Power design three-dimensional model lightweight processing method based on hybrid cloud architecture

Abstract

The invention belongs to the technical field of electric power, and relates to an electric power design three-dimensional model lightweight processing method based on a hybrid cloud architecture, and the method comprises the steps: analyzing an original three-dimensional design model, recognizing different components and hierarchical structures of the original three-dimensional design model, carrying out the hierarchical processing of the model according to a function module, and decomposing the model into a plurality of independent sub-models. Then, the sub-models are compressed by adopting a selected data compression algorithm, so that the volume of a model file is remarkably reduced; then, the compressed sub-models are stored in a hybrid cloud architecture, sensitive data are stored in private cloud to ensure data security, and non-sensitive data are stored in public cloud to utilize high expansibility and low cost. And finally, constructing a data synchronization mechanism to ensure that the public data in the public cloud and the private cloud are consistent, thereby realizing efficient data sharing and cooperative work. Through the above steps, the method not only reduces the storage and transmission cost, but also improves the data security and access efficiency.

Description

Technical Field

[0001] The present application belongs to the field of electric power technology, and more specifically, relates to a lightweight processing method for electric power design three-dimensional models based on a hybrid cloud architecture. Background Art

[0002] With the development of the power industry and the improvement of the level of informatization, the application of power design 3D models in power engineering is becoming more and more extensive. The 3D design model can intuitively display the structure, layout and operation of the power system, greatly improving the efficiency of design, construction and maintenance. However, with the increase in the scale and complexity of the model, the volume of the 3D design model is also expanding, which brings huge challenges to storage, transmission and processing.

[0003] The 3D model of power design usually contains a large amount of geometric data, material information and topological relationships, which makes the model file huge. Under the traditional storage and transmission methods, the huge model files often take up a lot of storage space and network bandwidth, resulting in high storage costs and slow transmission speeds, which affects the efficiency of design and construction. Summary of the invention

[0004] The present invention provides a lightweight processing method for three-dimensional models of power design based on a hybrid cloud architecture, which is intended to solve the technical problem that under traditional storage and transmission methods, huge model files often require a large amount of storage space and network bandwidth, resulting in high storage costs and slow transmission speeds, which affects the efficiency of design and construction.

[0005] A method for lightweight processing of a three-dimensional model of power design based on a hybrid cloud architecture comprises the following steps:

[0006] Step 1: parse the original 3D design model, identify the different components and hierarchical structures of the original 3D design model, hierarchically process the original 3D design model according to functional modules, and decompose multiple independent sub-models;

[0007] Step 2: Use the selected data compression algorithm to compress the decomposed independent sub-models;

[0008] Step 3: Store the compressed sub-model in a hybrid cloud architecture, where the sensitive data of the sub-model is in the private cloud and the non-sensitive data is in the public cloud;

[0009] Step 4: Build a data synchronization mechanism to ensure that the public data in the public cloud and the private cloud remain consistent.

[0010] The present invention solves the problems of high storage cost and slow transmission speed caused by the huge model files in the traditional storage and transmission mode through a lightweight processing method of power design three-dimensional model based on hybrid cloud architecture. First, by parsing the original three-dimensional design model, identifying its different components and hierarchical structures, the model is hierarchically processed according to functional modules and decomposed into multiple independent sub-models. Then, the sub-models are compressed using a selected data compression algorithm to significantly reduce the size of the model file. Then, the compressed sub-models are stored in the hybrid cloud architecture, where sensitive data is stored in the private cloud to ensure data security, and non-sensitive data is stored in the public cloud to take advantage of its high scalability and low cost. Finally, a data synchronization mechanism is constructed to ensure that the public data in the public cloud and the private cloud are consistent, thereby achieving efficient data sharing and collaborative work. Through the above steps, the method not only reduces the storage and transmission costs, but also improves data security and access efficiency.

[0011] Preferably, the step 1 comprises the following steps:

[0012] Analyze the model: read and analyze the original 3D design model M, extract the geometric information and attribute information;

[0013] Identify components and hierarchies: Identify different components and hierarchies in the original 3D design model M;

[0014] Layered processing: Layered processing is performed on the original 3D model M according to the functional modules;

[0015] Decompose sub-models: decompose each functional module into independent sub-models in one step.

[0016] Preferably, the identifying components and hierarchical structures comprises the following steps:

[0017] Identify components: Determine the geometric objects belonging to the same component by reading the labels or attribute information in the original 3D design model M; classify closely connected geometric objects as one component based on the relative positions and connection relationships between the geometric objects;

[0018] Hierarchical structure recognition: The hierarchy represents the inclusion and dependency relationships between components. The components of the original 3D design model are organized into a tree according to their inclusion relationships. The root node represents the entire original 3D design model, and the leaf nodes represent the most basic components. The parent and child nodes are determined based on the inclusion relationship and functional division of geometric objects.

[0019] Preferably, the layering process includes:

[0020] Functional module division: divide components into different functional modules according to their functional attributes;

[0021] Establish a hierarchical structure: within each functional module, establish a multi-level organization based on the hierarchical structure of the components; and within each functional module, determine the relationship and dependency between each level.

[0022] Preferably, in step 2, the geometry data is compressed by combining quantization compression and predictive coding to compress vertex position data; and the attribute data is compressed by combining color compression and texture compression.

[0023] Preferably, the steps for compressing the geometric data are as follows:

[0024] Quantization compression: Quantization compression is performed based on the original vertex position information of the geometric data:

[0025]

[0026] Where: v represents the original vertex position; v min and v max Indicates the minimum and maximum values ​​of the vertex; n indicates the number of quantization bits; v q Represents the quantized vertex position;

[0027] Predictive coding: Predictive coding is performed based on the quantized vertex positions using a prediction formula to achieve compression of geometric data:

[0028]

[0029] Where: v p represents the predictive coding value; Represents the predicted value, which is the average of adjacent vertices or vertices.

[0030] Preferably, the compression of the attribute data comprises the following steps:

[0031] Color quantization: Color quantization is performed based on the original raw values ​​and the maximum and minimum color values:

[0032]

[0033] Where: c represents the original color value; c min and c max Indicates the minimum and maximum values ​​of the color value; m indicates the number of quantization bits; c q Represents the quantized color value;

[0034] Texture resampling: Resample the color quantized texture data to obtain new texture data T r :

[0035]

[0036] Where: T represents the original texture data; s x and y Represents the resampling ratio; T r (x, y) represents the pixel value of the resampled texture data at position (x, y), where x represents the abscissa of the resampled texture data, and y represents the ordinate of the resampled texture data; Respectively represent the horizontal and vertical coordinates corresponding to the original texture data;

[0037] The resampled texture data is compressed using the PNG texture compression format to generate a compressed texture file.

[0038] The beneficial effects of the present invention include:

[0039] The present invention solves the problems of high storage cost and slow transmission speed caused by the huge model files in the traditional storage and transmission mode through a lightweight processing method of power design three-dimensional model based on hybrid cloud architecture. First, by parsing the original three-dimensional design model, identifying its different components and hierarchical structures, the model is hierarchically processed according to functional modules and decomposed into multiple independent sub-models. Then, the sub-models are compressed using a selected data compression algorithm to significantly reduce the size of the model file. Then, the compressed sub-models are stored in the hybrid cloud architecture, where sensitive data is stored in the private cloud to ensure data security, and non-sensitive data is stored in the public cloud to take advantage of its high scalability and low cost. Finally, a data synchronization mechanism is constructed to ensure that the public data in the public cloud and the private cloud are consistent, thereby achieving efficient data sharing and collaborative work. Through the above steps, the method not only reduces the storage and transmission costs, but also improves data security and access efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 An overall step block diagram provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0043] See also Figure 1 As shown, the best embodiment of the present invention is further described;

[0044] A method for lightweight processing of a three-dimensional model of power design based on a hybrid cloud architecture comprises the following steps:

[0045] Step 1: parse the original 3D design model, identify the different components and hierarchical structures of the original 3D design model, hierarchically process the original 3D design model according to functional modules, and decompose multiple independent sub-models;

[0046] Step 2: Use the selected data compression algorithm to compress the decomposed independent sub-models;

[0047] Step 3: Store the compressed sub-model in a hybrid cloud architecture, where the sensitive data of the sub-model is in the private cloud and the non-sensitive data is in the public cloud;

[0048] Step 4: Build a data synchronization mechanism to ensure that the public data in the public cloud and the private cloud remain consistent.

[0049] The present invention solves the problems of high storage cost and slow transmission speed caused by the huge model files in the traditional storage and transmission mode through a lightweight processing method of power design three-dimensional model based on hybrid cloud architecture. First, by parsing the original three-dimensional design model, identifying its different components and hierarchical structures, the model is hierarchically processed according to functional modules and decomposed into multiple independent sub-models. Then, the sub-models are compressed using a selected data compression algorithm to significantly reduce the size of the model file. Then, the compressed sub-models are stored in the hybrid cloud architecture, where sensitive data is stored in the private cloud to ensure data security, and non-sensitive data is stored in the public cloud to take advantage of its high scalability and low cost. Finally, a data synchronization mechanism is constructed to ensure that the public data in the public cloud and the private cloud are consistent, thereby achieving efficient data sharing and collaborative work. Through the above steps, the method not only reduces the storage and transmission costs, but also improves data security and access efficiency.

[0050] As a possible implementation of this embodiment, step 1 includes the following steps:

[0051] Analyze the model: read and analyze the original 3D design model M, extract the geometric information and attribute information;

[0052] Assume that the model file is M, which contains various parts of the power design, such as substations, transmission lines, switchgear, etc. The model file can be in CAD, BIM or other 3D design file formats.

[0053] The parsing process includes reading the model file and extracting the geometric information and attribute information. The geometric information includes basic data such as vertices, edges, and faces, while the attribute information includes materials, colors, labels, etc.

[0054] Identify components and hierarchies: Identify different components and hierarchies in the original 3D design model M;

[0055] After parsing the model, it is necessary to identify the different components and hierarchical structures in the model. Assume that the model M can be represented as a set of multiple components C: M = {C1, C2, …, Cn} where Ci represents the i-th component in the model. The hierarchical structure can be represented as a tree T, where each node represents a component or subcomponent and the root node represents the entire model.

[0056] T={V,E}, where V is a node set representing components; E is an edge set representing the hierarchical relationship between components.

[0057] Layered processing: Layered processing is performed on the original 3D model M according to the functional modules;

[0058] The model is layered according to the functional modules. The functional modules can be defined according to the specific power design requirements. For example, substations, transmission lines, distribution systems, etc. can be used as different functional modules. Assume that the set of functional modules is {F1, F2, …, Fk}.

[0059] The model M is hierarchically processed according to the functional modules, which can be expressed as:

[0060]

[0061] Among them, F i represents the i-th functional module; k represents the number of functional modules in the set;

[0062] Decompose sub-models: decompose each functional module into independent sub-models in one step.

[0063] On the basis of hierarchical processing, each functional module is further decomposed into multiple independent sub-models. Each sub-model represents an independent part of the model and can be processed and stored separately.

[0064] Assume that the i-th functional module F i It can be decomposed into m i Sub-model S ij :

[0065]

[0066] Where: S ij Represents the jth sub-model in the i-th functional module.

[0067] As a possible implementation of this embodiment, the identification component and hierarchy includes the following steps:

[0068] Identify components: Components are the basic units that make up the model. Each component usually corresponds to an independent physical object or functional unit. For example, in power design, a transformer or a switchgear can be considered a component.

[0069] By reading the label or attribute information in the original 3D design model M, the geometric objects belonging to the same component are determined; for example, all geometric objects marked as "transformer" can be classified as one component.

[0070] Closely connected geometric objects are grouped as one component based on their relative positions and connection relationships; for example, geometric objects that are physically connected to each other are more likely to belong to the same component.

[0071] Hierarchy recognition: Hierarchy represents the inclusion and dependency relationships between components; for example, a substation contains multiple transformers, switchgear, and transmission lines.

[0072] The various components of the original 3D design model are organized into a tree according to their containment relationship. The root node represents the entire original 3D design model, and the leaf nodes represent the most basic components. The parent and child nodes are determined according to the containment relationship and functional division of the geometric objects. For example, a substation node can be a parent node, and its child nodes include transformers, switchgear, etc.

[0073] As a possible implementation of this embodiment, the layered processing includes:

[0074] Functional module division: Functional modules are divided according to different functional requirements of power design. Common functional modules include substations, transmission lines, distribution systems, etc. Components are divided into different functional modules according to their functional attributes; for example, all transformers and related switchgear can be divided into substation module F1, and transmission line components can be divided into transmission module F2.

[0075] Establish a hierarchical structure: within each functional module, establish a multi-level organization based on the hierarchical structure of components;

[0076] For example, within the substation module F1, sub-layers such as transformer layer and switchgear layer can be established.

[0077] And within each functional module, the relationship and dependency between each layer are determined. For example, the transformer layer is the base layer, and the switchgear layer depends on the transformer layer.

[0078] Through the above steps, the components and hierarchical structure of the original 3D design model can be identified, as well as hierarchical processing based on functional modules, which lays the foundation for subsequent data compression and storage.

[0079] As a possible implementation method of this embodiment, in step 2, the compression of geometric data uses a combination of quantization compression and predictive coding to compress vertex position data; for the compression of attribute data, color compression and texture compression methods are used for compression.

[0080] As a possible implementation of this embodiment, the steps for compressing the geometric data are as follows:

[0081] Before data compression, the decomposed independent sub-models need to be preprocessed to ensure the integrity and consistency of the data and prepare for subsequent compression processing.

[0082] Data cleaning:

[0083] Remove redundant data: Remove duplicate or redundant information by analyzing sub-model data.

[0084] Data normalization: Normalize geometric data and attribute data into a unified format for subsequent processing.

[0085] Data classification:

[0086] Geometric data classification: classify and organize the geometric data (such as vertices, edges, faces, etc.) in the sub-model.

[0087] Attribute data classification: classify and organize the attribute data (such as material, color, texture, etc.) in the sub-model.

[0088] Quantization compression: Quantization compression is performed based on the original vertex position information of the geometric data:

[0089]

[0090] Where: v represents the original vertex position; v min and v max Indicates the minimum and maximum values ​​of the vertex; n indicates the number of quantization bits; v q Represents the quantized vertex position;

[0091] Predictive coding: Predictive coding is performed based on the quantized vertex positions using a prediction formula to achieve compression of geometric data:

[0092]

[0093] Where: v p represents the predictive coding value; Represents the predicted value, which is the average of adjacent vertices or vertices.

[0094] Preferably, the compression of the attribute data comprises the following steps:

[0095] Color quantization: Color quantization is performed based on the original raw values ​​and the maximum and minimum color values:

[0096]

[0097] Where: c represents the original color value; c min and c max Indicates the minimum and maximum values ​​of the color value; m indicates the number of quantization bits; c q Represents the quantized color value;

[0098] Texture resampling: Resample the color quantized texture data to obtain new texture data T r :

[0099]

[0100] Where: T represents the original texture data; s x and y Represents the resampling ratio; T r (x, y) represents the pixel value of the resampled texture data at position (x, y), where x represents the abscissa of the resampled texture data, and y represents the ordinate of the resampled texture data; Respectively represent the horizontal and vertical coordinates corresponding to the original texture data;

[0101] The resampled texture data is compressed using the PNG texture compression format to generate a compressed texture file.

[0102] As a possible implementation of this embodiment, step 3 includes the following steps:

[0103] After parsing the original 3D design model and processing it in layers, the data in each sub-model is identified.

[0104] Metadata identification: records the type, source, purpose and other information of each data.

[0105] Data labeling: Add labels to data, such as "sensitive", "non-sensitive", "public", etc.

[0106] Identify sensitive data based on pre-defined classification standards; use data classification tools or algorithms to automatically scan and identify sensitive data. For example, use natural language processing (NLP) technology to identify sensitive information in text data.

[0107] Classify the identified data according to sensitivity:

[0108] Sensitive data: includes all data that meets the sensitive data criteria.

[0109] Non-sensitive data: data that does not meet sensitive data standards and can be stored in the public cloud.

[0110] Public data: data required by both parties and needs to be stored synchronously.

[0111] Data classification example

[0112] Example 1: Design Drawings

[0113] Sensitive data: detailed design drawings, technical specifications, and patented design parts.

[0114] Non-sensitive data: general description documents, public component information.

[0115] Example 2: Device parameters

[0116] Sensitive data: unique parameters and configuration files of a specific device.

[0117] Non-sensitive data: general parameters of the device, non-confidential configuration files.

[0118] According to the classification results, formulate a storage strategy:

[0119] Private cloud storage: Store all sensitive data to ensure data security and privacy.

[0120] Public cloud storage: Stores non-sensitive data and public data to improve data access and processing efficiency.

[0121] Data synchronization mechanism: Ensure the consistency of public data between the public cloud and the private cloud by using data synchronization tools or custom synchronization logic.

[0122] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A lightweight processing method for a three-dimensional model of power design based on a hybrid cloud architecture, characterized in that: The following steps are involved: Step 1: parse the original 3D design model, identify the different components and hierarchical structures of the original 3D design model, hierarchically process the original 3D design model according to functional modules, and decompose multiple independent sub-models; Step 2: Use the selected data compression algorithm to compress the decomposed independent sub-models; Step 3: Store the compressed sub-model in a hybrid cloud architecture, where the sensitive data of the sub-model is in the private cloud and the non-sensitive data is in the public cloud; Step 4: Build a data synchronization mechanism to ensure that the public data in the public cloud and the private cloud remain consistent.

2. According to claim 1, a method for lightweight processing of electric power design three-dimensional models based on hybrid cloud architecture is characterized in that: The step 1 comprises the following steps: Analyze the model: read and analyze the original 3D design model M, extract the geometric information and attribute information; Identify components and hierarchies: Identify different components and hierarchies in the original 3D design model M; Layered processing: The original 3D model M is layered according to the functional modules; Decompose sub-models: decompose each functional module into independent sub-models in one step.

3. According to claim 2, a method for lightweight processing of a three-dimensional power design model based on a hybrid cloud architecture is characterized in that: The identification components and hierarchies include the following steps: Identify components: Determine the geometric objects belonging to the same component by reading the labels or attribute information in the original 3D design model M; classify closely connected geometric objects as one component based on the relative positions and connection relationships between the geometric objects; Hierarchical structure recognition: The hierarchy represents the inclusion and dependency relationships between components. The components of the original 3D design model are organized into a tree according to their inclusion relationships. The root node represents the entire original 3D design model, and the leaf nodes represent the most basic components. The parent and child nodes are determined based on the inclusion relationship and functional division of geometric objects.

4. According to claim 2, a method for lightweight processing of a three-dimensional model of electric power design based on a hybrid cloud architecture is characterized in that: The layered processing includes: Functional module division: divide components into different functional modules according to their functional attributes; Establish a hierarchical structure: Within each functional module, establish a multi-level organization based on the hierarchical structure of the components; and within each functional module, determine the relationship and dependency between each level.

5. According to the method for lightweight processing of electric power design three-dimensional model based on hybrid cloud architecture in claim 1, it is characterized in that: In step 2, the geometry data is compressed by combining quantization compression and predictive coding to compress vertex position data; and the attribute data is compressed by combining color compression and texture compression.

6. The method for lightweight processing of electric power design three-dimensional model based on hybrid cloud architecture according to claim 5 is characterized in that: The steps for compressing the geometric data are as follows: Quantization compression: Quantization compression is performed based on the original vertex position information of the geometric data: Where: v represents the original vertex position; v min and v max Indicates the minimum and maximum values ​​of the vertex; n indicates the number of quantization bits; v q Represents the quantized vertex position; Predictive coding: Predictive coding is performed based on the quantized vertex positions using a prediction formula to achieve compression of geometric data: Where: v p represents the predictive coding value; Represents the predicted value, which is the average of adjacent vertices or vertices.

7. The method for lightweight processing of electric power design three-dimensional model based on hybrid cloud architecture according to claim 5 is characterized in that: The compression of the attribute data includes the following steps: Color quantization: Color quantization is performed based on the original raw values ​​and the maximum and minimum color values: Where: c represents the original color value; c min and c max Indicates the minimum and maximum values ​​of the color value; m indicates the number of quantization bits; c q Represents the quantized color value; Texture resampling: Resample the color quantized texture data to obtain new texture data T r : Where: T represents the original texture data; s x and y Represents the resampling ratio; T r (x, y) represents the pixel value of the resampled texture data at position (x, y), where x represents the abscissa of the resampled texture data, and y represents the ordinate of the resampled texture data; Respectively represent the horizontal and vertical coordinates corresponding to the original texture data; The resampled texture data is compressed using the PNG texture compression format to generate a compressed texture file.

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