A pipe insulation design optimization method and system based on the integration of intelligent pipe PID and E3D

Abstract

This invention relates to the field of digital design technology for power engineering and industrial pipelines, and particularly to a pipeline insulation design optimization method and system based on the integration of intelligent pipeline PID and E3D. The method generates insulation design results at the intelligent PID end by calling a knowledge base based on process parameters, and binds these results to the pipeline's unique identifier code to generate a data package. This data package is then transmitted to the E3D platform via middleware, where the identifier code is used to accurately match the 3D pipeline model, and the design results are written into the model's custom attributes to establish a mapping relationship. An engineering-grade insulation material library containing material properties and loss algorithms is built within E3D. Finally, the model's geometric parameters and insulation properties are automatically extracted, and the material library algorithm is used to calculate the final material consumption. This invention breaks down the data silos between 2D processes and 3D models, achieving end-to-end automation from insulation selection to engineering quantity calculation, significantly improving calculation accuracy and enabling agile response to design changes.

Description

Technical Field

[0001] This invention relates to the field of digital design technology for power engineering and industrial pipelines, and in particular to a pipeline insulation design optimization method and system based on the integration of intelligent pipeline PID and E3D. Background Technology

[0002] In power engineering projects, the design of pipeline insulation, painting, and anti-corrosion engineering is a crucial aspect of ensuring power production safety and reducing energy consumption, with material costs and construction costs accounting for a significant proportion. With the advancement of digital design, intelligent pipeline PID (Piping and Instrumentation Diagram) systems and 3D design platforms (E3D, Everything3D) have been widely applied. However, existing technologies still have the following significant shortcomings in pipeline insulation design and material statistics processes: First, while existing intelligent pipeline PID systems can perform preliminary process design, the insulation design data they generate, such as insulation thickness and material properties, are disconnected from the E3D 3D design platform. Designers must manually select and calculate, and information such as insulation thickness, insulation weight, protective layer thickness, and paint coatings is transmitted through manually submitted documents. The 3D model only contains geometric information and lacks process attribute mapping, resulting in a broken data chain. Second, conventional 3D platforms do not have embedded calculation rule libraries for insulation material characteristics. Traditional insulation material statistics are mostly performed manually after the model is built, measuring pipe length and outer diameter, and then using external tools for calculations outside the 3D platform. This cannot directly combine the theoretical overlap rate and construction loss rate of materials for precise engineering planning. Finally, when changes in PID process parameters such as temperature and medium lead to changes in the insulation design, the lack of automated traversal and linkage calculation mechanisms prevents the 3D model and backend material statistics from being updated in real time. This requires significant manpower for re-verification and quantity calculation, easily leading to calculation errors and project procurement delays.

[0003] Therefore, there is an urgent need for an integrated method that can deeply integrate PID process parameter calculation, three-dimensional model mapping, and engineering consumption statistics algorithms. Summary of the Invention

[0004] This invention proposes a pipeline insulation design optimization method and system based on the integration of intelligent pipeline PID and E3D, aiming to solve the problems of data disconnect between the PID system and the three-dimensional design platform, cumbersome manual quantity calculation, and lack of engineering rule support in traditional pipeline insulation design.

[0005] One method for optimizing pipe insulation design based on the integration of intelligent pipe PID and E3D includes the following steps: S1. Obtain the process piping parameters of the target pipeline, and based on the intelligent pipeline PID system, call the preset insulation knowledge base to generate insulation design results, and bind the insulation design results with the unique identifier code of the target pipeline to generate a pipeline characteristic data package; S2. Transmit the pipeline characteristic data packet to the E3D 3D design platform through the data transmission middleware and parse it to obtain the unique identifier code and insulation design result. Based on the unique identifier code, match it in the 3D model database in the E3D 3D design platform to obtain the first 3D pipeline model, and construct the mapping relationship between the insulation design result and the first 3D pipeline model. S3. Obtain preset thermal insulation material performance parameter data and loss calculation algorithm, and build an engineering thermal insulation material library within the E3D 3D design platform; S4. Traverse the 3D model database to obtain the second 3D pipeline model with mapping relationship, extract the geometric and physical parameters of the second 3D pipeline model and the corresponding thermal insulation design results as input variables, and combine the corresponding thermal insulation material performance parameter data and loss calculation algorithm in the engineering thermal insulation material library to calculate the final thermal insulation design result of the target pipeline.

[0006] Furthermore, step S1 includes the following sub-steps: S101. Obtain the process pipeline parameters of the target pipeline, the process pipeline parameters including: target pipeline medium type parameters, target pipeline flow rate parameters, target pipeline design temperature parameters, and target pipeline geographical environment parameters; S102. Input the process piping parameters into the insulation knowledge base, calculate the insulation thickness using the standard insulation thickness algorithm integrated in the insulation knowledge base, and automatically select the insulation material type, protective layer material type, protective layer thickness, and paint matching type to obtain the insulation design result; S103. Encapsulate the insulation design results and corresponding process piping parameters into JSON format, and use the power plant identification system KKS code as the unique equipment identification code to build the association relationship and generate a piping characteristic data package.

[0007] Furthermore, step S2 includes the following sub-steps: S201. The intelligent pipeline PID system pushes pipeline characteristic data packages to the enterprise service bus in the middleware and publishes them as services through message queues; S202. The integrated microservices of the 3D design platform extract data from the message queue to complete data packet reception, and obtain unique identifier codes and thermal insulation design results through reverse parsing; S203. Using the unique identifier code as the search keyword, traverse the 3D model database to find the corresponding first 3D pipe model. Call the application programming interface of the 3D design platform to execute the attribute writing instruction, and write the insulation material type, insulation thickness, protective layer material type, protective layer thickness, and paint matching type into the custom attribute fields corresponding to the first 3D pipe model, so as to obtain the mapping relationship between the insulation design result and the first 3D pipe model.

[0008] Furthermore, the thermal insulation material performance parameter data and loss calculation algorithm mentioned in step S3 include: For thermal insulation materials, the performance parameters of thermal insulation materials include material density, and the loss calculation algorithm includes the construction loss rate of thermal insulation materials and the calculation formula for the consumption of thermal insulation materials. For protective layer materials, the performance parameters of thermal insulation materials include the standard length and standard width of the roll material, and the loss calculation algorithm includes the protective layer overlap rate, the protective layer construction loss rate, and the formula for calculating the theoretical coverage area of ​​the protective layer.

[0009] Furthermore, step S4 includes the following sub-steps: S401. Scan the 3D model database, determine whether the custom attribute fields of each 3D model are empty, and extract the non-empty models as 3D pipeline models carrying mapping relationships. S402. Extract the actual outer diameter of the pipe and the length of the pipe arranged in three-dimensional space from the geometric attribute interface of the three-dimensional pipe model, which together constitute the geometric physical parameters.

[0010] Furthermore, for thermal insulation materials, the loss calculation algorithm is expressed as follows: ; ; in, Indicates the volume of the insulation material. Indicates the outer diameter of the target pipe. Indicates the target pipe length. Indicates the thickness of the insulation material. This indicates the amount of insulation material wasted. Indicates the density of the insulation material. This indicates the pre-set construction loss rate.

[0011] Furthermore, for the protective layer material, its loss calculation algorithm is expressed as follows: ; ; in, Indicates the required area of ​​the protective layer. Indicates the thickness of the protective layer. This indicates the preset overlap rate of the protective layer. This indicates the pre-set construction loss rate. Indicates the outer diameter of the target pipe. Indicates the target pipe length. Indicates the thickness of the insulation material. This indicates the amount of protective layer material lost. Indicates the standard length of the protective layer. This indicates the standard width of the protective layer.

[0012] This invention also provides a pipeline insulation design optimization system based on the integration of intelligent pipeline PID and E3D. This system is implemented based on the pipeline insulation design optimization method based on the integration of intelligent pipeline PID and E3D described above, and includes: The intelligent pipeline PID design and data generation module is used to obtain the process pipeline parameters of the target pipeline. Based on the intelligent pipeline PID system, it calls the preset insulation knowledge base to generate insulation design results and binds the insulation design results with the unique identifier code of the target pipeline to generate pipeline characteristic data package. The data transmission and model assignment module is used to transmit pipeline characteristic data packets to the 3D design platform through middleware. After the 3D design platform parses out the unique identifier code and insulation design results, it matches the target 3D pipeline model in the 3D model database based on the unique identifier code and writes the insulation design results into the custom attributes of the target 3D pipeline model. The thermal insulation material library extension module is built inside the 3D design platform to pre-store various physical property parameters for different materials and corresponding statistical calculation rules. The traversal and statistical calculation module is used to automatically traverse the 3D model database, identify and filter 3D pipe models with mapping relationships, extract the geometric and physical parameters of the 3D pipe models and the thermal insulation design results in the mapping relationships, use the geometric and physical parameters and thermal insulation design results as input variables, call the corresponding statistical calculation rules in the thermal insulation material library extension module to perform calculations, obtain the corresponding material consumption and generate material statistical reports.

[0013] The present invention also provides a computer-readable storage medium for storing a computer program that, when run on a computer, causes the computer to execute any of the above-mentioned pipe insulation design optimization methods based on the integration of intelligent pipe PID and E3D.

[0014] The present invention also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement a pipe insulation design optimization method based on the integration of intelligent pipe PID and E3D as described above.

[0015] The beneficial effects of this invention are: This invention utilizes unique identifier coding (KKS coding) and data transmission middleware to seamlessly and automatically map the process calculation results of 2D intelligent PID control to custom attributes of the E3D 3D piping model, achieving end-to-end data connectivity from process to 3D layout. By constructing an engineering-based insulation material library within the E3D 3D platform, engineering rules such as material density, overlap rate, and construction losses are directly linked to 3D geometric extraction. This overcomes the traditional process of manual calculation and external formula application, effectively improving design accuracy and accelerating design efficiency. Based on an automatic traversal mechanism, when the PID front-end design parameters change, the E3D back-end calculation results can be dynamically updated by re-analyzing and executing formulas, greatly reducing the manual recalculation costs in project change management. This forms a complete closed-loop piping insulation design system encompassing process piping system KKS coding, intelligent design rule library, and 3D piping model, providing a highly valuable data foundation for the construction and operation of digital twin factories. Attached Figure Description

[0016] Figure 1 This is a flowchart of a pipeline insulation design optimization method based on the integration of intelligent pipeline PID and E3D, as proposed in an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of the terminal equipment structure of a pipeline insulation design optimization method based on the integration of intelligent pipeline PID and E3D proposed in an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram of a computer-readable storage medium structure for a pipeline insulation design optimization method based on the integration of intelligent pipeline PID and E3D proposed in an embodiment of the present invention.

[0019] In the diagram, 200 is the terminal device, 210 is the memory, 211 is the RAM, 212 is the cache memory, 213 is the ROM, 214 is the program / utility, 215 is the program module, 220 is the processor, 230 is the bus, 240 is the external device, 250 is the I / O interface, 260 is the network adapter, and 300 is the program product. Detailed Implementation

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, and not all of them. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention. It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0023] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or machine that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or machine. Without further limitation, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or machine that includes said element.

[0024] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0025] Example 1: like Figure 1 As shown, this embodiment provides a pipe insulation design optimization method based on the integration of intelligent pipe PID and E3D, including the following steps: S1. Obtain the process piping parameters of the target pipeline, and based on the intelligent pipeline PID system, call the preset insulation knowledge base to generate insulation design results, and bind the insulation design results with the unique identifier code of the target pipeline to generate a pipeline characteristic data package; Furthermore, step S1 includes the following sub-steps: S101. Obtain the process pipeline parameters of the target pipeline, the process pipeline parameters including: target pipeline medium type parameters, target pipeline flow rate parameters, target pipeline design temperature parameters, and target pipeline geographical environment parameters; S102. Input the process piping parameters into the insulation knowledge base, calculate the insulation thickness using the standard insulation thickness algorithm integrated in the insulation knowledge base, and automatically select the insulation material type, protective layer material type, protective layer thickness, and paint matching type to obtain the insulation design result; S103. Encapsulate the insulation design results and corresponding process piping parameters into JSON format, and use the power plant identification system KKS code as the unique equipment identification code to build the association relationship and generate a piping characteristic data package.

[0026] Specifically, this step addresses the challenges of obtaining insulation parameters and over-reliance on manual experience in traditional pipeline process design by deploying a knowledge base integrating standard insulation algorithms in the intelligent PID front-end design stage. Existing technologies often require designers to consult numerous specifications and manuals to calculate surface temperature and heat loss when selecting pipeline insulation, and the design results are mostly in the form of two-dimensional drawings or independent documents, lacking structured expression. This method achieves automated reasoning from process parameters to insulation materials and thicknesses through a front-end knowledge base rule engine, and creatively uses KKS encoding as the data framework to structurally encapsulate scattered design parameters, breaking down the "data barrier" between traditional process design and digital delivery, and providing a standardized underlying data source for subsequent cross-platform data flow.

[0027] Specifically, the implementation principle described above is as follows: First, the system acquires multi-dimensional process pipeline parameters, including the medium characteristics, fluid conditions, and environmental constraints of the target pipeline, and inputs them into a pre-defined insulation knowledge base. The knowledge base's built-in standard insulation thickness algorithm automatically derives insulation design results that meet economic and thermal requirements based on the input parameters. Finally, the system extracts the pipeline's KKS code as the primary key and packages the insulation design results into a lightweight JSON data packet. The process pipeline parameters include the target pipeline medium type, flow rate, design temperature, and geographical environment parameters. The insulation design results include insulation thickness, insulation material type, protective layer material type, and protective layer thickness. The degree and paint matching types; the generation of pipeline characteristic data package specifically involves encapsulating the insulation design results and the corresponding process pipeline parameters into JSON format, and using the power plant identification system KKS code as the unique equipment identifier to build an association relationship; further, it should be noted that the pipeline system of power and chemical projects is extremely large, and the KKS code is the unique "digital ID card" for identifying engineering objects. By binding the dynamically generated process insulation parameters with the static KKS code at the underlying level, it is actually giving the abstract process parameters a definite physical address pointer, thereby ensuring that massive amounts of data will not be misaligned or lost in subsequent complex three-dimensional spatial mapping.

[0028] S2. Transmit the pipeline characteristic data packet to the E3D 3D design platform through the data transmission middleware and parse it to obtain the unique identifier code and insulation design result. Based on the unique identifier code, match it in the 3D model database in the E3D 3D design platform to obtain the first 3D pipeline model, and construct the mapping relationship between the insulation design result and the first 3D pipeline model. Furthermore, step S2 includes the following sub-steps: S201. The intelligent pipeline PID system pushes pipeline characteristic data packages to the enterprise service bus in the middleware and publishes them as services through message queues; S202. The integrated microservices of the 3D design platform extract data from the message queue to complete data packet reception, and obtain unique identifier codes and thermal insulation design results through reverse parsing; S203. Using the unique identifier code as the search keyword, traverse the 3D model database to find the corresponding first 3D pipe model. Call the application programming interface of the 3D design platform to execute the attribute writing instruction, and write the insulation material type, insulation thickness, protective layer material type, protective layer thickness, and paint matching type into the custom attribute fields corresponding to the first 3D pipe model, so as to obtain the mapping relationship between the insulation design result and the first 3D pipe model.

[0029] Specifically, this step addresses the data silo problem between the 2D PID platform and the 3D layout platform by constructing a cross-platform data transmission path based on Enterprise Service Bus (ESB) and Message Queues (MQ). In existing technologies, 3D pipeline modelers typically have to manually input or supplement insulation information into the 3D model based on 2D drawings, which is highly prone to human error and completely unable to handle frequent design changes. This method achieves asynchronous decoupled data transmission through middleware and utilizes "digital keys" (KKS encoding) to achieve precise addressing and automated value annotation in a massive 3D database, replacing the traditional manual table rewriting and drawing matching process. Specifically, the implementation principle described above is as follows: First, the sending end pushes a JSON data packet carrying the KKS code and insulation design results to a message queue for queuing. Then, the microservice deployed on the E3D platform listens for and parses the message, extracting the KKS code and corresponding attributes such as insulation thickness and material. Finally, the microservice triggers the E3D internal API interface to match the KKS code in the database. If the first 3D pipeline model that has not yet been assigned weights is found, the underlying write command is executed to solidify the process attributes into the model's UDA field. Furthermore, it should be noted that through the message queue mechanism, the calculation of the PID system and the assignment of values ​​by the E3D system are effectively decoupled, ensuring that the system will not crash even when massive amounts of data are transmitted concurrently in ultra-large projects. Moreover, by utilizing E3D's underlying UDA (User Defined Attribute) extension mechanism, not only is the integrity of the model's original geometric topology guaranteed, but the model is also endowed with structured business attributes that can be used for later advanced calculations.

[0030] S3. Obtain preset thermal insulation material performance parameter data and loss calculation algorithm, and build an engineering thermal insulation material library within the E3D 3D design platform; Furthermore, the corresponding thermal insulation material performance parameter data and loss calculation algorithm mentioned in step S3 include: For thermal insulation materials, the performance parameters of thermal insulation materials include material density, and the loss calculation algorithm includes the construction loss rate of thermal insulation materials and the calculation formula for the consumption of thermal insulation materials. For protective layer materials, the performance parameters of thermal insulation materials include the standard length and standard width of the roll material, and the loss calculation algorithm includes the protective layer overlap rate, the protective layer construction loss rate, and the formula for calculating the theoretical coverage area of ​​the protective layer.

[0031] Specifically, this step addresses the deficiency in conventional 3D design software regarding the lack of engineering quantity calculation capabilities for thermal insulation consumables. It extends the E3D platform by constructing an engineering-grade thermal insulation material library containing quantity calculation rules. Existing 3D component specification libraries typically only include parameters for rigid geometric entities such as pipes, flanges, and valves, failing to cover complex engineering variables on construction sites, such as insulation material loss rates, overlap redundancy, and board layout calculations. This invention internalizes historical material loss data, previously isolated in external cost estimation software or other sources, into a standard rule library that can be called by the program, enabling accurate calculation of the 3D model.

[0032] Specifically, the implementation principle described above is as follows: First, a dedicated category for thermal insulation materials is created in the backend specification library of the E3D 3D design platform. Then, the physical constants of various insulation and protective layer materials (such as material density, standard factory dimensions, etc.) are entered. Finally, the associated engineering loss factors (such as an overlap rate of 15%, a construction loss rate of 10%, etc.) and related calculation algorithms are configured to form a multi-dimensional engineering-grade thermal insulation material library. Furthermore, it should be noted that this engineering-grade thermal insulation material library is not a static data storage table, but a computational unit with computational methods. It defines the boundary conditions and conversion logic for converting 3D geometric entities to actual engineering implementation, transforming the 3D platform from a simple drafting tool into an intelligent engineering platform with cost estimation capabilities.

[0033] S4. Traverse the 3D model database to obtain the second 3D pipeline model with mapping relationship, extract the geometric and physical parameters of the second 3D pipeline model and the corresponding thermal insulation design results as input variables, and combine the corresponding thermal insulation material performance parameter data and loss calculation algorithm in the engineering thermal insulation material library to calculate the final thermal insulation design result of the target pipeline.

[0034] Furthermore, step S4 includes the following sub-steps: S401. Scan the 3D model database, determine whether the custom attribute fields of each 3D model are empty, and extract the non-empty models as 3D pipeline models carrying mapping relationships. S402. Extract the actual outer diameter of the pipe and the length of the pipe arranged in three-dimensional space from the geometric attribute interface of the three-dimensional pipe model, which together constitute the geometric physical parameters.

[0035] Furthermore, for thermal insulation materials, the loss calculation algorithm is expressed as follows: ; ; in, Indicates the volume of the insulation material. Indicates the outer diameter of the target pipe. Indicates the target pipe length. Indicates the thickness of the insulation material. This indicates the amount of insulation material wasted. Indicates the density of the insulation material. This indicates the pre-set construction loss rate.

[0036] Furthermore, for the protective layer material, its loss calculation algorithm is expressed as follows: ; ; in, Indicates the required area of ​​the protective layer. Indicates the thickness of the protective layer. This indicates the preset overlap rate of the protective layer. This indicates the pre-set construction loss rate. Indicates the outer diameter of the target pipe. Indicates the target pipe length. Indicates the thickness of the insulation material. This indicates the amount of protective layer material lost. Indicates the standard length of the protective layer. This indicates the standard width of the protective layer.

[0037] Specifically, this step addresses the problems of extremely low efficiency, high error rate, and inability to respond to design changes caused by the reliance on manual measurement of 3D pipeline lengths and offline calculations in traditional material statistics. It develops an automatic quantity calculation engine based on full model automatic traversal and formula linkage. Existing technologies often require the entire quantity calculation process to be restarted manually when pipeline routes change, resulting in huge costs. This invention automatically couples the actual geometric attributes (outer diameter, length) of the pipeline spatial layout with the assigned UDA process attributes (thickness) at the underlying level and directly applies engineering algorithms to obtain the calculation results, enabling every design change to be transformed into accurate data for actual engineering applications.

[0038] Specifically, the implementation principle described above is as follows: First, the entire project's 3D model database is automatically traversed, and conditional filters are used to capture all second-dimensional pipe models with annotated UDA fields. Then, for each captured pipe, the geometry interface is called to read its actual outer diameter and wiring length, and process parameters such as insulation thickness in the UDA are read simultaneously. Finally, these two types of data are input as variables into the preset formula of the engineered insulation material library. Combined with constants such as material density and overlap rate, rigorous calculus and geometric algebra calculations are used to calculate the weight of insulation material required for the pipe section and the number of standard protective layer sheets to be purchased, and the results are summarized and output.

[0039] Example 2

[0040] As a preferred embodiment of the above embodiments, a pipe insulation design optimization system based on the integration of intelligent pipe PID and E3D is provided. This system is implemented based on any of the pipe insulation design optimization methods based on the integration of intelligent pipe PID and E3D described above, including: The intelligent pipeline PID design and data generation module is used to obtain the process pipeline parameters of the target pipeline. Based on the intelligent pipeline PID system, it calls the preset insulation knowledge base to generate insulation design results and binds the insulation design results with the unique identifier code of the target pipeline to generate pipeline characteristic data package. The data transmission and model assignment module is used to transmit pipeline characteristic data packets to the 3D design platform through middleware. After the 3D design platform parses out the unique identifier code and insulation design results, it matches the target 3D pipeline model in the 3D model database based on the unique identifier code and writes the insulation design results into the custom attributes of the target 3D pipeline model. The thermal insulation material library extension module is built inside the 3D design platform to pre-store various physical property parameters for different materials and corresponding statistical calculation rules. The traversal and statistical calculation module is used to automatically traverse the 3D model database, identify and filter 3D pipe models with mapping relationships, extract the geometric and physical parameters of the 3D pipe models and the thermal insulation design results in the mapping relationships, use the geometric and physical parameters and thermal insulation design results as input variables, call the corresponding statistical calculation rules in the thermal insulation material library extension module to perform calculations, obtain the corresponding material consumption and generate material statistical reports.

[0041] Specifically, the implementation principle of the above embodiments is as follows: First, the intelligent pipeline PID design and data generation module deployed in the front-end design environment obtains the initial process pipeline parameters such as the medium type, flow rate, design temperature and geographical environment of the target pipeline, calls the built-in knowledge base rule engine to perform automated calculations, and deduce the insulation design results that meet the specifications (including material type, thickness and paint matching, etc.). In order to ensure that this part of non-geometric process data can be accurately identified and located in the subsequent complex three-dimensional environment, the module binds the generated insulation design results with the KKS unique identifier code of the corresponding pipeline at the underlying level and encapsulates it into a structured JSON pipeline characteristic data package. Then, the data transmission and model assignment module deployed in the cross-platform communication architecture takes over the data flow. Its core mechanism is to use asynchronous communication technology based on Enterprise Service Bus (ESB) and Message Queue (MQ) to transmit the pipeline characteristic data packet to the E3D 3D design platform without loss. After receiving and unpacking the packet, the integrated microservice on the E3D platform uses KKS encoding as a "digital probe" to accurately address the data in the huge 3D model database, match the corresponding target 3D pipeline model (i.e., the first 3D pipeline model), and write the insulation design result into the custom attribute (UDA) of the model by calling the underlying API interface, thereby completing the mapping between process attributes and 3D geometric entities. Next, to enable the 3D model to perform engineering quantity calculations, the insulation material library extension module deployed on the 3D platform base pre-built an engineering database within the system. This module not only statically stores the standard dimensions, material density, and other physical property parameters of various insulation and protective layer materials, but also deeply embeds dynamic rule algorithms based on engineering practice experience (such as construction overlap rate, material loss rate, etc.), enabling the E3D platform, which was originally a geometric display, to possess the boundary conditions and conversion logic for underlying cost estimation. Finally, the traversal and statistical calculation module deployed on the quantity calculation end triggers a fully automated measurement process. The module's background execution engine automatically traverses the entire 3D model database, accurately filtering out 3D pipe models (i.e., second-dimensional pipe models) whose UDA fields have been annotated. Subsequently, the engine simultaneously extracts two sets of key data: one is the actual physical geometric parameters of the 3D spatial wiring (such as pipe outer diameter and actual laying length), and the other is the process insulation design results from the UDA (such as insulation thickness and material type). This module uses these two sets of data as input variables, dynamically calling the corresponding statistical calculation formulas in the insulation material library extension module. Through calculus and algebraic operations, it calculates the precise material consumption, including volume, weight, and the number of standard sheet materials, and categorizes and summarizes these to generate a material statistical report for centralized engineering procurement. Furthermore, throughout the entire system operation, KKS encoding is consistently used in the entire lifecycle of PID process calculations, cross-platform transmission, and 3D model mapping. When design changes occur to the parameters at the process front end, the system automatically triggers an update link through the data transmission and model assignment module, overwriting and refreshing the old attributes in the E3D model, and linking the traversal and statistical calculation module for second-level recalculation. This mechanism fundamentally breaks down the traditional barriers to collaboration between disciplines, ensuring high-frequency linkage and real-time optimization collaboration between pipeline process design, 3D spatial layout, and engineering material quantity calculation in terms of physical architecture and data logic.

[0042] Example 3

[0043] Based on Examples 1 and 2, there is a practical application scenario for pipeline insulation in a certain power engineering project. This virtual power plant includes a high-pressure water supply pipeline with KKS code 10LAB30BR010. This scenario adopts a pipeline insulation design optimization method and system based on the integration of intelligent pipeline PID and E3D as described in the above examples, specifically including the following steps: S1: Intelligent PID System Design and Standard Insulation Data Generation In an intelligent PID design system, a high-pressure water supply pipeline with KKS code 10LAB30 BR010 was designed for a power engineering project. Based on input parameters such as the pipeline medium (high-pressure water supply), flow rate, design temperature (251.5°C), and project location, the system automatically calculated the pipeline dimensions (Φ194*20, material 20G) and selected the insulation material, protective layer, and paint. The results are as follows: Insulation material: rock wool felt, thickness: 80mm; Protective layer: 0.5mm aluminum-zinc coated color steel plate; Paint kit: Inorganic zinc-rich primer + organosilicon heat-resistant intermediate coat; The system encapsulates data such as pipe specifications and dimensions, insulation materials and thickness, protective layer materials and thickness, and paint specifications into a JSON data packet, which is then bound to the KKS code of the water supply pipe.

[0044] S2: Data Transfer and Model Assignment The full characteristic data packet of the water supply pipeline, including the insulation results, is pushed to the message queue via the Enterprise Service Bus. The E3D client consumes this message through the Integration Service (a microservice), parses the KKS encoding 10LAB30BR010, uses the E3D system's default API interface Project.FindElement to locate the pipeline, and executes the following code: pipe.SetAttribute("UDA_ITSK_MATERIAL", "Rock wool blanket"); pipe.SetAttribute("UDA_ITSK_THK", "80"); pipe.SetAttribute("UDA_CLADDING", "Aluminium-zinc coated color steelplate, 0.5mm"); pipe.SetAttribute("UDA_ITSK_Oil paint", "inorganic zinc-rich primer,1degree","organic silicone heat-resistant intermediate coating,1 degree") # Write pipe-related attributes; S3: Construction of an expanded insulation material warehouse: The administrator creates a record for the material "Rock wool blanket" in the E3D specification library and fills in: Density: 100 kg / m³; Wastage_Factor: 0.2 (i.e., 20% loss); Create a record for the material Aluminium-zinc coated color steel plate, 0.5mm, and fill in: Standard Width: 1000 mm; Standard Length: 2000 mm (roll); Lap_Rate: 0.15 (15% overlap); Wastage_Factor: 0.05 (5% loss); S4: Automatic Traversal and Fast Statistics The project manager generates an insulation material purchase requisition in E3D by running the "Insulation Material Statistics" plugin as needed. Iterate through the entire project database and filter for all pipes where UDA_ITSK_MATERIAL is not empty; For pipe 10LAB30 BR010, its outer diameter is 194mm, length is 120m, and insulation thickness is 80mm. Calculate the insulation volume: (π / 4) (0.194+2) 0.08) 2 -0.194 2 ) 120 = 8.26 m³; Calculated insulation weight: 8.26 m³ 100 kg / m³ (1+0.2)= 991.2 kg; Calculate the area of ​​the protective layer: π (0.194+2 0.08 + 0.0005) 120 (1+0.15) (1+0.05) = 161.4 m²; Calculation of the number of aluminum-zinc coated steel sheets: 161.4 m² / (1m) 2m) = 80.7 → 81 sheets; Circulate through all pipes and summarize the total volume and total number of sheets for Rock wool blanket and Aluminium-zinc coated colorsteel plate, 0.5mm respectively; Generate reports and export them to Excel for procurement use.

[0045] Example 4

[0046] like Figure 2 As shown in the figure, this embodiment proposes a terminal device for a pipeline insulation design optimization method based on the integration of intelligent pipeline PID and E3D. The terminal device includes at least one memory, at least one processor, and a bus connecting different platform systems.

[0047] The memory may include readable media in the form of volatile memory, such as RAM 211 and / or cache memory, and may further include ROM 213.

[0048] The memory also stores a computer program that can be executed by a processor, causing the processor to perform any of the above-mentioned pipe insulation design optimization methods based on the integration of intelligent pipe PID and E3D in the embodiments of this application. The specific implementation method and the achieved technical effects are consistent with those described in the embodiments of the above methods, and some details will not be repeated here. The memory may also include a program / utility having a set (at least one) of program modules, including but not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0049] Accordingly, the processor can execute the aforementioned computer programs, as well as executable programs / utilities.

[0050] A bus can represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus structures.

[0051] The terminal device can also communicate with one or more external devices, such as keyboards, pointing devices, Bluetooth devices, etc., and with one or more devices capable of interacting with the terminal device, and / or with any device that enables the terminal device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed through I / O interfaces. Furthermore, the terminal device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter can communicate with other modules of the terminal device via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the terminal device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0052] Example 5

[0053] like Figure 3 As shown in Example 1, this example proposes a computer-readable storage medium for a pipe insulation design optimization method based on the integration of intelligent pipe PID and E3D. The computer-readable storage medium stores instructions that, when executed by a processor, implement any of the aforementioned pipe insulation design optimization methods based on the integration of intelligent pipe PID and E3D. The specific implementation method and the achieved technical effects are consistent with those described in the examples above, and some details will not be repeated.

[0054] This embodiment provides a program product for implementing the above-described method, which may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this embodiment, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device. The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0055] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. Program code for performing operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as a standalone software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to user computing devices via any type of network, including local area networks (LANs) or wide area networks (WANs), or they can be connected to external computing devices (e.g., via the Internet through an Internet service provider).

[0056] This invention is described from the perspectives of its intended use, effectiveness, progress, and novelty. Its practical and progressive features meet the functional enhancement and use requirements emphasized by the Patent Law. The above description and drawings are merely preferred embodiments of this application and are not intended to limit this application. Therefore, all structures, devices, features, etc., that are similar to or identical to those of this application, i.e., all equivalent substitutions or modifications made in accordance with the scope of this patent application, shall fall within the scope of protection of this patent application.

[0057] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A pipe insulation design optimization method based on the integration of intelligent pipe PID and E3D, characterized in that, Includes the following steps: S1. Obtain the process piping parameters of the target pipeline, and based on the intelligent pipeline PID system, call the preset insulation knowledge base to generate insulation design results, and bind the insulation design results with the unique identifier code of the target pipeline to generate a pipeline characteristic data package; S2. Transmit the pipeline characteristic data packet to the E3D 3D design platform through the data transmission middleware and parse it to obtain the unique identifier code and insulation design result. Based on the unique identifier code, match it in the 3D model database in the E3D 3D design platform to obtain the first 3D pipeline model, and construct the mapping relationship between the insulation design result and the first 3D pipeline model. S3. Obtain preset thermal insulation material performance parameter data and loss calculation algorithm, and build an engineering thermal insulation material library within the E3D 3D design platform; S4. Traverse the 3D model database to obtain the second 3D pipeline model with mapping relationship, extract the geometric and physical parameters of the second 3D pipeline model and the corresponding thermal insulation design results as input variables, combine the corresponding thermal insulation material performance parameter data and loss calculation algorithm in the engineering thermal insulation material library, and calculate the final thermal insulation design result of the target pipeline. Step S2 includes the following sub-steps: S201. The intelligent pipeline PID system pushes pipeline characteristic data packages to the enterprise service bus in the middleware and publishes them as services through message queues; S202. The integrated microservices of the 3D design platform extract data from the message queue to complete data packet reception, and obtain unique identifier codes and thermal insulation design results through reverse parsing; S203. Using the unique identifier code as the search keyword, traverse the three-dimensional model database, find the corresponding first three-dimensional pipe model, call the application interface of the three-dimensional design platform to execute the attribute writing instruction, and write the insulation material type, insulation thickness, protective layer material type, protective layer thickness and paint matching type into the custom attribute fields corresponding to the first three-dimensional pipe model, so as to obtain the mapping relationship between the insulation design result and the first three-dimensional pipe model. Step S4 includes the following sub-steps: S401. Scan the 3D model database, determine whether the custom attribute fields of each 3D model are empty, and extract the non-empty models as 3D pipeline models carrying mapping relationships. S402. Extract the actual outer diameter of the pipe and the length of the pipe arranged in three-dimensional space from the geometric attribute interface of the three-dimensional pipe model, which together constitute the geometric physical parameters.

2. The pipeline insulation design optimization method based on the integration of intelligent pipeline PID and E3D as described in claim 1, characterized in that, Step S1 includes the following sub-steps: S101. Obtain the process pipeline parameters of the target pipeline, the process pipeline parameters including: target pipeline medium type parameters, target pipeline flow rate parameters, target pipeline design temperature parameters, and target pipeline geographical environment parameters; S102. Input the process piping parameters into the insulation knowledge base, calculate the insulation thickness using the standard insulation thickness algorithm integrated in the insulation knowledge base, and automatically select the insulation material type, protective layer material type, protective layer thickness, and paint matching type to obtain the insulation design result; S103. Encapsulate the insulation design results and corresponding process piping parameters into JSON format, and use the power plant identification system KKS code as the unique equipment identification code to build the association relationship and generate a piping characteristic data package.

3. The pipeline insulation design optimization method based on the integration of intelligent pipeline PID and E3D as described in claim 1, characterized in that, Step S3 includes the following: thermal insulation material performance parameter data and loss calculation algorithm. For thermal insulation materials, the performance parameters of thermal insulation materials include material density, and the loss calculation algorithm includes the construction loss rate of thermal insulation materials and the calculation formula for the consumption of thermal insulation materials. For protective layer materials, the performance parameters of thermal insulation materials include the standard length and standard width of the roll material, and the loss calculation algorithm includes the protective layer overlap rate, the protective layer construction loss rate, and the formula for calculating the theoretical coverage area of ​​the protective layer.

4. The pipeline insulation design optimization method based on the integration of intelligent pipeline PID and E3D as described in claim 1, characterized in that, For thermal insulation materials, the loss calculation algorithm is expressed as follows: ； ； in, Indicates the volume of the insulation material. Indicates the outer diameter of the target pipe. Indicates the target pipe length. Indicates the thickness of the insulation material. This indicates the amount of insulation material wasted. Indicates the density of the insulation material. This indicates the pre-set construction loss rate.

5. The pipeline insulation design optimization method based on the integration of intelligent pipeline PID and E3D as described in claim 1, characterized in that, For the protective layer material, the loss calculation algorithm is expressed as follows: ； ； in, Indicates the required area of ​​the protective layer. Indicates the thickness of the protective layer. This indicates the preset overlap rate of the protective layer. This indicates the pre-set construction loss rate. Indicates the outer diameter of the target pipe. Indicates the target pipe length. Indicates the thickness of the insulation material. This indicates the amount of protective layer material lost. Indicates the standard length of the protective layer. This indicates the standard width of the protective layer.

6. A pipe insulation design optimization system based on the integration of intelligent pipe PID and E3D, wherein the system is implemented based on the pipe insulation design optimization method based on the integration of intelligent pipe PID and E3D as described in any one of claims 1-5, characterized in that, include: The intelligent pipeline PID design and data generation module is used to obtain the process pipeline parameters of the target pipeline. Based on the intelligent pipeline PID system, it calls the preset insulation knowledge base to generate insulation design results and binds the insulation design results with the unique identifier code of the target pipeline to generate pipeline characteristic data package. The data transmission and model assignment module is used to transmit pipeline characteristic data packets to the 3D design platform through middleware. After the 3D design platform parses out the unique identifier code and insulation design results, it matches the target 3D pipeline model in the 3D model database based on the unique identifier code and writes the insulation design results into the custom attributes of the target 3D pipeline model. The thermal insulation material library extension module is built inside the 3D design platform to pre-store various physical property parameters for different materials and corresponding statistical calculation rules. The traversal and statistical calculation module is used to automatically traverse the 3D model database, identify and filter 3D pipe models with mapping relationships, extract the geometric and physical parameters of the 3D pipe models and the thermal insulation design results in the mapping relationships, use the geometric and physical parameters and thermal insulation design results as input variables, call the corresponding statistical calculation rules in the thermal insulation material library extension module to perform calculations, obtain the corresponding material consumption and generate material statistical reports.

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