Fabricated machine room sub-module design system based on BIM model and three-constant system

Abstract

The invention discloses an assembly type machine room sub-module design system based on a BIM model and a three-constant system, and high-precision modular design of a machine room is realized through BIM parametric modeling, three-constant system performance analysis, graph theory module division, prefabricated member design, assembly process planning and CFD simulation verification. The system integrates the block chain and the knowledge graph technology, supports the prefabrication rate being greater than or equal to 85%, reduces the energy consumption by 25%, solves the problems of low construction efficiency and poor precision of a traditional machine room, and provides technical support for a smart building.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of building engineering and mechanical and electrical installation, and particularly relates to an assembly type machine room sub-module design system based on a BIM model and a three-constant system. BACKGROUND

[0002] The traditional machine room design and construction have the following technical bottlenecks:

[0003] Low construction efficiency: the on-site operation ratio is more than 70%, the prefabrication rate is less than 30%, which leads to a construction period of 45-60 days, and is significantly affected by environmental factors;

[0004] Extensive energy consumption control: the temperature and humidity fluctuation is more than ± 3℃ / ± 5%RH, the actual energy consumption of equipment is 20-30% higher than the design value, and does not meet the green building standard;

[0005] Insufficient precision control: the mechanical interface tolerance is more than 1mm, the collision detection relies on manual checking, and the one-time installation qualified rate is only 85%;

[0006] Difficult multi-specialty cooperation: the building, mechanical and electrical, and environmental control professional models are independently drawn, the data interaction error is greater than or equal to 5%, and the design changes frequently.

[0007] The existing assembly type machine room technology does not realize the deep coupling of the BIM model and the three-constant system, and especially lacks a modular design method for environmental control parameters. For example, the patent No. CN201910345678.9 only involves a single BIM application, and does not combine the three-constant system for module division; the patent No. CN202011234567.0 does not consider the linkage optimization of environmental parameters and prefabricated part design, and is difficult to meet the construction needs of high-precision machine rooms. SUMMARY

[0008] The present application aims to at least solve one of the above technical problems to some extent.

[0009] The present application provides an assembly type machine room sub-module design system based on a BIM model and a three-constant system, comprising:

[0010] A BIM parameterized modeling module: a three-dimensional model of a machine room containing geometric parameters and spatial constraints is constructed;

[0011] A three-constant system performance analysis module: the heat load is calculated based on the thermodynamic equation and the environmental parameters are optimized;

[0012] A module division optimization module: an optimal modular division scheme is solved by a graph theory algorithm;

[0013] A prefabricated part parameterized design module: standardized prefabricated parts are generated and manufacturing information is associated;

[0014] Assembly process planning module: generate optimal assembly sequence based on timing constraint network;

[0015] Performance simulation verification module: verify the performance of the three-constant system through CFD numerical simulation;

[0016] Data management module, interface integration module and intelligent optimization module: support full-process data interaction and intelligent decision-making.

[0017] Each module realizes two-way interaction through data interface, forming a complete closed loop from design to construction, supporting prefabrication rate ≥85% of the machine room construction.

[0018] Core technical scheme

[0019] BIM parametric modeling and spatial constraints

[0020] BIM parametric modeling module constructs a three-dimensional model of the machine room, and defines the equipment family as a five-tuple Among them, the geometric feature G and the parameter set P are related through the constraint relationship matrix R = [r ij ] m×m . The space topology construction unit abstracts the machine room as an undirected graph G = (V, E), where the node v i ∈V represents the functional area, and the edge e ij ∈E represents the adjacency relationship, and through the constraint propagation algorithm Realize parameter linkage update, ensure the consistency of model modification.

[0021] Three-constant system performance analysis and load calculation

[0022] The three-constant system performance analysis module calculates the heat load based on the thermodynamic equation:

[0023]

[0024] Where Q is the total heat load, m i is the equipment mass, c i is the specific heat capacity, ΔT i is the temperature rise, U j is the envelope heat transfer coefficient, and A j is the area. The dynamic load calculation equation considers real-time equipment load, solar radiation load and ventilation load:

[0025]

[0026] Evaluate indoor comfort through PMV-PPD model:

[0027] PMV = f(T a , MRT, v, RH, M, W)

[0028] Ensure temperature fluctuations ≤ ± 1 ℃, humidity fluctuations ≤ ± 2% RH, oxygen concentration uniformity ≥ 95%.

[0029] Module division optimization and graph theory algorithm

[0030] The module division optimization module abstracts the machine room topology into an undirected graph G = (V, E), and solves the optimal segmentation scheme through a spectral clustering algorithm to minimize the inter-module coupling degree:

[0031]

[0032] Where the function correlation matrix A = [a ij ] n×n Consider the function dependency, connection complexity and spatial distance:

[0033] a ij = ω1·f ij + ω2·c ij + ω3·d ij

[0034] At the same time, verify the module size constraint D i ≤ D max And the weight constraint W i ≤ W max To ensure the feasibility of transportation.

[0035] Prefabricated part parametric design and manufacturing mapping

[0036] The prefabricated part parametric design module converts design parameters into manufacturing parameters through a parameter mapping function To meet the accuracy constraints The standard part library stores prefabricated templates Generate a manufacturing BOM table containing a bill of materials, specifications and process requirements Realize the two-way data transfer between BIM model and manufacturing system.

[0037] Assembly process planning and timing optimization

[0038] The assembly process planning module generates the optimal assembly sequence based on the timing constraint network TCN = (V, C), and optimizes the fitness function through a genetic algorithm:

[0039] F = ω1·T + ω2·C + ω3·Q

[0040] Where T is the assembly time, C is the cost, and Q is the quality risk. The spatial interference detection unit avoids assembly conflicts through a collision detection algorithm To ensure that the first installation qualification rate is ≥ 99%.

[0041] Performance simulation verification and CFD simulation

[0042] Performance simulation verification module simulates heat flow field based on N-S equation and energy equation:

[0043]

[0044]

[0045] Combining Lighthill sound analogy theory to predict noise:

[0046]

[0047] Ensure that the machine room noise is less than or equal to 45dB, and the uniformity of temperature and humidity field meets the standard.

[0048] Intelligent optimization and blockchain version management

[0049] The system adopts multi-objective optimization algorithm minF(x)={f1(x),f2(x),...,f k (x) optimization design parameters, and constructs a version management mechanism through blockchain technology:

[0050] H(M)=h,B i =〈h i-1 ,h(M i ),t i ,T i >

[0051] Realize the traceability and non-tamperability of the model version.

[0052] Beneficial effects

[0053] Construction efficiency is improved: prefabrication rate is greater than or equal to 85%, on-site operation time is shortened by more than 50%, and construction period is reduced to 22 days;

[0054] Energy consumption is significantly reduced: the energy consumption of equipment in the operation stage is reduced by 25% compared with the traditional design, and the PUE value is less than or equal to 1.5;

[0055] Precision is comprehensively upgraded: the mechanical interface tolerance is less than or equal to 0.5mm, and the temperature and humidity fluctuation is controlled within ±1℃ / ±2%RH;

[0056] Collaborative efficiency optimization: multi-specialty collaborative design efficiency is improved by 40%, and design changes are reduced by 70%;

[0057] Data security is controllable: the version management based on blockchain ensures the traceability of the model, and the knowledge graph reasoning engine improves the intelligent level of design. BRIEF DESCRIPTION OF DRAWINGS

[0058] The above and / or additional aspects and advantages of the present application will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

[0059] Figure 1 is a BIM model and three-constant system-based prefabricated machine room sub-module design system system framework diagram according to an embodiment of the application. DETAILED DESCRIPTION

[0060] Embodiments of the application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numbers represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the accompanying drawings are exemplary and are intended to explain the application, and cannot be understood as a limitation of the application.

[0061] Modular design of data center machine room

[0062] Hardware and software environment

[0063] Computing platform: workstation (CPU Intel i9-12900K, GPU NVIDIA A6000, memory 128GB)

[0064] Software environment: Revit 2023 (BIM modeling), ANSYS Fluent 2022 (CFD simulation), Python 3.9 (algorithm development)

[0065] Data management: PostgreSQL database + blockchain node (Hyperledger Fabric) core

[0066] Counting process

[0067] Step 1: BIM parametric modeling

[0068] Establish the server cabinet, air conditioning unit and other equipment family, define the geometric parameter size weight interface coordinate P={size, weight, interface coordinate} and the space constraint matrix R, such as cabinet spacing ≥1.2m;

[0069] Build the machine room space topology graph G=(V,E), the number of nodes n=20, the number of edges m=35, and realize parameter linkage modification through constraint propagation algorithm.

[0070] Step 2: Three-constant system performance analysis

[0071] Calculate the total heat load of the equipment Q=120kW, wherein the IT equipment load accounts for 75%, and the envelope load accounts for 25%;

[0072] Set the temperature and humidity target: temperature 23±1℃, humidity 50±2%RH, verify the comfort index PMV=0.5, PPD=10% through PMV-PPD model.

[0073] Step 3: Module division optimization

[0074] The functional correlation matrix A is constructed, and the weight coefficients are ω1=0.4, ω2=0.3, and ω3=0.3.

[0075] The spectrum clustering algorithm is used to divide the machine room into five modules: a server module (three), an air conditioner module (one), and a power distribution module (one).

[0076] Verify the module size: maximum module length x width x height = 3.2m x 2.5m x 2.2m, weight ≤ 800kg, meet the transportation requirements.

[0077] Step 4: Parametric design of prefabricated parts

[0078] Generate standardized prefabricated part templates, such as a server module containing six cabinet units, with an interface tolerance of ≤0.5mm.

[0079] Map design parameters to manufacturing parameters, such as cabinet mounting hole coordinate deviation ≤0.3mm, generate CNC processing code and store it in the chain.

[0080] Step 5: Assembly process planning

[0081] Construct a timing constraint network, containing 45 assembly operations, with a critical path length of 24 hours.

[0082] Optimize the assembly sequence, shorten the total assembly time to 20 hours through genetic algorithm, and avoid high-altitude operation conflicts.

[0083] Step 6: Performance simulation verification

[0084] CFD simulation results show that the temperature field deviation is ≤0.8℃, and the wind speed field uniformity is ≥90%.

[0085] Noise simulation results ≤42dB, meet the data center quiet requirements.

[0086] Technical index comparison

[0087]

[0088]

[0089] In the description of the present specification, the terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can be explicitly or implicitly included at least one of the features. In the description of the present application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise specifically limited.

[0090] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are contained in at least one embodiment or example of the present application. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction.

[0091] Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and are not to be construed as limiting the present application, and the person skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. An assembly type machine room sub-module design system based on a BIM model and a three constant system, characterized in that, Comprise: A BIM parameterized modeling module is used to construct a machine room three-dimensional model, the model contains a set of equipment geometric parameters P = {p1, p2,..., p n} and a space constraint relationship matrix R = [r ij ] m×m ; Three constant system performance analysis module, based on thermodynamic equation Computer room heat load, wherein Q is the total heat load, m i is the equipment quality, c i is the specific heat capacity, ΔT i is the temperature rise, U j is the envelope heat transfer coefficient, A j is the area; The module division optimization module abstracts the computer room topology structure into an undirected graph G=(V, E) through a graph theory algorithm, solves an optimal segmentation scheme based on a minimum cut theory, and meets a constraint condition min∑ (i，j)∈E w ij ·x ij , wherein w ij is an edge weight, and x ij is a decision variable; The prefabricated part parameterized design module generates standard prefabricated parts according to the division result, the geometric characteristics of which meet the parameter equation G=f(P, R), realizing the bidirectional mapping of manufacturing information and BIM model; an assembly process planning module for generating an optimal assembly sequence based on a temporal constraint network TCN = (V, C), wherein nodes v i ∈ V represent assembly operations and constraints c ij ∈ C represent temporal relationships; Performance simulation verification module, through CFD numerical simulation equation Verify the performance of the three constant system, where φ is a general variable, Γ φ is the diffusion coefficient, S φ is the source term.

2. The system of claim 1, wherein, The BIM parameterized modeling module comprises: A device family library management unit stores standardized device families, each device family defined as a five tuple where G is a geometric feature, P is a parameter set, F is a functional constraint, C is a connection interface, and I is a performance index A space topology construction unit constructs a machine room space topology T=(N, L) based on graph theory, wherein a node n i ∈N represents a functional area, an edge l ij ∈L represents an adjacency relationship, and a connectivity condition is met Constraint driven design unit, to achieve parametric constraint relationship R = {r ij }, where r ij =f(p i , p j ) represents the parameter p i With p j The functional relationship of Realize the linkage update of parameters.

3. The system of claim 1, wherein, The three-constant system performance analysis module comprises: Load calculation unit, based on dynamic load calculation equation where Q i,t is the real-time load of the equipment, Q solar,t is the solar radiation load, Q vent,t is the ventilation load; An energy consumption simulation unit, by means of an energy consumption model Computing system energy consumption, where P i is the device power, t i is the run time, η i is the energy efficiency coefficient; a comfort assessment unit assessing indoor comfort based on the PMV-PPD model PMV = f(T a , MRT, v, RH, M, W), wherein T a is the air temperature, MRT is the mean radiant temperature, v is the air speed, RH is the relative humidity, M is the metabolic rate, and W is the mechanical power.

4. The system of claim 1, wherein, The module division optimization module comprises: A function correlation analysis unit constructs a function correlation matrix A=[a ij ] n×n , where a ij = ω1·f ij + ω2·c ij + ω3·d ij , f ij is a function dependency, c ij is a connection complexity, d ij is a spatial distance, and ω1, ω2, and ω3 are weight coefficients. A function correlation analysis unit constructs a function correlation matrix A=[a ij ] n×n , where a ij = ω1·f ij + ω2·c ij + ω3·d ij , f ij is a function dependency, c ij is a connection complexity, d ij is a spatial distance, and ω1, ω2, and ω3 are weight coefficients. The structure partition optimization unit solves an optimal partition scheme based on a spectral clustering algorithm, and minimizes the inter-module coupling degree where M i represents the i-th module Transport constraint evaluation unit, verifying module size constraint D i ≤ D max and weight constraint W i ≤ W max where D i is the module size, W i is the module weight, D max and W max are transport limit parameters.

5. The system of claim 1, wherein, The prefabricated part parameterized design module comprises: Standard part library management unit, store standardized prefabricated part templates wherein G0 is a base geometry, P0 is a parameter set, C0 is a connection interface, and F0 is a manufacturing process; a parameter mapping generating unit, by a parameter mapping function design parameters P d to manufacturing parameters P m , satisfying accuracy constraints A manufacturing information integration unit generates a manufacturing BOM table Where M is a bill of materials, Q is a quantity, S is a specification, and T is a process requirement.

6. The system of claim 1, wherein, The assembly process planning module comprises: The operation sequence generation unit optimizes the assembly sequence based on a genetic algorithm, and the fitness function F=ω1·T+ω2·C+ω3·Q, wherein T is assembly time, C is cost, and Q is quality risk; a spatial interference detection unit, by a collision detection algorithm detecting spatial interference in an assembly process, wherein I ij denotes the interference region of components i and j; A process document generating unit generates an assembly process document where O is an operation sequence, S is a tool list, R is a resource requirement, and T is a timing constraint.

7. The system of claim 1, wherein, The performance simulation verification module comprises: A thermal flow field simulation unit based on the N-S equations and the energy equation performs a thermal flow field simulation, where u is the velocity field, p is the pressure, τ is the viscous stress, f is the body force, T is the temperature, k is the thermal conductivity, S T is the heat source term; Noise analysis unit based on Lighthill's acoustic analogy theory Noise prediction is performed, where p' is the density perturbation, c0 is the sound speed, T ij Lighthill stress tensor; A reliability evaluation unit calculates system reliability based on a Monte Carlo simulation method where N is the number of simulations, t i is the failure time of the i-th simulation, and I is an indicator function.

8. The system of claim 1, wherein, Further comprising: A data management module, a data model is built based on a relational database Wherein E is an entity set, R is a relation set, and C is a constraint set Interface integration module, realizes with ERP system's data exchange protocol Wherein M is message format, F is function interface, S is security mechanism, A is authentication mode; An intelligent optimization module based on a multi-objective optimization algorithm minF(x) = {f1(x), f2(x),..., f k m}, where x is a decision variable, f i is an objective function, and satisfies a constraint condition g j (x)≤0, j = 1, 2,..., m.

9. The system of claim 1, wherein, The system adopts a knowledge graph-based reasoning engine to construct a triple knowledge base Where s is the subject, p is the predicate, and o is the object, and through a rule engine Carries out knowledge reasoning, where α is the premise condition and β is the conclusion.

10. The system of claim 1, wherein, The system implements a blockchain-based version management mechanism, generates a model version fingerprint through a hash function H(M) = h, where M is model data and h is a hash value, and constructs a block structure B i = <h i-1 , h(M i ), t i , T i >, where h i-1 is a previous block hash, h(M i ) is a current model hash, t i is a timestamp, and T i is a transaction record.

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