data to generate a three-dimensional data local feature mapping matrix; performing global registration and mapping on the standard multi-source three-dimensional data set using the three-dimensional data local feature mapping matrix to generate a three-dimensional space registration model;

[0081] In an embodiment of the present invention, a spatial virtual data matrix is partitioned by local features. This can be achieved using spatial gridding, cluster analysis, or other partitioning techniques, dividing the matrix into several regions based on the spatial characteristics of the data. The boundaries and characteristics of each region are determined, including spatial distribution, data density, geometric shape, etc. Based on the local feature partitioning, virtual spatial partition region data is generated. This data includes a feature description and spatial location of each partition, which is used for subsequent local mapping. The generated virtual spatial partition region data is used to perform local mapping on a standard multi-source three-dimensional dataset. This involves matching the virtual spatial partition region data with the corresponding regions in the three-dimensional dataset. Through local mapping, a three-dimensional data local feature mapping matrix is generated. This matrix describes the location and characteristics of each partition region in the three-dimensional dataset. Using the three-dimensional data local feature mapping matrix, the standard multi-source three-dimensional dataset is globally aligned. Global alignment algorithms can include feature point-based alignment, the application of transformation models, etc. Through global alignment, a three-dimensional spatial alignment model is generated. This model describes the spatial layout and alignment information of the entire three-dimensional dataset, ensuring the global consistency and accuracy of the data. The three-dimensional spatial alignment model is used to perform global mapping on the data. The mapping process integrates all local feature data into a unified spatial framework.

[0082] Step S3: Based on the 3D spatial registration model, data cache allocation is performed on the standard multi-source 3D dataset to generate a synchronized distributed cache dataset; time series load prediction is performed on the synchronized distributed cache dataset to obtain 3D storage data call load prediction data; based on the 3D storage data call load prediction data, stage cache data sharing is performed on the synchronized distributed cache dataset to generate a stage 3D cache data sharing strategy;

[0083] In an embodiment of the present invention, data cache allocation is performed for a standard multi-source three-dimensional dataset by utilizing a three-dimensional spatial registration model. This model provides information about the distribution of data in three-dimensional space and can be used to optimize the configuration of cache resources. Data is divided into several cache blocks based on the spatial location and access pattern of the data. A distributed cache system can be used to distribute the data blocks to different computing nodes. The standard multi-source three-dimensional dataset is divided into data blocks and distributed to each cache node. The generated synchronized distributed cache dataset contains copies of all data blocks, ensuring data availability and consistency. A cache data synchronization mechanism is implemented to ensure data consistency across all cache nodes and reduce data access latency. Time-series load prediction is performed on the synchronized distributed cache dataset. Prediction algorithms (such as time series analysis and machine learning models) are applied to predict future data call loads. Historical load data is analyzed to identify load patterns and trends, and a load prediction model is generated. Based on the time-series load prediction, three-dimensional storage data call load prediction data is generated. This data describes the cache load over a period of time, including the expected access frequency and load demand for each data block. Based on the three-dimensional storage data call load prediction data, staged cache data sharing is performed on the synchronized distributed cache dataset. The stage sharing strategy involves adjusting and optimizing data blocks based on load forecasts. Data access requirements are divided into different stages, and the data caching strategy is adjusted based on the data access volume and load forecast results for each stage. Based on the stage cache data sharing requirements, a stage-by-stage three-dimensional cache data sharing strategy is generated. This strategy defines cache data allocation and sharing rules for different stages to optimize system performance and resource utilization. The generated sharing strategy is applied to the distributed cache system to achieve dynamic adjustment and optimization of the data cache.

[0084] Step S4: Share the synchronized distributed cache data set according to the stage three-dimensional cache data sharing strategy to generate three-dimensional shared data; encrypt the shared data packet of the three-dimensional shared data to generate a three-dimensional shared encrypted compressed data packet; dynamically decode and restore the three-dimensional shared encrypted compressed data packet in real time to generate dynamically decoded three-dimensional data; upload the dynamically decoded three-dimensional data to the Internet for collaborative operation and shared display, thereby generating an interactive shared three-dimensional data set to perform three-dimensional data sharing operations.

[0085] In this embodiment of the present invention, data sharing is performed on the synchronized distributed cache dataset using the staged 3D cache data sharing policy generated in step S3. This policy specifies how data is shared between different cache nodes to ensure data consistency and availability. Data blocks are extracted from the synchronized distributed cache dataset, distributed and synchronized according to the sharing policy, and 3D shared data is generated. The shared data contains the necessary information for the entire dataset to support collaborative operations and visualization. The 3D shared data is compressed to reduce the size and bandwidth requirements of the data transmission. The data is compressed using an efficient compression algorithm, such as GZIP or LZ4. The compressed 3D shared data is encrypted to ensure data security during transmission. An encryption algorithm, such as AES (Advanced Encryption Standard) or RSA (Reliable Public Key Encryption), is used to generate a 3D shared encrypted compressed data packet. The encrypted data is packaged into a data packet for transmission over the network. The encryption algorithm and key management are ensured to comply with security standards to protect data privacy and integrity. Data header information, including the data format and encryption algorithm identifier, is extracted from the 3D shared encrypted compressed data packet. This information is used to guide the decryption and decompression process. The encryption algorithm identifier is used to decrypt the data header information and encrypted data using distributed decryption technology. Generate a 3D shared decrypted dataset. Dynamically decompress the decrypted data and restore it to its original 3D data format. Use a dynamic decompression algorithm, such as Huffman coding or LZW, to generate a dynamically decompressed 3D dataset. Render the dynamically decompressed 3D data to generate a 3D model or scene. This includes applying rendering techniques such as ray tracing and texture mapping to improve visual effects. Upload the dynamically decoded 3D data to the Internet and store it on a cloud platform or data center. Ensure data integrity and security during the upload process. Support real-time collaborative operations on data, allowing multiple users to access and edit 3D data simultaneously. Use collaborative tools and platforms, such as WebGL, Three.js, or virtual reality (VR) technology, to achieve collaborative work and presentation of data. Generate an interactive shared 3D dataset for users to browse, analyze, and visualize. Provide a user interface and interactive functions, such as zoom, rotation, and measurement tools, to enhance user experience and data utilization.

[0086] Preferably, step S1 includes the following steps:

[0087] Step S11: Acquire a multi-source three-dimensional data set;

[0088] Step S12: performing data preprocessing on the multi-source three-dimensional dataset to generate a standard multi-source three-dimensional dataset, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization;

[0089] Step S13: extracting spatial characteristics of the standard multi-source 3D dataset to obtain 3D spatial characteristic data; performing preliminary spatial data virtualization on the standard multi-source 3D dataset based on the 3D spatial characteristic data to generate a preliminary virtual space unit set;

[0090] Step S14: performing multi-dimensional feature extraction on the preliminary virtual space unit set to obtain a multi-dimensional feature space matrix; performing virtual space global optimization on the preliminary virtual space unit set according to the multi-dimensional feature space matrix to generate a spatial virtual data matrix.

[0091] In an embodiment of the present invention, data sources are selected and confirmed, including but not limited to laser scanners, cameras, sensors, etc. The selected equipment is used to collect three-dimensional data from different angles and positions to ensure that all relevant details of the target area are covered. The three-dimensional data from different devices and angles are integrated into a preliminary data set to ensure the integrity and consistency of the data. Noise and outliers are identified and removed. Redundant data is eliminated to reduce the amount of data and improve processing efficiency. Filters (such as median filters or Gaussian filters) are applied to remove noise in the data, and smoothing techniques (such as curve fitting) are used to improve data quality. Linear interpolation, spline interpolation, etc. are applied to fill missing values in the data. The data is scaled to a standard range to improve data consistency, and the mean and standard deviation of the data are adjusted to make the data conform to the standard normal distribution. Geometric information such as shape, curvature, and surface normal vectors are extracted, and surface texture and detail information are analyzed. The extracted feature data is constructed into a preliminary three-dimensional grid model, and the three-dimensional grid model is divided into several virtual space units for further processing. The geometric dimension information (such as volume and area) of the space unit is extracted, and the topological structure information (such as adjacency relationship) of the space unit is extracted. Apply optimization algorithms (such as genetic algorithms and particle swarm optimization) to globally optimize virtual space units. Adjust virtual space data based on objective functions (such as minimizing error and maximizing consistency) to generate the final spatial virtual data matrix.

[0092] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0093] Step S21: performing local feature partitioning on the spatial virtual data matrix to generate virtual space partition area data; performing local three-dimensional data mapping on the standard multi-source three-dimensional data set using the virtual space partition area data to generate a three-dimensional data local feature mapping matrix;

[0094] Step S22: extracting cross-region data from the standard multi-source three-dimensional data set to obtain three-dimensional cross-region data; performing multi-scale registration on the three-dimensional cross-region data using the three-dimensional data local feature mapping matrix to generate a multi-scale registration matrix;

[0095] Step S23: performing nonlinear global optimization on the multi-scale registration matrix to generate a global mapping network;

[0096] Step S24: using a global mapping network to perform global registration and mapping on the local feature mapping matrix of the three-dimensional data, thereby generating a three-dimensional space registration model.

[0097] In an embodiment of the present invention, a spatial virtual data matrix is divided into several local feature regions by applying a spatial partitioning algorithm (such as K-means clustering, DBSCAN). The boundaries and features of each region are determined based on the geometric and topological characteristics of the data. Feature data of each partition region is extracted from the spatial virtual data matrix to generate virtual spatial partition region data. Descriptive information (such as boundaries, center points, and feature vectors) is created for each partition region. The standard multi-source three-dimensional data set is locally mapped using the information in the virtual spatial partition region data. Mapping techniques (such as affine transformation and perspective transformation) can be used to map data to partition regions. Based on the local mapping results, a three-dimensional data local feature mapping matrix is generated to record the feature information of each local region. Data spanning multiple partition regions is extracted from the standard multi-source three-dimensional data set to obtain complete three-dimensional cross-region data, and the cross-region data is fused into a continuous data set to ensure data integrity and consistency. The three-dimensional data local feature mapping matrix is applied to the three-dimensional cross-region data using a multi-scale registration technology (such as pyramid registration and local matching). A multi-scale registration matrix is generated through the registration process, which describes the matching information at different scales. Apply nonlinear optimization algorithms (such as Levenberg-Marquardt algorithm, conjugate gradient method) to globally optimize the multi-scale registration matrix. Minimize the global error and adjust the registration matrix to improve the registration accuracy and consistency. Construct a global mapping network and define the network structure (such as convolutional network and fully connected network) to represent the global mapping relationship of three-dimensional space. Use the optimized registration matrix to train the global mapping network to improve the mapping performance of the network. Use the global mapping network to globally register the local feature mapping matrix of three-dimensional data to ensure the spatial consistency of the entire data set. Generate the final three-dimensional space registration model based on the global mapping network. Verify the generated three-dimensional space registration model to check the accuracy and applicability of the model. Apply the three-dimensional space registration model to practical scenarios, such as data fusion, virtual reality, etc.

[0098] Preferably, step S23 includes the following steps:

[0099] Step S231: performing a local nonlinear distortion analysis on the multi-scale registration matrix to generate registration matrix distortion data; performing a nonlinear distortion correction on the multi-scale registration matrix based on the registration matrix distortion data to generate a local correction matrix;

[0100] Step S232: performing global nonlinear mapping coordination on the local correction matrix to generate a global coordination matrix;

[0101] Step S233: Using the global coordination matrix, perform nonlinear global mapping optimization on the multi-scale registration matrix to generate a global mapping network.

[0102] In an embodiment of the present invention, local distortions in a multi-scale registration matrix are detected by using nonlinear distortion analysis techniques (such as local feature analysis and deformation field analysis). Local distortions are recorded and analyzed to generate registration matrix distortion data, which describe the severity and distribution of distortions in the matrix. A nonlinear distortion correction algorithm (such as B-spline fitting and radial basis function) is applied to correct the registration matrix distortion data. A local correction matrix is generated based on the correction results, and the matrix is used to correct the distortions and distortions in the registration matrix. A global coordination algorithm (such as image optimization and global smoothing technology) is applied to perform global nonlinear mapping coordination on the local correction matrix to generate a global coordination matrix, which ensures the mapping consistency between different local areas and corrects the distortion in the global range. A global mapping optimization algorithm (such as global least squares method and nonlinear optimization algorithm) is used to perform nonlinear global mapping optimization on the multi-scale registration matrix. A global mapping network is generated based on the optimization results, which accurately describes the global mapping relationship of the three-dimensional space and improves the accuracy of data registration.

[0103] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0104] Step S31: Based on the 3D space registration model, intelligent edge computing node technology is used to allocate node caches for the standard multi-source 3D data set to generate initial edge cache data blocks;

[0105] Step S32: performing distributed cache coordination on the initial edge cache data block to generate three-dimensional cache coordination data; performing data synchronization on the three-dimensional cache coordination data to generate a synchronized distributed cache data set;

[0106] Step S33: performing resource call load balancing detection on the synchronized distributed cache data set to generate three-dimensional storage data load balancing detection data; performing time series load prediction on the three-dimensional storage data load balancing detection data to obtain three-dimensional storage data call load prediction data;

[0107] Step S34: dividing the synchronized distributed cache data set into three-dimensional data call types to generate three-dimensional data call types, where the three-dimensional data call types include read-only call, analysis call, and rendering call;

[0108] Step S35: performing phase cache data sharing on the synchronous distributed cache data set based on the three-dimensional storage data call load prediction data and the three-dimensional data call type, thereby generating a phase three-dimensional cache data sharing strategy.

[0109] In an embodiment of the present invention, data cache allocation is performed by utilizing edge computing nodes (such as edge servers and edge devices). A standard multi-source three-dimensional data set is divided into multiple data blocks and allocated according to the computing power and storage resources of the nodes. An initial cache data block is generated on the edge node based on the division results to support subsequent data processing and analysis. A distributed cache coordination algorithm (such as consistent hashing, cache coordination protocol) is applied to optimize the distribution of cache data blocks. Three-dimensional cache coordination data is generated based on the coordination results to ensure consistency and efficient access of cache data in the distributed system. Data synchronization technology (such as distributed consistency protocol, synchronization mechanism) is implemented to synchronize the three-dimensional cache coordination data and generate a synchronized distributed cache data set to ensure consistency and update of data on all cache nodes. Load balancing detection tools (such as performance monitoring system, load balancing algorithm) are used to analyze the load of data calls and record the load balancing detection results, including the load situation and performance indicators of each node. A time series prediction model (such as time series analysis, machine learning model) is applied to predict future load changes, and the load prediction data generated based on the prediction model is used to guide future data calls and resource allocation. 3D data calls are divided into different types, including: read-only calls for data reading only and not data modification; analysis calls for data analysis and computational processing; and rendering calls for data rendering and visualization. Data in the synchronized distributed cache dataset is categorized by call type, and call type information for each data block is generated to support subsequent cache strategy optimization and resource management. Based on the 3D storage data call load prediction data and 3D data call type, a phased cache data sharing strategy is designed. Cached data is shared and scheduled in different phases and for different call types to optimize data access efficiency and resource utilization. The generated phased 3D cache data sharing strategy is implemented to ensure efficient sharing and management of data across different nodes and phases.

[0110] Preferably, step S35 includes the following steps:

[0111] Step S351: Obtaining total system load data; calculating call load rates for read-only calls, analysis calls, and rendering calls based on the three-dimensional storage data call load prediction data to obtain read-only call load rate prediction data, analysis call load rate prediction data, and rendering call load rate prediction data;

[0112] Step S352: performing a remaining load analysis on the read-only call load rate prediction data, the analysis call load rate prediction data, and the rendering call load rate prediction data based on the total system load data, and generating read-only call load rate difference data, analysis call load rate difference data, and rendering call load rate difference data;

[0113] Step S353: Based on the read-only call load rate difference data, the analysis call load rate difference data, and the rendering call load rate difference data, call processing priority data is divided into read-only calls, analysis calls, and rendering calls.

[0114] Step S354: Conflict detection is performed on read-only calls, analysis calls, and rendering calls according to the call processing priority data to generate call conflict detection data; emergency call priority adjustment is performed on the call processing priority data based on the call conflict detection data, thereby generating a stage three-dimensional cache data sharing strategy.

[0115] In an embodiment of the present invention, the total load data of the current system is obtained from a system monitoring tool or a load management system. This includes CPU usage, memory usage, storage IO, etc. The total load data of the current system is obtained from a system monitoring tool or a load management system. This includes CPU usage, memory usage, storage IO, etc. The load prediction data is called based on the three-dimensional storage data, and the load rate prediction data of the read-only call is calculated. A prediction model (such as time series prediction, regression analysis) is used to estimate the future call load rate. The load rate prediction data of the analysis call is calculated. The complexity of the analysis task and the demand for computing resources are considered. The load rate prediction data of the rendering call is calculated. The prediction is made based on the computing and memory requirements of the rendering task. The load difference is calculated using the total system load data and the load rate prediction data. The difference between the current system load and the predicted load is determined, and read-only call load rate difference data, analysis call load rate difference data and rendering call load rate difference data are generated. These data reflect the difference between the system load and the load of each type of call. Based on the difference data, the call processing priority is divided. Call types with high load differences should be given higher priority to ensure that the system can effectively handle key calls and generate call processing priority data to guide cache and resource allocation. Based on the call processing priority data, conflict detection is performed on read-only calls, analysis calls, and rendering calls. Priority conflicts and resource contention issues are identified, call conflict detection data is generated, and detected conflicts and contentions are recorded. Based on the conflict detection data, emergency call priority adjustments are made to the call processing priority data. Priority strategies are adjusted to resolve detected conflicts and resource contention issues. Finally, a three-dimensional cache data sharing strategy is generated in the stage to optimize cache data sharing and resource management to ensure stable system operation.

[0116] Preferably, step S354 includes the following steps:

[0117] Step S3541: performing call request frequency analysis on read-only calls, analysis calls, and rendering calls to generate call request frequency data; performing urgency priority classification on the read-only calls, analysis calls, and rendering calls based on the call request frequency data to generate call urgency priority data;

[0118] Step S3542: performing priority call conflict detection on the read-only call, the analysis call, and the rendering call according to the call processing priority data and the call urgency priority data, and generating call conflict detection data;

[0119] Step S3543: Conflict monitoring is performed on the call conflict detection data. When the call conflict detection data is detected, the call processing priority data is urgently adjusted based on the call conflict detection data, thereby generating a stage three-dimensional cache data sharing strategy.

[0120] In an embodiment of the present invention, the request frequency of read-only calls, analysis calls, and rendering calls is analyzed. This can be accomplished through log analysis, real-time monitoring tools, or a request statistics system. Request frequency data for each call type is recorded to understand its activity level in the system. Based on the call request frequency data, read-only calls, analysis calls, and rendering calls are prioritized by urgency. Call types with higher request frequencies are typically assigned higher urgency priorities, and call urgency priority data is generated to guide subsequent priority processing. Based on the call processing priority data and call urgency priority data, potential priority conflicts are detected. This includes evaluating resource contention between calls of different priorities under high load conditions and recording conflict detection results, including detected priority conflicts and resource contention issues. Call conflict detection data is monitored in real time to detect whether priority conflicts and resource contention exist in the system. When a call conflict is detected, the call processing priority data is adjusted for urgent call priority based on the call conflict detection data. The adjustment strategy should resolve the conflict and optimize resource allocation. Ultimately, an adjusted stage 3D cache data sharing strategy is generated to ensure effective data sharing and management across all stages, improving system stability and performance.

[0121] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0122] Step S41: Sharing the synchronized distributed cache data set according to the stage 3D cache data sharing strategy to generate 3D shared data;

[0123] Step S42: compressing the three-dimensional shared data to generate three-dimensional shared compressed data; encrypting the three-dimensional shared compressed data packet to generate a three-dimensional shared encrypted compressed data packet;

[0124] Step S43: dynamically decoding and recovering the three-dimensional shared encrypted compressed data packet in real time to generate dynamically decoded three-dimensional data;

[0125] Step S44: Upload the dynamically decoded three-dimensional data to the Internet for collaborative operation and shared display, thereby generating an interactive shared three-dimensional data set to perform a three-dimensional data sharing operation.

[0126] In an embodiment of the present invention, data sharing is performed on synchronized distributed cache datasets based on a staged 3D cache data sharing strategy. This ensures that data is distributed to the required nodes or systems according to a preset strategy. After data sharing is completed, 3D shared data is generated. This data is a processed data set ready for further operations. The 3D shared data is compressed to reduce data volume and improve transmission efficiency. An appropriate compression algorithm, such as gzip or bzip2, is used, selecting the most effective compression method based on the data characteristics. After compression, 3D shared compressed data is generated. This data has a smaller size and is more suitable for network transmission and storage. The 3D shared compressed data is encrypted to ensure data security and privacy. Encryption algorithms, such as AES (Advanced Encryption Standard) or RSA (asymmetric encryption), are used to protect the data from unauthorized access. The encrypted data generates a 3D shared encrypted compressed data packet, which is prepared for secure transmission. The 3D shared encrypted compressed data packet is dynamically decoded to recover the 3D data. The decoding process requires processing the inverse operations of encryption and compression to ensure correct data restoration. Through the decoding and recovery process, dynamically decoded 3D data is generated. This data is the original 3D data that can be used for subsequent processing and visualization. Upload dynamically decoded 3D data to the internet. Use appropriate network protocols and storage solutions, such as cloud storage services or distributed file systems. Ensure that the uploaded data is accessible to all relevant nodes or users for collaborative operations and shared presentation. Generate interactive, shared 3D datasets based on the uploaded data, allowing users to view and manipulate them online. Support real-time collaboration and data presentation, facilitating data sharing.

[0127] Preferably, step S43 includes the following steps:

[0128] Step S431: extracting data header information from the three-dimensional shared encrypted compressed data packet to obtain three-dimensional shared parsed data header information and encryption algorithm identifier;

[0129] Step S432: performing distributed decryption on the three-dimensional shared parsed data header information using the encryption algorithm identifier to generate a three-dimensional shared decrypted data set;

[0130] Step S433: dynamically decompressing the three-dimensional shared decrypted data set to generate a dynamically decompressed three-dimensional data set;

[0131] Step S434: performing three-dimensional rendering on the dynamically decompressed three-dimensional data set to generate dynamically decoded three-dimensional data.

[0132] In an embodiment of the present invention, data header information is extracted from a three-dimensional shared encrypted compressed data packet. The data header contains key information such as data format, compression method, encryption algorithm identifier, etc. The encryption algorithm identifier is extracted from the data header information to guide the subsequent decryption process. Based on the extracted encryption algorithm identifier, the three-dimensional shared parsed data header information is distributedly decrypted. Distributed decryption can utilize multiple nodes for parallel processing to improve decryption efficiency. After decryption, a three-dimensional shared decrypted data set is generated. This data set is the data after the encryption protection is removed and is ready for further processing. The three-dimensional shared decrypted data set is dynamically decompressed. The decompression process restores the original structure of the data and removes the data compression effect caused by compression. After decompression is completed, a dynamically decompressed three-dimensional data set is generated, and this data is ready for rendering and display. The dynamically decompressed three-dimensional data set is rendered in three dimensions. The rendering process visualizes the three-dimensional data as a graphic image, showing the three-dimensional structure and details of the data. After rendering is completed, dynamically decoded three-dimensional data is generated. This data set can be used for subsequent visualization and analysis to support user interaction and display needs.

[0133] In this specification, an Internet-based 3D data sharing system is provided for executing the above-mentioned Internet-based 3D data sharing method. The Internet-based 3D data sharing system includes:

[0134] The data space virtualization module is used to obtain a multi-source three-dimensional data set; perform data preprocessing on the multi-source three-dimensional data set to generate a standard multi-source three-dimensional data set; perform preliminary spatial data virtualization on the standard multi-source three-dimensional data set to generate a preliminary virtual space unit set; perform virtual space global optimization on the preliminary virtual space unit set to generate a spatial virtual data matrix;

[0135] A global mapping module is used to perform local feature partitioning on the spatial virtual data matrix to generate virtual space partition area data; use the virtual space partition area data to perform local 3D data mapping on the standard multi-source 3D data set to generate a 3D data local feature mapping matrix; and use the 3D data local feature mapping matrix to perform global registration and mapping on the standard multi-source 3D data set to generate a 3D space registration model.

[0136] The cache call module is used to allocate data cache for standard multi-source 3D datasets based on the 3D spatial registration model to generate a synchronized distributed cache dataset; perform time series load prediction on the synchronized distributed cache dataset to obtain 3D storage data call load prediction data; perform stage cache data sharing on the synchronized distributed cache dataset based on the 3D storage data call load prediction data, thereby generating a stage 3D cache data sharing strategy;

[0137] The three-dimensional data sharing module is used to share data of the synchronized distributed cache data set according to the stage three-dimensional cache data sharing strategy to generate three-dimensional shared data; encrypt the shared data packet of the three-dimensional shared data to generate a three-dimensional shared encrypted compressed data packet; dynamically decode and restore the three-dimensional shared encrypted compressed data packet in real time to generate dynamically decoded three-dimensional data; upload the dynamically decoded three-dimensional data to the Internet for collaborative operation and shared display, thereby generating an interactive shared three-dimensional data set to perform three-dimensional data sharing operations.

[0138] The beneficial effect of the present invention is that through data preprocessing, preliminary spatial data virtualization, and global optimization in the data space virtualization module, multi-source three-dimensional datasets can be integrated into a standardized dataset and optimized into a spatial virtual data matrix. This process ensures data consistency, accuracy, and availability, providing a reliable foundation for subsequent analysis and processing. The global mapping module generates a three-dimensional spatial registration model through local feature partitioning and global registration mapping. This enables three-dimensional data from different sources to be effectively aligned and integrated, improving the spatial consistency and accuracy of the data and providing accurate spatial information for comprehensive analysis. The data cache allocation and load prediction in the cache call module ensure efficient data storage and use. By implementing a staged cache data sharing strategy, cache usage can be optimized, improving the system's storage efficiency and response speed, and reducing data access latency. The three-dimensional data sharing module achieves secure, reliable, and efficient data transmission through data sharing, encryption compression, dynamic decoding, and internet upload. The generated interactive shared three-dimensional dataset not only ensures data security during transmission but also provides real-time collaborative operation and display functions, enhancing the user's operating experience and data visualization. In summary, this process, through the systematic integration of multi-source data, spatial registration, data cache optimization, and secure data sharing and real-time display, not only improves data processing efficiency and accuracy, but also enhances the system's responsiveness and user experience, ensuring the successful execution of 3D data sharing operations. Therefore, this invention improves the efficiency and security of data sharing by optimizing data preprocessing, global registration, intelligent cache management, and secure sharing.

[0139] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0140] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A three-dimensional data sharing method based on the Internet, characterized in that: The following steps are involved: Step S1: Acquire a multi-source three-dimensional dataset; perform data preprocessing on the multi-source three-dimensional dataset to generate a standard multi-source three-dimensional dataset; Perform preliminary virtualization of spatial data on standard multi-source three-dimensional data sets to generate a preliminary virtual space unit set; perform virtual space global optimization on the preliminary virtual space unit set to generate a spatial virtual data matrix; Step S2: performing local feature partitioning on the spatial virtual data matrix to generate virtual space partition area data; performing local three-dimensional data mapping on the standard multi-source three-dimensional data set using the virtual space partition area data to generate a three-dimensional data local feature mapping matrix; Perform global registration and mapping of standard multi-source 3D data sets through the 3D data local feature mapping matrix to generate a 3D spatial registration model; Step S3: Allocate data cache for the standard multi-source 3D dataset based on the 3D space registration model to generate a synchronized distributed cache dataset; perform time series load prediction on the synchronized distributed cache dataset to obtain 3D storage data call load prediction data; Based on the three-dimensional storage data, load prediction data is called to perform stage cache data sharing on the synchronized distributed cache data set, thereby generating a stage three-dimensional cache data sharing strategy; Step S4: Sharing the synchronized distributed cache data set according to the stage 3D cache data sharing strategy to generate 3D shared data; Encrypting the three-dimensional shared data into a shared data packet to generate a three-dimensional shared encrypted compressed data packet; Dynamically decode and recover the three-dimensional shared encrypted compressed data packets in real time to generate dynamically decoded three-dimensional data; The dynamically decoded 3D data is uploaded to the Internet for collaborative operation and shared display, thereby generating an interactive shared 3D dataset to perform 3D data sharing operations.

2. The Internet-based three-dimensional data sharing method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire a multi-source three-dimensional data set; Step S12: performing data preprocessing on the multi-source three-dimensional dataset to generate a standard multi-source three-dimensional dataset, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: extracting spatial characteristics of the standard multi-source 3D dataset to obtain 3D spatial characteristic data; performing preliminary spatial data virtualization on the standard multi-source 3D dataset based on the 3D spatial characteristic data to generate a preliminary virtual space unit set; Step S14: performing multi-dimensional feature extraction on the preliminary virtual space unit set to obtain a multi-dimensional feature space matrix; performing virtual space global optimization on the preliminary virtual space unit set according to the multi-dimensional feature space matrix to generate a spatial virtual data matrix.

3. The Internet-based three-dimensional data sharing method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing local feature partitioning on the spatial virtual data matrix to generate virtual space partition area data; performing local three-dimensional data mapping on the standard multi-source three-dimensional data set using the virtual space partition area data to generate a three-dimensional data local feature mapping matrix; Step S22: extracting cross-region data from the standard multi-source three-dimensional data set to obtain three-dimensional cross-region data; performing multi-scale registration on the three-dimensional cross-region data using the three-dimensional data local feature mapping matrix to generate a multi-scale registration matrix; Step S23: performing nonlinear global optimization on the multi-scale registration matrix to generate a global mapping network; Step S24: using a global mapping network to perform global registration and mapping on the local feature mapping matrix of the three-dimensional data, thereby generating a three-dimensional space registration model.

4. The Internet-based three-dimensional data sharing method according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: performing a local nonlinear distortion analysis on the multi-scale registration matrix to generate registration matrix distortion data; performing a nonlinear distortion correction on the multi-scale registration matrix based on the registration matrix distortion data to generate a local correction matrix; Step S232: performing global nonlinear mapping coordination on the local correction matrix to generate a global coordination matrix; Step S233: Using the global coordination matrix, perform nonlinear global mapping optimization on the multi-scale registration matrix to generate a global mapping network.

5. The Internet-based three-dimensional data sharing method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Based on the 3D space registration model, intelligent edge computing node technology is used to allocate node caches for the standard multi-source 3D data set to generate initial edge cache data blocks; Step S32: performing distributed cache coordination on the initial edge cache data block to generate three-dimensional cache coordination data; performing data synchronization on the three-dimensional cache coordination data to generate a synchronized distributed cache data set; Step S33: performing resource call load balancing detection on the synchronized distributed cache data set to generate three-dimensional storage data load balancing detection data; performing time series load prediction on the three-dimensional storage data load balancing detection data to obtain three-dimensional storage data call load prediction data; Step S34: dividing the synchronized distributed cache data set into three-dimensional data call types to generate three-dimensional data call types, where the three-dimensional data call types include read-only call, analysis call, and rendering call; Step S35: performing phase cache data sharing on the synchronous distributed cache data set based on the three-dimensional storage data call load prediction data and the three-dimensional data call type, thereby generating a phase three-dimensional cache data sharing strategy.

6. The Internet-based three-dimensional data sharing method according to claim 5, characterized in that: Step S35 includes the following steps: Step S351: Obtaining total system load data; calculating call load rates for read-only calls, analysis calls, and rendering calls based on the three-dimensional storage data call load prediction data to obtain read-only call load rate prediction data, analysis call load rate prediction data, and rendering call load rate prediction data; Step S352: performing a remaining load analysis on the read-only call load rate prediction data, the analysis call load rate prediction data, and the rendering call load rate prediction data based on the total system load data, and generating read-only call load rate difference data, analysis call load rate difference data, and rendering call load rate difference data; Step S353: Based on the read-only call load rate difference data, the analysis call load rate difference data, and the rendering call load rate difference data, call processing priority data is divided into read-only calls, analysis calls, and rendering calls. Step S354: Conflict detection is performed on read-only calls, analysis calls, and rendering calls according to the call processing priority data to generate call conflict detection data; emergency call priority adjustment is performed on the call processing priority data based on the call conflict detection data, thereby generating a stage three-dimensional cache data sharing strategy.

7. The Internet-based three-dimensional data sharing method according to claim 6, characterized in that: Step S354 includes the following steps: Step S3541: performing call request frequency analysis on read-only calls, analysis calls, and rendering calls to generate call request frequency data; performing urgency priority classification on the read-only calls, analysis calls, and rendering calls based on the call request frequency data to generate call urgency priority data; Step S3542: performing priority call conflict detection on the read-only call, the analysis call, and the rendering call according to the call processing priority data and the call urgency priority data, and generating call conflict detection data; Step S3543: Conflict monitoring is performed on the call conflict detection data. When the call conflict detection data is detected, the call processing priority data is urgently adjusted based on the call conflict detection data, thereby generating a stage three-dimensional cache data sharing strategy.

8. The Internet-based three-dimensional data sharing method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Sharing the synchronized distributed cache data set according to the stage 3D cache data sharing strategy to generate 3D shared data; Step S42: compressing the three-dimensional shared data to generate three-dimensional shared compressed data; encrypting the three-dimensional shared compressed data packet to generate a three-dimensional shared encrypted compressed data packet; Step S43: dynamically decoding and recovering the three-dimensional shared encrypted compressed data packet in real time to generate dynamically decoded three-dimensional data; Step S44: Upload the dynamically decoded three-dimensional data to the Internet for collaborative operation and shared display, thereby generating an interactive shared three-dimensional data set to perform a three-dimensional data sharing operation.

9. The Internet-based three-dimensional data sharing method according to claim 8, characterized in that: Step S43 includes the following steps: Step S431: extracting data header information from the three-dimensional shared encrypted compressed data packet to obtain three-dimensional shared parsed data header information and encryption algorithm identifier; Step S432: performing distributed decryption on the three-dimensional shared parsed data header information using the encryption algorithm identifier to generate a three-dimensional shared decrypted data set; Step S433: dynamically decompressing the three-dimensional shared decrypted data set to generate a dynamically decompressed three-dimensional data set; Step S434: performing three-dimensional rendering on the dynamically decompressed three-dimensional data set to generate dynamically decoded three-dimensional data.

10. A three-dimensional data sharing system based on the Internet, characterized in that: For executing the Internet-based three-dimensional data sharing method according to claim 1, the Internet-based three-dimensional data sharing system comprises: The data space virtualization module is used to obtain a multi-source three-dimensional data set; perform data preprocessing on the multi-source three-dimensional data set to generate a standard multi-source three-dimensional data set; perform preliminary spatial data virtualization on the standard multi-source three-dimensional data set to generate a preliminary virtual space unit set; perform virtual space global optimization on the preliminary virtual space unit set to generate a spatial virtual data matrix; A global mapping module is used to perform local feature partitioning on the spatial virtual data matrix to generate virtual space partition area data; use the virtual space partition area data to perform local 3D data mapping on the standard multi-source 3D data set to generate a 3D data local feature mapping matrix; and use the 3D data local feature mapping matrix to perform global registration and mapping on the standard multi-source 3D data set to generate a 3D space registration model. The cache call module is used to allocate data cache for standard multi-source 3D datasets based on the 3D spatial registration model to generate a synchronized distributed cache dataset; perform time series load prediction on the synchronized distributed cache dataset to obtain 3D storage data call load prediction data; perform stage cache data sharing on the synchronized distributed cache dataset based on the 3D storage data call load prediction data, thereby generating a stage 3D cache data sharing strategy; The three-dimensional data sharing module is used to share data of the synchronized distributed cache data set according to the stage three-dimensional cache data sharing strategy to generate three-dimensional shared data; encrypt the shared data packet of the three-dimensional shared data to generate a three-dimensional shared encrypted compressed data packet; dynamically decode and restore the three-dimensional shared encrypted compressed data packet in real time to generate dynamically decoded three-dimensional data; upload the dynamically decoded three-dimensional data to the Internet for collaborative operation and shared display, thereby generating an interactive shared three-dimensional data set to perform three-dimensional data sharing operations.

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