Multi-objective intelligent optimization method and system for piping support hanger

Abstract

The application provides a pipeline system support hanger multi-target intelligent optimization method and system, and the method comprises the following steps: S1, a finite element mechanics analysis model is established, the mechanical response of the pipeline system under multiple design conditions is calculated, and support hanger load data and mechanical evaluation data are obtained; S2, a support hanger load structured data file and a key performance evaluation structured data file are constructed; S3, a standardized data interface file is constructed; S4, a hybrid coding structure composed of real number coding and integer coding is constructed as an optimization variable; S5, a target function and an engineering constraint condition are defined; S6, a multi-target genetic algorithm is used to iteratively optimize the optimization variable, automatically obtain the mechanical evaluation data, automatically calculate the target function, and automatically evaluate the engineering constraint condition; and S7, the population updating, simulation calculation and iterative evolution process are repeatedly executed, and an optimal support hanger arrangement scheme is output. The application realizes full closed-loop intelligent optimization, reduces manual intervention, and significantly reduces the dependence on the experience of engineers.

Description

Technical Field

[0001] This invention relates to the field of pipeline system support and hanger arrangement in nuclear power plants, and particularly to a multi-objective intelligent optimization method and system for pipeline system supports and hangers. Background Technology

[0002] In the nuclear power field, although the arrangement of pipe supports in the pipeline system of a nuclear power plant is determined by the initial design, in order to ensure safety, it is necessary to carry out pipeline mechanical analysis and adjust the arrangement of pipe supports according to the analysis results, iterating repeatedly until the mechanical calculation results of all operating conditions meet the limit and specification requirements.

[0003] Because of the large number of supports and hangers in a pipeline system, their arrangement is a key factor in influencing seismic analysis. For example, increasing the number of supports and hangers can improve the mechanical safety under seismic dynamic conditions, but excessive arrangement of supports and hangers can limit the flexibility of the pipeline system and increase the stress level under thermal expansion conditions.

[0004] Traditional adjustment schemes rely on engineers' experience with "meeting the limits" as the endpoint, making it difficult to obtain the optimal support and hanger layout. Existing methods for optimizing support and hanger positions have the following limitations: the optimization variables only consider the location and do not cover key design parameters such as support and hanger type and quantity; the optimization objective is singular, mainly focusing on stress or valve acceleration.

[0005] In view of this, the inventors of this application have designed a multi-objective intelligent optimization method and system for pipeline system supports and hangers in order to overcome the above-mentioned technical problems. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology in the mechanical analysis of nuclear power plant pipeline systems, which relies on engineers' experience to repeatedly modify the arrangement of supports and hangers, making it difficult to obtain the optimal support and hanger arrangement scheme. The present invention provides a multi-objective intelligent optimization method and system for pipeline system supports and hangers.

[0007] The present invention solves the above-mentioned technical problems through the following technical solution: A multi-objective intelligent optimization method for pipeline system supports and hangers, characterized in that the method includes the following steps: S1. Establish a finite element mechanical analysis model of the pipeline system, calculate the mechanical response of the pipeline system under various design conditions, and obtain support and hanger load data and mechanical evaluation data. S2. Based on the support and hanger load data, construct a structured data file for the support and hanger load; based on the mechanical evaluation data, construct a structured data file for key performance evaluation. S3. Based on the finite element analysis model and the mechanical evaluation data, construct a standardized data interface file; S4. Based on the standardized data interface file, construct a hybrid encoding structure consisting of real number encoding and integer encoding as an optimization variable; S5. Define the objective function and engineering constraints; S6. The optimization variables are iteratively optimized using a multi-objective genetic algorithm, and the optimization results are written back to the standardized data interface file to update the support and hanger layout model. The mechanical analysis software is then called to automatically complete the simulation calculation, automatically acquire mechanical evaluation data, automatically calculate the objective function, and automatically evaluate the engineering constraints. S7. Repeat the iterative evolution process of population update, simulation calculation and objective function and constraint condition evaluation until the preset convergence condition is met, and output the optimal support and hanger layout scheme.

[0008] According to an embodiment of the present invention, in step S1, the mechanical evaluation data includes pipeline stress, flange weld stress, equipment and valve nozzle loads, acceleration at the valve's center of gravity, through-hole loads and displacements, and the natural frequency of the pipeline system.

[0009] According to an embodiment of the present invention, in step S2, the structured data file of the support load records the force, moment and displacement response of each support under different design conditions; the structured data file of the key performance evaluation records the analysis results of pipeline stress, flange weld stress, equipment and valve pipe load, acceleration at the center of gravity of valve mass, through-part load and displacement, and natural frequency of the pipeline system.

[0010] According to an embodiment of the present invention, step S3 includes: S 31 Based on the finite element analysis model and the mechanical evaluation data, identify one or more target pipe sections that require support and hanger layout optimization, and determine the optimization range of the support and hanger layout; S 32 Based on the optimization range and in conjunction with on-site physical installation constraints, the pipe segment information, on-site physical installation constraints of supports and hangers, and optimization operation rules within the optimization range are converted into standardized data interface files.

[0011] According to one embodiment of the present invention, the standardized data interface file is as follows: all original pipe segments within the optimization range are integrated, multiple spatially continuous pipe segments with consistent axial directions are merged into one optimized pipe segment, each optimized pipe segment is arranged in the modeling order, and described using a unified structured data template.

[0012] According to one embodiment of the present invention, the structured data template includes a pipe segment identifier segment, a node identifier segment, and an operation instruction segment.

[0013] According to one embodiment of the present invention, the pipe segment identification segment includes: establishing a unique identifier for the pipe segment, extracting and optimizing the geometric coordinates of the start and end points of the pipe segment, defining variables, storing the variables according to the geometric coordinates, and defining the configurable support and hanger types for the pipe segment.

[0014] According to one embodiment of the present invention, the node identification segment includes: establishing a unique node identifier, extracting the node numbers and geometric coordinates of all unit nodes within the optimized pipe segment, defining variables, storing the node numbers and geometric coordinates as variables, and configuring a support status identifier for each node to characterize whether a support is installed at the node and its name.

[0015] According to one embodiment of the present invention, the operation instruction segment is used to define support optimization operations that can be performed within the pipe section, including deleting supports, adding supports, and moving supports.

[0016] According to one embodiment of the present invention, the deletion of the support includes: a deletion operation identifier, locating the target support based on the node's unique number, and controlling its execution status through an enable flag.

[0017] According to one embodiment of the present invention, adding supports and hangers includes: adding support and hanger operation identifiers, installation positions, support and hanger attributes, support and hanger names, and support and hanger direction parameters, and controlling their execution status by enabling flags.

[0018] According to one embodiment of the present invention, the movable support includes: a movable support operation identifier, a target node identifier, a maximum movement boundary parameter, and an actual movement parameter, and its execution state is controlled by an enable flag.

[0019] According to an embodiment of the present invention, the coding process of the hybrid coding structure in the reconstructed support optimization mode in step S4 includes: S 41 Remove existing supports and hangers between the starting nodes of the pipe segment to be optimized, and generate multiple candidate installation positions on the pipe segment based on the minimum support and hanger spacing constraint; S 42 1. Configure an optional support type at each of the candidate installation locations; S 43 The location of the support and hanger is represented by a real number code, and the code value is the ratio of the axial distance of the support and hanger installation position from the starting node of the pipe section to the total length of the pipe section; S 44 Integer codes are used to identify the type of support and hanger; S 45 The real-number encoding and integer encoding of each pipe segment to be optimized constitute the hybrid encoding data structure of the pipe segment to be optimized, and the hybrid encoding data structure of all pipe segments to be optimized forms the complete chromosome structure of the optimization range.

[0020] According to an embodiment of the present invention, the encoding process of the hybrid encoding structure in the fine-tuning support optimization mode in step S4 includes: S 41’ The original design supports and hangers are retained as the basis, and their positions are encoded with real numbers, while the operations of the supports and hangers at their positions are encoded with integers. S 42’ The real-number encoding and integer encoding of each pipe segment to be optimized constitute the hybrid encoding data structure of the pipe segment to be optimized, and the hybrid encoding data structure of all pipe segments to be optimized forms the complete chromosome structure of the optimization range.

[0021] According to an embodiment of the present invention, step S5 includes: S 51 Based on the structured data file of the support and hanger load, an economic objective function is constructed with the goal of minimizing the total number of supports and hangers. S 52 Based on the structured data file for the key performance evaluation, construct one or more safety objective functions related to stress, displacement, load, acceleration, and frequency.

[0022] According to one embodiment of the present invention, the security objective function and the engineering constraints support multiple configuration modes, including: as an engineering constraint only, as a security objective function only, or as both a security objective function and an engineering constraint.

[0023] According to an embodiment of the present invention, step S6 includes: S 61 Based on the hybrid coding structure, multiple individuals are constructed to form an initial population, which is then used as the parent population for the genetic algorithm. S 62 Perform crossover and mutation operations on the parent population to generate offspring individuals, decode the encoded information of the offspring individuals into support and hanger operation instructions, and update the standardized data interface file to synchronously modify the support and hanger layout model; each offspring individual corresponds to a support and hanger layout scheme; S 63 The system automatically calls finite element mechanical analysis software to perform mechanical simulation calculations on the support and hanger layout scheme corresponding to each of the child individuals, automatically performs mechanical evaluation, automatically obtains the stress, displacement, load and acceleration response of the pipeline system, and generates the corresponding mechanical evaluation results. S 64 Calculate the function value of the objective function based on the simulation results, and verify whether it meets the preset engineering constraints. S 65The fitness of the offspring individuals is evaluated based on the function value of the objective function and the satisfaction of engineering constraints, and the superior offspring individuals are selected to enter the next generation of the population.

[0024] According to an embodiment of the present invention, the convergence condition in step S7 includes: the change in the objective function value of the optimal individual over N consecutive generations is less than a set threshold, or the maximum number of iterations is reached.

[0025] According to an embodiment of the present invention, step S 62 The crossover operation process includes: randomly selecting two parent individuals and copying them to obtain two copies of the parent individuals; Perform a crossover operation between the first parent individual and the copy of the second parent individual, and between the second parent individual and the copy of the first parent individual.

[0026] According to one embodiment of the present invention, a mutation operation is added to the offspring individuals generated by the crossover operation; in the reconstructed support and hanger optimization mode, the mutation operation includes the mutation types of adding supports and hangers, deleting supports and hangers, and moving supports and hangers; in the fine-tuning support and hanger optimization mode, the mutation operation includes the mutation types of deleting supports and hangers and moving supports and hangers.

[0027] The present invention also provides a multi-objective intelligent optimization system for pipeline system supports and hangers, characterized in that the intelligent optimization system is configured to execute the multi-objective intelligent optimization method for pipeline system supports and hangers as described above, and the intelligent optimization system includes: The data modeling module is used to build a finite element model of the pipeline system and generate support and hanger load data and mechanical evaluation data. The structured data generation module constructs a support load file based on the support load data; constructs a key performance evaluation structured data file based on the mechanical evaluation data; and constructs a standardized data interface file based on the finite element analysis model and the mechanical evaluation data. The population initialization module is used to randomly generate an initial set of candidate solutions based on the optimization variable encoding rules. The simulation evaluation module is used to automatically call the pipeline mechanics analysis model and calculate the objective function value and the state of satisfaction of engineering constraints corresponding to the initial candidate scheme set. The evolutionary iteration module is used to perform non-dominated sorting and crowding calculation based on the objective function value, select parent individuals in combination with the engineering constraints, and perform genetic operations on the parent individuals to generate a new generation of optimization variables. The output module is used to output the optimal layout scheme and update it to the 3D design system. The simulation evaluation module and the evolution iteration module work together to perform the evaluation and iteration process repeatedly until the termination condition is met and the optimal candidate solution is output.

[0028] The present invention also provides an electronic device, characterized in that the electronic device includes: a processor and a memory, the memory storing programs or instructions that can be executed on the processor, the programs or instructions being executed by the processor to implement the multi-objective intelligent optimization method for pipeline system supports and hangers as described above.

[0029] This invention also provides a readable storage medium, characterized in that a program or instructions are stored on the readable storage medium, and when the program or instructions are executed by a processor, the multi-objective intelligent optimization method for pipe system supports and hangers described above is implemented. The positive and progressive effects of this invention are as follows: The multi-objective intelligent optimization method and system for pipeline system supports and hangers of the present invention has the following advantages: First, it achieves fully closed-loop intelligent optimization, reduces human intervention, and significantly reduces reliance on engineers' experience.

[0030] Second, the optimized variables cover a wider range, improving the authenticity and feasibility of the plan. At the same time, the location, type and quantity of supports and hangers are optimized to generate a layout plan that is more in line with the actual project.

[0031] Third, a multi-objective collaborative optimization mechanism is constructed to balance safety and economy. Under the premise of ensuring that the mechanical performance of all working conditions meets the standards, the number of supports and hangers can be significantly reduced. Attached Figure Description

[0032] The above and other features, properties and advantages of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings and embodiments, in which the same reference numerals always denote the same features, wherein: Figure 1 This is a flowchart of the multi-objective intelligent optimization method for pipeline system supports and hangers according to the present invention.

[0033] Figure 2 This is a table parsing the key performance evaluation data file content in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0034] Figure 3 This is a schematic diagram of the hybrid coding data structure for support and hanger optimization under the reconstructed support and hanger mode in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0035] Figure 4 This is a schematic diagram of the hybrid coding data structure for support and hanger optimization in the fine-tuning support and hanger mode of the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0036] Figure 5 This is a flowchart of the cross-operation in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0037] Figure 6 This is a flowchart of the cross-initialization phase in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0038] Figure 7 This is a flowchart of the intersection point selection stage in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0039] Figure 8 This is a schematic diagram illustrating the generation of offspring during cross operations in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0040] Figure 9 This is a schematic diagram illustrating the addition of a support / hanger variation operation in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0041] Figure 10 This is a schematic diagram illustrating the deletion of support / hanger mutation operations in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0042] Figure 11 This is a schematic diagram of the variation operation of moving supports in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0043] Figure 12 This is a schematic diagram of the process of generating the pipeline model after coding optimization in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0044] Figure 13 This is a flowchart of the iterative loop based on the genetic optimization algorithm in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0045] Figure 14 This is a schematic diagram of the pipeline model before optimization in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0046] Figure 15 This is a schematic diagram of the optimized pipeline model in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0047] Figure 16 This is a schematic diagram showing the stress ratio before and after optimization in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0048] Figure 17 This is a schematic diagram of the model before the number of supports and hangers is optimized in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention.

[0049] Figure 18This is a schematic diagram of the optimized model of the number of supports and hangers in the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention. Detailed Implementation

[0050] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0051] Embodiments of the invention will now be described in detail with reference to the accompanying drawings. Preferred embodiments of the invention will now be described in detail, examples of which are shown in the drawings. Wherever possible, the same reference numerals will be used in all the drawings to denote the same or similar parts.

[0052] Furthermore, although the terminology used in this invention is selected from commonly known and used terms, some terms mentioned in this specification may have been selected by the applicant in his or her judgment, and their detailed meanings are explained in the relevant sections of the description herein.

[0053] Furthermore, the invention should be understood not only through the actual terminology used, but also through the meaning implied by each term.

[0054] like Figure 1 As shown, this invention discloses a multi-objective intelligent optimization method for pipeline system supports and hangers, which includes the following steps: Step S1: Establish a finite element mechanical analysis model of the pipeline system, calculate the mechanical response of the pipeline system under various design conditions, and obtain support and hanger load data and mechanical evaluation data.

[0055] Finite element analysis software is used to analyze and calculate the initial pipeline model. For example, Pipestress software is used in this embodiment to obtain support and hanger load data and mechanical evaluation data. The mechanical evaluation data preferably includes pipeline stress, flange weld stress, equipment and valve nozzle loads, acceleration at the valve's center of gravity, through-hole loads and displacements, and the natural frequencies of the pipeline system.

[0056] Step S2: Based on the support load data, construct a structured data file for the support load; based on the mechanical evaluation data, construct a structured data file for the key performance evaluation.

[0057] The above calculation results are constructed into two types of structured data files: A structured data file for support and hanger loads is constructed based on the support and hanger load data to record the force, moment, and displacement responses of each support and hanger under different design conditions. A structured data file for key performance evaluation is constructed based on the mechanical evaluation data to record the analysis results of pipe stress, flange weld stress, equipment and valve nozzle loads, acceleration at the valve's center of gravity, through-hole loads and displacements, and the natural frequencies of the piping system. These are stored in a standardized structural format. For a summary of the details, please refer to [link to relevant documentation]. Figure 2 .

[0058] The structured data files for support and hanger loads and the structured data files for key performance evaluation are organized in a standardized format, supporting automatic parsing by downstream systems. They serve as the data carrier for setting objective functions and constraint thresholds, driving the closed-loop optimization of the support and hanger layout model.

[0059] Besides the Pipestress software mentioned above, other general-purpose pipe stress analysis software such as CAESAR II, ANSYS, or AutoPIPE can also be used, and it is not limited to Pipestress. The key to this step is to obtain the complete mechanical response through finite element analysis and convert it into a structured data format to support subsequent intelligent optimization.

[0060] Furthermore, the mechanical response results can be supplemented with natural frequency analysis results. These natural frequency analysis results are added to the structured data file of the key performance evaluation. By constructing an objective function related to the natural frequencies of the pipeline system, the lowest-order natural frequency is maximized, or it is brought closer to a preset target range. This can improve the pipeline system's anti-resonance or anti-seismic performance, and, based on the natural frequency range requirements of the pipeline system, can help avoid resonance with the equipment operating frequency or the dominant frequency of seismic excitation.

[0061] Step S3: Based on the finite element analysis model and the mechanical evaluation data, construct a standardized data interface file.

[0062] Preferably, step S3 includes: Step S 31 Based on the finite element analysis model and the mechanical evaluation data, identify one or more target pipe sections that require support and hanger layout optimization, and determine the optimization range of the support and hanger layout.

[0063] Step S 32 Based on the optimization scope and in conjunction with on-site physical installation constraints, the pipe segment information, support and hanger layout status, and optimization operation rules within the optimization scope are converted into standardized data interface files.

[0064] Specifically, based on the data distribution characteristics of the pipeline system's three-dimensional model and finite element analysis results, one or more target pipe sections requiring optimized support and hanger layout are selected, and their information is structured to generate standardized data interface files. The on-site physical installation constraints preferably include: the existence of physical space obstacles in the support and hanger installation area, resulting in only allowing the installation of specific types of supports or prohibiting their installation.

[0065] The standardized data interface file serves as the data exchange carrier for the entire optimization closed-loop process, from supporting the pipe support and hanger layout model to optimization variables and vice versa. It supports the automatic parsing of optimization variables into the pipe support and hanger layout model, triggering intelligent updates and closed-loop optimization execution of the model. The standardized data interface file provides input parameters for the optimization algorithm, receives optimization results, and drives the automatic updating of the pipe layout model.

[0066] All operation instructions in the standardized data interface file include a Valid field as an enable status indicator to control whether the system executes the operation. The standardized data interface file is organized in a structured format, including but not limited to XML or JSON format.

[0067] Preferably, the standardized data interface file integrates all original pipe segments within the optimization range, merging multiple spatially continuous pipe segments with consistent axial directions into one optimized pipe segment. These optimized pipe segments are arranged according to the modeling order and described using a unified structured data template to ensure format consistency and scalability. It includes the optimized pipe segment's geometric information, finite element node information, and support information, supporting efficient integration and automated processing of pipeline system information within the optimization range. Furthermore, the structured data template also includes standardized operation instruction information for adding, deleting, and moving supports.

[0068] The structured data template includes a pipe segment identifier segment, a node identifier segment, and an operation instruction segment. The following provides a detailed explanation: 1. Pipe Segment: This section defines the spatial location of the optimized pipe segment and the types of supports and hangers that can be configured. Starting coordinates: StartX, StartY, StartZ; End point coordinates: EndX, EndY, EndZ; Allows setting a specific type: AllowedSupportType; All coordinate values ​​are defined based on the global coordinate system.

[0069] The pipe segment identification segment establishes a unique identifier for the pipe segment, extracts and optimizes the geometric coordinates of the starting and ending points of the pipe segment, defines variables, stores the variables according to the geometric coordinates, and defines the configurable support and hanger types for the pipe segment, including at least one of four types: unidirectional constraint type, bidirectional constraint type, tridirectional constraint type, and fixed constraint type.

[0070] II. Node Identifier Segment Information (PT), used to record information about all finite element nodes within the pipe segment: Node unique identifier and number: id (e.g., id=N105); Node coordinates: AX, AY, AZ; Support status indicator: HasSupport, with a value of "true" or "false", indicating whether a support has been installed at this node; If already installed, record the support name: SupportName (e.g., CCS-PH-XXX).

[0071] The node identification segment preferably includes: establishing a unique node identifier, extracting the node numbers and geometric coordinates of all unit nodes within the optimized pipe section, defining variables, storing the node numbers and geometric coordinates as variables, and configuring a support status identifier for each node to indicate whether a support is installed at that node and its name.

[0072] 3. Operations section defines the support optimization operations that can be performed within this pipe section, including deleting supports, adding supports, and moving supports.

[0073] (1) Remove All Supports: This function clears all supports in the current pipe section as a prerequisite for rearrangement. Attribute: Valid, with a value of "Yes" indicating that the operation is enabled and "No" indicating that it is not executed.

[0074] The deletion command includes a deletion operation identifier, locates the target support based on the node's unique number, and controls its execution status through an enable flag. When the enable flag is valid, the system is triggered to remove the support entity from the corresponding node in the finite element model, which can trigger the 3D design system or finite element analysis system to automatically execute the deletion operation of the support location.

[0075] (2) Add support brackets (InsertSupport): Location: Installation location, expressed as a percentage of the total length of the pipe segment's axial distance from the starting point (0~1). Anchor: Whether it is a fixed constraint support or hanger. "Yes" indicates fixed, and "No" indicates movable. Name: Support and hanger name, conforming to the engineering coding rules (e.g., CCS-PH-XXX); When Anchor="Yes", the system does not parse the direction constraint field; when Anchor="No", the constraint direction needs to be further defined.

[0076] Support direction definition (SupportDir), only valid when Anchor="No", is used to define the constraint degrees of freedom of the support: XDir, YDir, ZDir: These indicate whether constraints are applied in the X, Y, and Z directions, respectively. One-way constraint: Only one direction is enabled; Two-way constraint: Enables two directions; Tri-directional constraints: all three directions are enabled; Attribute: Valid: Whether to enable it; CoordinateSystem: Defines the reference coordinate system. "Local" represents the local pipeline coordinate system, and "Global" represents the global coordinate system.

[0077] The instruction to add supports and hangers includes adding support and hanger operation identifiers, installation locations, support and hanger attributes, support and hanger names, and support and hanger direction parameters, and controls its execution status through an enable flag.

[0078] The installation position is defined by a position identifier, indicating the axial offset of the support from the starting point of the pipe segment, expressed as a percentage of the total pipe segment length. The support attributes are defined by a fixed-constraint support identifier. When the fixed-constraint support identifier is "Yes," it indicates that the support is a fixed-constraint support, ending the definition of adding a support. When the fixed-constraint support identifier is "No," it indicates that the support is one of unidirectional, bidirectional, or tridirectional constraint types.

[0079] The constraint direction identifier of the support / hanger in the local coordinate system is further defined by including a field for the support / hanger constraint direction identifier. This field includes a reference coordinate system identifier and a constraint direction identifier. When the activation condition is met, the 3D design system or finite element analysis system is triggered to automatically execute the operation of adding the support / hanger.

[0080] The standardized data interface file is updated based on the optimization algorithm results, and the corresponding operations are automatically triggered by the 3D design system or finite element analysis system.

[0081] All operation instructions include an enable status flag field, which serves as an enable status flag to indicate whether the operation is allowed to be executed.

[0082] (3) Delete a specified support / hanger (DeleteSupport): PT: Target node ID number, used to locate the support to be deleted.

[0083] Attribute: Valid: Whether to enable it.

[0084] (4) Move Support: PT: Target node ID; LeftMax: The maximum allowable displacement to the left (closest to the previous adjacent support) (unit: mm); RightMax: The maximum permissible displacement to the right (closest to the next adjacent support); Distance: The actual amount of movement. A positive value indicates movement to the right, and a negative value indicates movement to the left.

[0085] The moving support command includes a moving support operation identifier, a target node identifier, maximum movement boundary parameters, and actual movement parameters, and its execution status is controlled by an enable flag. Based on the axial distance between the support and its adjacent supports, its movable boundary range is automatically determined. The movement direction and distance are defined by signed displacement values, where negative values ​​indicate movement towards the previous adjacent support, and positive values ​​indicate movement towards the next adjacent support. When the enable condition is met, the 3D design system or finite element analysis system is triggered to automatically update the support position.

[0086] All operation instructions include a Valid field as an enable status indicator to control whether the system executes the operation. After the optimization algorithm outputs the optimal layout scheme, it automatically updates the operation instructions and their Valid status in the standardized data interface file and saves them in a standardized format.

[0087] The system detects changes in the standardized data interface file through the program interface. Once a valid operation command is identified, the 3D design system or finite element analysis software is triggered to automatically execute the corresponding model update operation, realizing an intelligent closed loop of "optimization result → design model".

[0088] Step S4: Based on the standardized data interface file, construct a hybrid encoding structure consisting of real number encoding and integer encoding as an optimization variable, perform iterative optimization on the optimization variable using a genetic algorithm, and write the optimization result back to the standardized data interface file.

[0089] Preferably, the coding process of the hybrid coding structure in the reconstructed support optimization mode in step S4 includes: Step S 41Remove all existing supports and hangers between the starting nodes of the pipe segment to be optimized, set the minimum support and hanger spacing constraint according to your requirements (e.g., not less than 0.1 meters), and generate multiple (e.g., N) candidate installation positions on the pipe segment.

[0090] Step S 42 Each of the candidate installation locations is configured with an optional support type.

[0091] The types mentioned include unidirectional constraint, bidirectional constraint, tridirectional constraint, or fixed constraint. The support and hanger types mentioned here primarily refer to the three types of rigid supports and hangers, and are only examples, not limitations. The support and hanger types can also include spring supports, damper supports, etc., with added support and hanger type numbers and associated design parameters. All types of support and hangers are within the scope of protection of this application.

[0092] Step S 43 The position of the support / hanger is represented by a real number encoding, where the encoded value is the ratio of the axial distance of the support / hanger installation position from the starting node of the pipe segment to the total length of the pipe segment. For example, the ratio ranges from [0,1]. Alternatively, the real number encoding of the support / hanger position can also be achieved by encoding the position offset relative to the previous support / hanger.

[0093] Step S 44 Integer codes are used to identify the type of support and hanger.

[0094] Step S 45 The real-number encoding and integer encoding of each pipe segment to be optimized constitute the hybrid encoding data structure of the pipe segment to be optimized, and the hybrid encoding data structure of all pipe segments to be optimized forms the complete chromosome structure of the optimization range.

[0095] Specifically, such as Figure 3 The diagram shown illustrates the optimized hybrid coding data structure for supports and hangers under reconfigurable support / hanger modes. The preferred coding format for the reconfigurable mode is: Location coding: Real number coding is used, which is the ratio of the axial distance of the support installation position from the starting node of the pipe segment to the total length of the pipe segment. The value of the ratio is preferably a real number in the interval [0,1]. For example, if a support is located at 60% of the total length of the pipe segment, its location code is 0.6.

[0096] Type encoding: Integer encoding is used, and the mapping relationship is as follows: 0 → One-way constraint type; 1 → Two-way constraint type; 2 → Three-way constraint type; 3 → Fixed constraint type; Each support is represented by a pair of data: (real number encoding for position, integer encoding for type). Multiple supports are arranged in axial order to form the encoding sequence of the pipe segment. The encoding sequences of all optimized pipe segments are combined to form a complete chromosome structure.

[0097] Preferably, the encoding process of the hybrid encoding structure in the fine-tuning support optimization mode in step S4 includes: Step S 41’ The original design's supports and hangers are retained as a foundation. Their positions are encoded using real numbers, while operations on these positions are encoded using integers. The support and hanger positions are also coded using a [0,1] normalized encoding. The fine-tuning support and hanger optimization mode is used for local optimization based on the original design. Support and hanger optimization only includes moving and deleting supports and hangers. The specific encoding format is as follows: Figure 4 The diagram shown illustrates the optimized hybrid coding data structure for supports and hangers under the fine-tuning support and hanger mode.

[0098] Step S 42’ The real-number encoding and integer encoding of each pipe segment to be optimized constitute the hybrid encoding data structure of the pipe segment to be optimized, and the hybrid encoding data structure of all pipe segments to be optimized forms the complete chromosome structure of the optimization range.

[0099] Furthermore, step S3 also includes: establishing a position index and a type index, both of which are numbered using natural numbers starting from 0 (i.e., encoded using natural numbers starting from 0), for accurately locating the support type and operation element in subsequent crossover and mutation operations. The position index and type index maintain a one-to-one correspondence in the encoding sequence.

[0100] The position index is used to identify the logical position of the support in the chromosome, supporting the location of the exchange point in the crossover operation and the addition, deletion and modification processing in the mutation operation.

[0101] Step S5: Define the objective function and engineering constraints. These constraints include stress limits, displacement limits, acceleration limits, load limits, etc., set based on specifications, design documents, or requirements.

[0102] Preferably, step S5 includes: Step S 51 Based on the structured data file of the support and hanger load, an economic objective function is constructed with the goal of minimizing the total number of supports and hangers.

[0103] Step S 52 Based on the structured data file for the key performance evaluation, construct one or more safety objective functions related to stress, displacement, load, acceleration, and frequency.

[0104] The security objective function includes a minimization or maximization expression. The security objective function and engineering constraints support multiple configuration modes, including: serving only as engineering constraints, serving only as the objective function, or serving as both as the objective function and engineering constraints. Specifically, when the same performance indicator serves as both the objective function and the engineering constraint, the objective function guides the optimization direction, while the engineering constraints ensure the compliance of the solution, achieving coordinated optimization of the objective and constraints.

[0105] The objective function is selected by combining structured data from key performance assessments. For example, the stress ratio, pipe load, or valve acceleration under a specific operating condition can be used as the optimization objective and can be configured accordingly. The engineering constraints use maximizing the stress ratio as the objective function term, guiding the optimization scheme to fully utilize the material strength margin. Simultaneously, the value range of the engineering constraints is limited to within the allowable values ​​specified in the standards, achieving synergistic optimization of safety and economy. Furthermore, minimizing the number of supports and hangers is a default optimization principle attribute of this algorithm and is not selected here.

[0106] Step S6: Using a multi-objective genetic algorithm and integrating finite element analysis software, simulation calculations are performed on each updated support and hanger layout scheme to automatically acquire mechanical evaluation data and automatically calculate the objective function.

[0107] Preferably, step S6 includes: Step S 61 Based on the hybrid coding structure, multiple individuals are constructed to form an initial population, which is then used as the parent population for the genetic algorithm.

[0108] For example, the population size is set to P, meaning P independent individuals are randomly generated, each representing a complete support and hanger arrangement scheme. The initial population serves as the parent population for the genetic algorithm. Crossover and mutation operations are performed on the parent individuals to generate offspring. The overall process of the crossover operation is as follows: Figures 5 to 7 As shown.

[0109] Step S 62 Individuals in the parent population are replicated, and offspring individuals are generated through crossover and mutation operations. Each offspring individual corresponds to a support and hanger arrangement scheme.

[0110] Preferably, step S 62 The crossover operation includes: randomly selecting two parent individuals (i.e., the first parent individual and the second parent individual), and recording them as parent 1 and parent 2. Then, replicating each parent individual to obtain copies of the two parent individuals (i.e., copies of the first parent individual and the second parent individual), and recording them as parent 1 copy and parent 2 copy.

[0111] Perform a crossover operation between the first parent individual (i.e., parent 1) and the copy of the second parent individual (i.e., copy of parent 2), and perform a crossover operation between the second parent individual (i.e., parent 2) and the copy of the first parent individual (i.e., copy of parent 1).

[0112] like Figure 8 As shown, the crossover operation includes: Determine the number of intersection points: Select the minimum number of candidate support / hanger positions between the two parent individuals participating in the intersection operation. For example, let the minimum value be k, such as... Figure 9 In this case, k is set to: k=4.

[0113] Determine the intersection point location: Randomly select a set of exchange point location indices in the first parent individual (i.e., parent 1). The location index is a non-repeating index sequence randomly selected from the candidate locations of the support bracket, and is used as the source index. The index order is not required to be continuous or ordered.

[0114] In the copy of the second parent individual (i.e., the copy of parent 2), a set of position indices is randomly selected. The number of position indices is the same as the source index. The position indices are a non-repeating index sequence randomly selected from the candidate positions of the support and hanger, and the index order is not required to be continuous or ordered. These indices are used as the target indexes.

[0115] For example, k position indices are randomly selected in parent 1 as source indices, and k target indices of different orders are randomly selected in the parent 2 copy.

[0116] Perform a crossover operation: Take the real and integer codes of the supports at each source index position of the first parent individual (i.e., parent 1) and replace them with the codes of the second parent's copy (i.e., parent 2 copy) at the corresponding target index positions. This essentially changes the candidate positions and types of the supports in parent 2 copy, forming a new child individual, recorded as child 1. Positions not replaced retain their original values.

[0117] In the same manner, a crossover operation is performed between the second parent individual (i.e., parent 2) and its paired copy of the first parent individual (i.e., copy of parent 1) to generate another new child individual, recorded as child 2.

[0118] In addition, the crossover operation can also be performed as follows: the encoding sequences of the two parent individuals are divided into multiple blocks of the same length, and the blocks are rotated and crossed.

[0119] In the crossover operation, the hybrid coding and genetic operation method achieves multi-point heterogeneous gene fragment exchange by selecting disordered and discontinuous source and target indices between the parent and its replicas.

[0120] Subsequently, mutation operations are added to the offspring individuals generated by the crossover operation. In the reconstructed support / hanger optimization mode, the mutation types include: adding supports / hangers, deleting supports / hangers, and moving supports / hangers. In the fine-tuning support / hanger optimization mode, the mutation types include: deleting supports / hangers and moving supports / hangers.

[0121] like Figure 9 As shown, the preferred method for adding support and hanger mutation operation is to randomly generate a new support and hanger candidate position (satisfying the minimum spacing) and its corresponding support and hanger type, determine its insertion position in the chromosome sequence according to its real number encoding value, and insert the position code and type code of the support and hanger into the corresponding position to generate a new individual, that is, insert it into the appropriate position of the chromosome according to the normalized value.

[0122] like Figure 10 As shown, the preferred method for deleting a support / hanger mutation operation is to randomly select an existing support / hanger (i.e., an existing support / hanger) from the current individual and remove its position real number code and corresponding type integer code.

[0123] like Figure 11 As shown, the preferred method for the mobile support mutation operation is to apply a preset or random position offset, such as a random offset within the range of [0,1], to the real number code of the selected support position, and simultaneously update its type integer code to generate a new individual.

[0124] The newly generated individuals constitute the offspring population, and each offspring individual corresponds to a candidate support and hanger arrangement scheme.

[0125] The above Figures 9 to 11 The variation operation shown applies only to the location and type of a single support / hanger, and is merely an example and not intended to be limiting. The variation operation can be applied to one or more supports / hangers; multiple supports / hangers selected randomly or sequentially by location are all within the scope of protection of this application.

[0126] In the mutation operation, newly added supports are inserted into the chromosome sequence in axial order according to their position encoding values, ensuring the physical rationality of the solution. This encoding and operation mechanism achieves coordinated optimization of the position, type, and quantity of supports, supporting multi-mode intelligent optimization from global reconstruction to local fine-tuning.

[0127] like Figure 12 As shown, new genetic codes are obtained through crossover and mutation in the optimization algorithm, the codes are mapped to a standardized data interface file, and then the pipeline model is updated based on the standardized data interface file to obtain a new layout scheme, from the initial model to the model update process.

[0128] Step S 63The system automatically calls finite element analysis software to perform mechanical simulation calculations on the support and hanger layout scheme corresponding to each of the child individuals, automatically conducts mechanical evaluation, automatically obtains the stress, displacement, load and acceleration response of the pipeline system, and generates the corresponding mechanical evaluation results.

[0129] A multi-objective genetic algorithm (such as NSGA-II) is used as the core optimization engine and integrated with finite element analysis software to perform mechanical simulation calculations on each updated support and hanger arrangement scheme generated by the optimization algorithm. Here, the finite element analysis software simulation process is integrated with the genetic algorithm module through a program interface. During the simulation, the system automatically acquires mechanical evaluation data of the pipeline system, including response results such as stress, displacement, load, and acceleration.

[0130] Step S 64 Calculate the function value of the objective function based on the simulation results, and verify whether it meets the preset engineering constraints.

[0131] Based on the acquired mechanical evaluation data, the system automatically calculates the function values ​​of the economic objective function and the safety objective function, and simultaneously evaluates whether the preset engineering constraints are met.

[0132] Step S 65 The fitness of the offspring individuals is evaluated based on the function value of the objective function and the satisfaction of engineering constraints, and the superior offspring individuals are selected to enter the next generation of the population.

[0133] The fitness of individuals in the current population is evaluated based on the objective function value and constraint satisfaction, and a new generation of population is generated through selection, crossover, and mutation operations.

[0134] Step S7: Repeat the iterative evolution process of population update, simulation calculation and objective function, and engineering constraint condition evaluation until the preset convergence condition is met, and output the optimal support and hanger layout scheme.

[0135] The iterative evolutionary process of "population update - simulation calculation - objective function and engineering constraint evaluation" is repeated until a preset convergence condition is met. The convergence condition may include the objective function value of the optimal individual changing less than a set threshold for multiple consecutive generations (e.g., N generations), or reaching the maximum number of iterations. When the convergence condition is met, the iteration terminates, and the system outputs one or more support and hanger arrangement schemes that achieve an optimal balance between safety and economy as the final optimization result.

[0136] Furthermore, the technical solution of this application can also use the ratio of non-standard to standard supports and hangers as an optimization objective, and verify the support load according to the standard support and hanger load manual to count the number of non-standard supports and hangers that can be formed by each scheme. Finally, the final layout scheme is selected based on the objective function and engineering requirements.

[0137] like Figure 13 The diagram shows a flowchart of support and hanger layout optimization based on the genetic optimization algorithm (NSGA-II fast non-dominated sorting genetic algorithm). An example with a population size of 20 and 100 generations is used for illustration.

[0138] Assume the population size (N) is 20 individuals; assume the number of generations (E) is 100.

[0139] Population initialization: Generate 20 individuals, each of which calls the pipeline mechanics analysis software once, for a total of 20 initialization calls.

[0140] Assuming we enter the iterative cycle phase (generations 1-100), the iterative process for each generation is as follows: Step 1: Generate 20 offspring through crossover mutation; The second step is to call the pipeline mechanics analysis software once for each child generation; Step 3: Parent generation (e.g., 20) + Child generation (e.g., 20) = 40 individuals; Step 4: Select the 20 best ones from the 40 to enter the next generation; Step 5: Each generation is called 20 times.

[0141] Based on the above description, the effectiveness of this application will be further described in detail through two specific examples.

[0142] Example 1: Stress ratio optimization

[0143] The pipeline model before optimization is as follows: Figure 14 As shown, the model fails the calculation case where the stress ratio in load condition 330 (self-weight + earthquake) exceeds 1. After adopting the method of this invention, the pipeline model optimized by the genetic algorithm is as follows: Figure 15 As shown. By Figure 15 It can be seen that the position and type of the supports and hangers have been optimized.

[0144] The stress ratio before and after optimization is as follows: Figure 16 As shown, the stress ratios for conditions 10 (self-weight), 15 (hydraulic test), and 330 (self-weight + earthquake) are significantly reduced, while the stress ratio for condition 321 (self-weight + thermal expansion) is slightly increased, but still meets the requirement of being less than 1.

[0145] Example 2: Optimization of the number of supports and hangers

[0146] like Figure 17 The model shown has a stress ratio that meets the requirements of mechanical analysis, but the number of supports and hangers is excessive. For example... Figure 18 As shown, after optimization by the present invention, the number of supports and hangers is reduced from 7 to 1, while still meeting the mechanical stress ratio requirement.

[0147] Based on the above description, the multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention begins with a finite element simulation model, and sequentially completes the generation of load and evaluation data, identification of the optimization range, construction of a standardized data interface, definition of the objective function and constraints, and optimization solution using a genetic algorithm. The optimization results are written back to the interface file, triggering automatic updates of the 3D model, and are verified through simulation and feedback iteration until convergence is achieved, outputting the optimal support and hanger layout scheme.

[0148] The multi-objective intelligent optimization method for pipeline system supports and hangers of the present invention can achieve the following advantages: I. Achieving fully closed-loop intelligent optimization reduces manual intervention and significantly lowers reliance on engineers' experience. This is mainly achieved through the following means: using standardized data interface files as the data carrier for "optimization variables and model layout," supporting automatic parsing of algorithm results and triggering updates to the 3D design or simulation system; employing deep integration of multi-objective genetic algorithms and finite element simulation, automatically calling pipeline mechanics analysis software to execute simulations through program interfaces, achieving closed-loop execution; and using programmatic expressions for operation command segments, with deletion, movement, and addition of supports and hangers all defined by structured commands, supporting automatic execution by the 3D platform.

[0149] Second, the optimized variables provide more comprehensive coverage, improving the realism and feasibility of the scheme. Simultaneously, the location, type, and quantity of supports and hangers are optimized to generate a layout scheme that better suits the actual engineering situation. This is mainly achieved through the following methods: A hybrid coding structure is adopted, using real numbers to represent location ([0,1] interval) and integers to identify type (one-way, two-way, three-way, fixed), achieving joint coding of location and type; on-site physical installation constraint modeling is used, defining the allowed support and hanger types in the pipe segment identification section to ensure that the optimized variables conform to the on-site space constraints.

[0150] Third, a multi-objective collaborative optimization mechanism is constructed to balance safety and economy, significantly reducing the number of supports and hangers while ensuring that the mechanical performance meets the standards under all working conditions. This is mainly achieved through the following means: A multi-objective function system is adopted (e.g., economic objective function and safety objective function). The economic objective function aims to minimize the total number of supports and hangers, while the safety objective function is used to construct minimization or maximization objectives related to stress, acceleration, and displacement. An objective-constraint collaborative configuration mechanism is adopted, where the same index can simultaneously serve as both an objective and a constraint (e.g., maximizing the stress ratio + engineering constraint ≤ 0.8), guiding the algorithm to release structural load-bearing margin under compliance conditions.

[0151] This invention also provides a multi-objective intelligent optimization system for pipeline system supports and hangers, configured to execute the multi-objective intelligent optimization method for pipeline system supports and hangers as described above. The intelligent optimization system includes: a data modeling module for establishing a finite element model of the pipeline system and generating support and hanger load data and mechanical evaluation data; a structured data generation module for constructing support and hanger load files based on the support and hanger load data; constructing a key performance evaluation structured data file based on the mechanical evaluation data; and constructing a standardized data interface file based on the finite element analysis model and the mechanical evaluation data.

[0152] The population initialization module randomly generates an initial candidate scheme set based on the optimization variable encoding rules. The simulation evaluation module automatically calls the pipeline mechanics analysis model to calculate the objective function value and the state of engineering constraint satisfaction corresponding to the initial candidate scheme set. The evolutionary iteration module performs non-dominated sorting and crowding calculation based on the objective function value, selects parent individuals based on the engineering constraint satisfaction state, and performs genetic operations on the parent individuals to generate a new generation of optimization variables. The output module outputs the optimal layout scheme and updates it to the 3D design system.

[0153] The simulation evaluation module and the evolution iteration module work together to perform the evaluation and iteration process repeatedly until the termination condition is met and the optimal candidate solution is output.

[0154] refer to Figure 1 The working process of the multi-objective intelligent optimization system for pipeline system supports and hangers is as follows: First, the data modeling module establishes a finite element mechanical analysis model of the pipeline system and performs initial calculations to obtain basic mechanical response data.

[0155] Subsequently, the structured data generation module constructs a support load file, a key performance evaluation structured data file, and a standardized data interface file as the core data carrier based on the data.

[0156] Next, the optimized variable encoding module reads the standardized data interface file, generates a hybrid encoding structure and an initial population, and inputs it into the multi-objective genetic algorithm engine module. The multi-objective genetic algorithm engine module performs crossover and mutation operations on the population to generate new codes.

[0157] The process then enters an iterative closed loop: the optimization variable encoding module decodes the new encoding into operation instructions, updates the standardized data interface file, and drives the finite element model to change synchronously. The finite element simulation calling module automatically calls the analysis software to perform multi-condition simulations on the updated model and obtain the latest mechanical data. The objective function evaluation module calculates economic (e.g., number of supports and hangers) and safety (e.g., stress level) indicators based on the simulation results and verifies the engineering constraints. The evaluation results are then fed back to the multi-objective genetic algorithm engine module, which selects dominant individuals to form a new generation population based on a non-dominated sorting strategy.

[0158] The above process of "encoding evolution → decoding update → simulation calculation → evaluation feedback" is executed cyclically until the preset convergence condition is met.

[0159] Finally, the output module extracts the coding scheme of the optimal individual, converts it into the final engineering design data, outputs the optimal support and hanger layout scheme and automatically updates it to the three-dimensional design system, completing the intelligent optimization closed loop.

[0160] The present invention also provides an electronic device comprising: a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions being executed by the processor to implement the multi-objective intelligent optimization method for pipe system supports as described above.

[0161] This invention also provides a readable storage medium storing a program or instructions, which, when executed by a processor, implements the multi-objective intelligent optimization method for pipeline system supports and hangers as described above. In summary, the multi-objective intelligent optimization method and system for pipeline system supports and hangers of this invention have the following numerous advantages: First, it achieves fully closed-loop intelligent optimization, reduces human intervention, and significantly reduces reliance on engineers' experience.

[0162] Second, the optimized variables cover a wider range, improving the authenticity and feasibility of the plan. At the same time, the location, type and quantity of supports and hangers are optimized to generate a layout plan that is more in line with the actual project.

[0163] Third, a multi-objective collaborative optimization mechanism is constructed to balance safety and economy. Under the premise of ensuring that the mechanical performance of all working conditions meets the standards, the number of supports and hangers can be significantly reduced.

[0164] For those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0165] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0166] Similarly, it should be noted that, in order to simplify the description of the embodiments disclosed in this application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of this application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this disclosure method does not imply that the subject matter of this application requires more features than those mentioned in the claims. In fact, the embodiments have fewer features than all the features of a single embodiment disclosed above. Some embodiments use numbers describing the number of components or attributes; it should be understood that such numbers used in the description of embodiments are modified in some examples by the terms "approximately," "about," or "generally."

[0167] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but all such changes and modifications fall within the scope of protection of the present invention.

Claims

1. A multi-objective intelligent optimization method for pipe system supports and hangers, characterized in that, The multi-objective intelligent optimization method for pipeline system supports and hangers includes the following steps: S1. Establish a finite element mechanical analysis model of the pipeline system, calculate the mechanical response of the pipeline system under various design conditions, and obtain support and hanger load data and mechanical evaluation data. S2. Based on the support and hanger load data, construct a structured data file for the support and hanger load; based on the mechanical evaluation data, construct a structured data file for key performance evaluation. S3. Based on the finite element analysis model and the mechanical evaluation data, construct a standardized data interface file; S4. Based on the standardized data interface file, construct a hybrid encoding structure consisting of real number encoding and integer encoding as an optimization variable; S5. Define the objective function and engineering constraints; S6. The optimization variables are iteratively optimized using a multi-objective genetic algorithm, and the optimization results are written back to the standardized data interface file to update the support and hanger layout model. The mechanical analysis software is then called to automatically complete the simulation calculation, automatically acquire mechanical evaluation data, automatically calculate the objective function, and automatically evaluate the engineering constraints. S7. Repeat the iterative evolution process of population update, simulation calculation and objective function and constraint condition evaluation until the preset convergence condition is met, and output the optimal support and hanger layout scheme.

2. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 1, characterized in that, In step S1, the mechanical evaluation data includes pipeline stress, flange weld stress, equipment and valve nozzle loads, acceleration at the valve's center of gravity, through-hole loads and displacements, and the natural frequency of the pipeline system.

3. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 1, characterized in that, In step S2, the structured data file of the support load records the force, moment and displacement response of each support under different design conditions; the structured data file of the key performance evaluation records the analysis results of pipeline stress, flange weld stress, equipment and valve pipe load, acceleration at the center of gravity of valve mass, through-part load and displacement, and natural frequency of the pipeline system.

4. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 1, characterized in that, Step S3 includes: S 31 Based on the finite element analysis model and the mechanical evaluation data, identify one or more target pipe sections that require support and hanger layout optimization, and determine the optimization range of the support and hanger layout; S 32 Based on the optimization range and in conjunction with on-site physical installation constraints, the pipe segment information, on-site physical installation constraints of supports and hangers, and optimization operation rules within the optimization range are converted into standardized data interface files.

5. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 4, characterized in that, The standardized data interface file is as follows: all original pipe segments within the optimization range are integrated and processed, and multiple pipe segments that are spatially continuous and have the same axial direction are merged into one optimized pipe segment. Each optimized pipe segment is arranged in the modeling order and described using a unified structured data template.

6. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 5, characterized in that, The structured data template includes a pipe segment identifier segment, a node identifier segment, and an operation instruction segment.

7. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 6, characterized in that, The pipe segment identification segment includes: establishing a unique identifier for the pipe segment, extracting and optimizing the geometric coordinates of the start and end points of the pipe segment, defining variables, storing the variables according to the geometric coordinates, and defining the configurable support and hanger types for the pipe segment.

8. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 6, characterized in that, The node identification segment includes: establishing a unique node identifier, extracting the node numbers and geometric coordinates of all unit nodes within the optimized pipe section, defining variables, storing the node numbers and geometric coordinates as variables, and configuring a support status identifier for each node to indicate whether a support is installed at the node and its name.

9. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 6, characterized in that, The operation instruction segment is used to define the support optimization operations that can be performed within the pipe section, including deleting supports, adding supports, and moving supports.

10. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 9, characterized in that, The deletion of the support includes: a deletion operation identifier, which locates the target support based on the node's unique number, and controls its execution status through an enable flag.

11. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 9, characterized in that, The addition of supports and hangers includes: adding support and hanger operation identifiers, installation locations, support and hanger attributes, support and hanger names, and support and hanger direction parameters, and controlling their execution status through an enable flag.

12. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 9, characterized in that, The movable support includes: movable support operation identifier, target node identifier, maximum movement boundary parameter and actual movement parameter, and its execution status is controlled by an enable flag.

13. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 1, characterized in that, The coding process of the hybrid coding structure in the reconstructed support optimization mode in step S4 includes: S 41 Remove existing supports and hangers between the starting nodes of the pipe segment to be optimized, and generate multiple candidate installation positions on the pipe segment based on the minimum support and hanger spacing constraint; S 42 1. Configure an optional support type at each of the candidate installation locations; S 43 The location of the support and hanger is represented by a real number code, and the code value is the ratio of the axial distance of the support and hanger installation position from the starting node of the pipe section to the total length of the pipe section; S 44 Integer codes are used to identify the type of support and hanger; S 45 The real-number encoding and integer encoding of each pipe segment to be optimized constitute the hybrid encoding data structure of the pipe segment to be optimized, and the hybrid encoding data structure of all pipe segments to be optimized forms the complete chromosome structure of the optimization range.

14. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 1, characterized in that, The encoding process of the hybrid encoding structure in the fine-tuning support optimization mode in step S4 includes: S 41’ The original design supports and hangers are retained as the basis, and their positions are encoded with real numbers, while the operations of the supports and hangers at their positions are encoded with integers. S 42’ The real-number encoding and integer encoding of each pipe segment to be optimized constitute the hybrid encoding data structure of the pipe segment to be optimized, and the hybrid encoding data structure of all pipe segments to be optimized forms the complete chromosome structure of the optimization range.

15. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 1, characterized in that, Step S5 includes: S 51 Based on the structured data file of the support and hanger load, an economic objective function is constructed with the goal of minimizing the total number of supports and hangers. S 52 Based on the structured data file for the key performance evaluation, construct one or more safety objective functions related to stress, displacement, load, acceleration, and frequency.

16. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 15, characterized in that, The security objective function and engineering constraints support multiple configuration modes, including: as an engineering constraint only, as a security objective function only, or as both a security objective function and an engineering constraint.

17. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 1, characterized in that, Step S6 includes: S 61 Based on the hybrid coding structure, multiple individuals are constructed to form an initial population, which is then used as the parent population for the genetic algorithm. S 62 Perform crossover and mutation operations on the parent population to generate offspring individuals, decode the encoded information of the offspring individuals into support and hanger operation instructions, and update the standardized data interface file to synchronously modify the support and hanger layout model; each offspring individual corresponds to a support and hanger layout scheme; S 63 The system automatically calls finite element mechanical analysis software to perform mechanical simulation calculations on the support and hanger layout scheme corresponding to each of the child individuals, automatically performs mechanical evaluation, automatically obtains the stress, displacement, load and acceleration response of the pipeline system, and generates the corresponding mechanical evaluation results. S 64 Calculate the function value of the objective function based on the simulation results, and verify whether it meets the preset engineering constraints. S 65 The fitness of the offspring individuals is evaluated based on the function value of the objective function and the satisfaction of engineering constraints, and the superior offspring individuals are selected to enter the next generation of the population.

18. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 1, characterized in that, The convergence conditions in step S7 include: the change in the objective function value of the best individual over N consecutive generations is less than a set threshold, or the maximum number of iterations is reached.

19. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 17, characterized in that, The step S 62 The crossover operation process includes: randomly selecting two parent individuals and copying them to obtain two copies of the parent individuals; Perform a crossover operation between the first parent individual and the copy of the second parent individual, and between the second parent individual and the copy of the first parent individual.

20. The multi-objective intelligent optimization method for pipeline system supports and hangers as described in claim 17, characterized in that, Add mutation operations to the offspring individuals generated by the crossover operation; in the reconstructed support and hanger optimization mode, the mutation operations include the mutation types of adding supports and hangers, deleting supports and hangers, and moving supports and hangers; in the fine-tuning support and hanger optimization mode, the mutation operations include the mutation types of deleting supports and hangers and moving supports and hangers.

21. A multi-objective intelligent optimization system for pipeline system supports and hangers, characterized in that, The intelligent optimization system is configured to execute the multi-objective intelligent optimization method for pipeline system supports and hangers as described in any one of claims 1-20, the intelligent optimization system comprising: The data modeling module is used to build a finite element model of the pipeline system and generate support and hanger load data and mechanical evaluation data. The structured data generation module constructs a support load file based on the support load data; constructs a key performance evaluation structured data file based on the mechanical evaluation data; and constructs a standardized data interface file based on the finite element analysis model and the mechanical evaluation data. The population initialization module is used to randomly generate an initial set of candidate solutions based on the optimization variable encoding rules. The simulation evaluation module is used to automatically call the pipeline mechanics analysis model and calculate the objective function value and the state of satisfaction of engineering constraints corresponding to the initial candidate scheme set. The evolutionary iteration module is used to perform non-dominated sorting and crowding calculation based on the objective function value, select parent individuals in combination with the engineering constraints, and perform genetic operations on the parent individuals to generate a new generation of optimization variables. The output module is used to output the optimal layout scheme and update it to the 3D design system. The simulation evaluation module and the evolution iteration module work together to perform the evaluation and iteration process repeatedly until the termination condition is met and the optimal candidate solution is output.

22. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions being executed by the processor to implement the multi-objective intelligent optimization method for pipeline system supports and hangers as described in any one of claims 1-20.

23. A readable storage medium, characterized in that, The program or instructions are stored on the readable storage medium, and when the program or instructions are executed by the processor, they implement the multi-objective intelligent optimization method for pipeline system supports and hangers as described in any one of claims 1-20.

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