A 3D model data management system and method for engineering surveying

Abstract

This invention discloses a 3D model data management system and method for engineering surveying. This invention relates to the field of data management technology and solves the technical problems of the inability to manage multi-source data in a unified manner, increasing the difficulty of data management, and the lack of targeted data storage. This invention classifies model data according to the acquisition method and then performs secondary classification based on model version, enabling more accurate management of different types of 3D model data, improving the orderliness of data management and query efficiency. By extracting and replacing the features of modified data, it helps reduce data redundancy and better reflects the model's change process. Secondly, it selects the best-performing compatible reading format as the standard format, improving data storage and retrieval efficiency and reducing waiting time. Finally, it encrypts and stores different types of data, improving data storage security and efficiency, and better protecting the confidentiality and integrity of engineering surveying data.

Description

Technical Field

[0001] This invention relates to the field of data management technology, specifically to a three-dimensional model data management system and method for engineering surveying. Background Technology

[0002] In the field of engineering surveying, with the continuous development of technology, the application of 3D models is becoming increasingly widespread. However, managing 3D model data presents numerous challenges. Traditional data management methods are often inefficient and unable to meet the ever-growing demand for 3D model data processing.

[0003] According to CN118094711A, a three-dimensional model data management system and method for engineering surveying are disclosed, relating to the field of engineering surveying technology. The invention includes: S10: determining the construction location in the three-dimensional geological model; S20: predicting the surveying data of the construction location before construction; S30: analyzing the construction difficulty of each construction location based on the construction content and surveying data; S40: determining the construction management intensity of each construction location.

[0004] However, some existing data management systems face challenges. Firstly, the diverse sources of different types of 3D model data, including architectural models, terrain models, and mechanical models, with varying acquisition methods, complicate data classification and management. Secondly, 3D models may undergo continuous optimization during use, generating different versions of data, further increasing the difficulty of data management.

[0005] Meanwhile, in terms of data storage and retrieval, the performance differences between different compatible reading formats need to be considered to ensure that data can be stored and retrieved efficiently. Furthermore, encrypted data storage is also a crucial aspect of ensuring data security, but traditional methods lack specificity and efficiency when encrypting and storing different types of data. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a three-dimensional model data management system and method for engineering surveying, which solves the problems of the inability to manage multi-source data in a unified manner, increasing the difficulty of data management, and the lack of targeted data storage.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a three-dimensional model data management system for engineering surveying, comprising:

[0008] The 3D model data acquisition unit is used to acquire model data corresponding to different 3D models, and at the same time transmit the acquired model data to the adaptive classification management unit.

[0009] The adaptive classification management unit is used to classify different model data transmitted by the 3D model data acquisition unit. It classifies the model data based on different acquisition methods to obtain the same type of model data, and performs secondary classification on the same type of model data according to the model version to obtain secondary classification data. At the same time, the secondary classification data is transmitted to the storage analysis management unit.

[0010] The storage analysis and management unit is used to analyze secondary classification data, identify modified model data and unmodified model data of the same type, generate replacement templates based on the modified data corresponding to the modified data in the modified data of the same type, replace the modified data to obtain replacement data, and combine the replacement data with the unmodified data of the same type to obtain combined data of the same type.

[0011] Simultaneously, the comprehensive performance index corresponding to the compatible reading format of the same type of combined data is calculated, and the standard format is generated with the maximum value of the comprehensive performance index. At the same time, the standard format information is generated, and then the standard format information is transmitted to the data encryption storage and analysis unit.

[0012] The data encryption storage and analysis unit is used to segment the same type of recombined data into capacity segmentation packets according to standard format information, identify the data types in the capacity segmentation packets to generate single-type data and multi-type data, encrypt and store the single-type data and multi-type data respectively, generate storage information, and then transmit the storage information to the three-dimensional model data management output unit.

[0013] The 3D model data management output unit is used to store the acquired storage information.

[0014] As a further aspect of the present invention: the adaptive classification management unit obtains the secondary classification data in the following specific manner:

[0015] Acquire different model data, and classify them into the same type according to the acquisition method to obtain the same type of model data. At the same time, obtain the model version corresponding to the same type of model data, and the data version represents the model version before and after the 3D model optimization. Then, perform secondary classification on the same type of model data based on different model versions to obtain secondary classification data.

[0016] As a further aspect of the present invention: the specific method by which the storage analysis management unit analyzes the secondary classification data is as follows:

[0017] Obtain secondary classification data, and classify the secondary classification data into modified model data and unmodified model data of the same type based on whether the secondary classification data has been modified. At the same time, obtain all modified model data of the same type and label them as i, where i = 1, 2, ..., j, and j represents the number of modified model data of the same type. Obtain the corresponding updated data and original data.

[0018] Next, the modified data in the updated data is obtained and marked using the original data as the standard, and the data features of the modified data are extracted. Then, a replacement template is generated based on the obtained data features, and the modified data is replaced according to the replacement template to obtain the replacement data. At the same time, the replacement data is combined with the same type of unmodified data to obtain the same type of recombined data.

[0019] As a further aspect of the present invention: the specific method by which the storage analysis management unit generates standard format information is as follows:

[0020] Acquire all recombinant data of the same type, and simultaneously obtain the compatible reading formats corresponding to the recombinant data of the same type. Label the compatible reading formats as 'a', where a = 1, 2, ..., b, and b represents the number of compatible reading formats. Then, obtain the reading speed Va, reading latency Wa, and response time Ta corresponding to the compatible reading formats, and substitute the obtained parameters into the formula. The comprehensive performance index Pa is calculated, and k1, k2 and k3 are weighting coefficients. Vmax, Wmax and Tmax are the maximum values ​​of read speed, read latency and response time in all possible cases, respectively. The importance of each parameter can be adjusted according to actual needs, and k1+k2+k3=1.

[0021] Based on the calculated comprehensive performance index Pa, the compatible reading format corresponding to the largest comprehensive performance index value is selected as the standard format, and standard format information is generated.

[0022] As a further aspect of the present invention: the specific method by which the data encryption storage and analysis unit generates single-type data and multi-type data is as follows:

[0023] The maximum read speed corresponding to the standard format information is obtained, and a capacity segmentation standard is generated based on the maximum read speed value. Then, any group of recombined data of the same type is taken as the analysis target. At the same time, the data capacity of the analysis target is segmented according to the capacity segmentation standard to obtain multiple capacity segmentation packets, denoted as n, where n=1, 2, ..., m. Then, the data types corresponding to the multiple capacity segmentation packets n are identified, and type identification information is generated. The type identification information includes single-type data and multi-type data. The single-type data and multi-type data obtained by classification are encrypted and stored respectively.

[0024] As a further aspect of the present invention: the specific method by which the data encryption storage and analysis unit encrypts and stores data of a single type is as follows:

[0025] A binary conversion is performed on a single type of data to obtain a base-converted data. At the same time, the data capacity of the single type of data is obtained, and a base segmentation standard is generated based on the data capacity value. Then, the base-converted data is segmented according to the base segmentation standard to obtain base segments. Simultaneously, base segments with the same last digit are bundled to obtain an encrypted bundle, and the encrypted bundle is stored to generate storage information.

[0026] As a further aspect of the present invention: the specific method by which the data encryption storage and analysis unit encrypts and stores multiple types of data is as follows:

[0027] Duplicate data is extracted from multiple types of data. The number of duplicate data is labeled as o, where o = 1, 2, ..., u, and u represents the type of duplicate data. Data features of duplicate data are extracted, and replacement templates are generated based on these features. The replacement templates are then used to replace the duplicate data to obtain multiple types of replacement data. Finally, the multiple types of replacement data are stored, and storage information is generated.

[0028] A method for managing 3D model data in engineering surveying, comprising the following steps:

[0029] Step 1: Classify the model data based on the acquisition method to obtain similar model data, and then classify the similar model data a second time according to the model version to obtain secondary classification data;

[0030] Step 2: Identify the secondary classification data to obtain modified model data and unmodified model data of the same type. Generate a replacement template based on the modified data corresponding to the modified data in the modified data of the same type. Replace the modified data to obtain replacement data. Combine the replacement data with the unmodified data of the same type to obtain combined data of the same type.

[0031] Step 3: Calculate the comprehensive performance index corresponding to the compatible reading format of the same type of combined data, and generate the standard format with the maximum value of the comprehensive performance index, and generate the standard format information at the same time;

[0032] Step 4: Divide the recombined data of the same type into capacity partition packets according to the standard format information, identify the data types in the capacity partition packets to generate single-type data and multi-type data, encrypt and store the single-type data and multi-type data respectively, and generate storage information.

[0033] Beneficial effects

[0034] This invention provides a three-dimensional model data management system and method for engineering surveying. Compared with the prior art, it has the following advantages:

[0035] This invention classifies model data into similar types based on the acquisition method and then performs secondary classification based on model version. This enables more accurate management of different types of 3D model data, improving the orderliness of data management and query efficiency. By employing SVM (Support Vector Machine) from the supervised learning algorithm to extract and replace features of modified data, it helps reduce data redundancy and better reflects the model's change process. Secondly, it selects the best-performing compatible reading format as the standard format, improving data storage and reading efficiency and reducing waiting time. Finally, it encrypts and stores different types of data, improving data storage security and efficiency, and better protecting the confidentiality and integrity of engineering surveying data. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the system of the present invention;

[0037] Figure 2 This is a flowchart of the steps of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] Example 1, please refer to Figure 1 This application provides a 3D model data management system for engineering surveying, including: a 3D model data acquisition unit, an adaptive classification management unit, a storage and analysis management unit, a data encryption storage and analysis unit, and a 3D model data management output unit, and also based on... Figure 1 As is known, the above-mentioned functional units are connected electrically in one direction only.

[0040] The 3D model data acquisition unit is used to acquire model data corresponding to different 3D models and transmit the acquired model data to the adaptive classification management unit.

[0041] The adaptive classification management unit is used to classify the acquired model data, and the specific classification methods are as follows:

[0042] Different model data are acquired, and then categorized according to the acquisition method to obtain similar model data. Here, acquisition method refers to different 3D models, such as architectural model data, terrain model data, and mechanical model data. Simultaneously, the corresponding model version is acquired, indicating whether the 3D model has undergone subsequent optimization. Then, based on different model versions, the similar model data is further categorized to obtain secondary classification data. For example, architectural model data within the similar model data will be further categorized if subsequent architectural model data is obtained after optimization. Model data without optimization is not processed.

[0043] The secondary classification data obtained from the classification is then transmitted to the storage analysis and management unit.

[0044] The storage analysis management unit is used to perform specific analysis on the acquired secondary classification data, which includes all model data of the same type. The specific analysis method is as follows:

[0045] All secondary classification data are acquired, and the secondary classification data are judged to obtain the same type of modified model data and the same type of unmodified model data. Here, the modified model data refers to the model data obtained from the optimized 3D model, while the unmodified model data refers to the model data obtained from the same 3D model. At the same time, all the same type of modified model data are acquired and labeled as i, where i = 1, 2, ..., j, and j represents the number of the same type of modified model data. Then, the same type of modified model data with any label is acquired and labeled as the target data. At the same time, the corresponding updated data and original data in the target data are acquired. Here, the updated data refers to the data obtained after the 3D model optimization, while the original data is the data that has not been modified, specifically represented as a single data group.

[0046] Next, the modified data in the updated data is obtained and marked using the original data as the standard. The data location of the modified data is also obtained; this location is for the convenience of recording the modified data. The data features of the modified data are extracted using a machine learning algorithm. Specifically, the machine learning algorithm used in this application is SVM (Support Vector Machine), a supervised learning algorithm. The principle is as follows: by finding a hyperplane to classify or regress the data, in a high-dimensional space, SVM attempts to find a hyperplane that maximizes the margin between different categories. SVM can use kernel functions to map low-dimensional data to a high-dimensional space, thereby better performing classification or regression. For example, when dealing with text classification problems, SVM can learn from features such as the bag-of-words model and TF-IDF that are most effective in distinguishing different categories of text, extracting important features for classification. Then, a replacement template is generated based on the obtained data features, and the modified data is replaced according to the replacement template to obtain replacement data. Simultaneously, the replacement data is combined with unmodified data of the same type to obtain recombined data of the same type.

[0047] Similarly, all modified data of the same type are processed to obtain recombined data of the same type, and the recombined data of the same type is transmitted to the data encryption storage and analysis unit.

[0048] Acquire all recombined data of the same type, and simultaneously obtain the compatible reading formats corresponding to the recombined data of the same type. Here, compatible reading formats refer to those that can be read by different software. Label the compatible reading formats as 'a', where a = 1, 2, ..., b, and b represents the number of compatible reading formats. Next, obtain the reading speed Va, reading latency Wa, and response time Ta corresponding to the compatible reading formats. These parameters are obtained under the condition of the same data volume. The response time represents the time taken to read all data. Substitute the obtained parameters into the formula... The comprehensive performance index Pa is calculated, and k1, k2 and k3 are weighting coefficients. Vmax, Wmax and Tmax are the maximum values ​​of read speed, read latency and response time in all possible cases, respectively. The importance of each parameter can be adjusted according to actual needs, and k1+k2+k3=1.

[0049] For example, k1=0.6, k2=0.2, k3=0.2, Va=50, Vmax=100, Wa=2, Wmax=5, Ta=1, Tmax=3, then substitute the above parameters into the formula. Further calculations yielded a comprehensive performance index of 0.153 for the compatible reading format Pa.

[0050] Based on the calculated comprehensive performance index Pa, the compatible reading format corresponding to the largest comprehensive performance index value is selected as the standard format. At the same time, standard format information is generated and transmitted to the data encryption storage and analysis unit.

[0051] The data encryption storage and analysis unit is used to perform encryption storage and analysis on recombined data of the same type based on the acquired standard format information. The specific encryption storage and analysis method is as follows:

[0052] The maximum read speed corresponding to the standard format information is obtained, and a capacity segmentation standard is generated based on the maximum read speed value. Then, any set of recombined data of the same type is taken as the analysis target. At the same time, the data capacity of the analysis target is segmented into multiple capacity segment packets based on the capacity segmentation standard, and denoted as n, where n=1, 2, ..., m. Then, the data types corresponding to the multiple capacity segment packets n are identified, and type identification information is generated. The type identification information includes single-type data and multi-type data. Here, the data type represents the modified data and unmodified data types in the capacity segment packet. For example, if a capacity segment packet contains both modified data and unmodified data, it is classified as multi-type data. If the capacity segment packet contains only one of modified data or unmodified data, it is classified as single-type data. The single-type data and multi-type data obtained by classification are encrypted and stored respectively.

[0053] For the single-category data obtained from the classification, the single-category data is converted from binary to base-based data. At the same time, the data capacity of the single-category data is obtained, and a base segmentation standard is generated based on the data capacity value. Then, the base-based converted data is segmented according to the base segmentation standard. Here, the segmentation is based on the corresponding number of binary characters of the base-based converted data. For example, if the base segmentation standard is 30, then the segmentation is based on 30 binary characters to obtain base segment segments. At the same time, base segment segments with the same last character are bundled to obtain an encrypted bundle, and the encrypted bundle is stored to generate storage information.

[0054] For the multi-class data obtained from classification, duplicate data in the multi-class data is extracted, and the number of duplicate data is labeled as o, where o = 1, 2, ..., u, and u represents the type of duplicate data. The data features of the duplicate data are extracted, and a replacement template is generated based on the data features. The replacement template is then used to replace the duplicate data to obtain multi-class replacement data. The multi-class replacement data is then stored, and storage information is generated.

[0055] The generated storage information is then transmitted to the 3D model data management output unit.

[0056] The 3D model data management output unit is used to store the acquired storage information.

[0057] Example 2, please refer to Figure 2 A method for managing 3D model data in engineering surveying, the method specifically includes the following steps:

[0058] Step 1: Based on the acquisition method of model data, classify the data to obtain model data of the same type, and then classify the model data of the same type according to the model version to obtain secondary classification data. The method of generating secondary classification data here is the same as the processing method of the adaptive classification management unit in Implementation Example 1.

[0059] Step 2: Identify the secondary classification data to obtain modified model data and unmodified model data of the same type. Generate a replacement template based on the modified data corresponding to the modified data in the modified data of the same type. Replace the modified data to obtain replacement data. Combine the replacement data with the unmodified data of the same type to obtain combined data of the same type. The processing method here is the same as the processing method of the storage analysis management unit in Example 1.

[0060] Step 3: Calculate the comprehensive performance index corresponding to the compatible reading format of the same type of combined data, and generate the standard format with the maximum value of the comprehensive performance index. At the same time, generate the standard format information. The processing method here is the same as the processing method of the storage analysis management unit in Example 1.

[0061] Step 4: Segment the recombined data of the same type according to the standard format information to obtain capacity segmentation packets, identify the data types in the capacity segmentation packets to generate single-type data and multi-type data, encrypt and store the single-type data and multi-type data respectively, and generate storage information. The method of generating storage information here is the same as the processing method of the data encryption storage analysis unit in Embodiment 1.

[0062] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0063] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A three-dimensional model data management system for engineering mapping, characterized by, The application relates to a three-dimensional model data management system and method. The adaptive classification management unit is used for classifying different model data transmitted by the three-dimensional model data acquisition unit, classifying the same type of model data based on the acquisition mode of the different model data, and classifying the same type of model data based on the model version to obtain secondary classification data, and transmitting the secondary classification data to the storage analysis management unit; The storage analysis management unit is used for analyzing the secondary classification data, identifying the same type of modified model data and the same type of unmodified model data, generating a replacement template according to the modified data in the same type of modified data, replacing the modified data to obtain replacement data, and combining the replacement data and the same type of unmodified data to obtain the same type of combined data; Meanwhile, the comprehensive performance index corresponding to the compatible reading format of the same type of combined data is calculated, the maximum value of the comprehensive performance index is taken as the standard format, the standard format information is generated, and then the standard format information is transmitted to the data encryption storage analysis unit, and the specific processing mode is as follows: The same type of combination data and the corresponding compatible reading format label are marked as a, and a=1, 2, …, b, wherein b represents the number of compatible reading formats, and the formula is The comprehensive performance index Pa is calculated, wherein Va is the reading speed, Wa is the reading delay, Ta is the reaction time, k1, k2 and k3 are weight coefficients, Vmax, Wmax and Tmax are the maximum values of the reading speed, the reading delay and the reaction time respectively; The comprehensive performance index value corresponding to the compatible reading format is selected as the standard format, and the standard format information is generated; The data encryption storage analysis unit is used for segmenting the same type of combined data according to the standard format information to obtain a capacity segmentation package, identifying the data types in the capacity segmentation package to generate single-type data and multi-type data, respectively encrypting and storing the single-type data and the multi-type data, and generating storage information, and then transmitting the storage information to the three-dimensional model data management output unit.

2. The three-dimensional model data management system for engineering mapping of claim 1, wherein, The three-dimensional model data acquisition unit and the three-dimensional model data management output unit are further included. The three-dimensional model data acquisition unit is used for acquiring model data corresponding to different three-dimensional models, and transmitting the acquired model data to the adaptive classification management unit; The three-dimensional model data management output unit is used for storing the obtained storage information.

3. The three-dimensional model data management system for engineering mapping of claim 1, wherein, The specific mode of obtaining the secondary classification data by the adaptive classification management unit is as follows: Different model data is acquired, the same type of model data is classified based on the acquisition mode of the model data, the model version corresponding to the same type of model data is acquired, the data version is represented as the model version before and after three-dimensional model optimization, and then the same type of model data is classified based on different model versions to obtain secondary classification data.

4. The three-dimensional model data management system for engineering mapping of claim 1, wherein, The specific mode of analyzing the secondary classification data by the storage analysis management unit is as follows: Secondary classification data is acquired, the same type of modified model data and the same type of unmodified model data are classified based on whether the secondary classification data is modified, all the same type of modified model data is acquired, and is marked as i, i=1, 2, …, j, wherein j represents the number of the same type of modified model data, and the corresponding updated data and original data are acquired; Then the changed data in the updated data is acquired and marked according to the original data as a standard, and the data characteristics of the changed data are extracted, then a replacement template is generated according to the obtained data characteristics, and the changed data is replaced according to the replacement template to obtain replacement data, and the replacement data is combined with the same type of unmodified data to obtain the same type of combined data.

5. The three-dimensional model data management system for engineering mapping of claim 1, wherein, The specific way in which the data encryption storage analysis unit generates single-category data and multi-category data is: Obtain the maximum reading speed corresponding to the standard format information, and generate a capacity segmentation standard with the maximum reading speed value, then take any group of same type combined data as the analysis target, and segment the data capacity of the analysis target with the capacity segmentation standard to obtain multiple groups of capacity segmentation packages, denoted as n, and n=1, 2, …, m, then identify the data categories corresponding to the multiple groups of capacity segmentation packages n, and generate category identification information, which includes single-category data and multi-category data, and the single-category data and multi-category data are respectively encrypted and stored.

6. The three-dimensional model data management system for engineering mapping of claim 5, wherein, The specific way in which the data encryption storage analysis unit encrypts and stores single-category data is: The single-category data is converted to binary to obtain binary conversion data, and the data capacity of the single-category data is obtained, and a binary segmentation standard is generated with the numerical value of the data capacity, then the binary conversion data is segmented according to the binary segmentation standard to obtain binary segmentation segments, and the binary segmentation segments with the same mantissa character are bundled to obtain an encryption bundle, and the encryption bundle is stored to generate storage information.

7. The three-dimensional model data management system for engineering mapping of claim 5, wherein, The specific way in which the data encryption storage analysis unit encrypts and stores multi-category data is: The repeated data in the multi-category data is extracted, and the number of repeated data is labeled as o, and o=1, 2, …, u, and u represents the category of repeated data, and the data characteristics of the repeated data are extracted, and a replacement template is generated with the data characteristics, and the replacement template is replaced with the repeated data to obtain multi-category replacement data, and the multi-category replacement data is further stored to generate storage information.

8. A method for managing three-dimensional model data for engineering surveying, applied to the three-dimensional model data management system for engineering surveying according to any one of claims 1-7, characterized in that, The method comprises the following steps: Step one: classify the same type of model data based on the model data acquisition method, and further classify the same type of model data according to the model version to obtain secondary classification data; Step two: identify the secondary classification data to obtain same type modified model data and same type unmodified model data, and generate a replacement template according to the changed data corresponding to the modified data in the same type modified data, replace the changed data to obtain replacement data, and combine the replacement data with the same type unmodified data to obtain same type combined data; Step three: calculate the comprehensive performance index corresponding to the compatible reading format of the same type combination data, and generate the standard format with the maximum value of the comprehensive performance index, and generate the standard format information, obtain the same type combination data and the corresponding compatible reading format label a, and a=1, 2, …, b, wherein b represents the number of compatible reading formats, according to the formula The comprehensive performance index Pa is calculated, wherein Va is the reading speed, Wa is the reading delay, Ta is the reaction time, k1, k2 and k3 are weight coefficients, wherein Vmax, Wmax and Tmax are the corresponding maximum values respectively; And select the compatible reading format corresponding to the maximum comprehensive performance index value as the standard format, and generate standard format information; Step four: segment the same type combined data according to the standard format information to obtain capacity segmentation packages, identify the data categories in the capacity segmentation packages to obtain single-category data and multi-category data, and encrypt and store the single-category data and multi-category data respectively, and generate storage information.

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