Building holographic mathematical model storage method and system, terminal and storage medium

Abstract

The invention relates to a building holographic mathematical model storage method and system, a terminal and a storage medium. The method comprises the following steps: decomposing a holographic model into a series of two-dimensional slices; compressing each two-dimensional slice; the compressed two-dimensional slices are stored in storage devices of different levels; constructing a metadata table, wherein the metadata table comprises information of each compressed two-dimensional slice; the method disclosed by the invention has the following advantages: high efficiency: through compression and hierarchical storage, the storage space requirement is obviously reduced; reliability: the reliability is improved by storing the slices in the storage devices of different levels; and quick access: the metadata table allows quick access to required slices, so that the rendering performance is improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of computer graphics and holographic technology, and more specifically, to a method, system, terminal and storage medium for storing a holographic mathematical model of a building. Background Art

[0002] The architectural holographic model is a digital representation of the multi-dimensional information of the building, which can provide realistic visual views and two-dimensional plane views, including the geometric and physical characteristics of the building. However, storing holographic models is a challenging task. Conventional compressed storage methods have the disadvantages of large storage space requirements, high computational overhead and low reliability. A method for storing holographic mathematical models of buildings that can better solve the above defects is needed. Summary of the invention

[0003] The technical problem to be solved by the present invention is that, in view of the above-mentioned defects of the prior art, a method for storing a holographic mathematical model of a building is provided, and a holographic mathematical model storage system for a building, a holographic mathematical model storage terminal for a building and a computer-readable storage medium are also provided.

[0004] The technical solution adopted by the present invention to solve its technical problem is:

[0005] A method for storing a holographic mathematical model of a building is constructed, which includes the following steps:

[0006] Decomposing the holographic model into a series of two-dimensional slices;

[0007] Compress each 2D slice;

[0008] Storing the compressed two-dimensional slices in storage devices at different levels;

[0009] A metadata table is constructed, which contains information about each compressed two-dimensional slice.

[0010] The method for storing the holographic mathematical model of a building according to the present invention comprises the following steps: compressing each slice based on a dictionary method and a prediction method.

[0011] The method for storing the holographic mathematical model of a building according to the present invention, wherein the dictionary stores the repeated segments and matching information in the historical data, and the dictionary is continuously updated and maintained during the compression process;

[0012] Dictionary compression uses:

[0013] Check the data in the two-dimensional slice one by one, looking for fragments that match the dictionary;

[0014] If a match is found, the corresponding instruction is used to indicate the match information.

[0015] The method for storing the holographic mathematical model of a building according to the present invention, wherein the prediction method compression adopts:

[0016] After training the prediction model using machine learning:

[0017] By analyzing the sequence characteristics of the sliced ​​data, the value of the data can be predicted more accurately.

[0018] Perform statistical analysis on slice data, including frequency analysis, probability analysis, etc., to find repeated patterns and regularities in the data and improve prediction accuracy.

[0019] A building holographic mathematical model storage system, wherein the system comprises a data processing unit, a data storage unit and a metadata server;

[0020] The data processing unit is used to decompose the holographic model into a series of two-dimensional slices and compress each two-dimensional slice;

[0021] The data storage unit is used to store the compressed two-dimensional slices in storage devices at different levels;

[0022] The metadata server is used to construct a metadata table, which contains compressed information of each two-dimensional slice.

[0023] The building holographic mathematical model storage system of the present invention is characterized in that the compression of each slice is based on a dictionary method and a prediction method.

[0024] The building holographic mathematical model storage system of the present invention, wherein the dictionary stores repeated segments and matching information in historical data, and the dictionary is continuously updated and maintained during the compression process;

[0025] Dictionary compression uses:

[0026] Check the data in the two-dimensional slice one by one, looking for fragments that match the dictionary;

[0027] If a match is found, the corresponding instruction is used to indicate the match information.

[0028] The building holographic mathematical model storage system of the present invention, wherein the prediction method compression adopts:

[0029] After training the prediction model using machine learning:

[0030] By analyzing the sequence characteristics of the sliced ​​data, the value of the data can be predicted more accurately.

[0031] Perform statistical analysis on slice data, including frequency analysis, probability analysis, etc., to find repeated patterns and regularities in the data and improve prediction accuracy.

[0032] A building holographic mathematical model storage terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0033] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0034] The beneficial effects of the present invention are:

[0035] Efficient: Through compression and tiered storage, storage space requirements are significantly reduced.

[0036] Reliable: Reliability is improved by storing slices in different levels of storage devices.

[0037] Fast access: Metadata tables allow quick access to required tiles, improving rendering performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. The drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work:

[0039] Figure 1 This is a flow chart of a method for storing a holographic mathematical model of a building according to a preferred embodiment of the present invention;

[0040] Figure 2 It is a principle block diagram of a building holographic mathematical model storage system according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the following will be described clearly and completely in combination with the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are partial embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0042] The method for storing the building holographic mathematical model in the preferred embodiment of the present invention is as follows: Figure 1 As shown, the following steps are included:

[0043] S01: Decompose the holographic model into a series of two-dimensional slices;

[0044] S02: compress each 2D slice;

[0045] S03: storing the compressed two-dimensional slices in storage devices at different levels;

[0046] S04: constructing a metadata table, wherein the metadata table contains information about each compressed two-dimensional slice; the metadata table includes information about the position, size and compression rate of each slice;

[0047] The method of the present invention has the following advantages:

[0048] Efficient: Through compression and tiered storage, storage space requirements are significantly reduced.

[0049] Reliable: Reliability is improved by storing slices in different levels of storage devices.

[0050] Fast access: Metadata tables allow quick access to required tiles, improving rendering performance.

[0051] Preferably, compression of each slice is based on a dictionary method and a prediction method.

[0052] The dictionary method is a content analysis method based on a predefined vocabulary (or dictionary). It usually includes the following steps:

[0053] Build a dictionary: Depending on the specific task or domain, build a dictionary containing keywords or phrases. For example, in sentiment analysis, the dictionary may contain positive and negative sentiment words.

[0054] Content matching: Compare the content to be analyzed with the words in the dictionary to identify relevant keywords in the content.

[0055] Calculation results: Perform corresponding analysis based on the matched keywords, such as statistical sentiment scores, keyword frequencies, etc.

[0056] Prediction law is a content analysis method based on statistical learning or machine learning. It usually includes the following steps:

[0057] Data collection: Collect a large amount of labeled data, usually content data containing specific tags.

[0058] Feature extraction: converting content into numerical features;

[0059] Model training: Use machine learning algorithms (such as Naive Bayes, support vector machines, deep learning models, etc.) to learn the training data and build a prediction model.

[0060] Result prediction: Input new text into the model and perform classification or regression prediction.

[0061] The advantage of the dictionary method is that it is simple and easy to use, and is particularly suitable for small-scale text analysis. However, its disadvantage is that it has poor flexibility and is difficult to handle some semantically complex or ambiguous situations.

[0062] The advantage of the prediction method is that it is highly adaptable, can handle more diverse and complex text data, and generally has better performance. However, it requires a large amount of labeled data and high computing resources.

[0063] Preferably, the dictionary stores repeated segments and matching information in the historical data, and the dictionary is continuously updated and maintained during the compression process;

[0064] Dictionary compression uses:

[0065] Check the data in the two-dimensional slice one by one, looking for fragments that match the dictionary;

[0066] If a match is found, the corresponding instructions are used to represent the match information instead of directly storing the matched data. The dictionary contains representations of various common patterns and data fragments. When compressing data, the contents of the dictionary can be referenced to achieve better compression effects;

[0067] The dictionary can contain various information such as string, match length, offset, etc., which is used to find similar fragments in the data.

[0068] Preferably, the predictive compression method adopts:

[0069] After training the prediction model using machine learning:

[0070] By analyzing the sequence characteristics of the sliced ​​data, the value of the data can be predicted more accurately.

[0071] Perform statistical analysis on slice data, including frequency analysis, probability analysis, etc., to find repeated patterns and regularities in the data and improve prediction accuracy.

[0072] The methods of the present invention can be used in a variety of applications, including:

[0073] AI architectural design and virtual construction, virtual reality and augmented reality, 3D modeling and visualization, medical imaging, scientific computing, and other scenarios;

[0074] A building holographic mathematical model storage system, such as Figure 2 As shown, the system includes a data processing unit 100, a data storage unit 101 and a metadata server 102;

[0075] The data processing unit 100 is used to decompose the holographic model into a series of two-dimensional slices and compress each two-dimensional slice;

[0076] The data storage unit 101 is used to store the compressed two-dimensional slices in storage devices at different levels; wherein the storage devices may include a solid state drive (SSD) and a hard disk drive (HDD), etc.;

[0077] The metadata server 102 is used to construct a metadata table, which contains compressed information of each two-dimensional slice.

[0078] The system of the present invention has the following advantages:

[0079] Efficient: Through compression and tiered storage, storage space requirements are significantly reduced.

[0080] Reliable: Reliability is improved by storing slices in different levels of storage devices.

[0081] Fast access: Metadata tables allow quick access to required tiles, improving rendering performance

[0082] Preferably, compression of each slice is based on a dictionary method and a prediction method.

[0083] Preferably, the dictionary stores repeated segments and matching information in the historical data, and the dictionary is continuously updated and maintained during the compression process;

[0084] Dictionary compression uses:

[0085] Check the data in the two-dimensional slice one by one, looking for fragments that match the dictionary;

[0086] If a match is found, the corresponding instruction is used to indicate the match information.

[0087] Preferably, the predictive compression method adopts:

[0088] After training the prediction model using machine learning:

[0089] By analyzing the sequence characteristics of the sliced ​​data, the value of the data can be predicted more accurately.

[0090] Perform statistical analysis on slice data, including frequency analysis, probability analysis, etc., to find repeated patterns and regularities in the data and improve prediction accuracy.

[0091] A building holographic mathematical model storage terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0092] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0093] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A method for storing a holographic mathematical model of a building, characterized in that: The following steps are involved: Decomposing the holographic model into a series of two-dimensional slices; Compress each 2D slice; Storing the compressed two-dimensional slices in storage devices at different levels; A metadata table is constructed, which contains information about each compressed two-dimensional slice.

2. The method for storing a holographic mathematical model of a building according to claim 1, characterized in that: The compression of each slice is based on a dictionary method and a prediction method.

3. The method for storing a holographic mathematical model of a building according to claim 2, characterized in that: The dictionary stores repeated segments and matching information in historical data, and the dictionary is continuously updated and maintained during the compression process; Dictionary compression uses: Check the data in the two-dimensional slice one by one, looking for fragments that match the dictionary; If a match is found, the corresponding instruction is used to indicate the match information.

4. The method for storing a holographic mathematical model of a building according to claim 2, characterized in that: The prediction compression method adopts: After training the prediction model using machine learning: By analyzing the sequence characteristics of the sliced ​​data, the value of the data can be predicted more accurately. Perform statistical analysis on slice data, including frequency analysis and probability analysis, to find repeated patterns and regularities in the data and improve prediction accuracy.

5. A building holographic mathematical model storage system, characterized in that: The system includes a data processing unit, a data storage unit and a metadata server; The data processing unit is used to decompose the holographic model into a series of two-dimensional slices and compress each two-dimensional slice; The data storage unit is used to store the compressed two-dimensional slices in storage devices at different levels; The metadata server is used to construct a metadata table, which contains compressed information of each two-dimensional slice.

6. The building holographic mathematical model storage system according to claim 5, characterized in that: The compression of each slice is based on a dictionary method and a prediction method.

7. The building holographic mathematical model storage system according to claim 6, characterized in that: The dictionary stores repeated segments and matching information in historical data, and the dictionary is continuously updated and maintained during the compression process; Dictionary compression uses: Check the data in the two-dimensional slice one by one, looking for fragments that match the dictionary; If a match is found, the corresponding instruction is used to indicate the match information.

8. The building holographic mathematical model storage system according to claim 1, characterized in that: The prediction compression method adopts: After training the prediction model using machine learning: By analyzing the sequence characteristics of the sliced ​​data, the value of the data can be predicted more accurately. Perform statistical analysis on slice data, including frequency analysis and probability analysis, to find repeated patterns and regularities in the data and improve prediction accuracy.

9. A building holographic mathematical model storage terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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