A method and system for deepening design of large space ceiling pipelines based on BIM technology

Abstract

The present invention provides a method and system for deepening design of large-space ceiling pipelines based on BIM technology, which belongs to the technical field of pipeline arrangement, and includes the following steps: S1, using BIM technology to establish a BIM model for large-space ceiling pipelines according to construction drawings and actual building parameters; S2, using structural analysis software to analyze the initial structure of pipelines of different professions; S3, staff of pipelines of different professions perform collaborative operation of pipeline construction simulation; S4, pipeline construction simulation through BIM model; S5, multiple iterative simulations according to the optimized construction plan; S6, using structural analysis software to perform final structural analysis on the optimized plan. The problem that there are many types of pipelines and collision with other professional pipelines is not considered, and design changes may occur during the construction process, which will affect the original pipeline arrangement and the overall progress of the construction is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of pipeline arrangement, and in particular, relates to a large-space ceiling pipeline in-depth design method and system based on BIM technology. Background Art

[0002] With the development of society, the construction requirements for building projects are getting higher and higher. The increase in equipment demand makes the layout of pipelines more and more complicated. At the same time, considering the aesthetics and practicality of the project, a pipeline layout plan design is required before the pipeline is laid out, and the layout construction is carried out based on the design plan. Therefore, the accuracy of the plan affects the subsequent construction standards. If there is a deviation in the plan, there may be potential application risks, and rework and adjustment will be required later.

[0003] The layout of pipelines is very important in building construction. On the one hand, correct pipeline layout can ensure the smooth implementation of various functions of the building. On the other hand, incorrect layout may lead to delays in the construction process, increased costs, safety hazards and other problems. Therefore, the importance of pipeline layout in building construction cannot be ignored.

[0004] However, pipeline layout is difficult during the construction process. There are many types of pipelines, and each professional supports and installs them according to their own professional drawings, without considering the collision with other professional pipelines. The demolition and modification during the construction process is serious, and design changes may occur during the construction process, which will affect the original pipeline layout. The layout of the pipeline needs to be re-evaluated and adjusted, affecting the overall progress of the construction. Summary of the invention

[0005] The embodiment of the present invention provides a large-space ceiling pipeline in-depth design method and system based on BIM technology, which solves the problem that there are many types of pipelines and collision with other professional pipelines is not considered. During the construction process, design changes may occur, which will affect the original pipeline layout and the overall progress of the construction.

[0006] In view of the above problems, the technical solution proposed by the present invention is:

[0007] The present invention provides a large space ceiling pipeline in-depth design method based on BIM technology, comprising the following steps:

[0008] S1, using BIM technology to establish BIM model for large space ceiling pipelines according to construction drawings and actual building parameters;

[0009] S2, using structural analysis software to analyze the initial structures of pipelines of different specialties and obtain the structural optimization scheme of the pipelines;

[0010] S3, using the pipeline optimization plan imported into the BIM model, the staff of different professional pipelines conduct collaborative operations on pipeline construction simulation;

[0011] S4, conduct pipeline construction simulation through BIM model and optimize pipeline construction plan according to the simulation situation;

[0012] S5, perform multiple iterative simulations based on the optimized construction plan, and optimize again based on the faults that occur in the simulation;

[0013] S6, using structural analysis software to perform final structural analysis on the optimized solution to obtain the final optimized solution;

[0014] The collaborative operation in step S3 is specifically as follows:

[0015] S31, the staff is divided into three parts: the service end, the design end and the operation end. The service end manages the staff data, the staff designs the pipeline plan through the design end, and the staff adjusts the pipeline plan through the operation end;

[0016] S32, staff members of different professions perform different operations on the BIM model of the pipeline through task permissions;

[0017] The steps of the step S32 performed by staff of different professions are as follows:

[0018] S321, when different professional staff operate, each staff has his own work task, and the current task is indicated by the task register;

[0019] S322, when it is necessary to switch the operation of staff members of different professions, the staff member currently operating needs to actively request to temporarily give up the execution right to switch the operator;

[0020] S323, installing a timer interrupt to remind the operator to implement task switching when an interrupt signal is generated, so as to realize the collaborative work of different professional staff;

[0021] The specific steps of pipeline construction simulation in step S4 are as follows:

[0022] S41, performing a collision test on the pipeline through dynamic collision detection, which is specifically:

[0023] S411, based on different pipeline design drawings, use BIM technology to simulate pipeline construction;

[0024] S412, collision detection based on BIM technology based on the comprehensive pipeline model established in the BIM platform;

[0025] S413, checking conflict points between pipelines in the simulated collision detection and optimizing them;

[0026] S42, perform a leak test on the pipeline, specifically:

[0027] S421, basic parameters of the collection pipeline, including materials, dimensions, connection methods and working conditions;

[0028] S422, importing basic parameters of the pipeline into simulation software for simulation;

[0029] S423, setting restriction conditions, calculating the pressure and flow distribution of the pipeline, and obtaining the pressure distribution, flow distribution and stress information of the pipeline;

[0030] S424, by changing the parameters of the restriction condition, observing the change of the pipeline, and optimizing the pipeline according to the simulated structure of the pipeline;

[0031] S43, conduct heat loss test on pipeline, specifically:

[0032] S431, use COMSOL Multiphysics or ANSYS Fluen software to establish a heat transfer model for the pipeline;

[0033] S432, input pipeline parameters to simulate the heat loss of the pipeline;

[0034] The specific steps of step S5 are as follows:

[0035] S51, based on the simulation software and model of step S4, separately changing various parameters;

[0036] S52, after changing the parameters, observe the changes in pipeline construction models of different professions to determine whether there is a fault in the model;

[0037] S53, optimizing the pipeline construction plan at the fault location until the pipeline construction model is in a stable state;

[0038] The design system applied to the above method includes a construction simulation module, a detailed design module, a large database and a user layer. The construction simulation module uses Revit software to simulate pipeline construction. The construction simulation module includes a collision simulation unit, a leakage simulation unit and a heat loss simulation unit.

[0039] The in-depth design module performs in-depth design on the design scheme of the pipeline based on the simulation situation of the construction simulation module, and the in-depth design module includes a scheme analysis unit, a scheme prediction unit and a scheme output unit.

[0040] As a preferred technical solution of the present invention, the structural analysis software in step S2 is one of SAP2000 or ETABS, and the structural analysis steps of the pipeline in step S2 are as follows:

[0041] S21, establish structural models of pipelines of different specialties through structural analysis software;

[0042] S22, apply load parameters and condition constraints, and perform static, dynamic or buckling analysis according to requirements;

[0043] S23, numerical analysis of the model using the finite element method;

[0044] S24, optimizes the structures of pipelines of different specialties according to the output structural numerical analysis structures of pipelines of different specialties.

[0045] As a preferred technical solution of the present invention, the simulation software in step S422 is one of ANSYS Fluent, Auto PIPE or GHD Piping System Analysis.

[0046] As a preferred technical solution of the present invention,

[0047] The collision simulation unit uses the BIM model to perform construction simulation to check the construction collision situation of the pipeline;

[0048] The leakage simulation unit uses simulation software to simulate and calculate the pressure, temperature and flow rate of the pipeline;

[0049] The heat loss simulation unit uses simulation software to establish a heat transfer model of the pipeline and calculate the heat loss of the pipeline;

[0050] The scheme analysis unit is used to analyze the construction scheme data of the construction simulation module;

[0051] The scheme prediction unit uses the scheme of the construction simulation module to learn and predict the data;

[0052] The solution output unit is used to visualize the prediction data of the solution prediction unit;

[0053] The large database is used to store information parameters of various pipelines and type parameters of building structures;

[0054] The user layer is used to store the staff's information and classify the staff's permissions. The user layer includes a user terminal and a collaboration module. The user terminal is used for the staff to register an account and input information. The collaboration module is used for different professional pipeline workers to exchange information and simulate and optimize the construction simulation module for different professions.

[0055] As a preferred technical solution of the present invention, the construction simulation module also includes a distribution box simulation unit, a cable simulation unit, an air duct simulation unit, a water pipe simulation unit and a lighting unit. The distribution box simulation unit, the cable simulation unit, the air duct simulation unit, the water pipe simulation unit and the lighting unit are used to perform separate structural simulations for pipelines of different professions.

[0056] As a preferred technical solution of the present invention, the solution prediction unit predicts the simulation state of the pipeline using deep learning technology, specifically:

[0057] A. Determine the pipeline type to be predicted and select parameters;

[0058] B, using convolutional neural network to build a deep learning model;

[0059] C. Use the training set to iterate the model multiple times and adjust the data of pipeline design. The specific steps of model training are:

[0060] By passing in the training set x and the training set label y, the model is trained using the fit method, where epochs is the number of iterations, and Early Stopping is used to stop at the appropriate epoch in time to reduce overfitting. The batch size is the number of training samples randomly sampled in each epoch. A for loop is used to implement a grid search-like hyperparameter adjustment to verify the effect of different numbers of neurons in the hidden layer.

[0061] D, minimize the error of the predicted data through one of the mean square error loss function or the cross entropy loss function;

[0062] E. Use trained data to predict new data and provide optimization solutions for pipeline design:

[0063] Compared with the prior art, the beneficial effects of the present invention are:

[0064] (1) Considering that the pipeline design of the space ceiling is relatively complex and there are many pipelines of different professions, the present invention brings together the design work of pipeline workers of different professions in a collaborative manner, reduces the collision of pipeline construction of different professions, and completes the task more efficiently through reasonable division of labor and collaboration, avoiding duplication of work.

[0065] (2) The present invention simulates collision, leakage and heat loss of pipeline construction of different professions separately, and uses deep learning technology to learn and predict the pipeline BIM model of the current construction plan. It can predict the usage status of the pipeline and then optimize the construction plan of the pipeline to obtain the most stable pipeline construction plan.

[0066] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic diagram of a large space ceiling pipeline in-depth design method based on BIM technology disclosed in the present invention;

[0068] Figure 2 It is a block diagram of a large space ceiling pipeline in-depth design system based on BIM technology disclosed in the present invention;

[0069] Figure 3 It is a block diagram of a construction simulation module of a large space ceiling pipeline in-depth design system based on BIM technology disclosed in the present invention;

[0070] Explanation of the accompanying drawings: 101, construction simulation module; 1011, collision simulation unit; 1012, leakage simulation unit; 1013, heat loss simulation unit; 1014, distribution box simulation unit; 1015, cable simulation unit; 1016, air duct simulation unit; 1017, water pipe simulation unit; 1018, lighting simulation unit; 102, in-depth design module; 1021, solution analysis unit; 1022, solution prediction unit; 1023, solution output unit; 103, big database; 104, user layer; 1041, user end; 1042, collaboration module. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0074] Embodiment 1

[0075] Refer to the attached Figure 1 As shown, the present invention provides a technical solution: a large space ceiling pipeline in-depth design method based on BIM technology, comprising the following steps:

[0076] S1, based on the construction drawings and actual building parameters, the BIM model of the large space ceiling pipelines is established using BIM technology. The model includes separate models for different types of pipelines and a comprehensive model for all different types of pipelines;

[0077] S2, using structural analysis software to analyze the initial structure of different professional pipelines. The structural analysis software is one of SAP2000 or ETABS, which is integrated with BIM technology to obtain the structural optimization plan of the pipeline, specifically:

[0078] S21, establish structural models of pipelines of different specialties through structural analysis software;

[0079] S22, apply load parameters and condition constraints, and perform static, dynamic or buckling analysis according to requirements;

[0080] S23, numerical analysis of the model using finite element method, including displacement, stress, pressure, etc.;

[0081] S24, optimizing the structures of different professional pipelines according to the output structural numerical analysis structures of different professional pipelines;

[0082] S3, using the pipeline optimization plan to import into the BIM model, to build the overall model, different professional pipeline workers to conduct pipeline construction simulation collaborative operation;

[0083] S4, conduct construction simulation of integrated pipelines through BIM model, and optimize the integrated pipeline construction plan according to the simulation situation;

[0084] S5, perform multiple iterative simulations based on the optimized construction plan, and optimize again based on the faults that occur in the simulation;

[0085] S6, use structural analysis software to conduct final structural analysis on the optimized solution until the comprehensive pipeline design solution achieves the required effect, obtains the final optimized solution, and conducts field construction;

[0086] The collaborative operation in step S3 is specifically as follows:

[0087] S31, the staff is divided into three parts: the service end, the design end and the operation end. The staff data is managed through the service end, the staff designs the pipeline plan through the design end, and the staff adjusts the pipeline plan through the operation end;

[0088] S32, staff members of different professions perform different operations on the BIM model of the pipeline through task permissions;

[0089] Step S32: The steps for different professional staff to perform operations are:

[0090] S321, when different professional staff operate, each staff has his own work task, and the current task is indicated by the task register;

[0091] S322, when it is necessary to switch the operation of staff members of different professions, the staff member currently operating needs to actively request to temporarily give up the execution right to switch the operator;

[0092] S323, installing a timer interrupt to remind the operator to implement task switching when an interrupt signal is generated, so as to realize the collaborative work of different professional staff;

[0093] In addition, when processing multiple tasks, two or more tasks can be executed at the same time. Even if one task is not completed, the next task can be executed. When the current task is to be switched out, all the states of the current task must be protected for future recovery. The task being switched to must also be restored to the state when it was interrupted, thereby preventing untimely data storage and untimely information updates between pipeline designs of different disciplines.

[0094] The specific steps of pipeline construction simulation in step S4 are as follows:

[0095] S41, performing collision test on pipelines through dynamic collision detection, specifically:

[0096] S411, based on different pipeline design drawings, use BIM technology to simulate pipeline construction;

[0097] S412, collision detection based on BIM technology based on the comprehensive pipeline model established in the BIM platform;

[0098] S413, checking the conflict points between pipelines in the simulated collision detection, and optimizing them, and repeating the above work until there is no collision;

[0099] S42, perform a leak test on the pipeline, specifically:

[0100] S421, basic parameters of the collection pipeline, including materials, dimensions, connection methods and working conditions;

[0101] S422, importing basic parameters of the pipeline into simulation software for simulation, where the simulation software is one of ANSYS Fluent, Auto PIPE or GHD Piping System Analysis;

[0102] S423, setting restriction conditions, including pressure, temperature, flow rate, etc., calculating the pressure and flow distribution of the pipeline, and obtaining the pressure distribution, flow distribution and stress information of the pipeline;

[0103] S424, by changing the parameters of the restriction conditions and observing the changes in the pipeline BIM model, the pipeline is optimized until there is no leakage in the pipeline, thereby reducing the risk of leakage;

[0104] S43, conduct heat loss test on pipeline, specifically:

[0105] S431, use COMSOL Multiphysics or ANSYS Fluen software to establish a heat transfer model for the pipeline;

[0106] S432, input pipeline parameters and simulate the heat loss of the pipeline.

[0107] In addition, most of the ceiling pipes in large spaces are installed indoors without the influence of external wind. The surface heat transfer coefficient of the pipes is calculated by the following formula:

[0108] Q=U×A×(T W +T F )×t

[0109] Where Q is the heat loss per unit time, U is the heat transfer coefficient, A is the cross-sectional area of ​​the pipe, and T W Surface temperature, T F t is the surface temperature, and t is the time. The monthly or annual heat loss of the pipeline can be calculated based on the cross-sectional area of ​​the insulated pipeline and time. According to the heat loss, the damaged insulation part can be found and repaired or the insulation structure can be adjusted to achieve the purpose of heat loss detection.

[0110] In addition, considering that there are many types of pipelines in large-space suspended ceilings, the construction simulation of the pipelines needs to be simulated separately first and then centralized to prevent collision, leakage and heat loss between pipelines of the same type, as well as collision between different types of pipeline construction.

[0111] The embodiment of the present invention is also implemented through the following technical solutions.

[0112] In an embodiment of the present invention, the specific steps of pipeline fault simulation in step S5 are as follows:

[0113] S51, based on the simulation software and model of step S4, separately changing various parameters, the parameters including temperature, pressure, connection method, material, etc.;

[0114] S52, after changing the parameters, observe the changes in pipeline construction models of different professions to determine whether there is a fault in the model;

[0115] S53, optimizing the pipeline construction plan at the fault location until the pipeline construction model is in a stable state.

[0116] Embodiment 2

[0117] Refer to the attached Figure 2-3 As shown, an embodiment of the present invention further provides a large space ceiling pipeline in-depth design system based on BIM technology, the system includes a construction simulation module 101, a in-depth design module 102, a big database 103 and a user layer 104;

[0118] The construction simulation module 101 uses Revit software to simulate pipeline construction. The construction simulation module 101 includes a collision simulation unit 1011, a leakage simulation unit 1012, and a heat loss simulation unit 1013;

[0119] The collision simulation unit 1011 uses the BIM model to perform construction simulation and check the construction collision situation of the pipeline;

[0120] The leakage simulation unit 1012 uses simulation software to simulate and calculate the pressure, temperature and flow rate of the pipeline;

[0121] The heat loss simulation unit 1013 uses simulation software to establish a heat transfer model of the pipeline and calculate the heat loss of the pipeline;

[0122] The in-depth design module 102 performs in-depth design on the design scheme of the pipeline based on the simulation situation of the construction simulation module 101. The in-depth design module 102 includes a scheme analysis unit 1021, a scheme prediction unit 1022 and a scheme output unit 1023;

[0123] The scheme analysis unit 1021 is used to analyze the construction scheme data of the construction simulation module 101 and analyze the construction scheme using structural analysis software;

[0124] The scheme prediction unit 1022 uses the scheme of the construction simulation module 101 to learn and predict the data;

[0125] The solution output unit 1023 is used to visualize the prediction data of the solution prediction unit 1022, and display the predicted data on the operator's operation terminal in the form of images or charts;

[0126] The big database 103 is used to store information parameters of various pipelines and type parameters of building structures;

[0127] The user layer 104 is used to store the information of the staff and classify the permissions of the staff. The user layer 104 includes a user terminal 1041 and a collaboration module 1042. The user terminal 1041 is used by the staff to register an account and input information. The collaboration module 1042 is used for different professional pipeline workers to exchange information and perform different professional simulations and optimizations on the simulation of the construction simulation module 101. The user terminal 1041 includes a service terminal, a design terminal and an operation terminal, and divides different professional pipeline operators into different operation permissions, so that pipeline workers of different professions can operate the pipeline model with different permissions and tasks.

[0128] In an embodiment of the present invention, the construction simulation module 101 also includes a distribution box simulation unit 1014, a cable simulation unit 1015, an air duct simulation unit 1016, a water pipe simulation unit 1017 and a lighting unit. The distribution box simulation unit 1014, the cable simulation unit 1015, the air duct simulation unit 1016, the water pipe simulation unit 1017 and the lighting unit are used to perform separate structural simulations for pipelines of different professions.

[0129] In an embodiment of the present invention, the solution prediction unit 1022 uses deep learning technology to predict the simulation state of the pipeline, specifically:

[0130] A. Determine the pipeline type to be predicted and select parameters;

[0131] B, using convolutional neural network to build a deep learning model;

[0132] C. Use the training set to iterate the model multiple times and adjust the data of pipeline design. The specific steps of model training are:

[0133] The model is trained by passing in the training set x and the training set label y, and using the fit method. Epochs is the number of iterations, and Early Stopping is used to stop at the appropriate epoch in time to reduce overfitting. Batch size is the number of training samples randomly sampled in each epoch. A for loop is used to implement grid search-like hyperparameter adjustment, verifying the effect of different numbers of neurons in the hidden layer (hyperparameter k).

[0134] D, through one of the mean square error loss function or cross entropy loss function, minimize the error of the predicted data, that is, the prediction error of the evaluation model;

[0135] E. Predict new data using trained data, and compare the actual value of the test set with the predicted value. If the values ​​are close to the same, the model prediction effect is good. If the values ​​are inconsistent, it means that there is a problem with the model, and then provide an optimization solution for pipeline design.

[0136] In addition, the expression of the mean square error loss function is as follows:

[0137]

[0138] The expression of the cross entropy loss function is as follows:

[0139]

[0140] The mean square error loss function is used to calculate the average square of the predicted value f(x; w) of each sample i minus the actual value y. By minimizing the mean square error loss function, the difference between the model predicted value and the actual value can be made as small as possible. The cross entropy loss function is used to make the model prediction distribution as consistent as possible with the empirical distribution of the actual data (maximum likelihood estimation).

[0141] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0142] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0143] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein can all be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above around their functions. Whether such functions are implemented as hardware or software depends on specific applications and the design constraints imposed on the entire system. A skilled person can implement the described functions in an alternative manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of the present disclosure.

Claims

1. A method for deepening the design of large space ceiling pipelines based on BIM technology, characterized in that: The following steps are involved: S1, using BIM technology to establish BIM model for large space ceiling pipelines according to construction drawings and actual building parameters; S2, using structural analysis software to analyze the initial structures of pipelines of different specialties and obtain the structural optimization scheme of the pipelines; S3, using the pipeline optimization plan imported into the BIM model, the staff of different professional pipelines conduct collaborative operations on pipeline construction simulation; S4, conduct pipeline construction simulation through BIM model and optimize pipeline construction plan according to the simulation situation; S5, perform multiple iterative simulations based on the optimized construction plan, and optimize again based on the faults that occur in the simulation; S6, using structural analysis software to perform final structural analysis on the optimized solution to obtain the final optimized solution; The collaborative operation in step S3 is specifically as follows: S31, the staff is divided into three parts: the service end, the design end and the operation end. The staff data is managed through the service end, the staff designs the pipeline plan through the design end, and the staff adjusts the pipeline plan through the operation end; S32, staff members of different professions perform different operations on the BIM model of the pipeline through task permissions; The steps of the step S32 performed by staff of different professions are as follows: S321, when different professional staff operate, each staff has his own work task, and the current task is indicated by the task register; S322, when it is necessary to switch the operation of staff members of different professions, the staff member currently operating needs to actively request to temporarily give up the execution right to switch the operator; S323, installing a timer interrupt to remind the operator to implement task switching when an interrupt signal is generated, so as to realize the collaborative work of different professional staff; The specific steps of pipeline construction simulation in step S4 are as follows: S41, performing collision test on pipelines through dynamic collision detection, specifically: S411, based on different pipeline design drawings, use BIM technology to simulate pipeline construction; S412, collision detection based on BIM technology based on the comprehensive pipeline model established in the BIM platform; S413, checking conflict points between pipelines in the simulated collision detection and optimizing them; S42, perform a leak test on the pipeline, specifically: S421, basic parameters of the collection pipeline, including materials, dimensions, connection methods and working conditions; S422, importing basic parameters of the pipeline into simulation software for simulation; S423, setting restriction conditions, calculating the pressure and flow distribution of the pipeline, and obtaining the pressure distribution, flow distribution and stress information of the pipeline; S424, by changing the parameters of the restriction condition, observing the change of the pipeline, and optimizing the pipeline according to the simulated structure of the pipeline; S43, conduct heat loss test on pipeline, specifically: S431, use COMSOL Multiphysics or ANSYS Fluen software to establish a heat transfer model for the pipeline; S432, input pipeline parameters to simulate the heat loss of the pipeline; The specific steps of pipeline fault simulation in step S5 are as follows: S51, based on the simulation software and model of step S4, separately changing various parameters; S52, after changing the parameters, observe the changes in pipeline construction models of different professions to determine whether there is a fault in the model; S53, optimizing the pipeline construction plan at the fault location until the pipeline construction model is in a stable state; The design system applied to the above method comprises a construction simulation module (101), a detailed design module (102), a large database (103) and a user layer (104), wherein the construction simulation module (101) uses Revit software to simulate pipeline construction, and the construction simulation module (101) comprises a collision simulation unit (1011), a leakage simulation unit (1012) and a heat loss simulation unit (1013); The in-depth design module (102) performs in-depth design on the design scheme of the pipeline based on the simulation situation of the construction simulation module (101), and the in-depth design module (102) includes a scheme analysis unit (1021), a scheme prediction unit (1022) and a scheme output unit (1023).

2. According to the BIM technology-based large space ceiling pipeline in-depth design method of claim 1, it is characterized by: The structural analysis software in step S2 is one of SAP2000 and ETABS. The structural analysis steps of the pipeline in step S2 are as follows: S21, establish structural models of pipelines of different specialties through structural analysis software; S22, apply load parameters and condition constraints, and perform static, dynamic or buckling analysis according to requirements; S23, numerical analysis of the model using the finite element method; S24, optimizes the structures of pipelines of different specialties according to the output structural numerical analysis structures of pipelines of different specialties.

3. According to the BIM technology-based large space ceiling pipeline in-depth design method of claim 1, it is characterized by: The simulation software in step S422 is one of ANSYS Fluent, Auto PIPE or GHD Piping System Analysis.

4. According to the BIM technology-based large space ceiling pipeline in-depth design method of claim 1, it is characterized in that: The collision simulation unit (1011) uses the BIM model to perform construction simulation to check the construction collision situation of the pipeline; The leakage simulation unit (1012) uses simulation software to simulate and calculate the pressure, temperature and flow rate of the pipeline; The heat loss simulation unit (1013) uses simulation software to establish a heat transfer model of the pipeline and calculates the heat loss of the pipeline; The scheme analysis unit (1021) is used to analyze the construction scheme data of the construction simulation module (101); The scheme prediction unit (1022) uses the scheme of the construction simulation module (101) to learn and predict data; The solution output unit (1023) is used to visually display the prediction data of the solution prediction unit (1022); The large database (103) is used to store information parameters of various pipelines and type parameters of building structures; The user layer (104) is used to store the information of the staff and classify the permissions of the staff. The user layer (104) includes a user terminal (1041) and a collaboration module (1042). The user terminal (1041) is used for the staff to register an account and input information. The collaboration module (1042) is used for different professional pipeline staff to exchange information and simulate and optimize the simulation of the construction simulation module (101) in different professions.

5. According to claim 4, a large space ceiling pipeline in-depth design method based on BIM technology is characterized in that: The construction simulation module (101) further includes a distribution box simulation unit (1014), a cable simulation unit (1015), an air duct simulation unit (1016), a water pipe simulation unit (1017) and a lighting unit. The distribution box simulation unit (1014), the cable simulation unit (1015), the air duct simulation unit (1016), the water pipe simulation unit (1017) and the lighting unit are used to perform separate structural simulations for pipelines of different professions.

6. According to claim 5, a method for in-depth design of large space ceiling pipelines based on BIM technology is characterized in that: The scheme prediction unit (1022) predicts the simulation state of the pipeline using deep learning technology, specifically: A. Determine the pipeline type to be predicted and select parameters; B, using convolutional neural network to build a deep learning model; C. Use the training set to iterate the model multiple times and adjust the data of pipeline design. The specific steps of model training are: By passing in the training set x and the training set label y, the model is trained using the fit method, where epochs is the number of iterations, and Early Stopping is used to stop at the appropriate epoch in time to reduce overfitting. The batch size is the number of training samples randomly sampled in each epoch. A for loop is used to implement a grid search-like hyperparameter adjustment to verify the effect of different numbers of neurons in the hidden layer. D, minimize the error of the predicted data through one of the mean square error loss function or the cross entropy loss function; E. Use trained data to predict new data and provide optimization solutions for pipeline design.

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