Intelligent self-adaptive freezing pipeline pre-integration system

Abstract

The invention provides an intelligent self-adaptive freezing pipeline pre-integration system, and relates to the technical field of building environment and energy application engineering. The intelligent self-adaptive freezing pipeline pre-integration system comprises the following modules: a structure design module, a fluid simulation module, a material optimization module, an intelligent alignment module, an energy recovery module, a heat management module and a prefabricated integration module. And the structure design module is used for optimizing the geometric parameters of the ripple compensation structure by adopting a multi-target genetic algorithm based on the pipeline installation error statistical data, analyzing and verifying the structural strength through finite element stress, and generating a three-dimensional ripple structure parameter set. A multi-target genetic algorithm is combined with finite element stress analysis to optimize ripple geometric parameters, the axial compensation amount is improved to + / -20mm on the premise of ensuring the structural strength, the compensation capability is improved by 40% compared with that of a traditional empirical design method, a k-omega SST turbulence model and a particle image velocity measurement technology are adopted for collaborative verification, the flow field simulation precision is improved to 92%, and the flow field simulation precision is improved to 92%. And the pressure drop calculation error is controlled within + / -3%.

Description

Technical Field

[0001] This invention relates to the field of building environment and energy application engineering technology, specifically to an intelligent adaptive refrigeration pipe pre-integration system. Background Technology

[0002] Building environment and energy application engineering is a technical field focusing on the regulation of the internal environment of buildings and the efficient use of energy. It focuses on the optimization design of the internal thermal and humidity environment, air quality and energy system of buildings. It involves multiple disciplines such as fluid transportation, heat and mass transfer, materials engineering and intelligent control. It uses technologies such as computational fluid dynamics simulation, development of new composite materials and modular prefabrication assembly. Among them, an intelligent adaptive chilled water pipe pre-integrated system refers to a prefabricated chilled water pipe component that integrates a flexible compensation structure and a biomimetic drag-reducing surface. Through the optimization of corrugated geometric parameters, biomimetic microstructure lining and intelligent alignment compensation technology, it solves the fluid loss problem caused by misalignment of traditional pipe installation. Its purpose is to improve the installation efficiency of chilled water transportation system by 55% while reducing turbulence intensity by 40%. It is mainly used in scenarios with strict requirements for fluid transportation energy efficiency, such as data centers and commercial complexes.

[0003] Traditional piping systems rely on empirical formulas for corrugated structure design, lacking multi-physics coupling optimization. This makes the compensation structure prone to plastic deformation when subjected to thermal stress. Existing hose materials use homogeneous rubber structures with an inner wall roughness Ra≥1.2μm, resulting in an additional 3.2m head loss under a flow velocity of 2.5m / s. Manual alignment and adjustment rely on visual inspection, and installation deviations often exceed ±5mm. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent adaptive pre-integrated refrigeration pipeline system. This system solves the problem that traditional pipeline systems rely on empirical formulas for corrugated structure design, lack multi-physics field coupling optimization, and thus the compensation structure is prone to plastic deformation when subjected to thermal stress.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent adaptive refrigeration pipeline pre-integrated system, comprising the following modules: structural design module, fluid simulation module, material optimization module, intelligent alignment module, energy recovery module, thermal management module, and prefabrication integration module; The structural design module, based on pipeline installation error statistics, uses a multi-objective genetic algorithm to optimize the geometric parameters of the corrugated compensation structure, verifies the structural strength through finite element stress analysis, and generates a three-dimensional corrugated structure parameter set. The fluid simulation module, based on a three-dimensional corrugated structure parameter set, uses fluid calculation software to perform turbulence simulation, employs the k-ωSST model to calculate pressure drop distribution, verifies flow field characteristics through particle image velocimetry, and generates a flow field feature cloud map dataset. The material optimization module, based on the flow field feature cloud map dataset, uses response surface methodology to screen composite material formulations, prepares biomimetic drag-reducing surfaces through nanoimprinting, verifies flexibility performance through three-point bending tests, and generates a composite material performance parameter table. The intelligent alignment module, based on the composite material performance parameter table, uses the iterative nearest point algorithm to match laser point clouds, determines the compensation amount through rigid body transformation matrix calculation, drives a high-precision servo mechanism to complete pose adjustment, and generates three-dimensional pose compensation parameters; the energy recovery module, based on the three-dimensional pose compensation parameters, designs a piezoelectric ceramic array layout scheme, uses the equivalent circuit modeling method to optimize energy harvesting efficiency, realizes energy storage through maximum power point tracking technology, and generates an energy recovery efficiency curve; The thermal management module selects energy storage materials based on energy recovery efficiency curves, uses phase change enthalpy analysis, prepares composite pipe walls using multi-layer co-extrusion process, detects thermal insulation performance using infrared thermal imaging technology, and generates thermal resistance characteristic parameter packages. The prefabricated integrated module, based on thermal resistance characteristic parameter package, constructs a building information model component library, uses tolerance accumulation analysis method to determine prefabrication accuracy, and completes modular assembly through a six-axis robotic arm to generate pre-integrated piping units.

[0006] Preferably, the structural design module includes a parametric modeling submodule, a mechanical analysis submodule, and a geometric optimization submodule; the parametric modeling submodule, based on pipeline installation error statistics, uses the Monte Carlo random sampling method to establish a three-dimensional error distribution model and generate an error probability density function; The mechanical analysis submodule calculates the stress concentration factor of the corrugated structure based on the error probability density function and uses the finite element contact algorithm to generate stress cloud distribution data. The geometry optimization submodule, based on stress cloud map distribution data, uses a non-dominated sorting genetic algorithm to optimize the corrugation height / wavelength ratio and generate a three-dimensional corrugation structure parameter set.

[0007] Preferably, the fluid simulation module includes a model building submodule, a turbulence simulation submodule, and a pressure drop calculation submodule; the model building submodule, based on the three-dimensional ripple structure parameter set, uses parametric modeling technology to construct the flow channel geometric model and generate a standardized flow channel model file; The turbulence simulation submodule, based on a standardized flow channel model file, uses the large eddy simulation method to calculate transient flow field characteristics and generate vorticity isosurface datasets. The pressure drop calculation submodule, based on the vorticity isosurface dataset, uses field synergy theory to calculate local drag loss and generate a flow field characteristic cloud map dataset.

[0008] Preferably, the material optimization module includes a matrix selection submodule, an enhancement design submodule, and an interface processing submodule; the matrix selection submodule, based on the flow field feature cloud map dataset, uses the fuzzy comprehensive evaluation method to screen the hardness of the silicone matrix and generate a matrix material performance matrix; The enhanced design submodule, based on the matrix material property matrix, uses molecular dynamics simulation to determine the carbon nanotube doping ratio and generate an enhanced phase distribution map; The interface processing submodule, based on the enhanced phase distribution map, uses plasma grafting technology to improve the interfacial bonding strength of the material and generates a composite material performance parameter table.

[0009] Preferably, the intelligent alignment module includes a laser scanning submodule, a deviation calculation submodule, and a servo drive submodule; the laser scanning submodule, based on the composite material performance parameter table, uses laser triangulation to acquire three-dimensional point cloud data of the pipe end face and generate a high-precision point cloud model; The deviation calculation submodule, based on a high-precision point cloud model, uses an iterative nearest point registration algorithm to match the theoretical and measured models and generate a pose deviation matrix. The servo drive submodule, based on the pose deviation matrix, uses a PID control algorithm to drive the servo motor to complete six-degree-of-freedom pose adjustment and generate three-dimensional pose compensation parameters.

[0010] Preferably, the energy recovery module includes a piezoelectric layout submodule, a circuit optimization submodule, and an energy storage configuration submodule; the piezoelectric layout submodule, based on three-dimensional pose compensation parameters, uses the finite element analysis method to determine the optimal mounting position of the PZT piezoelectric sheet and generates a piezoelectric array topology diagram. The circuit optimization submodule, based on the piezoelectric array topology, uses impedance matching technology to design the rectifier circuit and generate the maximum power output parameters. The energy storage configuration submodule, based on the maximum power output parameters, tracks the maximum power point in real time using the perturbation observation method and generates an energy recovery efficiency curve.

[0011] Preferably, the thermal management module includes a phase change selection submodule, a lamination process submodule, and a thermal insulation testing submodule; The phase change selection submodule uses differential scanning calorimetry to screen phase change materials based on energy recovery efficiency curves and generates a phase change material performance table. The lamination process submodule, based on the phase change material performance table, uses melt co-extrusion technology to prepare a five-layer composite pipe wall and generate a composite pipe wall sample; The thermal insulation test submodule, based on composite pipe wall samples, uses the steady-state heat flow method to determine the thermal conductivity and generate a thermal resistance characteristic parameter package.

[0012] Preferably, the prefabricated integration module includes a model integration submodule, a tolerance allocation submodule, and a process planning submodule; the model integration submodule, based on thermal resistance characteristic parameter package, uses parametric modeling technology to build a BIM component library and generate a standardized pipeline model; The tolerance allocation submodule, based on a standardized pipeline model, uses Monte Carlo simulation to perform tolerance chain analysis and generate an assembly tolerance table. The process planning submodule, based on the assembly tolerance table, optimizes the robot arm's motion trajectory using the RRT path planning algorithm to generate pre-integrated piping units.

[0013] This invention provides an intelligent adaptive pre-integrated refrigeration piping system. It has the following beneficial effects: This invention optimizes corrugated geometry parameters using a multi-objective genetic algorithm combined with finite element stress analysis, achieving an axial compensation of ±20mm while maintaining structural strength, a 40% improvement in compensation capability compared to traditional empirical design methods. The k-ωSST turbulence model and particle image velocimetry are used for collaborative verification, improving flow field simulation accuracy to 92% and controlling pressure drop calculation error within ±3%, an 8% reduction in error compared to the conventional k-ε model. Based on response surface methodology for composite material formulation selection and supplemented by nanoimprinting, a biomimetic drag-reducing surface is prepared, reducing surface roughness to Ra0.6μm and lowering the friction coefficient by 35% compared to traditional hose materials. The iterative closest point algorithm combined with rigid body transformation matrix calculation achieves installation deviation detection accuracy of ±0.5mm. Combined with a high-precision servo mechanism, pipe alignment time can be reduced to 12 minutes, a 60% improvement in efficiency compared to manual adjustment. The piezoelectric ceramic array layout scheme is optimized through equivalent circuit modeling, achieving 18mW / cm² under a flow velocity of 2m / s. 2 The energy recovery density is five times more efficient than traditional waste energy recovery methods. The composite pipe wall, prepared using phase change energy storage materials and a multi-layer co-extrusion process, reduces the thermal conductivity to 0.04 W / m·K, decreasing heat loss by 63% compared to ordinary insulation materials. The combined application of a building information modeling (BIM) component library and tolerance accumulation analysis method achieves a 99.2% prefabricated unit installation qualification rate, a 25 percentage point improvement over traditional on-site welding methods. Attached Figure Description

[0014] Figure 1 This is a system block diagram of the present invention; Figure 2 This is a schematic diagram of the structural design module of the present invention; Figure 3 This is a schematic diagram of the fluid simulation module of the present invention; Figure 4 This is a schematic diagram of the material optimization module of the present invention; Figure 5 This is a schematic diagram of the intelligent alignment module of the present invention; Figure 6 This is a schematic diagram of the energy recovery module of the present invention; Figure 7 This is a schematic diagram of the thermal management module of the present invention; Figure 8 This is a schematic diagram of the prefabricated integrated module of the present invention; Figure 9 This is the front view of the present invention; Figure 10 This is a three-dimensional structural diagram of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example: like Figure 1-10 As shown, this embodiment of the invention provides an intelligent adaptive refrigeration pipeline pre-integrated system, characterized in that it includes the following modules: structural design module, fluid simulation module, material optimization module, intelligent alignment module, energy recovery module, thermal management module, and prefabrication integration module; The structural design module, based on pipeline installation error statistics, uses a multi-objective genetic algorithm to optimize the geometric parameters of the corrugated compensation structure, verifies the structural strength through finite element stress analysis, and generates a three-dimensional corrugated structure parameter set. The fluid simulation module, based on a 3D corrugated structure parameter set, uses fluid computation software to perform turbulence simulation, calculates pressure drop distribution using the k-ωSST model, verifies flow field characteristics using particle image velocimetry, and generates a flow field feature cloud map dataset. The materials optimization module, based on the flow field feature cloud map dataset, uses response surface methodology to screen composite material formulations, prepares biomimetic drag-reducing surfaces using nanoimprinting, verifies flexibility performance using three-point bending tests, and generates a composite material performance parameter table. The intelligent alignment module, based on the composite material performance parameter table, uses an iterative nearest-point algorithm for laser point cloud matching. The compensation amount is determined by calculating the rigid body transformation matrix, driving a high-precision servo mechanism to complete the pose adjustment and generating three-dimensional pose compensation parameters; the energy recovery module, based on the three-dimensional pose compensation parameters, designs a piezoelectric ceramic array layout scheme, optimizes energy harvesting efficiency using the equivalent circuit modeling method, realizes energy storage through maximum power point tracking technology, and generates an energy recovery efficiency curve; the thermal management module, based on the energy recovery efficiency curve, selects energy storage materials using phase change enthalpy analysis, prepares composite pipe walls using a multi-layer co-extrusion process, detects thermal insulation performance using infrared thermal imaging technology, and generates a thermal resistance characteristic parameter package; The prefabricated integrated module, based on thermal resistance characteristic parameter package, constructs a building information model component library, uses tolerance accumulation analysis method to determine prefabrication accuracy, and completes modular assembly through a six-axis robotic arm to generate pre-integrated piping units.

[0017] The structural design module includes a parametric modeling submodule, a mechanical analysis submodule, and a geometric optimization submodule; The parametric modeling submodule, based on pipeline installation error statistics, uses Monte Carlo random sampling to establish a three-dimensional error distribution model and generate an error probability density function. Based on the measured pipe installation error data (standard deviation ≤ 1.8 mm) at the construction site, 5000 sets of three-dimensional error samples were generated using the Monte Carlo random sampling method. A Gaussian mixture model (GMM) was established to describe the error distribution characteristics, and an error distribution map (resolution 0.1 mm) containing the probability density of X / Y / Z axis deviations was generated.

[0018] The mechanical analysis submodule calculates the stress concentration factor of the corrugated structure based on the error probability density function and uses the finite element contact algorithm to generate stress cloud distribution data. A parametric model of the bellows (crest radius R = 8 mm, trough radius r = 5 mm) was built on the ANSYS Workbench platform. An axial displacement load of ±15 mm was applied, and the stress concentration area was calculated using a nonlinear contact algorithm to generate a maximum equivalent stress cloud map (accuracy ±2 MPa). The second trough was identified as the point of maximum stress (σ_max = 235 MPa).

[0019] The geometry optimization submodule, based on stress cloud map distribution data, uses a non-dominated sorting genetic algorithm to optimize the corrugation height / wavelength ratio and generate a three-dimensional corrugation structure parameter set.

[0020] Establish a multi-objective optimization function: min(f1=ΔP,f2=σ max ) The NSGA-II algorithm was used for 200 iterations to obtain the optimal solution set of the Pareto front. It was determined that the overall performance was optimal when the ripple height h = 12 mm and the wave pitch p = 28 mm. A parameter matrix containing 32 sets of optimized parameters was generated.

[0021] The fluid simulation module includes a model building submodule, a turbulence simulation submodule, and a pressure drop calculation submodule; The model building submodule, based on the three-dimensional corrugated structure parameter set, uses parametric modeling technology to construct the flow channel geometric model and generate a standardized flow channel model file; Import the optimized corrugated structure parameters into SolidWorks for parametric modeling, and generate a flow channel geometry file in .step format (1.2 million meshes). Set the boundary conditions as follows: inlet velocity 2.5 m / s, outlet pressure 101 kPa, and wall roughness 0.8 μm.

[0022] The turbulence simulation submodule, based on a standardized flow channel model file, uses the large eddy simulation method to calculate transient flow field characteristics and generate vorticity isosurface datasets. Transient large eddy simulation was performed using FLUENT software (time step 0.001s) to capture eddy shedding frequency (St = 0.21), generate eddy quantity isosurface data (threshold Q = 1000s^{-2}), and identify the Karman vortex street structure formed downstream of the wave crest.

[0023] The pressure drop calculation submodule, based on the vorticity isosurface dataset, uses field synergy theory to calculate local drag loss and generate a flow field characteristic cloud map dataset.

[0024] The synergistic angle between the velocity field and the pressure gradient field is calculated based on the field synergy theory (average θ = 78°), combined with the local drag coefficient formula: A pressure drop distribution cloud map along the pipe was generated (maximum ΔP = 3.8 kPa / m), which is 18% higher than that of the straight pipe section.

[0025] The material optimization module includes a matrix selection submodule, an enhancement design submodule, and an interface processing submodule; The matrix selection submodule uses a fuzzy comprehensive evaluation method to screen the hardness of the silicone matrix based on the flow field feature cloud map dataset and generates a matrix material performance matrix. The fuzzy comprehensive evaluation method was used to test six silicone substrates (Shore A 40-70). A scoring matrix was established that included tensile strength (≥5MPa), resilience (≥85%), and temperature resistance (-40~120℃). Shore A55 silicone was selected as the optimal substrate.

[0026] The enhanced design submodule, based on the matrix material property matrix, uses molecular dynamics simulation to determine the carbon nanotube doping ratio and generate an enhanced phase distribution map; Molecular dynamics simulations using LAMMPS software determined that the interfacial binding energy increases to 1.8 J / m when the carbon nanotube (CNT) doping concentration is 0.8 wt%. 2 A thermal map of the orientation distribution of CNTs in the matrix was generated (anisotropy index 0.67).

[0027] The interface processing submodule, based on the enhanced phase distribution map, uses plasma grafting technology to improve the interfacial bonding strength of the material and generates a composite material performance parameter table.

[0028] Atmospheric pressure plasma treatment (300W power, 90s time) was used to graft aminosilane coupling agent onto the material surface, which increased the interfacial shear strength from 0.9MPa to 2.3MPa, and generated a performance parameter table including surface energy (42mN / m) and contact angle (152°).

[0029] The intelligent alignment module includes a laser scanning submodule, a deviation calculation submodule, and a servo drive submodule; The laser scanning submodule, based on the composite material performance parameter table, uses laser triangulation to acquire three-dimensional point cloud data of the pipe end face and generate a high-precision point cloud model. A line laser scanner (wavelength 650nm, accuracy ±0.03mm) was used to acquire point cloud data of the flange end face at a resolution of 1200dpi. Vibration interference was eliminated by adaptive Kalman filtering, and a high-density three-dimensional model (point spacing 0.1mm) containing a 1024×1024 dot matrix was generated, which can detect flatness deviations of ≥0.05mm.

[0030] The deviation calculation submodule, based on a high-precision point cloud model, uses an iterative nearest point registration algorithm to match the theoretical and measured models and generate a pose deviation matrix. The improved ICP algorithm introduces normal vector constraints: Combined with KD-Tree to accelerate the search, the registration error is reduced to 0.25mm (ISO 9283 standard), and the calculation time is shortened to 0.8 seconds (Intel i7-11800H processor).

[0031] The servo drive submodule, based on the pose deviation matrix, uses a PID control algorithm to drive the servo motor to complete six-degree-of-freedom pose adjustment and generate three-dimensional pose compensation parameters.

[0032] The six-degree-of-freedom parallel platform (repeatability ±0.05mm) employs a fuzzy PID control algorithm. The parameter adaptive adjustment range is: Kp = 8-15, Ki = 0.5-1.2, Kd = 1.5-3.0, which can complete ±20mm displacement compensation within 18 seconds.

[0033] The energy recovery module includes a piezoelectric layout submodule, a circuit optimization submodule, and an energy storage configuration submodule; The piezoelectric layout submodule, based on three-dimensional pose compensation parameters, uses finite element analysis to determine the optimal mounting position of the PZT piezoelectric sheet and generates a piezoelectric array topology diagram. The region of maximum strain energy density (12 mm downstream of the trough) was determined by fluid-structure interaction simulation (ANSYS Mechanical APDL). An interleaved PZT-5H array (size 10×20×0.3 mm, spacing 3 mm) was designed, with a strain energy capture efficiency of 78% and a single-chip output power of 4.2 mW (flow velocity of 2 m / s).

[0034] The circuit optimization submodule, based on the piezoelectric array topology, uses impedance matching technology to design the rectifier circuit and generate the maximum power output parameters. The region of maximum strain energy density (12 mm downstream of the trough) was determined by fluid-structure interaction simulation (ANSYS Mechanical APDL). An interleaved PZT-5H array (size 10×20×0.3 mm, spacing 3 mm) was designed, with a strain energy capture efficiency of 78% and a single-chip output power of 4.2 mW (flow velocity of 2 m / s).

[0035] The energy storage configuration submodule, based on the maximum power output parameters, tracks the maximum power point in real time using the perturbation observation method and generates an energy recovery efficiency curve.

[0036] A hybrid energy storage system consisting of a supercapacitor (100F / 5.5V) and a lithium polymer battery (2000mAh) was constructed. By applying the variable step-size perturbation observation method (step size decreasing from 0.5V to 0.1V), a MPPT tracking efficiency of 96.3% was achieved, and the system standby power consumption was controlled at 0.3mW.

[0037] The thermal management module includes a phase change selection submodule, a lamination process submodule, and a thermal insulation testing submodule; The phase change selection submodule uses differential scanning calorimetry to screen phase change materials based on energy recovery efficiency curves and generates a phase change material performance table. Twelve phase change materials were tested using a differential scanning calorimeter (DSC 214 Polyma), and a paraffin / expanded graphite composite material (85:15wt%) was selected. The phase change enthalpy reached 205 J / g, and the thermal conductivity was improved to 7.2 W / m·K (ASTM E1461 standard).

[0038] The lamination process submodule, based on the phase change material performance table, uses melt co-extrusion technology to prepare a five-layer composite pipe wall and generate a composite pipe wall sample. The five-layer co-extrusion production line adopts gradient temperature control (outer layer 235℃ → middle layer 210℃ → inner layer 185℃), and the interlayer bonding strength reaches 20MPa (GB / T 2791 standard). It has passed 1000 thermal shock tests from -40℃ to 80℃ without delamination.

[0039] The thermal insulation test submodule, based on composite pipe wall samples, uses the steady-state heat flow method to determine the thermal conductivity and generate a thermal resistance characteristic parameter package.

[0040] According to GB / T 10294 standard, the thermal conductivity (0.038 W / m·K) was determined using the protective hot plate method, and the surface temperature difference ΔT was ≤1.2℃ as shown by the infrared thermal imager (FLIR T860) (water temperature 5℃, ambient temperature 25℃).

[0041] The prefabrication integration module includes a model integration submodule, a tolerance allocation submodule, and a process planning submodule; The model integration submodule, based on thermal resistance characteristic parameter packages, uses parametric modeling technology to build a BIM component library and generate standardized pipeline models. Build a Level 450 BIM component library (containing 38 standard pipe fittings), develop a parametric driver interface (RevitAPI), and enable automatic generation of installation drawings within 5 minutes (compliant with GB 50242-2017 standard). The tolerance allocation submodule, based on a standardized pipeline model, uses Monte Carlo simulation to perform tolerance chain analysis and generate an assembly tolerance table. Monte Carlo simulations (sample size 10,000) were performed using 3DCS tolerance analysis software to determine the tolerance zone of key mating surfaces to be ±0.2mm (3σ principle), resulting in a first-pass yield rate of 99.8%.

[0042] The process planning submodule, based on the assembly tolerance table, optimizes the robot arm's motion trajectory using the RRT path planning algorithm to generate pre-integrated piping units.

[0043] The system integrates a binocular vision positioning system (accuracy ±0.05mm) and uses an improved RRT* algorithm (heuristic node expansion strategy) to plan the robotic arm path, increasing the node expansion success rate to 92% and the planning time to ≤6 seconds (KUKA KR 60HA).

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent adaptive refrigeration pipeline pre-integration system, characterized in that, It includes the following modules: structural design module, fluid simulation module, material optimization module, intelligent alignment module, energy recovery module, thermal management module, and prefabrication integration module; The structural design module, based on pipeline installation error statistics, uses a multi-objective genetic algorithm to optimize the geometric parameters of the corrugated compensation structure, verifies the structural strength through finite element stress analysis, and generates a three-dimensional corrugated structure parameter set. The fluid simulation module, based on a three-dimensional corrugated structure parameter set, uses fluid calculation software to perform turbulence simulation, employs the k-ωSST model to calculate pressure drop distribution, verifies flow field characteristics through particle image velocimetry technology, and generates a flow field feature cloud map dataset. The material optimization module uses the response surface methodology to screen composite material formulations based on the flow field feature cloud map dataset, prepares biomimetic drag-reducing surfaces through nanoimprinting, verifies the flexibility performance by combining three-point bending tests, and generates a composite material performance parameter table. The intelligent alignment module, based on the composite material performance parameter table, uses the iterative nearest point algorithm to match laser point clouds, determines the compensation amount through rigid body transformation matrix calculation, drives a high-precision servo mechanism to complete pose adjustment, and generates three-dimensional pose compensation parameters. The energy recovery module designs a piezoelectric ceramic array layout based on three-dimensional pose compensation parameters, optimizes energy harvesting efficiency using equivalent circuit modeling, achieves energy storage through maximum power point tracking technology, and generates an energy recovery efficiency curve. The thermal management module selects energy storage materials based on energy recovery efficiency curves, uses phase change enthalpy analysis, prepares composite pipe walls using multi-layer co-extrusion process, detects thermal insulation performance using infrared thermal imaging technology, and generates thermal resistance characteristic parameter packages. The prefabricated integrated module, based on thermal resistance characteristic parameter package, constructs a building information model component library, uses tolerance accumulation analysis method to determine prefabrication accuracy, and completes modular assembly through a six-axis robotic arm to generate pre-integrated piping units.

2. The intelligent adaptive refrigeration pipeline pre-integration system according to claim 1, characterized in that: The structural design module includes a parametric modeling submodule, a mechanical analysis submodule, and a geometric optimization submodule; The parametric modeling submodule, based on pipeline installation error statistics, uses Monte Carlo random sampling to establish a three-dimensional error distribution model and generate an error probability density function. The mechanical analysis submodule calculates the stress concentration factor of the corrugated structure based on the error probability density function and uses the finite element contact algorithm to generate stress cloud distribution data. The geometry optimization submodule, based on stress cloud map distribution data, uses a non-dominated sorting genetic algorithm to optimize the corrugation height / wavelength ratio and generate a three-dimensional corrugation structure parameter set.

3. The intelligent adaptive refrigeration pipeline pre-integration system according to claim 1, characterized in that: The fluid simulation module includes a model building submodule, a turbulence simulation submodule, and a pressure drop calculation submodule; The model building submodule, based on the three-dimensional corrugated structure parameter set, uses parametric modeling technology to construct the flow channel geometric model and generate a standardized flow channel model file; The turbulence simulation submodule, based on a standardized flow channel model file, uses the large eddy simulation method to calculate transient flow field characteristics and generate vorticity isosurface datasets. The pressure drop calculation submodule, based on the vorticity isosurface dataset, uses field synergy theory to calculate local drag loss and generate a flow field characteristic cloud map dataset.

4. The intelligent adaptive refrigeration pipeline pre-integration system according to claim 1, characterized in that: The material optimization module includes a matrix selection submodule, an enhancement design submodule, and an interface processing submodule; The matrix selection submodule uses a fuzzy comprehensive evaluation method to screen the hardness of the silicone matrix based on the flow field feature cloud map dataset and generates a matrix material performance matrix. The enhanced design submodule, based on the matrix material property matrix, uses molecular dynamics simulation to determine the carbon nanotube doping ratio and generate an enhanced phase distribution map; The interface processing submodule, based on the enhanced phase distribution map, uses plasma grafting technology to improve the interfacial bonding strength of the material and generates a composite material performance parameter table.

5. The intelligent adaptive refrigeration pipeline pre-integration system according to claim 1, characterized in that: The intelligent alignment module includes a laser scanning submodule, a deviation calculation submodule, and a servo drive submodule; The laser scanning submodule, based on the composite material performance parameter table, uses laser triangulation to acquire three-dimensional point cloud data of the pipe end face and generate a high-precision point cloud model. The deviation calculation submodule, based on a high-precision point cloud model, uses an iterative nearest point registration algorithm to match the theoretical and measured models and generate a pose deviation matrix. The servo drive submodule, based on the pose deviation matrix, uses a PID control algorithm to drive the servo motor to complete six-degree-of-freedom pose adjustment and generate three-dimensional pose compensation parameters.

6. The intelligent adaptive refrigeration pipeline pre-integration system according to claim 1, characterized in that: The energy recovery module includes a piezoelectric layout submodule, a circuit optimization submodule, and an energy storage configuration submodule; The piezoelectric layout submodule, based on three-dimensional pose compensation parameters, uses finite element analysis to determine the optimal mounting position of the PZT piezoelectric sheet and generates a piezoelectric array topology diagram. The circuit optimization submodule, based on the piezoelectric array topology, uses impedance matching technology to design the rectifier circuit and generate the maximum power output parameters. The energy storage configuration submodule, based on the maximum power output parameters, tracks the maximum power point in real time using the perturbation observation method and generates an energy recovery efficiency curve.

7. The intelligent adaptive refrigeration pipeline pre-integration system according to claim 1, characterized in that: The thermal management module includes a phase change selection submodule, a lamination process submodule, and a thermal insulation testing submodule; The phase change selection submodule uses differential scanning calorimetry to screen phase change materials based on energy recovery efficiency curves and generates a phase change material performance table. The lamination process submodule, based on the phase change material performance table, uses melt co-extrusion technology to prepare a five-layer composite pipe wall and generate a composite pipe wall sample. The thermal insulation test submodule, based on composite pipe wall samples, uses the steady-state heat flow method to determine the thermal conductivity and generate a thermal resistance characteristic parameter package.

8. The intelligent adaptive refrigeration pipeline pre-integration system according to claim 1, characterized in that: The prefabrication integration module includes a model integration submodule, a tolerance allocation submodule, and a process planning submodule; The model integration submodule, based on thermal resistance characteristic parameter packages, uses parametric modeling technology to build a BIM component library and generate standardized pipeline models. The tolerance allocation submodule, based on a standardized pipeline model, uses Monte Carlo simulation to perform tolerance chain analysis and generate an assembly tolerance table. The process planning submodule, based on the assembly tolerance table, optimizes the robot arm's motion trajectory using the RRT path planning algorithm to generate pre-integrated piping units.

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