Runner plate assembly layout and pipeline optimization method

Abstract

The invention discloses a runner plate assembly layout and pipeline optimization method, and relates to the field of new energy automobiles. The invention provides a runner plate assembly layout and pipeline optimization method based on optimization modeling and an intelligent search algorithm. A decision framework based on Laplacian matrix spectrum characteristics is adopted, and a center layout generation algorithm or a force-oriented layout generation algorithm is adaptively selected. According to the method, a detailed mathematical model is established to define a layout space, components, interfaces and a connection relationship, a runner plate design problem is converted into a constraint optimization problem, and an efficient optimization algorithm is used for solving, so that a layout scheme with minimum flow resistance is automatically generated.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicles, and in particular to a method for optimizing the layout and pipeline of flow channel plate components. Background Technology

[0002] The integrated thermal management system for new energy vehicles is crucial for maintaining the power battery pack, electric motor, and electrical controller at optimal temperatures, which is key to delaying battery capacity degradation and ensuring driving range. In this core technology, the flow channel plate, as an integrated carrier, is responsible for arranging and connecting cooling system components (such as water pumps and valve bodies). The design of the flow channel plate requires simultaneous consideration of multiple factors, including the plate's shape, size, maximum allowable number of layers, precise placement of components, flow channel configuration characteristics, and fluid flow characteristics.

[0003] Current flow channel plate designs primarily rely on the experience of designers and manual adjustments. Designers need to manually experiment with the layout of components and plan the flow channel paths connecting the interfaces of each component, based on the system configuration schematic and in a computer-aided design (CAD) environment.

[0004] Existing methods are inefficient and time-consuming in achieving the desired results. Flow channel plate design is a massive multivariable coupled constraint optimization problem. Designers must spend a significant amount of time manually trying and adjusting to meet geometric constraints such as component non-interference and non-crossing of flow channels. This unsystematic iterative process unnecessarily lengthens the design cycle.

[0005] Existing methods struggle to guarantee globally optimal design performance. Although the design objective is to minimize the total flow resistance of the flow path, the lack of efficient, systematic optimization search tools prevents manual design from effectively exploring the large solution space. The resulting solution is often merely a "feasible solution," rather than a globally optimal or near-optimal solution with the best fluid performance, thus limiting the product's energy consumption and efficiency.

[0006] Therefore, those skilled in the art are dedicated to developing a method for optimizing the layout and pipeline of flow channel plates. Based on the Laplace matrix spectral characteristic decision-making, flow channel plate design can be transformed into a constrained optimization problem, which can be automatically solved using an intelligent search algorithm, achieving high efficiency, high performance, and high reliability in the design process. Summary of the Invention

[0007] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is the component layout and pipeline optimization of the flow channel plate, so as to achieve high efficiency, high performance and high reliability in the design process.

[0008] To achieve the above objectives, the present invention provides a method for optimizing the layout and pipeline of flow channel plate components, comprising the following steps: Step 1: Based on the component and connection data, establish overall and local indicators; rank the importance of components and establish a probabilistic model of component importance; Step 2: Using a decision framework based on the spectral characteristics of the Laplacian matrix, adaptively select either the center layout generation algorithm or the force-oriented layout generation algorithm; based on the probability model, select the most important components in sequence. Step 3: Perform collision detection on the initial layout generated by layout optimization to ensure that the collision constraints are met, and generate a new layout that meets the constraints; compare the new layout with the historical search layout. If it is locally consistent with the historical search layout, return to step 2 and regenerate according to the probability model; otherwise, proceed to step 4. Step 4: Use the optimal Chebyshev distance A The algorithm was optimized.

[0009] Furthermore, the overall indicators include the statistical percentage of fixed components and the size difference of components.

[0010] Furthermore, the local metrics include the size of the component, the number of interfaces, and the degree of component connectivity.

[0011] Furthermore, in step 2, a Laplace matrix is ​​constructed to describe the topological information of the component relationship network.

[0012] Furthermore, in step 2, the decision indicators include topological heterogeneity and layout rigidity.

[0013] Further, in step 2, the topological heterogeneity and layout rigidity are calculated, and the uniformity of component connections and the constraint strength of fixed components are quantified.

[0014] Furthermore, step 4 includes the following steps: Step 4.1, A based on Chebyshev distance Flow channel planning algorithm; Step 4.2: Flow channel merging strategy to optimize the pipeline system; Step 4.3: Flow channel priority sorting and layer number constraints.

[0015] Furthermore, in step 4.1, the flow channel plate is divided into a grid map, and the node cost is estimated using Chebyshev distance to explore and find the path with the minimum flow resistance.

[0016] Furthermore, in step 4.2, for flow channels with the same common interface, a new interface is opened at the location with the minimum flow resistance through calculation and analysis, and the flow channels are merged to reduce redundant pipelines.

[0017] Furthermore, in step 4.3, the flow channels are sorted and prioritized according to the number and type of constraints they bear.

[0018] Addressing the component layout problem, this invention employs a decision framework based on the spectral characteristics of the Laplace matrix for adaptively selecting either a center-based layout generation algorithm or a force-oriented layout generation algorithm. The invention comprises: 1. A Laplace matrix L: Constructing an L matrix to describe the topological structure information of the component relationship network. 2. Decision indices: Calculating topological heterogeneity and layout rigidity to quantify component connection uniformity and the strength of fixed component constraints.

[0019] This invention addresses pipeline planning and hierarchy issues. 1. It employs an A-type method based on Chebyshev distance. 1. Flow channel planning algorithm. 2. Propose a flow channel merging strategy to optimize the piping system. 3. Introduce strict flow channel priority ranking and layer constraints (such as layer constraints for water pumps and check valves). This invention 1. A 1. Algorithm and Chebyshev Distance: The flow channel plate is divided into a grid map, and the Chebyshev distance is used to estimate the node cost in order to explore and find the path with the minimum total cost (i.e., minimum flow resistance). 2. Flow Channel Merging: For flow channels with the same common interface, the location with the minimum flow resistance is found through calculation and analysis, and a new interface is opened to merge the flow channels, reducing redundant pipelines. 3. Priority / Constraints: Based on the number and type of constraints carried by the flow channels (e.g., pump outlets take priority over inlets), the flow channels are sorted and prioritized to ensure the effectiveness and feasibility of the design.

[0020] This invention provides a method for optimizing the layout and pipeline of flow channel plate components based on optimization modeling and intelligent search algorithms. By establishing a detailed mathematical model to define the layout space, components, interfaces, and connection relationships, this invention transforms the flow channel plate design problem into a constrained optimization problem, and uses an efficient optimization algorithm to solve it, thereby automatically generating a layout scheme with minimum flow resistance.

[0021] I. Establishment of the flow channel model

[0022] (1) Symbol description: All components set Components shape Components A collection of all interfaces, for each component Includes several interfaces Components The One interface, The collection of all pipeline connections Components The Interfaces and components The Interface connection,

[0023] (2) Problem parameters: Representation Component The The relative coordinates of the component center of each interface Representation Component The appearance can be represented by a set of coordinates of the component's shape. Components interface radius Pipeline connection radius Minimum clearance between heat pipes and other components or pipes Width and height of the flow channel plate (3) Decision variables: Representation Component The absolute coordinates of the geometric center on the flow channel plate, Representation Component interface Absolute coordinates on the flow channel plate, Representation Component The Interfaces and components The The route connected by each interface

[0024] (4) Optimization objective:

[0025] in Indicates from component The Interfaces and components The The flow resistance on the path of each interface.

[0026] (5) Pipeline constraints: ① Panel size constraints: All components must be laid out on the panel; ② Fixed component constraints: Some components are fixed in specific positions on the panel; ③ Non-interference of components: Each component occupies a specific volume space. In principle, these components must meet the non-interference condition in physical layout, that is, they cannot overlap in space, so as to ensure that all components can be arranged on the flow channel plate.

[0027]

[0028] (Some components, due to their specific space-saving installation methods (such as the WCC which can be rotated to be outside the panel), do not need to consider interference between their shape and other components.)

[0029] ④ Irreversibility of flow channels: Any newly planned flow channel must not cross other component interfaces other than its own source interface and target interface, nor may it cross other planned flow channels.

[0030] (Except for the starting point and the end point)

[0031] ⑤ Flow channel plate layer constraint: The maximum number of flow channel plates limits the vertical space that the flow channel design can span. During the process of searching for feasible connecting flow channels layer by layer, it is essential to strictly ensure that the number of layers containing the flow channel does not exceed the maximum number of layers of the flow channel plate to guarantee the effectiveness and feasibility of the design.

[0032] ⑥ Thermal Pipe Spacing Constraints: To ensure the stability and independence of fluid temperatures within pipes with different temperature properties, thermal pipe design must adhere to strict spacing requirements. Thermal pipes must maintain a certain safe distance from all surrounding flow channels and component interfaces. This distance is determined based on heat conduction theory, fluid dynamics principles, and practical engineering experience.

[0033] ⑦ Pump flow channel layer constraint: The number of flow channels at the pump outlet must be higher than or equal to the number of flow channels at the pump inlet.

[0034] ⑧ Constraint on the number of flow channels in a one-way valve: The number of flow channels at the outlet of a one-way valve shall not be less than the number of flow channels at the inlet of the one-way valve.

[0035] II. Flow channel plate layout and pipeline optimization algorithm design

[0036] (1) Overall Algorithm Framework Design

[0037] like Figure 1As shown, the layout and pipeline optimization problem is a strongly NP-hard problem. The main challenge lies in the coupling effect between component layout and pipeline under complex constraints. The layout affects the specific route of the pipeline, which in turn affects the flow resistance. Therefore, the impact of layout on flow resistance is indirect, and a mapping function from layout to flow resistance cannot be directly provided. Therefore, in the algorithm implementation, layout generation and pipeline path planning are decomposed into two interrelated problems for solution. The specific steps are as follows: (1) Based on the component and connection data, establish overall and local indicators. Overall indicators include: the statistical percentage of fixed components, the size difference of components, etc. Local indicators include: the size of each component, the number of interfaces, the degree of component connection, ranking the importance of components, and establishing a probabilistic model of component importance.

[0038] (2) Determine the layout optimization algorithm based on the overall indicators. If the components have large differences and there are many fixed components, the center layout generation algorithm is adopted. If the components have small differences, the force-directed layout generation algorithm is adopted. Based on the probability model, select the most important component in turn.

[0039] (3) Perform collision detection on the initial layout generated by layout optimization to ensure that the collision constraint conditions are met, and generate a new layout that meets the constraints. Compare the new layout with the historical search layout. If it is locally consistent with the historical search layout, return to (2) and regenerate according to the probability model. Otherwise, proceed to (4).

[0040] (4) Based on the layout in (3), adopt the improved A The algorithm can be optimized. The optimal Chebyshev distance method is adopted. algorithm.

[0041] Chebyshev distance: ,

[0042] (2) Algorithm selection decision framework based on L matrix features

[0043] Traditional placement algorithms rely too heavily on the geometric or functional properties of specific components (such as pumps and multi-way valves) for feature extraction, resulting in insufficient robustness and generalization performance when component configurations and connectivity (topology) change. Therefore, it is necessary to delve deeper into the common global features of component relationship networks to guide the selection of placement strategies.

[0044] 1. Construction of the Laplace matrix L

[0045] Introducing L to describe the graph The topological structure information, where node V represents a component and edge E represents a connection, is calculated using the following formula:

[0046] in: Adjacency matrix A:

[0047] Degree matrix D: Diagonal matrix Representation Component The total number of connections (degrees).

[0048]

[0049] 2. Two key decision indicators constructed based on the spectral features of L

[0050] (1) Topological heterogeneity

[0051] Topological heterogeneity The uniformity of component connectivity distribution is quantified by calculating the standard deviation of the non-zero eigenvalues ​​of L. A smaller size implies a homogeneous graph structure and uniform force, making it suitable for force-oriented layouts. A larger value indicates the presence of highly connected hub components, resulting in a heterogeneous graph structure suitable for a central layout. The calculation process is as follows: ① Perform eigenvalue decomposition on the real symmetric matrix L:

[0052] Obtain N eigenvalues , ,…, ,in The corresponding constant eigenvector.

[0053] ② Extract the set of all non-zero eigenvalues :

[0054] Let the number of non-zero eigenvalues ​​be... .

[0055] ③ The mean of non-zero eigenvalues :

[0056] ④ Define topological heterogeneity The standard deviation of non-zero eigenvalues:

[0057] (2) Layout rigidity

[0058] rigid layout Used to measure a set of fixed components FThe strength and consistency of constraints on the entire layout topology space. It is based on L. Fiedler vector Quantify it. Map all components in the graph onto a one-dimensional topological coordinate axis, representing the relative positions of the graph under optimal algebraic partitioning. If a large number of fixed components are in... value set on This indicates that these fixed components form strong constraints on the topology, and the layout has high rigidity. The calculation process is as follows: ① Define the index set of fixed components ,set up The number of fixed components.

[0059] ②Fixed components Vector value set :

[0060] ③Fixed components mean of values :

[0061] ④ Define layout rigidity For fixed components Variance on a vector:

[0062] 3. Decision-making logic

[0063] like Figure 2 As shown, the algorithm adaptively selects an optimization strategy based on the combined results of these two metrics: (1) If L exhibits high rigidity And high degree of heterogeneity ( (Large): This indicates that the topology of the flow channel plate is extremely non-uniform, and a few fixed components occupy a key and dominant position in the topology, strongly restricting the degrees of freedom of other components. In this case, layout optimization prioritizes allocation based on these fixed points, therefore a center layout generation algorithm is adopted.

[0064] (2) If L exhibits low rigidity And low heterogeneity This indicates that the component connectivity distribution is relatively uniform, and the constraints on fixed components are dispersed. In this case, the natural trend of the layout becomes more important. A force-directed layout generation algorithm is used to iteratively calculate the interaction forces between components, discovering natural clustering and optimal geometric embedding under a uniform graph structure.

[0065] Through this pre-decision mechanism based on L-spectrum features, the algorithm can adaptively select the most suitable optimization strategy according to the common global topological features of the flow channel plate, thereby significantly enhancing the algorithm's generalization ability and scheduling stability.

[0066] (3) Geometric center layout optimization algorithm

[0067] A rule-based layout algorithm is proposed. Each non-fixed component is assigned an importance score, calculated by weighting the number of flow channels on that component with its area. The importance scores of all non-fixed components are normalized, and these normalized scores are used as the probability basis for selecting each component. During the layout process, the algorithm selects components one by one based on these selection probabilities until the final layout sequence of all non-fixed components is determined.

[0068] In the layout sequence, each component is assigned a position one by one. First, all components connected to the component to be laid out and whose positions are already determined are identified and located. Then, the average center coordinates of these located connected components are calculated and used as a preset estimate of the current component's center position. Next, it is verified whether the current component will overlap or collide with its neighboring components when it is at this preset center position. If no collision occurs, this position is confirmed as the final position of the current component. Conversely, if a collision occurs, the current component needs to move step by step in the opposite direction of the collision until it reaches a position that does not collide with any surrounding components; this position is determined as the final layout position of the component.

[0069] After determining the position of the current component, the position of the next component in the layout sequence is calculated until the positions of all components are successfully determined. If, during this series of calculations, a suitable position cannot be found for a certain component, it indicates that the current layout attempt has failed. In this case, the layout procedure needs to be re-executed until all components can be properly laid out, thus achieving the condition of successful layout, which can be considered the final layout scheme of the flow channel board.

[0070] (4) Force-oriented layout optimization and improvement algorithm

[0071] The algorithm can work through undirected graphs The model is used to represent the graph, and the force-directed layout algorithm is applied to solve the layout problem. In undirected graphs... G In the diagram, each component corresponds to a set of nodes. V The flow channel connections between components are abstracted as a set of edges connecting these vertices. E .

[0072] The core of the force-directed algorithm lies in calculating and balancing the attractive and repulsive forces acting on each node. By iteratively adjusting the positions of the nodes, it ultimately achieves a balance between attractive and repulsive forces in the entire graph layout. Specifically, the strength of the attractive force between nodes is proportional to whether they are directly connected by an edge, i.e., whether there is a direct connection between the components. The strength of the repulsive force between nodes is proportional to the size of the minimum circumcircle radius of the component, reflecting the repulsive effect of the space occupied by the component in the layout space on other components.

[0073] Repulsion calculation:

[0074] in, It is a repulsive force. It is the repulsive constant. It is the distance between the centers of the two components. It is the minimum distance that the centers of two components can meet. At this point, the components will collide. The direction of the repulsive force is from the node towards the node moving away from it.

[0075] Gravity calculation:

[0076] in, It is a repulsive force. It is the gravitational constant. It is the distance between the centers of the two components. It is the expected optimal distance between the centers of the two components.

[0077] After obtaining the initial layout result using the force-directed placement algorithm, collision detection between components is required. If no collisions occur, the layout result can be considered the final layout result of the flow channel plate. If collisions are detected, an adjustment method consistent with the collision elimination strategy in the hybrid fixed-variable component placement algorithm must be adopted. Specifically, for components that have collided, their positions will be fine-tuned step by step in the opposite direction of the collision until all collisions are eliminated. When there are no more collisions between any two components in the system, the layout state at this point is considered the final layout result.

[0078] (5) A based on Chebyshev distance Flow channel planning algorithm

[0079] After the layout algorithm generates results, the planning order of the flow channels is sorted according to the component positions. This process is accomplished by setting priorities for the flow channels, generating a planning order from high to low priority. The specific sorting logic and priority settings are as follows: First, for flow channels involved in components with layer constraints, the outlet flow channel should be given priority over the inlet flow channel. This is because the outlet flow channel usually needs to be located at the highest layer of the flow channel plate, thus relaxing the constraints of the inlet flow channel. Second, regarding the number of layer constraints that need to be satisfied for the component flow channel, the more constraints a flow channel carries, the higher its priority, ensuring that flow channels with strict constraints are processed first. Furthermore, for components with component flow channel layer constraints, different types of flow channels also need to be assigned different priorities. Specifically, the pump flow channel has a higher priority than the one-way valve flow channel. Finally, for the remaining flow channels not subject to specific constraints, priority can be set according to the Euclidean distance between their source and target interfaces. The shorter the distance, the higher the priority of the flow channel.

[0080] The flow path planning problem can be abstracted as finding the optimal shortest path in a map. To solve this problem, we use A... The algorithm performs flow path planning. A The algorithm is a heuristic pathfinding algorithm. In its implementation, the flow channel plate is divided into a grid map according to a specific ratio, and the grid cells corresponding to the component interfaces and the planned flow channel sections are set as obstacles. Chebyshev distance is used to estimate the cost from the node to the initial point and the target point. By exploration, a path with the minimum total cost from the starting node to the target node is found, and this path is used as the final planned flow channel scheme.

[0081] To optimize the hydrodynamic performance of pipeline systems, particularly to reduce flow resistance, a channel merging strategy is proposed. Channels with the same source or target interface are considered potential merging targets. If the corresponding mergeable channel has already been planned, the specific merging location is further determined. Specifically, on the planned mergeable channels, the location with the minimum flow resistance from the non-common interface is found through calculation and analysis. Determining this location requires comprehensive consideration of factors such as the impassability of all channels and the shape of the merged channel. After determining the optimal opening location, a new interface is opened at this location in the pipeline to replace the original common interface and complete the connection of the current channel. This strategy effectively reduces redundant channels in the pipeline system, lowers flow resistance, improves fluid transmission efficiency, and enhances the overall performance of the pipeline system.

[0082] Compared with the prior art, the present invention has the following obvious substantive features and significant advantages: 1. The algorithm of this invention replaces manual iteration, which greatly shortens the design cycle.

[0083] 2. This invention enhances the algorithm's generalization ability and scheduling stability in the face of changes in component configuration and connection relationships.

[0084] 3. This invention achieves an interference-free layout of all components within a limited space.

[0085] 4. This invention successfully connects all flow channels and minimizes the total flow resistance of all flow channels.

[0086] 5. This invention effectively reduces redundant flow channels in the pipeline system, lowers flow resistance, and improves fluid transmission efficiency and the overall performance of the pipeline system.

[0087] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0088] Figure 1 This is a diagram illustrating the overall algorithm framework of a preferred embodiment of the present invention; Figure 2 This is a layout measurement analysis diagram of a preferred embodiment of the present invention; Figure 3 This is a layout scheme of a preferred embodiment of the present invention; Figure 4 This is another preferred embodiment of the layout scheme of the present invention. Detailed Implementation

[0089] The following description, with reference to the accompanying drawings, illustrates several preferred embodiments of the present invention to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0090] In the accompanying drawings, components with the same structure are indicated by the same numerical designation, and components with similar structures or functions are indicated by similar numerical designations. The dimensions and thicknesses of each component shown in the drawings are arbitrary, and the present invention does not limit the dimensions and thicknesses of each component. To make the illustrations clearer, the thickness of some components has been appropriately exaggerated in the drawings.

[0091] Example 1

[0092] enter: Panel size: [300, 300] Component information: { "Component symbol": "P", Number of interfaces: 1, Component area: 314 Component Category: 5000 Interface radius: 10, "interface": [ {"Interface ID": 1,"Interface Relative Position Coordinates":[0,0], "Interface Connection Angle Range": [0,360]} ] } { "Component Symbol": "SOV", Number of interfaces: 1, Component area: 1257 Component Category: 5008 Interface radius: 10, "interface": [ {"Interface ID": 1,"Interface Relative Position Coordinates":[0,0], "Interface Connection Angle Range": [0,360]} ], "Circular shape": [ {"Outline Number": 1, "Outline Radius": 20} ] } { "Component Symbol": "EXV", Number of interfaces: 1, Component area: 1257 Component Category: 5006 Interface radius: 10, "interface": [ {"Interface ID": 1,"Interface Relative Position Coordinates":[0,0], "Interface Connection Angle Range": [0,360]} ], "Circular shape": [ {"Outline Number": 1, "Outline Radius": 20} ] } { "Component symbol": "1WV", Number of interfaces: 2, Component area: 2551 "Interface radius": 8, "interface": [ {"Interface ID": 1,"Interface Relative Position Coordinates":[0,0], "Interface Connection Angle Range": [0,360]} ], "Circular shape": [ {"Outline Number": 1, "Outline Radius": 28.5} ] } { "Component Symbol": "TC", Number of interfaces: 1, Component area: 98 Component Category: 5010 Interface radius: 15 "interface": [ {"Interface ID": 1,"Interface Relative Position Coordinates":[0,0], "Interface Connection Angle Range": [0,360]} ] } Connection information:

[0093] Decision: Topological heterogeneity: 0.8725, Layout rigidity: 0.0, Number of fixed components: 0, Number of active components: 24. This problem has low rigidity and low heterogeneity, with fixed components accounting for 0%. The decision uses a force-directed layout generation algorithm. The results are as follows: Figure 3 As shown.

[0094] Example 2

[0095] enter: Panel size: [380, 260] Component information: { Component symbol: "5WV", Number of interfaces: 5, Component area: 8100 "Interface radius": 8, "interface": [ {"Interface ID": 1,"Interface relative position coordinates":[0,-29.5], "Interface connection angle range": [0,360]}, {"Interface ID": 2,"Interface Relative Position Coordinates":[0,0], "Interface Connection Angle Range": [0,360]}, {"Interface ID": 3,"Interface relative position coordinates":[0,29.5], "Interface connection angle range": [0,360]}, {"Interface ID": 4,"Interface Relative Position Coordinates":[-29.5,0], "Interface Connection Angle Range": [0,360]}, {"Interface ID": 5,"Interface Relative Position Coordinates":[29.5,0], "Interface Connection Angle Range": [0,360]} ], "Polygonal shape": [ {"Outline Number": 1, "Relative Coordinates of Outline Position": "(-45, -45), (45, -45), (45, 45),(-45, 45)"} ] } { "Component Symbol": "ECP", Number of interfaces: 2, Component area: 9503 "Interface radius": 8, "interface": [ {"Interface ID": 1,"Interface Relative Position Coordinates":[0,0], "Interface Connection Angle Range": [0,360],"Interface Attributes":"Entry Point"}, {"Interface ID": 2,"Interface Relative Position Coordinates":[0,-34.5], "Interface Connection Angle Range":[120,122],"Interface Attributes":"Exit", "inner_ring_radius": 34.5} ], "Circular shape": [ {"Outline Number": 1, "Outline Radius": 34.5}, {"Outline Number": 2, "Outline Radius": 55} ] } { "Component symbol": "EC", Number of interfaces: 2, Component area: 17 "Interface radius": 8, "interface": [ {"Interface ID": 1,"Interface relative position coordinates":[0,0], "Interface connection angle range": [0,360]}, {"Interface ID": 2,"Interface Relative Position Coordinates":[44,0], "Interface Connection Angle Range": [0,360]} ] } { "Component Symbol": "EW", Number of interfaces: 2, Component area: 17 "Interface radius": 8, "interface": [ {"Interface ID": 1,"Interface relative position coordinates":[0,0], "Interface connection angle range": [0,360]}, {"Interface ID": 2,"Interface Relative Position Coordinates":[0,137], "Interface Connection Angle Range": [0,360]} ] } { "Component symbol": "P", Number of interfaces: 1, Component area: 200 "Interface radius": 8, "interface": [ {"Interface ID": 1,"Interface Relative Position Coordinates":[0,0], "Interface Connection Angle Range": [0,360]} ] } { "Component symbol": "1WV", Number of interfaces: 2, Component area: 2551 "Interface radius": 8, "interface": [ {"Interface ID": 1,"Interface Relative Position Coordinates":[0,0], "Interface Connection Angle Range": [0,360]} ], "Circular shape": [ {"Outline Number": 1, "Outline Radius": 28.5} ] } Connection information:

[0096] Decision: Topological heterogeneity: 1.2481, Layout rigidity: 0.0428, Number of fixed components: 10, Number of active components: 6. This problem has high rigidity and high heterogeneity, with fixed components accounting for 62.5%. The decision uses a center-based layout generation algorithm. The results are as follows: Figure 4 As shown.

[0097] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for optimizing the layout and pipeline of a flow channel plate assembly, characterized in that, Includes the following steps: Step 1: Based on the component and connection data, establish overall and local indicators; rank the importance of components and establish a probabilistic model of component importance; Step 2: Adopt a decision framework based on the spectral characteristics of the Laplacian matrix to adaptively select either the center layout generation algorithm or the force-oriented layout generation algorithm; Based on the probability model, select the most important components in sequence; Step 3: Perform collision detection on the initial layout generated by layout optimization to ensure that the collision constraints are met, and generate a new layout that meets the constraints; compare the new layout with the historical search layout. If it is locally consistent with the historical search layout, return to step 2 and regenerate according to the probability model; otherwise, proceed to step 4. Step 4: Use the optimal Chebyshev distance A The algorithm was optimized.

2. The method for optimizing the layout and pipeline of the flow channel plate assembly as described in claim 1, characterized in that, The overall metrics include the statistical percentage of fixed components and the size differences of components.

3. The method for optimizing the layout and pipeline of the flow channel plate assembly as described in claim 1, characterized in that, The local metrics include component size, number of interfaces, and degree of component connectivity.

4. The method for optimizing the layout and pipeline of the flow channel plate assembly as described in claim 1, characterized in that, Step 2 involves constructing a Laplace matrix to describe the topological structure information of the component relationship network.

5. The method for optimizing the layout and pipeline of the flow channel plate assembly as described in claim 1, characterized in that, In step 2, the decision indicators include topological heterogeneity and layout rigidity.

6. The method for optimizing the layout and pipeline of the flow channel plate assembly as described in claim 5, characterized in that, Step 2 involves calculating topological heterogeneity and layout rigidity, quantifying component connection uniformity, and fixing component constraint strength.

7. The method for optimizing the layout and pipeline of the flow channel plate assembly as described in claim 1, characterized in that, Step 4 includes the following steps: Step 4.1, A based on Chebyshev distance Flow channel planning algorithm; Step 4.2: Flow channel merging strategy to optimize the pipeline system; Step 4.3: Flow channel priority sorting and layer number constraints.

8. The method for optimizing the layout and pipeline of the flow channel plate assembly as described in claim 7, characterized in that, In step 4.1, the flow channel plate is divided into a grid map, and the node cost is estimated using Chebyshev distance to explore and find the path with the minimum flow resistance.

9. The method for optimizing the layout and pipeline of the flow channel plate assembly as described in claim 7, characterized in that, In step 4.2, for flow channels with the same common interface, a new interface is opened at the location with the minimum flow resistance through calculation and analysis, and the flow channels are merged to reduce redundant pipelines.

10. The method for optimizing the layout and pipeline of the flow channel plate assembly as described in claim 1, characterized in that, In step 4.3, the flow channels are sorted and prioritized according to the number and type of constraints they bear.

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