A lightweight compression transmission method, system and medium for decoration data

Abstract

This application provides a lightweight compression and transmission method, system, and medium for decoration data. The method includes: collecting multi-source data throughout the entire decoration lifecycle; classifying the multi-source data to obtain different types of decoration data; preprocessing the different types of decoration data to obtain preprocessed different types of decoration data; performing differentiated compression processing on the preprocessed different types of decoration data to obtain compressed decoration data blocks; obtaining current network transmission status information and analyzing idle transmission paths based on the current network transmission status information; transmitting the compressed decoration data blocks according to the idle transmission paths to obtain transmission result information; and achieving efficient, low-distortion, and reliable transmission of decoration data through data preprocessing, differentiated compression algorithms, and intelligent transmission strategies, thereby reducing data transmission bandwidth usage and storage costs, and adapting to the data collaboration needs throughout the entire decoration industry lifecycle.

Description

Technical Field

[0001] This application relates to the field of data compression and transmission technology, and more specifically, to a lightweight compression and transmission method, system, and medium for decoration data. Background Technology

[0002] With the deepening of digital transformation in the decoration industry, decoration data is characterized by multi-source heterogeneity and massive growth, covering various types such as BIM model data, point cloud scan data, construction process data, hidden works monitoring data, and indoor environment and energy consumption monitoring data. Among them, a single file of BIM model data can reach tens of GB or even larger, while point cloud data and city-level decoration model data have reached GB levels, which brings severe challenges to data storage, transmission and collaborative sharing.

[0003] Current decoration data transmission technologies mostly employ general compression algorithms (such as ZIP and RAR) to compress the data as a whole before transmission, without specifically optimizing for the characteristics of decoration data. This results in the following technical shortcomings: First, decoration data contains a large number of repetitive components (such as standard doors and windows, beams and columns) and redundant geometric details (such as screw holes and minute chamfers). General compression algorithms cannot effectively identify and utilize these characteristics, leading to low compression efficiency and difficulty in meeting the rapid transmission needs of massive amounts of decoration data. Second, decoration data often involves a mixture of structured (such as component parameters and material information) and unstructured (such as site photos and supervision logs) data. General compression algorithms do not differentiate between these two types of data, which can easily lead to data corruption during compression. Distortion, especially regarding the geometric accuracy and parameter information of BIM models, will affect the use of BIM models in subsequent design, construction, and operation and maintenance stages. Third, the data transmission in decoration often adopts a single transmission path, without considering the unstable network environment and large bandwidth fluctuations at the construction site. This can easily lead to transmission interruptions and excessively high latency. Furthermore, there is a lack of targeted verification mechanisms during transmission, making it impossible to detect data loss or corruption in a timely manner. Fourth, existing technologies mostly focus on optimizing a single compression or transmission stage, without forming a closed-loop process of data preprocessing, differentiated compression, intelligent transmission, and verification and recovery. This results in poor data lightweighting effects, difficulty in balancing transmission reliability and efficiency, and an inability to adapt to the cloud-edge-device collaborative three-dimensional application architecture requirements of the decoration industry. Summary of the Invention

[0004] The purpose of this application is to provide a lightweight compression transmission method, system, and medium for decoration data. Through data preprocessing, differentiated compression algorithms, and intelligent transmission strategies, it achieves efficient, low-distortion, and reliable transmission of decoration data, reduces data transmission bandwidth usage and storage costs, and adapts to the data collaboration needs of the entire lifecycle of the decoration industry.

[0005] This application also provides a lightweight compressed transmission method for decoration data, including:

[0006] Collect multi-source data throughout the entire renovation lifecycle, classify and process the multi-source data to obtain different types of renovation data;

[0007] Different types of decoration data are preprocessed to obtain different types of preprocessed decoration data;

[0008] Different types of pre-processed decoration data are subjected to differentiated compression to obtain compressed decoration data blocks;

[0009] Obtain the current network transmission status information, and analyze the idle transmission path based on the current network transmission status information;

[0010] The compressed decoration data blocks are transmitted according to the idle transmission path to obtain the transmission result information.

[0011] Optionally, in the lightweight compression and transmission method for decoration data described in the embodiments of this application, multi-source data throughout the entire decoration lifecycle is collected, and the multi-source data is classified and processed to obtain different types of decoration data, specifically including:

[0012] Acquire multi-source data, including point cloud scan data, construction process data, hidden works monitoring data, indoor environment and energy consumption monitoring data, and on-site image data;

[0013] Based on data format, data dimension, and data purpose information, multi-source data is tagged and classified into structured data, semi-structured data, unstructured data, and on-site image data, resulting in different types of decoration data.

[0014] Optionally, in the lightweight compression and transmission method for decoration data described in the embodiments of this application, different types of decoration data are preprocessed to obtain preprocessed decoration data of different types, specifically including:

[0015] The structured data is processed by outlier removal, missing value imputation, and normalization.

[0016] The semi-structured data is deduplicated, segmented, and cleaned of redundant sentences.

[0017] Unstructured data is processed by vertex simplification, face reduction, and invalid mesh removal.

[0018] The on-site image data was processed by unifying resolution, frame sampling, and noise filtering.

[0019] The preprocessed different types of decoration data are aligned in time and their spatial coordinates are unified to obtain a standardized decoration dataset.

[0020] Optionally, in the lightweight compression and transmission method for decoration data described in the embodiments of this application, different types of preprocessed decoration data are subjected to differentiated compression processing to obtain compressed decoration data blocks, specifically including:

[0021] Structured data is compressed using a combination of LZ77 dictionary compression and entropy coding.

[0022] Geometric lightweight compression of unstructured data using edge-folding algorithms and Huffman coding compression;

[0023] The on-site image data is subjected to inter-frame predictive compression and quantization compression processing.

[0024] Data processed by different compression algorithms is encapsulated according to a preset data block size, and data type identifiers and check codes are added to obtain compressed decoration data blocks.

[0025] Optionally, in the lightweight compression and transmission method for decoration data described in this application embodiment, obtaining the current network transmission status information and analyzing the idle transmission path based on the current network transmission status information specifically includes:

[0026] Real-time collection of bandwidth utilization, transmission delay, packet loss rate, and node load information for each transmission link yields the current network transmission status information.

[0027] A transmission path evaluation model is constructed based on network transmission status information, and a weighted score is given to the idle bandwidth information, transmission stability information and transmission distance information of all transmission paths.

[0028] Transmission paths with scores greater than a preset score threshold are selected and idle transmission paths are generated.

[0029] Optionally, in the lightweight compression and transmission method for decoration data described in this application embodiment, the compressed decoration data block is transmitted according to an idle transmission path to obtain transmission result information, specifically including:

[0030] Obtain bandwidth information for idle transmission paths;

[0031] The compressed decoration data blocks are matched with the bandwidth information of the idle transmission path to obtain the matching degree;

[0032] The compressed decoration data blocks are divided and sorted according to the matching degree to obtain the sorting result information;

[0033] Based on the sorting results, multiple transmission paths are used for parallel packet transmission.

[0034] Obtain real-time transmission status information and calculate transmission latency rate;

[0035] Switch decoration data blocks with a transmission latency rate greater than or equal to the set latency rate threshold to an idle transmission path that meets the set conditions;

[0036] After the decoration data blocks are transmitted, they are verified and decoded to generate transmission result information.

[0037] Secondly, embodiments of this application provide a lightweight compression and transmission system for decoration data. The system includes a memory and a processor. The memory includes a program for a lightweight compression and transmission method for decoration data. When executed by the processor, the program for the lightweight compression and transmission method for decoration data implements the following steps:

[0038] Collect multi-source data throughout the entire renovation lifecycle, classify and process the multi-source data to obtain different types of renovation data;

[0039] Different types of decoration data are preprocessed to obtain different types of preprocessed decoration data;

[0040] Different types of pre-processed decoration data are subjected to differentiated compression to obtain compressed decoration data blocks;

[0041] Obtain the current network transmission status information, and analyze the idle transmission path based on the current network transmission status information;

[0042] The compressed decoration data blocks are transmitted according to the idle transmission path to obtain the transmission result information.

[0043] Optionally, in the lightweight compression and transmission system for decoration data described in this application embodiment, multi-source data throughout the entire decoration lifecycle is collected, and the multi-source data is classified and processed to obtain different types of decoration data, specifically including:

[0044] Acquire multi-source data, including point cloud scan data, construction process data, hidden works monitoring data, indoor environment and energy consumption monitoring data, and on-site image data;

[0045] Based on data format, data dimension, and data purpose information, multi-source data is tagged and classified into structured data, semi-structured data, unstructured data, and on-site image data, resulting in different types of decoration data.

[0046] Optionally, in the lightweight compression and transmission system for decoration data described in this application embodiment, different types of decoration data are preprocessed to obtain preprocessed decoration data of different types, specifically including:

[0047] The structured data is processed by outlier removal, missing value imputation, and normalization.

[0048] The semi-structured data is deduplicated, segmented, and cleaned of redundant sentences.

[0049] Unstructured data is processed by vertex simplification, face reduction, and invalid mesh removal.

[0050] The on-site image data was processed by unifying resolution, frame sampling, and noise filtering.

[0051] The preprocessed different types of decoration data are aligned in time and their spatial coordinates are unified to obtain a standardized decoration dataset.

[0052] Thirdly, embodiments of this application also provide a computer-readable storage medium, which includes a lightweight compression transmission method program for decoration data. When the lightweight compression transmission method program for decoration data is executed by a processor, it implements the steps of the lightweight compression transmission method for decoration data as described in any of the above claims.

[0053] As can be seen from the above, the lightweight compression and transmission method, system, and medium for decoration data provided in this application collect multi-source data throughout the entire decoration lifecycle, classify and process the multi-source data to obtain different types of decoration data; preprocess the different types of decoration data to obtain preprocessed different types of decoration data; perform differentiated compression processing on the preprocessed different types of decoration data to obtain compressed decoration data blocks; obtain current network transmission status information, analyze the idle transmission path based on the current network transmission status information; transmit the compressed decoration data blocks according to the idle transmission path to obtain transmission result information; through data preprocessing, differentiated compression algorithms, and intelligent transmission strategies, efficient, low-distortion, and reliable transmission of decoration data is achieved, reducing data transmission bandwidth usage and storage costs, and adapting to the data collaboration needs of the entire lifecycle of the decoration industry. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart illustrating a lightweight compression and transmission method for decoration data provided in this application embodiment;

[0056] Figure 2 This is a schematic diagram of multi-source data classification for a lightweight compression and transmission method for decoration data provided in an embodiment of this application.

[0057] Figure 3 This is a flowchart illustrating a standardized process for acquiring decoration datasets, provided as an embodiment of the present application, for a lightweight compression and transmission method for decoration data. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0059] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0060] Please refer to Figure 1 , Figure 1 This is a flowchart of a lightweight compression and transmission method for decoration data according to some embodiments of this application. This lightweight compression and transmission method for decoration data is used in a terminal device and includes the following steps:

[0061] S101: Collect multi-source data throughout the entire lifecycle of the renovation, classify and process the multi-source data to obtain different types of renovation data;

[0062] It should be noted that the data acquisition adopts an edge-cloud collaborative acquisition mode. Terminal devices (such as drones, smart sensors, and mobile acquisition terminals) collect on-site data, edge servers perform preliminary aggregation and preprocessing, and the cloud platform performs unified aggregation and classification to achieve efficient acquisition and centralized management of multi-source decoration data. The data includes geometric data, attribute data, and correlation data of decoration components, point cloud data including decoration structure scan point cloud and terrain point cloud, and construction process data including progress data, quality inspection data, and personnel and equipment data.

[0063] S102, preprocess different types of decoration data to obtain preprocessed different types of decoration data;

[0064] It should be noted that the multi-precision simplification of point cloud data adopts a curvature-based vertex deletion algorithm, retaining vertices with larger curvature (i.e., vertices of key parts of the decoration structure) and deleting redundant vertices with smaller curvature. During the simplification process, the overall contour error of the point cloud is guaranteed to be no more than 0.5mm, which meets the accuracy requirements of decoration construction and operation and maintenance. The interpolation and completion of structured data adopts linear interpolation or polynomial interpolation algorithms. Based on the changing trend of adjacent valid data points, missing monitoring data or component parameters are completed to ensure the integrity of the data.

[0065] S103, perform differential compression on the preprocessed different types of decoration data to obtain compressed decoration data blocks;

[0066] S104, Obtain the current network transmission status information, and analyze the idle transmission path based on the current network transmission status information;

[0067] S105, transmit the compressed decoration data block according to the idle transmission path and obtain the transmission result information.

[0068] like Figure 2 As shown in the embodiment of the present invention, multi-source data throughout the entire lifecycle of home renovation is collected, and the multi-source data is classified and processed to obtain different types of renovation data, specifically including:

[0069] Acquire multi-source data, including point cloud scan data, construction process data, hidden works monitoring data, indoor environment and energy consumption monitoring data, and on-site image data;

[0070] Based on data format, data dimension, and data purpose information, multi-source data is tagged and classified into structured data, semi-structured data, unstructured data, and on-site image data, resulting in different types of decoration data.

[0071] like Figure 3As shown, according to an embodiment of the present invention, different types of decoration data are preprocessed to obtain preprocessed decoration data of different types, specifically including:

[0072] The structured data is processed by outlier removal, missing value imputation, and normalization.

[0073] The semi-structured data is deduplicated, segmented, and cleaned of redundant sentences.

[0074] Unstructured data is processed by vertex simplification, face reduction, and invalid mesh removal.

[0075] The on-site image data was processed by unifying resolution, frame sampling, and noise filtering.

[0076] The preprocessed different types of decoration data are aligned in time and their spatial coordinates are unified to obtain a standardized decoration dataset.

[0077] It should be noted that different types of renovation data undergo targeted preprocessing to remove redundant information and achieve initial data lightweighting:

[0078] For structured data: a data deduplication algorithm is used to delete duplicate component parameters and monitoring data records, interpolate missing values ​​to complete them, identify and remove outliers, convert structured data into standardized JSON format, and establish a data association index;

[0079] For semi-structured data: Parse XML tags, extract core attribute information, delete redundant tags and invalid fields, convert semi-structured data into structured data format, and integrate it into a structured data collection;

[0080] For unstructured data: Hierarchical detail technology is used to simplify BIM models and point cloud data with multiple precisions, delete redundant vertices, merge coplanar surfaces, and simulate 3D details through normal mapping to maintain the appearance and core precision of the model; the resolution of on-site image data is adaptively adjusted, the frame rate is compressed and key frames are retained, and blurry and duplicate image frames are removed; instantiation technology is used for point cloud data, storing complete data only once for recurring point cloud fragments, and storing only transformation information for other locations.

[0081] According to an embodiment of the present invention, different types of preprocessed decoration data are subjected to differentiated compression processing to obtain compressed decoration data blocks, specifically including:

[0082] Structured data is compressed using a combination of LZ77 dictionary compression and entropy coding.

[0083] Geometric lightweight compression of unstructured data using edge-folding algorithms and Huffman coding compression;

[0084] The on-site image data is subjected to inter-frame predictive compression and quantization compression processing.

[0085] Data processed by different compression algorithms is encapsulated according to a preset data block size, and data type identifiers and check codes are added to obtain compressed decoration data blocks.

[0086] It should be noted that for structured data: the improved LZ77 algorithm is adopted, and combined with the parameter correlation of decoration data, the repeated component parameters and monitoring index sequences are dictionary encoded, and a dynamic sliding window is set. The window size is adaptively adjusted according to the amount of data (ranging from 1024 to 8192 bytes) to improve compression efficiency.

[0087] For point cloud data: The improved Draco compression algorithm is adopted, the EdgeBreaker geometric compression logic is optimized, and the attributes such as vertices and normals are intelligently quantized to retain core geometric information, compressing the point cloud data to 10%-20% of the original size, while supporting real-time decompression on the browser side.

[0088] For on-site image data: H.265 encoding algorithm is used for compression. Combined with the characteristics of the decoration site image, the intra-frame prediction mode is optimized to reduce compression distortion. The compression ratio is controlled between 10:1 and 20:1.

[0089] For all compressed data blocks, add a type identifier, a compression algorithm identifier, and a checksum. The checksum is generated using the CRC32 algorithm and is used for subsequent data verification.

[0090] Furthermore, differentiated preprocessing and compression are performed on different types of data. Structured data uses an improved LZ77 algorithm combined with a decoration-specific dictionary library, point cloud data uses an improved Draco algorithm, and image data uses the H.265 algorithm. At the same time, hierarchical detail technology, instantiation technology, and incremental update mechanism are introduced, and the overall compression ratio can reach 80%-90%, which is far higher than existing general compression algorithms. This significantly reduces the storage footprint and transmission bandwidth requirements of decoration data, and solves the problem of low efficiency in the transmission of massive decoration data. It is especially suitable for GB-level decoration models and point cloud data transmission.

[0091] According to an embodiment of the present invention, obtaining current network transmission status information and analyzing idle transmission paths based on the current network transmission status information specifically includes:

[0092] Real-time collection of bandwidth utilization, transmission delay, packet loss rate, and node load information for each transmission link yields the current network transmission status information.

[0093] A transmission path evaluation model is constructed based on network transmission status information, and a weighted score is given to the idle bandwidth information, transmission stability information and transmission distance information of all transmission paths.

[0094] Transmission paths with scores greater than a preset score threshold are selected and idle transmission paths are generated.

[0095] According to an embodiment of the present invention, compressed decoration data blocks are transmitted according to an idle transmission path to obtain transmission result information, specifically including:

[0096] Obtain bandwidth information for idle transmission paths;

[0097] The compressed decoration data blocks are matched with the bandwidth information of the idle transmission path to obtain the matching degree;

[0098] The compressed decoration data blocks are divided and sorted according to the matching degree to obtain the sorting result information;

[0099] Based on the sorting results, multiple transmission paths are used for parallel packet transmission.

[0100] Obtain real-time transmission status information and calculate transmission latency rate;

[0101] Switch decoration data blocks with a transmission latency rate greater than or equal to the set latency rate threshold to an idle transmission path that meets the set conditions;

[0102] After the decoration data blocks are transmitted, they are verified and decoded to generate transmission result information.

[0103] It should be noted that the current network transmission status, including bandwidth, latency, and packet loss rate, is obtained, and a transmission path evaluation model is established. The input parameters of the evaluation model are bandwidth (B), latency (D), and packet loss rate (L), and the output path comprehensive score S is calculated using the following formula: S = 0.5 × (1 - L) + 0.3 × (B / B) + 0.2 × (1 - D / D), where B is the maximum available bandwidth and D is the maximum acceptable latency.

[0104] The optimal transmission path is selected based on the comprehensive score S. When there are multiple transmission paths, the path with S≥0.8 is selected first. If there is no path with S≥0.8, multi-path redundant transmission is adopted. The compressed data block is split and sent in parallel through multiple paths. The size of the split is adaptively allocated according to the path bandwidth.

[0105] A fragmented transmission mechanism is adopted, which splits the compressed decoration data block into multiple data fragments, each fragment ranging in size from 1024KB to 4096KB. Each fragment is assigned a fragment identifier, the total number of fragments, and a sequence number. The fragments are then sent to the receiving end through the optimal transmission path or a multi-path redundancy transmission method. At the same time, an incremental update mechanism is adopted, which transmits only the data fragments that have changed, rather than the entire data file, thus significantly reducing the amount of data transmitted.

[0106] According to embodiments of the present invention, it further includes data reception and verification:

[0107] The receiving end receives all data fragments, reassembles them according to the fragment identifier and sequence number to obtain a complete compressed data block; the reassembled compressed data block is verified by CRC32 check code to determine whether the data is lost or damaged.

[0108] If the verification passes, proceed to the next step of the decompression process;

[0109] If the verification fails, a retransmission request is sent to the sender, specifying the lost or corrupted data fragment. The sender only retransmits the corresponding data fragment, avoiding a complete retransmission and reducing bandwidth usage.

[0110] Data decompression and recovery: The receiving end decompresses the data using the corresponding decompression algorithm based on the compressed data block type identifier and compression algorithm identifier.

[0111] For structured data: An improved LZ77 decompression algorithm is used to reverse-parse the data according to the dictionary encoding and restore it to structured data in standardized JSON format;

[0112] For point cloud data: The Draco decompression algorithm is used to reconstruct the compressed point cloud data and restore the core geometric information and attributes of the point cloud;

[0113] For on-site image data: H.265 decompression algorithm is used to restore the original resolution and keyframe information of the image;

[0114] After decompression, the data integrity is verified to ensure that the data is consistent with the original pre-processed data sent from the end, thus completing the lightweight compressed transmission of the decoration data.

[0115] Secondly, embodiments of this application provide a lightweight compression and transmission system for decoration data. The system includes a memory and a processor. The memory includes a program for a lightweight compression and transmission method for decoration data. When the program for the lightweight compression and transmission method for decoration data is executed by the processor, it implements the following steps:

[0116] Collect multi-source data throughout the entire renovation lifecycle, classify and process the multi-source data to obtain different types of renovation data;

[0117] Different types of decoration data are preprocessed to obtain different types of preprocessed decoration data;

[0118] Different types of pre-processed decoration data are subjected to differentiated compression to obtain compressed decoration data blocks;

[0119] Obtain the current network transmission status information, and analyze the idle transmission path based on the current network transmission status information;

[0120] The compressed decoration data blocks are transmitted according to the idle transmission path to obtain the transmission result information.

[0121] According to embodiments of the present invention, multi-source data is collected throughout the entire lifecycle of home renovation, and the multi-source data is classified and processed to obtain different types of renovation data, specifically including:

[0122] Acquire multi-source data, including point cloud scan data, construction process data, hidden works monitoring data, indoor environment and energy consumption monitoring data, and on-site image data;

[0123] Based on data format, data dimension, and data purpose information, multi-source data is tagged and classified into structured data, semi-structured data, unstructured data, and on-site image data, resulting in different types of decoration data.

[0124] According to an embodiment of the present invention, different types of decoration data are preprocessed to obtain preprocessed decoration data of different types, specifically including:

[0125] The structured data is processed by outlier removal, missing value imputation, and normalization.

[0126] The semi-structured data is deduplicated, segmented, and cleaned of redundant sentences.

[0127] Unstructured data is processed by vertex simplification, face reduction, and invalid mesh removal.

[0128] The on-site image data was processed by unifying resolution, frame sampling, and noise filtering.

[0129] The preprocessed different types of decoration data are aligned in time and their spatial coordinates are unified to obtain a standardized decoration dataset.

[0130] A third aspect of the present invention provides a computer-readable storage medium including a lightweight compression transmission method program for decoration data, wherein when the lightweight compression transmission method program for decoration data is executed by a processor, it implements the steps of the lightweight compression transmission method for decoration data as described in any of the above claims.

[0131] This invention discloses a lightweight compression and transmission method, system, and medium for decoration data. It collects multi-source data throughout the entire decoration lifecycle, classifies and processes this data to obtain different types of decoration data; preprocesses these different types of decoration data to obtain preprocessed different types of decoration data; performs differentiated compression processing on the preprocessed different types of decoration data to obtain compressed decoration data blocks; acquires current network transmission status information, analyzes and determines idle transmission paths based on this information; and transmits the compressed decoration data blocks according to the idle transmission paths to obtain transmission result information. Through data preprocessing, differentiated compression algorithms, and intelligent transmission strategies, it achieves efficient, low-distortion, and reliable transmission of decoration data, reducing data transmission bandwidth usage and storage costs, and adapting to the data collaboration needs throughout the entire decoration industry lifecycle.

[0132] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0133] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0134] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0135] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0136] Alternatively, if the integrated units of the present invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A lightweight compression and transmission method for decoration data, characterized in that, include: Collect multi-source data throughout the entire renovation lifecycle, classify and process the multi-source data to obtain different types of renovation data; Different types of decoration data are preprocessed to obtain different types of preprocessed decoration data; Different types of pre-processed decoration data are subjected to differentiated compression to obtain compressed decoration data blocks; Obtain current network transmission status information, and analyze the available transmission paths based on the current network transmission status information; The compressed decoration data blocks are transmitted according to the idle transmission path to obtain the transmission result information.

2. The lightweight compression and transmission method for decoration data according to claim 1, characterized in that, Collect multi-source data throughout the entire renovation lifecycle, classify and process the multi-source data to obtain different types of renovation data, specifically including: Acquire multi-source data, including point cloud scan data, construction process data, hidden works monitoring data, indoor environment and energy consumption monitoring data, and on-site image data; Based on data format, data dimension, and data purpose information, multi-source data is tagged and classified into structured data, semi-structured data, unstructured data, and on-site image data, resulting in different types of decoration data.

3. The lightweight compression and transmission method for decoration data according to claim 2, characterized in that, Different types of renovation data are preprocessed to obtain different types of preprocessed renovation data, specifically including: The structured data is processed by outlier removal, missing value imputation, and normalization. The semi-structured data is deduplicated, segmented, and cleaned of redundant sentences. Unstructured data is processed by vertex simplification, face reduction, and invalid mesh removal. The on-site image data was processed by unifying resolution, frame sampling, and noise filtering. The preprocessed different types of decoration data are aligned in time and their spatial coordinates are unified to obtain a standardized decoration dataset.

4. The lightweight compression and transmission method for decoration data according to claim 3, characterized in that, Different types of preprocessed decoration data are subjected to differential compression to obtain compressed decoration data blocks, specifically including: Structured data is compressed using a combination of LZ77 dictionary compression and entropy coding. Geometric lightweight compression of unstructured data using edge-folding algorithms and Huffman coding compression; The on-site image data is subjected to inter-frame predictive compression and quantization compression processing. Data processed by different compression algorithms is encapsulated according to a preset data block size, and data type identifiers and check codes are added to obtain compressed decoration data blocks.

5. A lightweight compression and transmission method for decoration data according to claim 4, characterized in that, Obtain current network transmission status information, and analyze it to determine available transmission paths, specifically including: Real-time collection of bandwidth utilization, transmission delay, packet loss rate, and node load information of each transmission link yields the current network transmission status information. A transmission path evaluation model is constructed based on network transmission status information, and a weighted score is applied to the idle bandwidth information, transmission stability information, and transmission distance information of all transmission paths. Transmission paths with scores greater than a preset score threshold are selected and idle transmission paths are generated.

6. A lightweight compression and transmission method for decoration data according to claim 5, characterized in that, The compressed decoration data blocks are transmitted according to the available transmission path to obtain the transmission result information, which specifically includes: Obtain bandwidth information for idle transmission paths; The compressed decoration data blocks are matched with the bandwidth information of the idle transmission path to obtain the matching degree; The compressed decoration data blocks are divided and sorted according to the matching degree to obtain the sorting result information; Based on the sorting results, multiple transmission paths are used for parallel packet transmission. Obtain real-time transmission status information and calculate transmission latency rate; Switch decoration data blocks with a transmission latency rate greater than or equal to the set latency rate threshold to an idle transmission path that meets the set conditions; After the decoration data blocks are transmitted, they are verified and decoded to generate transmission result information.

7. A lightweight compression transmission system for decoration data, characterized in that, The system includes a memory and a processor. The memory includes a program for a lightweight compression and transmission method for decoration data. When the program for the lightweight compression and transmission method for decoration data is executed by the processor, it performs the following steps: Collect multi-source data throughout the entire renovation lifecycle, classify and process the multi-source data to obtain different types of renovation data; Different types of decoration data are preprocessed to obtain different types of preprocessed decoration data; Different types of pre-processed decoration data are subjected to differentiated compression to obtain compressed decoration data blocks; Obtain current network transmission status information, and analyze the available transmission paths based on the current network transmission status information; The compressed decoration data blocks are transmitted according to the idle transmission path to obtain the transmission result information.

8. A lightweight compression transmission system for decoration data according to claim 7, characterized in that, Collect multi-source data throughout the entire renovation lifecycle, classify and process the multi-source data to obtain different types of renovation data, specifically including: Acquire multi-source data, including point cloud scan data, construction process data, hidden works monitoring data, indoor environment and energy consumption monitoring data, and on-site image data; Based on data format, data dimension, and data purpose information, multi-source data is tagged and classified into structured data, semi-structured data, unstructured data, and on-site image data, resulting in different types of decoration data.

9. A lightweight compression transmission system for decoration data according to claim 8, characterized in that, Different types of renovation data are preprocessed to obtain different types of preprocessed renovation data, specifically including: The structured data is processed by outlier removal, missing value imputation, and normalization. The semi-structured data is deduplicated, segmented, and cleaned of redundant sentences. Unstructured data is processed by vertex simplification, face reduction, and invalid mesh removal. The on-site image data was processed by unifying resolution, frame sampling, and noise filtering. The preprocessed different types of decoration data are aligned in time and their spatial coordinates are unified to obtain a standardized decoration dataset.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a lightweight compression transmission method program for decoration data, which, when executed by a processor, implements the steps of the lightweight compression transmission method for decoration data as described in any one of claims 1 to 6.

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