Automatic optimization method of pipe network generation model for digital city intelligent operation and maintenance

Abstract

The present application belongs to the field of public building heating ventilation and air conditioning system design optimization, and provides an automatic optimization method of pipe network generation model for digital city intelligent operation and maintenance. The method comprises the following steps: S1, obtaining air outlet position; S2, supplementing virtual air outlet and dividing grid according to the air outlet position; S3, separately processing small area grid to carry out first-stage branch pipe path planning; S4, second-stage branch pipe path planning based on A* algorithm; S5, secondary area connection and main pipe path planning; S6, air pipe hydraulic calculation and pipe size determination; and S7, pipe generation. The present application can effectively observe and predict the operation state of a solar PVT heat pump system in real time. The present application can successfully complete the automatic arrangement and optimization of the air pipe system in the actual design process. Compared with the traditional design method, the present application greatly saves manpower and material resources, and has great application value in the actual design process.

Description

Technical Field

[0001] This invention relates to the field of HVAC system design optimization for public buildings, specifically an automatic optimization method for a pipeline network generation model for intelligent operation and maintenance in digital cities. Background Technology

[0002] The introduction of BIM technology has enabled my country's architectural design approach to shift from abstract to concrete, and from two-dimensional CAD planar representation to three-dimensional solid models. From a theoretical perspective, BIM technology is an effective tool for improving design efficiency. However, due to the lack of targeted technical specifications and applicable BIM standards in China, as well as limitations in the domestic software environment and technological capabilities, BIM-based HVAC forward design is inefficient, and BIM modeling software has become largely relegated to "model duplication" and clash detection tools. However, with the increasing integration of automation technology with the architectural field, automation-related research in the HVAC field is gradually becoming more diverse. This invention discloses an automatic optimization method for pipeline network generation models oriented towards intelligent operation and maintenance in digital cities. Addressing the problems of low design efficiency and low automation in HVAC BIM design of air supply systems, this invention aims to improve the automation level and design efficiency of air supply system BIM design. It combines standardized and modular design concepts with computational and technological advancements to propose an automatic optimization method for pipeline network generation models oriented towards intelligent operation and maintenance in digital cities. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide an automatic optimization method for pipeline network generation model for intelligent operation and maintenance of digital cities.

[0004] The technical solution of the present invention:

[0005] An automatic optimization method for pipeline network generation models for intelligent operation and maintenance in digital cities, comprising the following steps:

[0006] S1. Obtain the location of the air vent.

[0007] Based on the existing building information model, extract information such as the location of air vents, the width-to-height ratio of ducts, and the location of air conditioning rooms that have been arranged according to requirements. The obtained information is then used in the subsequent automatic generation and optimization process of the pipeline network.

[0008] S2. Add virtual air vents and divide the grid according to the location of the air vents.

[0009] S2.1, Virtual Trend Supplement

[0010] Before dividing the grid, the existing air outlets need to be ordered. Virtual air outlets are introduced without affecting the original layout of the air outlets, so that the number of air outlets in each column is equal. The virtual air outlets will not be connected in the subsequent duct network generation, but they provide a reference for the location of the subsequent duct network.

[0011] S2.2, Mesh Generation

[0012] The entire area containing virtual air vents is progressively divided according to the number of air vents. This results in every four air vents, including the virtual ones, forming a separate small grid space.

[0013] S3, small-area grids are processed separately for primary branch pipeline route planning.

[0014] Each small grid area's air vents are individually connected, with different connection methods applied based on the actual number of vents. All small grid areas defined in S2.2 are traversed, and the actual air vents within each area are connected using the corresponding connection methods for different numbers of vents, thus generating a complete primary branch pipeline.

[0015] S4. Secondary branch pipeline path planning based on A* algorithm

[0016] The path planning of the secondary branch pipeline is performed using the heuristic search algorithm A*, and the estimation function f(x) of the current node x is introduced:

[0017] f(x)=g(x)+h(x))(1

[0018] Where: f(x) represents the total distance estimate from the starting point to the ending point; g(x) represents the actual distance traveled from the starting point to the current node x; h(x) represents the minimum distance estimate from node x to the ending point.

[0019] S4.1 First, add the starting point to the closelist. Then, calculate the f(x) values ​​of the four adjacent cells (white cells in the diagram) that can move forward in the four directions (up, down, left, and right) from the starting point, and set the parent node of these cells as the starting point. Then, add each cell with the calculated f(x) value to the openlist. Then, select the cell with the smallest f(x) value from the list (named "current cell") as the intended direction of movement (the cell on the right in Figure b). At the same time, remove this cell from the openlist and add it to the closelist.

[0020] S4.2 Calculate the f(x) value of the white cells (not in the closelist) among the four cells above, below, left, and right of the current cell, and check if they are already in the openlist. If not, set the parent node of the given cell to the current cell and add it directly to the openlist; if it is already in the openlist, compare its current g value with its previous g value (g0). If g > g0, do nothing. If g <= g0, update the f, g, and h values ​​of the cell and set its parent node to the current cell. Then remove the current cell from the openlist and add it to the closelist. At this point, the current cell is called the new starting point.

[0021] S4.3 Repeat steps S4.1 and S4.2 until the destination is found. Once the destination is found, the pathfinding ends. When multiple minimum f(x) values ​​exist in list0, select any cell with the minimum f(x) value as a preliminary direction until the minimum f(x) is updated to be unique.

[0022] S4.4 After reaching the destination, backtrack the path, that is, search for its parent nodes in reverse order from the destination until the starting point is found. In this way, the final secondary branch path is obtained.

[0023] S5, Secondary Area Connectivity and Main Road Route Planning

[0024] Connectivity of secondary sub-regions can still be achieved using the A* pathfinding algorithm or extended line intersection path planning to generate main pipeline routes. The main pipeline ultimately connecting all secondary branch pipes is generated based on the relative positions of the air-conditioning sections and air-conditioning rooms.

[0025] S6. Duct hydraulic calculation and pipe size determination

[0026] After the pipeline route is generated, it is necessary to generate pipelines, connect pipelines, and perform hydraulic calculations for the ductwork based on the pipeline route. Since only the route of the air supply duct is currently determined and the total pressure head of the fan is not yet determined, the assumed velocity method is used to perform preliminary hydraulic calculations to determine the dimensions of each section of the air supply duct.

[0027] L=3600abV(2)

[0028] ab=L / 3600V (3)

[0029] Where: L - air volume of the duct, m 3 / h; V - air velocity inside the duct, m / s; a, b - net width and net height of the duct cross-section, m. The specific dimensions of the duct can be determined based on the previously obtained duct width-to-height ratio.

[0030] After determining the pipe dimensions, calculate the friction resistance ΔP.m calculate.

[0031] ΔP m =Δp m l (4)

[0032]

[0033]

[0034]

[0035]

[0036] In the formula: Δp m — Frictional resistance per unit length, Pa / m; l — Duct length, m; λ — Frictional resistance coefficient; ρ — Air density, kg / m³; de — Equivalent duct diameter, m; F — Net cross-sectional area of ​​duct, m² 2 P – Wetted perimeter of the duct cross-section, m; K – Absolute roughness of the duct inner wall, m; Re – Reynolds number; v – Kinematic viscosity, m 2 / s. Based on equations (4), (5), (6), (7), and (8), the friction loss (pressure drop) ΔP for each section of the duct can be obtained. m The pressure drop is recorded in the corresponding pipeline.

[0037] S7, Pipeline Data Loading

[0038] Using the hydraulic calculation methods described above, the dimensions ab, flow rate L, flow velocity V, and pressure drop ΔP of each duct section were determined. m After the user specifies the center elevation of the pipeline, the corresponding duct will be automatically generated based on the route of each pipeline segment. After the duct is generated, the above-mentioned water calculation results will be written into the pipeline.

[0039] S8, All pipes generated

[0040] Using the specified elevation and the pipe dimensions calculated in S6, the entire pipe network is generated in the building information model. At the same time, obstacles such as beams and columns are automatically avoided, the various pipe segments are connected, and different corresponding connectors are generated to complete the generation and connection of all pipes.

[0041] The beneficial effects of this invention are:

[0042] This invention, through a pipeline network generation model and automatic optimization method based on Building Information Modeling (BIM), can successfully complete the automatic layout and optimization of duct systems in the actual design process. Compared with traditional design methods, it greatly saves manpower and material resources and has significant application value in practical design processes. Attached Figure Description

[0043] Figure 1 A flowchart illustrating an automated optimization method for pipeline network generation models aimed at intelligent operation and maintenance in digital cities.

[0044] Figure 2 A supplementary flowchart is provided for the virtual trend indicator.

[0045] Figure 3 A schematic diagram of the decomposition of a small area into a grid.

[0046] Figure 4 This is a schematic diagram of the actual air outlet connection method within a small area grid.

[0047] Figure 5(a) shows the initial path planning diagram for secondary branch pipelines based on the A* algorithm.

[0048] Figure 5(b) shows the initial pathfinding diagram for secondary branch pipeline route planning based on the A* algorithm.

[0049] Figure 5(c) shows the first grid diagram for the path planning and routing of secondary branch pipelines based on the A* algorithm.

[0050] Figure 5(d) shows the obstacles encountered during the path planning of the secondary branch pipeline based on the A* algorithm.

[0051] Figure 5(e) shows the path planning and routing diagrams for secondary branch pipelines based on the A* algorithm in various directions.

[0052] Figure 5(f) shows the correct path found by the secondary branch pipeline path planning based on the A* algorithm.

[0053] Figure 5(g) shows the path planning and route finding of the secondary branch pipeline based on the A* algorithm before reaching the destination.

[0054] Figure 5(h) shows the path planning and destination of the secondary branch pipeline based on the A* algorithm.

[0055] Figure 5(i) shows the final path obtained by the secondary branch pipeline path planning based on the A* algorithm.

[0056] Figure 6 This is a schematic diagram illustrating the generation effect of a secondary branch pipeline.

[0057] Figure 7(a) is a plan view of the duct generation.

[0058] Figure 7(b) shows the 3D model of the generated duct. Detailed Implementation

[0059] The specific embodiments of the present invention will be described in detail below with reference to the invention description and accompanying drawings.

[0060] See attached document Figure 1The present invention provides an automatic optimization method for a pipeline network generation model for intelligent operation and maintenance in digital cities, comprising the following steps:

[0061] S1. Obtain the location of the air vent.

[0062] Based on the existing building information model, extract information such as the location of air vents, the width-to-height ratio of ducts, and the location of air conditioning rooms that have been arranged according to requirements. The obtained information is then used in the subsequent automatic generation and optimization process of the pipeline network.

[0063] S2. Add virtual air vents and divide the grid according to the location of the air vents.

[0064] S2.1, Virtual Trend Supplement

[0065] Before dividing the grid, the existing air outlets need to be ordered. Virtual air outlets are introduced without affecting the original layout of the air outlets, so that the number of air outlets in each column is equal. The virtual air outlets will not be connected in the subsequent duct network generation, but they provide a reference for the location of the subsequent duct network.

[0066] S2.2, Mesh Generation

[0067] The entire area containing virtual air vents is progressively divided according to the number of air vents. This results in every four air vents, including the virtual ones, forming a separate small grid space.

[0068] S3, small-area grids are processed separately for primary branch pipeline route planning.

[0069] Each small grid area's air vents are individually connected, with different connection methods applied based on the actual number of vents. All small grid areas defined in S2.2 are traversed, and the actual air vents within each area are connected using the corresponding connection methods for different numbers of vents, thus generating a complete primary branch pipeline.

[0070] S4. Secondary branch pipeline path planning based on A* algorithm

[0071] The path planning of the secondary branch pipeline is performed using the heuristic search algorithm A*, and the estimation function f(x) of the current node x is introduced:

[0072] f(x)=g(x)+h(x))(1

[0073] Where: f(x) represents the total distance estimate from the starting point to the ending point; g(x) represents the actual distance traveled from the starting point to the current node x; h(x) represents the minimum distance estimate from node x to the ending point.

[0074] S4.1 First, add the starting point to the closelist. Then, calculate the f(x) values ​​of the four adjacent cells (white cells in the diagram) that can move forward in the four directions (up, down, left, and right) from the starting point, and set the parent node of these cells as the starting point. Then, add each cell with the calculated f(x) value to the openlist. Then, select the cell with the smallest f(x) value from the list (named "current cell") as the intended direction of movement (the cell on the right in Figure b). At the same time, remove this cell from the openlist and add it to the closelist.

[0075] S4.2 Calculate the f(x) value of the white cells (not in the closelist) among the four cells above, below, left, and right of the current cell, and check if they are already in the openlist. If not, set the parent node of the given cell to the current cell and add it directly to the openlist; if it is already in the openlist, compare its current g value with its previous g value (g0). If g > g0, do nothing. If g <= g0, update the f, g, and h values ​​of the cell and set its parent node to the current cell. Then remove the current cell from the openlist and add it to the closelist. At this point, the current cell is called the new starting point.

[0076] S4.3 Repeat steps S4.1 and S4.2 until the destination is found. Once the destination is found, the pathfinding ends. When multiple minimum f(x) values ​​exist in list0, select any cell with the minimum f(x) value as a preliminary direction until the minimum f(x) is updated to be unique.

[0077] S4.4 After reaching the destination, backtrack the path, that is, search for its parent nodes in reverse order from the destination until the starting point is found. In this way, the final secondary branch path is obtained.

[0078] S5, Secondary Area Connectivity and Main Road Route Planning

[0079] Connectivity of secondary sub-regions can still be achieved using the A* pathfinding algorithm or extended line intersection path planning to generate main pipeline routes. The main pipeline ultimately connecting all secondary branch pipes is generated based on the relative positions of the air-conditioning sections and air-conditioning rooms.

[0080] S6. Duct hydraulic calculation and pipe size determination

[0081] After the pipeline route is generated, it is necessary to generate pipelines, connect pipelines, and perform hydraulic calculations for the ductwork based on the pipeline route. Since only the route of the air supply duct is currently determined and the total pressure head of the fan is not yet determined, the assumed velocity method is used to perform preliminary hydraulic calculations to determine the dimensions of each section of the air supply duct.

[0082] L=3600abV (2)

[0083] ab=L / 3600V (3)

[0084] Where: L - air volume of the duct, m 3 / h; V - air velocity inside the duct, m / s; a, b - net width and net height of the duct cross-section, m. The specific dimensions of the duct can be determined based on the previously obtained duct width-to-height ratio.

[0085] After determining the pipe dimensions, calculate the friction resistance ΔP. m calculate.

[0086] ΔP m =Δp m l (4)

[0087]

[0088]

[0089]

[0090]

[0091] In the formula: Δp m — Frictional resistance per unit length, Pa / m; l — Duct length, m; λ — Frictional resistance coefficient; ρ — Air density, kg / m³; de — Equivalent duct diameter, m; F — Net cross-sectional area of ​​duct, m² 2 P – Wetted perimeter of the duct cross-section, m; K – Absolute roughness of the duct inner wall, m; Re – Reynolds number; v – Kinematic viscosity, m 2 / s. Based on equations (4), (5), (6), (7), and (8), the friction loss (pressure drop) ΔP for each section of the duct can be obtained. m The pressure drop is recorded in the corresponding pipeline.

[0092] S7, Pipeline Data Loading

[0093] Using the hydraulic calculation methods described above, the dimensions ab, flow rate L, flow velocity V, and pressure drop ΔP of each duct section were determined. m After the user specifies the center elevation of the pipeline, the corresponding duct will be automatically generated based on the route of each pipeline segment. After the duct is generated, the above-mentioned water calculation results will be written into the pipeline.

[0094] S8, All pipes generated

[0095] Using the specified elevation and the pipe dimensions calculated in S6, the entire pipe network is generated in the building information model. At the same time, obstacles such as beams and columns are automatically avoided, the various pipe segments are connected, and different corresponding connectors are generated to complete the generation and connection of all pipes.

Claims

1. An automatic optimization method for pipeline network generation models for intelligent operation and maintenance in digital cities, characterized in that, The steps are as follows: S1. Obtain the location of the air vent. Based on the building information model, extract the location information of the air outlets, the width-to-height ratio information of the ducts, and the location information of the air conditioning room that have been arranged according to the requirements. The obtained information is used in the subsequent automatic generation and optimization process of the pipeline network. S2. Add virtual air vents and divide the grid according to the location of the air vents. S2.1, Virtual Trend Supplement Before dividing the grid, the existing air outlets need to be ordered. Virtual air outlets are introduced without affecting the original layout of the air outlets, so that the number of air outlets in each column is equal. The virtual air outlets will not be connected in the subsequent duct network generation, but they provide a reference for the location of the subsequent duct network. S2.2, Mesh Generation The entire area containing virtual air vents is gradually divided according to the number of air vents, so that every four air vents, including the virtual air vents, become a separate small area grid space; S3, small-area grids are processed separately for primary branch pipeline route planning. Each vent in the divided small grid area is connected individually, and different connection methods are used depending on the actual number of vents. Traverse all the small area grids divided in S2.2, and connect the actual air outlets in each area through the connection method of different numbers of air outlets to generate a complete primary branch pipeline; S4. Secondary branch pipeline path planning based on A* algorithm The path planning of the secondary branch pipeline is performed using the heuristic search algorithm A*, and the estimation function f(x) of the current node x is introduced: f(x)=g(x)+h(x))(1 Where: f(x) represents the total distance estimate from the starting point to the ending point; g(x) represents the actual distance traveled from the starting point to the current node x; h(x) represents the minimum distance estimate from node x to the ending point; S5, Secondary Area Connectivity and Main Road Route Planning The connectivity of secondary sub-regions still uses the A* pathfinding algorithm or the method of extending line intersection path planning to generate the main pipeline route; the main pipeline that ultimately connects all secondary branch pipelines is generated based on the relative positions of the air-conditioning sections and air-conditioning rooms; S6. Duct hydraulic calculation and pipe size determination After the pipeline route is generated, it is necessary to generate pipelines, connect pipelines, and perform hydraulic calculations for the air ducts based on the pipeline route. Since only the route of the air supply pipeline is currently determined and the total pressure head of the fan is not yet determined, the assumed velocity method is used to perform preliminary hydraulic calculations to determine the size of each section of the air supply pipeline. After determining the pipe dimensions, calculate the friction resistance ΔP. m calculate; S7, Pipeline Data Loading Using the hydraulic calculation methods described above, the dimensions ab, flow rate L, flow velocity V, and pressure drop ΔP of each duct section were determined. m After the user specifies the center elevation of the pipeline, the corresponding duct will be automatically generated according to the route of each pipeline segment. After the duct is generated, the above-mentioned water calculation results will be written into the pipeline. S8, All pipes generated Using the specified elevation and the pipe dimensions calculated in S6, the entire pipe network is generated in the building information model. At the same time, existing beam and column obstacles are automatically avoided, the various pipe segments are connected, and different corresponding connectors are generated to complete the generation and connection of all pipes.

2. The automatic optimization method for pipeline network generation model for intelligent operation and maintenance of digital cities as described in claim 1, characterized in that, The S4 method for secondary branch pipeline path planning based on the A* algorithm is described in the following steps: S4.1 First, add the starting point to the closelist. Then, calculate the f(x) values ​​of the four adjacent cells in the four directions (up, down, left, right) that can move forward from the starting point, and set the parent node of these cells as the starting point. Then, add each cell with the calculated f(x) value to the openlist. Then, select the cell with the smallest f(x) value from the list (name it "current cell") as the intended direction of movement. At the same time, remove this cell from the openlist and add it to the closelist. S4.2 Calculate the f(x) value of the white cells that are not in the closelist among the four cells above, below, left and right of the current cell, and check whether they are already in the openlist; If not, set the parent node of the given cell as the current cell and add it directly to the openlist list; If it is already in the openlist, compare its current g value with the previous g value (g0). If g > g0, do nothing. If g <= g0, update the f, g, and h values ​​of the cell and set the parent node of the cell to the current cell. Then remove the current cell from the openlist and add it to the closelist. At this point, the current cell is called the new starting point. S4.3 Repeat steps S4.1 and S4.2 until the destination is found. Once the destination is found, the pathfinding ends. When multiple minimum f(x) values ​​are found in list0, select any cell with the minimum f(x) value as a preliminary direction until the minimum f(x) is updated to be unique. S4.4 After reaching the destination, backtrack the path, that is, search for its parent node in reverse order from the destination until the starting point is found; in this way, the final secondary branch path is obtained.

3. The automatic optimization method for pipeline network generation model for intelligent operation and maintenance of digital cities as described in claim 1 or 2, characterized in that, In step S6, the specific steps for determining the dimensions of each section of the air supply duct are as follows: L=3600abV (2) ab=L / 3600V (3) Where: L - air volume of the duct, m 3 / h; V - air velocity inside the duct, m / s; a, b - net width and net height of the duct cross-section, m; the specific dimensions of the duct can be determined based on the width-to-height ratio obtained earlier.

4. The automatic optimization method for pipeline network generation model for intelligent operation and maintenance of digital cities as described in claim 3, characterized in that, In step S6, after determining the pipe dimensions, the friction resistance is calculated. The specific steps are as follows: △P m =△p m l (4) In the formula: △p m — Frictional resistance per unit length, Pa / m; l — Duct length, m; λ — Frictional resistance coefficient; ρ — Air density, kg / m³; de — Equivalent duct diameter, m; F — Net cross-sectional area of ​​duct, m² 2 P—Wetted perimeter of the duct section, m; K—Absolute roughness of the duct inner wall, m; Re—Reynolds number; ν—Kinematic viscosity, m 2 / s; According to equations (4), (5), (6), (7), and (8), the friction loss (pressure drop) △P of each section of the duct can be obtained. m The pressure drop is recorded in the corresponding pipeline.

Images