A computer room modular design system, method and storage medium

Abstract

The present application discloses a computer room modular design system, method and storage medium, the system includes a warehouse management module for constructing a computer room material model and establishing model coding rules to encode and manage the computer room material model, the computer room material model includes: a component model, a pipeline model and a residual material pipeline model; an algorithm module is used to execute a design algorithm to obtain the computer room material model from the warehouse management module according to the design requirements to design the economic module design of the computer room; a learning optimization module optimizes the economic module design of the computer room by learning the collected case models to obtain the modular design of the computer room. The present application solution can reduce the complexity and risk of on-site construction while forming a customized and scalable design solution, respond to the actual product component requirements of the construction to achieve seamless connection between design and construction, improve the efficiency of computer room material use while evaluating the design performance of computer room modules.

Description

Technical Field

[0001] The present application relates to the application field of electromechanical engineering construction technology, and in particular to a modular design system, method and storage medium for a computer room. Background Art

[0002] With the continuous development of my country's economy and the rapid advancement of science and technology, super-high-rise and super-large buildings are rapidly emerging. With the ever-increasing requirements for construction quality and progress control of building mechanical and electrical installation, the continuous innovation of mechanical and electrical installation technology has become an inevitable trend for the development and survival of the building mechanical and electrical installation industry. How to reduce the complexity and risks of on-site construction while forming customized and scalable design solutions to improve the flexibility and plasticity of computer room design IDEs? How to achieve seamless integration of design and construction while responding to the actual product component requirements of construction? While improving the efficiency of computer room material utilization, evaluate computer room installation labor consumption and module design performance, directly link accumulated cases for intelligent adjustment and optimization, and improve the relevance and practicality of design solutions while further reducing material waste and labor costs are currently difficult problems that need to be solved. Summary of the Invention

[0003] In response to the above problems, the purpose of this application is to provide a modular design system, method and storage medium for a computer room, which can realize the electromechanical system functions of the computer room by splicing multiple independent modules, reduce the complexity and risk of on-site construction, and form customized and scalable design solutions, thereby improving the flexibility and reusability of computer room design; respond to the actual product component requirements of construction to achieve seamless connection between design and construction, improve the efficiency of computer room material utilization, and evaluate the labor consumption of computer room installation and module design performance; directly link accumulated cases for intelligent adjustment and optimization, improve the pertinence and practicality of design solutions, and further reduce material waste and labor costs.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present invention provides a modular design system for a computer room, comprising:

[0006] The warehouse management module is used to build a computer room material model and establish model coding rules to encode and manage the computer room material model. The computer room material model includes: a component model, a pipeline model, and a residual material pipeline model;

[0007] An algorithm module is used to execute a design algorithm to obtain a computer room material model from the warehouse management module for modular design according to design requirements;

[0008] The learning optimization module is used to learn the collected case models and optimize the economic module design of the computer room based on the learning results to obtain a modular design of the computer room.

[0009] Optionally, the warehouse management module includes:

[0010] A component library is used to process the acquired sample data to establish a data form, and to construct a component model consistent with the data in the data form;

[0011] A pipeline library is used to process the obtained bill of materials to form a pipeline bill of materials, and to construct a corresponding pipeline model based on the pipeline parameters of the pipeline bill of materials;

[0012] The residual material library is used to manage the pipeline models that do not meet the use requirements or the residual pipeline models of the remaining sections after the modular design of the algorithm module is completed;

[0013] The inventory model management unit is used to encode the models in the component library, the pipeline library and the residual material library to form a model code list.

[0014] Optionally, the algorithm module includes:

[0015] A standard module design unit is used to execute a standard connection algorithm to call the corresponding component model and pipeline model from the library management module to design a standard module design;

[0016] An economical module design unit, configured to execute a surplus material replacement algorithm to identify surplus material pipeline models corresponding to pipeline models in the standard module design from the warehouse management module and replace them to obtain an economical module design;

[0017] a module performance analysis unit, configured to perform module performance analysis on the economic module design;

[0018] An assembly cost verification unit is used to verify the assembly cost of the economic module design that passes the module performance analysis.

[0019] Optionally, the learning module includes:

[0020] A module design optimization unit, used for learning the collected module design case models to optimize the module design of the economic module design;

[0021] A module layout optimization unit is used to learn the collected layout design case models to optimize the layout design of the economic module design;

[0022] The module connection optimization unit is used to learn the collected connection design case models to optimize the pipeline connection of the economic module design.

[0023] In a second aspect, the present application provides a modular design method for a computer room, comprising:

[0024] Select corresponding component models and pipeline models based on received design requirements for standard module design;

[0025] Identifying and obtaining a surplus pipe model corresponding to the pipe model in the standard module design to perform economical module design;

[0026] The economic module design is optimized based on machine learning to obtain a modular design of the computer room.

[0027] Optionally, the step of selecting corresponding component models and pipeline models for standard module design based on the received design requirements includes:

[0028] Analyze the design requirements and determine the corresponding computer room material model;

[0029] Based on the parameters of the computer room material model, the corresponding component model and pipeline model are called from the warehouse management module;

[0030] Executing a standard connection algorithm to arrange the component model and the pipeline model in a linear path;

[0031] Based on the linear path arrangement, the component models and pipeline models are arranged one by one according to specifications to obtain a standard module design.

[0032] Optionally, the step of identifying and obtaining a surplus pipeline model corresponding to the pipeline model in the standard module design to perform economic module design includes:

[0033] Identifying selection parameters of a residual material pipeline model based on a model code of the pipeline model in the standard module design;

[0034] Identify and call a residual material pipeline model that meets the selected parameters based on the model code of the residual material pipeline model;

[0035] Comparing the lengths of the surplus material pipeline model and the corresponding pipeline model in the standard module design;

[0036] The residual material pipeline model whose comparison results meet the comparison rules is replaced with the corresponding pipeline model to obtain an economical module design.

[0037] Optionally, the step of optimizing the economic module design based on machine learning to obtain a modular design of a computer room includes:

[0038] After learning the collected design case models, optimizing the module design of the economic module design;

[0039] After learning the collected layout design case models, optimizing the layout design of the economic module design;

[0040] After studying the collected connection design case models, the pipeline connection designed by the economic module is optimized.

[0041] Optionally, before the step of optimizing the economic module design based on machine learning to obtain a modular design of a computer room, the step further includes:

[0042] Conduct module performance analysis for economic module design;

[0043] Verify assembly cost of economic module design that has passed module performance analysis;

[0044] When the assembly cost is verified, the economic module design is optimized to obtain a modular design of the computer room.

[0045] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the above-mentioned computer room modular design methods.

[0046] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0047] This application provides a modular design system, method and storage medium for a computer room. By utilizing BIM parametric software, it can realize the rapid modeling of complex electromechanical pipe fittings, realize the electromechanical system functions of the computer room by splicing multiple independent modules, reduce the complexity and risk of on-site construction, form a customized and scalable design scheme, and improve the flexibility and reusability of the computer room design; respond to the actual product component requirements of the construction to achieve seamless connection between design and construction, improve the efficiency of computer room material utilization, and evaluate the labor consumption of computer room installation and module design performance; directly link accumulated cases for intelligent adjustment and optimization, improve the pertinence and practicality of the design scheme, and further reduce material waste and labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A schematic diagram of the modular design system framework for a computer room provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of the workflow of the modular design system for a computer room provided in an embodiment of the present application;

[0051] Figure 3The main component intentions of the component library provided in the embodiment of this application;

[0052] Figure 4 The main component intentions of the component library provided in the embodiment of this application;

[0053] Figure 5 A schematic diagram of a method for producing a residual material pipeline provided in an embodiment of the present application;

[0054] Figure 6 Schematic diagram of the module design library provided in the embodiment of the present application;

[0055] Figure 7 A schematic diagram of a modular design method for a computer room provided in an embodiment of the present application;

[0056] Figure 8 A modular design method for a computer room and a workflow diagram are provided for an embodiment of the present application. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0058] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0059] refer to Figure 1 and Figure 2 As shown, the present application provides a modular design system for a computer room, comprising: a warehouse management module, an algorithm module and a learning optimization module, wherein the warehouse management module is used to construct models of various materials needed for the modular design of the computer room, such as component models, pipeline models and surplus material pipeline models, and at the same time establish model coding rules, and set unique codes for the constructed component models, pipeline models and surplus material pipeline models for management; wherein the algorithm module is used to execute the design algorithm to obtain the computer room material model from the warehouse management module according to the design requirements to design the economic module design of the computer room; the learning optimization module optimizes the economic module design of the computer room by learning the collected case models to obtain the modular design of the computer room.

[0060] Continue to refer Figure 1 As shown, in the embodiment of the present application, the warehouse management module includes: a component library, a pipeline library, a surplus material library and an inventory model management unit;

[0061] Among them, the component library needs to cover the sample range, such as component types including commonly used equipment in the computer room, pipe connectors, valves, equipment including water pumps, refrigeration units, heat exchangers, water processors, softening water devices, online cleaning devices and manifolds, etc.; such as pipe connectors including elbows, tees, crosses, reducers and flexible joints, etc., valves including butterfly valves, gate valves, filters, check valves, water flow indicators and stop valves, etc.; obtain detailed sample data from manufacturers, including drawings, technical specifications, performance parameters, etc. required for component library establishment; classify and organize the collected data, divide them according to component type, specification, material and other attributes and establish corresponding data forms, and import the data forms into BIM software to establish various types of equipment, pipe connectors, valves, and ensure that the geometric shape, size, material, performance information and other attributes of the component are consistent with the sample data during the modeling process to form a component library, thus completing the component library construction. Figure 3 、 Figure 4 shown.

[0062] Among them, the pipeline library processes the material list obtained from the material management department to form a pipeline material list, from which pipeline items for computer room construction are allocated. The pipeline items are usually the total length of various types of pipelines with different specifications. For example: 6m is used as 1 inventory to divide the finished product length of various types of pipelines according to specifications, and the part less than 6m is recorded as 1 inventory, thereby forming a pipeline material list containing pipeline types, specifications, and inventory quantities. Based on BIM software, API endpoints are created by writing code, and by reading and calling the list data, pipeline models of various types and specifications with the same inventory quantity are created in the BIM software to form a pipeline library, thus completing the construction of the pipeline library.

[0063] Among them, the surplus material library is used to manage the surplus material pipeline models of pipelines that do not meet the use requirements or the remaining sections after the design is completed. Specifically, the library management module provides the pipeline model of the pipeline library to the algorithm module for completing the standard module design. The algorithm module uses each 6m long pipeline module section by section during the standard module design process. If the remaining section is shorter than the use requirement, that is, it cannot meet the use requirement, or when there is still a surplus after the current standard module design is completed, the remaining section is returned to the library management module as a surplus material model and enters the surplus material library to form a surplus material library. In this embodiment, the surplus material library provides surplus material pipeline models for economic module design to replace the pipeline models intercepted by the standard pipelines provided by the pipeline library, thereby reducing the use of standard pipeline models. The surplus material is generated in the following way: Figure 5 As shown:

[0064] The inventory model management unit is used to encode the models in the component library, pipeline library, and surplus material library to form a model code list, where each model has a unique code. By encoding and identifying the models, entering and forming a model code management list, it facilitates subsequent model management and retrieval. During the management process, if a model is called out of the warehouse management module, the warehouse management module removes the code corresponding to the called-out model from the model code management list. If surplus material is returned to the warehouse management module, the warehouse management module encodes the surplus material and enters it into the model code management list.

[0065] In the embodiment of the present application, the model code structure is "KX.YZ001", where "K" represents the table code, indicating that the code belongs to the model library. "X" represents the major category code, indicating the library category to which the model belongs. There are three categories in total: GJ, GD, and YL. GJ corresponds to the component library, GD corresponds to the pipeline library, and YL corresponds to the residual material library. "Y" represents the medium category code, indicating the type of model to which the model belongs. There are five categories in total: SB, GJ, FM, GD, and YL. SB corresponds to equipment, GJ corresponds to pipe fittings, FM corresponds to valves, GD corresponds to pipelines, and YL corresponds to residual material. "Z" represents the subcategory code, indicating the specifications of this model type. There are eight categories: 50, 65, 100, 125, 150, 200, 250, and 300. 50 corresponds to a diameter of DN50, 65 to a diameter of DN65, 100 to a diameter of DN100, 125 to a diameter of DN125, 150 to a diameter of DN150, 200 to a diameter of DN200, 250 to a diameter of DN250, and 300 to a diameter of DN300. "001" represents the subcategory code, which covers three categories: equipment (SB), pipe fittings (GJ), and valves (FM), representing different individual models within this model type, ranging from 001 to 999. It also covers two categories: pipes (GD) and residual materials (YL), representing different length models within this model type, counted in centimeters, ranging from 001 to 600.

[0066] For example, in the model coding example, the model of the first water pump with a DN200 interface in the component library of the warehouse management module is coded as K-GJ.SB.200.001. The model of a pipe with a diameter of DN100 (length 6m, or 600cm) in the pipe library of the warehouse management module is coded as K-GD.GD.100.600. The model of a DN50 diameter and 320cm length surplus stock in the surplus stock module of the warehouse management module is coded as K-YL.YL.50.320. The model coding rules are shown in Table 1:

[0067]

[0068]

[0069] Table 1

[0070] Continue to refer Figure 1 As shown, the algorithm module includes: a standard module design unit, an economic module design unit, a module performance analysis unit and an assembly cost verification unit.

[0071] The standard module design unit is used to execute the standard connection algorithm to call the corresponding construction model and pipeline model from the library management module to design the standard module; the standard connection algorithm rules are as follows:

[0072] First, select the appropriate equipment model according to the functional requirements of the module design, determine the model and specifications of the equipment model based on the required functional parameters such as water flow, head, pressure, etc., and call the model from the library management module based on the model code formed by the equipment, model and specifications. When calling, refer to the model code management list described in step 4 to call out the corresponding model.

[0073] Then, the reserved connection interface positions of the module are set to the two ends of the top of the module, and the set interface positions and the equipment interface positions are connected with a linear path, so that the valve model, pipe fitting model and pipeline model are arranged along the linear path.

[0074] Finally, according to the location and specifications of the inlet and outlet interfaces of the selected equipment model, the valve model and pipe fitting model are called one by one in the conventional design logic sequence. When calling, the corresponding model is called out according to the model code management list described in step 4. The valve model and pipe fitting model are arranged one by one according to the minimum spacing allowed by the specification.

[0075] Call the pipeline model according to the inlet and outlet specifications of the selected equipment model. When calling, call the corresponding model according to the model code control list described in step 4. When arranging the pipeline model, compare and arrange it section by section from the pipeline model according to the spacing between the valve model and the pipe fitting model. At the same time, delete the corresponding code of the pipeline model from the model code management list.

[0076] The length of each pipe model is 6 meters. If the remaining segment during the cut process is shorter than the distance between the valve model and the pipe fitting model, the remaining segment is returned to the warehouse management module's residual stock as a residual model as described in step 3. It is coded and identified as described in step 4 and entered into the model code management list. A new pipe model is then called from the warehouse management module's pipeline library based on the model code. When the standard module design is completed, if there is still some remaining from the currently cut pipe model, the remaining segment is returned to the warehouse management module's residual stock as a residual model as described in step 3. It is coded and identified as described in step 4 and entered into the model code management list. This completes the standard module design.

[0077] The economic module design unit is used to execute a surplus material substitution algorithm to identify surplus material pipeline models corresponding to the pipeline models in the standard module design from the warehouse management module and replace them to design an economic module; wherein the rules of the surplus material substitution algorithm are as follows:

[0078] First, compare the model code of the pipeline model called during the current module standard design to identify the selected specifications of the surplus pipeline model, retrieve the length of the pipeline model cut section by section during the current standard module design, and identify the selected length of the surplus model.

[0079] Return the surplus model code identification of the model code management list to the surplus material warehouse through the surplus model, and verify the surplus model codes that meet the surplus model selection specifications one by one. The verification content is whether the small and medium category codes in the coding rules, that is, whether the specifications of the surplus model are met, the surplus model that does not meet the surplus model selection specifications will continue to be retained in the surplus material warehouse for use, and the surplus model that meets the surplus model selection specifications will be transferred out of the surplus material warehouse for use.

[0080] Finally, the spare material model to be used and the length of the module standard design pipeline model are called out in rotation and compared. The spare material model that meets the comparison rules is used to replace the pipe segment model to form a module. The replaced pipe segment model returns to the pipeline model segment cutting process, skipping the corresponding pipeline model cutting step.

[0081] The comparison process is as follows: compare the length of the first residual material model with the cut-off length of the first pipeline model, then compare the length of the first residual material model with the cut-off length of the second pipeline model, until the length of the first residual material model completes the comparison of all pipeline model lengths, then rotate the length of the second residual material model to compare with the cut-off length of the first pipeline model, until all pipeline model cut-off lengths are compared. During the process, (1) if the length of the residual material model to be used is the same as the cut-off length of the pipeline model, it is directly replaced, and the corresponding code of the residual material model is deleted from the model code management list, and the current cut-off length of the pipeline model exits the comparison; (2) if If the length of the standby stock model is less than the cut length of all pipeline models, the current standby stock model is returned to the stock library of the warehouse management module, and the model coding management list is not operated; (3) If the length of the standby stock model is longer than the cut length of the pipeline model, and the length of the extended part is less than 10cm, the cut length of the pipeline model is cut from the stock model to replace the pipeline, and the corresponding code of the stock model is deleted from the model coding management list. At the same time, the remaining section of the stock model is returned to the stock library of the warehouse management module as the stock model, and the coding is marked and entered into the model coding management list as described in step 4. The current pipe model cut length exits the comparison; (4) If the standby stock model is longer than the cut length of the pipeline model, and the length of the extended part is less than 10cm, the cut length of the pipeline model is cut from the stock model to replace the pipeline, and the corresponding code of the stock model is deleted from the model coding management list. At the same time, the remaining section of the stock model is returned to the stock library of the warehouse management module as the stock model, and the coding is marked and entered into the model coding management list as described in step 4. The current pipe model cut length exits the comparison; If the length of the material model is longer than the cut-off length of the pipeline model, and the length of the extended part is greater than or equal to 10cm and less than or equal to 20cm, then the length of the current residual material model is compared with the cut-off length of other pipeline models, and the cut-off length of the pipeline model closest to the current residual material model is selected. The cut-off length of the pipeline model is cut from the residual material model to replace the pipeline, and the corresponding code of the residual material model is deleted from the model coding management list. At the same time, the remaining section of the residual material model is returned to the residual material library of the library management module as the residual material model, and the coding is marked and entered into the model coding management list as described in step 4. The current pipeline model cut-off length exits the comparison; (5) If the residual material model to be used If the length is longer than the cut-off length of the pipeline model, and the length of the protruding part is greater than 20cm, the length of the current residual material model will be compared with the cut-off lengths of other pipeline models. The execution priority of the comparison process is as follows: if the comparison result shows that the length of the residual material model to be used is the same as the cut-off length of the pipeline model, execute (1); if the comparison result shows that the length of the protruding part is less than 10cm, execute (3); if the comparison result shows that the length of the protruding part is greater than or equal to 10cm and less than or equal to 20cm, execute (4); if the comparison result shows that the protruding part is greater than 20cm, the current residual material model to be used is returned to the residual material library of the library management module, and the model coding management list is not operated.

[0082] The module performance analysis unit is used to perform module performance analysis on the economic module. When performing module performance analysis, a scientific simulation calculation is used, and the formula preset by the algorithm module is automatically executed and the calculation result is output. The preset formula is as follows:

[0083]

[0084] Where z is the module flow resistance loss, in meters. y is the module pipe resistance loss, in meters. j is the resistance loss of the module components, in meters. f is the pipeline resistance coefficient, which is 0.03 during simulation verification. 001 is the detailed category code of the model coding as described in step 4, that is, the length. ∑001 is the sum of the detailed category codes, that is, the sum of the lengths, in cm. v is the liquid flow velocity in the module, and its value is 2.0 m / s. Z is the subcategory code of the model coding as described in step 4, that is, the specification, in mm. Y is the middle category code of the model coding as described in step 4, that is, the type. ∑Y is the sum of the corresponding values ​​of the middle category codes, that is, the sum of the corresponding values ​​of the types. The corresponding values ​​are: SB, that is, the value of equipment, is 32; GJ, that is, the value of pipe fittings, is 1.5; and FM, that is, the value of valves, is 9.

[0085] Among them, when conducting scientific simulation verification, according to the model codes corresponding to the component models, pipeline models and residual material models contained in the current economic module design, the information contained in the model codes is extracted, such as: the detailed category code of the pipeline model and the residual material model, that is, the length, and the total length of the pipeline model and the residual material model used by the module is obtained by adding the detailed category codes; the subcategory code of the pipeline model and the residual material model, that is, the specification, and the diameter of the pipeline model and the residual material model is obtained by the subcategory code; the middle category code corresponding to the equipment, pipe fittings, and valves, that is, the type

[0086] The sum of the values ​​corresponding to the class code is obtained by adding the corresponding values ​​one by one. The module flow resistance loss is the sum of the module pipeline resistance loss and the module component resistance loss. For example, the flow resistance loss of the economic module is set to no more than 100. If it is greater than 100, the algorithm module will issue an alarm. If it is less than or equal to 100, the analysis is passed, thus completing the module performance analysis.

[0087] The assembly cost verification unit is used to verify the assembly cost of the economic module that has passed the module performance analysis. In the embodiment of the present application, the assembly cost verification adopts the method of embedding manual quotas, that is, embedding preset manual quotas into the algorithm module to verify the module assembly cost. The details are as follows:

[0088] First, the model codes for the component, piping, and stock models of the current module are retrieved, and the information contained in the model codes is extracted. This information includes: obtaining the number of equipment, pipe fittings, and valves using the detailed category code; obtaining the length and number of pipes and stock using the number of codes corresponding to the detailed category code and the medium category code; and obtaining the number of assembly joints based on the total number of equipment, pipe fittings, valves, pipes, and stock. The algorithm module then inputs the labor quota rules, which are as follows: installer cost: 400 yuan / person / day, welder cost: 750 yuan / person / day, equipment assembly efficiency: 4 people * 0.5 days / unit, pipe fitting assembly efficiency: 1 person * 0.1 day / unit, valve assembly efficiency: 2 people * 0.2 days / unit, pipe assembly efficiency: 2 people * 1 day / 120 meters, stock assembly efficiency: 2 people * 1 day / 120 meters, and joint connection efficiency: 1 person * 0.4 day / unit. Equipment, pipe fittings, valves, pipes, and stock are assembled by installers, while joint connection is done by welders. Finally, establish the module assembly cost calculation formula: Module Assembly Cost = Equipment Assembly Cost + Pipe Fitting Assembly Cost + Valve Assembly Cost + Pipe Assembly Cost + Residual Material Assembly Cost + Joint Connection Cost. Equipment Assembly Cost = Installation Labor Cost * Equipment Assembly Efficiency * Number of Equipment; Pipe Fitting Assembly Cost = Installation Labor Cost * Pipe Fitting Assembly Efficiency * Number of Pipe Fittings; Valve Assembly Cost = Installation Labor Cost * Valve Assembly Efficiency * Number of Valves; Pipe Assembly Cost = Installation Labor Cost * Pipe Assembly Efficiency * Pipe Length; Residual Material Assembly Cost = Installation Labor Cost * Residual Material Assembly Efficiency * Residual Material Length; Joint Connection Cost = Welding Labor Cost * Joint Connection Efficiency * Number of Joints. For example, set the economic module's assembly cost to a maximum of 160,000. If it exceeds 160,000, the algorithm module will issue an alarm; if it is less than or equal to 160,000, the analysis will pass, thus completing the assembly cost verification.

[0089] Continue to refer Figure 1 As shown, the learning module includes: a module design optimization unit, a module layout optimization unit and a module connection optimization unit.

[0090] In the example of this application, in order to optimize the economic module to obtain the final modular design of the computer room, this application collects module design case models from multiple projects and multiple fields through various methods such as pre-construction, exchange, and purchase, collects various types of modular layout design case models of computer rooms through the company's accumulated project documents, and collects various types of connection design case models between computer room modules through the company's accumulated project documents, etc., and further uses machine learning to learn the acquired module design case models, layout design case models, and connection design case models, and performs targeted processing on the economic module based on the learning results.

[0091] In this embodiment, the module design optimization unit learns from collected module design case models to optimize the module design for economic module design. For example, the collected cases are categorized and organized according to functional modules to form a module design library. This library is then linked to machine learning. The obtained module component models are encoded by invoking the model encoding rules of the library management module and incorporated into the model encoding management list. Simultaneously, the machine learning ensures that the module models contained in the electromechanical module design library meet requirements by invoking the algorithm module's module performance analysis and assembly cost verification.

[0092] Furthermore, machine learning forms a table database by reading the information contained in the module model. The table data includes: the classification, type, specification and number of each component model of the module extracted from the coding of each component model of the economic module, the position coordinates of each component model of the module, and the starting point coordinates and end point coordinates of each component pipeline model and residual material model of the module.

[0093] Perform unique heat encoding on the category labels corresponding to the module functions to obtain the feature vector G , input the Embedding layer, and get the label information embedded W (1×N dimension). Construct three independent parameter fully connected layers FC for the component model, pipeline model, and residual model respectively. i (N×M i Dimension), where M i is the number of classifications under the corresponding model type. The label information is embedded into the fully connected layer FC i (Calculation, calculation result R i Complete the normalization process through Sigmod operation to obtain the category probability P i , compare the class probabilities P i Each element p ij With threshold T i The size of the output is greater than or equal to the corresponding threshold T i The predicted label P′ i , complete the selection of model categories in component models, pipeline models, and residual material models.

[0094] R i =FC i (Embedding(G))

[0095] P i =Sigmod(R i ), P i =[p i1 , p i2 ,...,p iMi ]

[0096] P′ i =[p′ i1 , p′i2 ,...,p′ iMi ]

[0097]

[0098] After the predicted label is uniquely encoded, it is concatenated with the label information embedding W to obtain the category-label encoding W′. It is then input into several fully connected layers, and the fully connected layers are connected using the ReLU() activation function to obtain the predicted values ​​of the specifications, number, coordinates and other parameters of the corresponding model type. The model selects the mean square error MSE() as the training loss function L and sets the learning bias vector α, where the value is 0.6 for the component model, 0.3 for the pipeline model, and 0.1 for the residual model. The overall loss function L selected by the model is 整体 =0.6*L 构件 +0.3*L 管道 +0.1*L 余料 For the prediction results, the cost verification algorithm in step 8 is called to obtain the assembly cost C under the model prediction assembly solution. The algorithm also optimizes L 整体 Together with the assembly cost C, a module combination solution with the lowest assembly cost is formed.

[0099] At the same time, machine learning also uses modular design libraries (such as Figure 6 As shown in the figure, new data is added to iteratively optimize the module composition design, thus completing the module design optimization.

[0100] In this embodiment, the module layout optimization unit learns from the collected layout design case models to optimize the layout design of the economic module design to achieve maximum space utilization and maximum equipment maintenance convenience. The module layout optimization unit extracts features that affect the optimization objectives from the case models, such as the size of the machine room, the type and number of module functions used, the location and orientation of the module layout, and the width and location of the reserved maintenance passage. It converts the influencing features into a table database, assigning a weight of 0.7 to space utilization and a weight of 0.3 to equipment maintenance convenience. The module layout optimization unit then reads the module function types contained in the above module design library. Based on the optimization objective, a genetic algorithm model is adopted. The table database is randomly generated with real number coding to establish a basic layout scheme group. Then, the shuffling crossover and continuous mutation methods are adopted. According to the optimization objectives and corresponding weight values ​​of maximum space utilization E and maximum equipment maintenance convenience M, each layout scheme in the layout scheme group is evaluated to obtain the fitness value of each scheme. The higher the fitness, the better the scheme. Among them, the maximum space utilization E is the ratio of the area S corresponding to the effective activity space of the computer room to the total area A of the computer room. The maximum equipment maintenance convenience M is reflected in the shortest sum D of the distances from each entrance p_i of the computer room to each equipment maintenance area q_j. It is related to the module function type and quantity, the layout position and orientation of each module, and the width and position of the reserved maintenance channel. The width and position of the reserved maintenance channel must meet the maintenance activities of maintenance personnel.

[0101]

[0102] M:min(∑dis(p i ,q j ))

[0103] The constraints set include:

[0104] (1) The module layout is mainly constrained by the space of the computer room. It cannot exceed the boundaries of the computer room, and must meet the positional relationship between adjacent modules that have distance requirements.

[0105] (2) Modules in the computer room do not interfere with each other and cannot be intertwined.

[0106] (3) Ensure that each module does not affect the entry and exit of maintenance personnel.

[0107] When the optimal individual fitness or group fitness no longer increases, the model iteration is stopped. Finally, the machine learning selects the module layout optimization plan based on the fitness. At the same time, the machine learning also iteratively optimizes the module layout position by adding new project documents, thus completing the module layout optimization.

[0108] In an embodiment of the present application, a module connection optimization unit learns from collected connection design case models to optimize pipeline connections in economical module designs. In this embodiment, the module connection optimization unit analyzes and identifies pipeline routing principles and pipeline bend configuration practices within the connection design case models, determining the goals of module connection optimization as minimum pipeline length and minimum pipeline bends. The learning module extracts pipeline layout features from engineering cases that influence the optimization goals. These features include pipeline straight path, pipeline length, and the number of pipeline bends.

[0109] First, we construct an N×N dimensional module connectivity matrix A, where N is the number of modules installed in the computer room, and the matrix element a is ij A value of 0 indicates that module i and module j are not connected, and a value of 1 indicates that they need to be connected. Construct an N×M-dimensional module information matrix B, where N is the number of modules installed in the room, and M is the number of features possessed by the module. The features include the three-dimensional spatial coordinates of the module layout, the module function type, etc. The matrix element b ij is the actual value of the corresponding feature of the module. The module connectivity matrix and the module information matrix are concat-concatenated to form an N×L-dimensional input matrix, where L=N+M. The input matrix is ​​calculated through two fully connected layers. The first fully connected layer FC1 is of L×N dimensions, and the second fully connected layer FC2 is of N×N dimensions. The calculation results are processed by the ReLU() function and rounded down to obtain the prediction matrix R of the number of bends in the connecting pipes between each module. The element r ij is the predicted number of bends between device i and device j.

[0110]

[0111]

[0112]

[0113] For a pair of modules, the number of bends in the connecting pipes between the modules is predicted based on r ij And the module three-dimensional space information, input several layers of fully connected layers, output r ijThe total length of the pipeline route is obtained by summing the three-dimensional spatial coordinates of each pipe bend and calculating the distance between the starting point, each bend, and the end point of each device. The model uses the mean square error (MSE) as the loss function, and compares the actual value of the pipeline routing in the existing design case with the model prediction value to finally complete the model training. The total cost is used to reflect the effect of the solution generated by the learning module by using the preset pipeline cost and pipeline bend cost as constants. This is used to consider the module connection optimization effect and provide reference data for module connection implementation. At the same time, the learning module has the ability to iteratively optimize the module connection route by adding new project documents, thus completing the module connection optimization.

[0114] refer to Figure 7 and Figure 8 As shown, this application provides a modular design method for a computer room, including:

[0115] Based on the received module design functional requirements, corresponding component models and pipeline models are selected to perform standard module design. In this embodiment, the standard module design is performed according to the following steps:

[0116] Analyze the functional requirements of the module design to determine the corresponding equipment model;

[0117] Based on the parameters of the equipment model, the corresponding component model and pipeline model are called from the library management module;

[0118] Executing a standard connection algorithm to arrange the component model and the pipeline model in a linear path;

[0119] Based on the linear path arrangement, the component models and pipeline models are arranged one by one according to specifications to obtain a standard module design.

[0120] In an embodiment of the present application, after receiving the design function requirements, the system determines the model and specifications of the equipment model based on functional parameters such as water flow, head, pressure, etc. Further, according to the model code formed by the equipment name, model and specifications, the corresponding model is called from the library management module to perform standard module design.

[0121] In this embodiment, by setting the module's reserved connection interface positions to the two ends of the module's top, the set interface positions and the device interface positions are connected with a linear path, so that the valve model, pipe fitting model, and pipeline model are arranged along the linear path. Furthermore, according to the selected device model's inlet and outlet interface positions and specifications, the valve model and pipe fitting model are called one by one in accordance with the conventional design logic sequence. When calling, the corresponding model is retrieved based on the model code and the model code management list. When the valve model and pipe fitting model are arranged, they are arranged one by one according to the minimum spacing allowed by the specification. Furthermore, according to the selected device model's inlet and outlet specifications, the pipeline model is called. When calling, the corresponding model is retrieved based on the model code and the model code management list. When the pipeline model is arranged, it is cut out section by section from the pipeline model according to the spacing between the valve model and the pipe fitting model, and the corresponding code of the pipeline model is deleted from the model code management list.

[0122] The length of each pipe model is 6 meters. If the remaining segment during the cut process is shorter than the distance between the valve model and the pipe fitting model, the remaining segment is returned to the warehouse management module's residual stock library as a residual model, coded and identified, and entered into the model code management list. Simultaneously, a new pipe model is retrieved from the warehouse management module's pipeline library based on the model code. When the standard module design is completed, if there is still some remaining from the currently cut pipe model, the remaining segment is returned to the warehouse management module's residual stock library as a residual model, coded and identified, and entered into the model code management list, as described in step 4. This completes the standard module design.

[0123] According to the preset rules, the residual material pipeline model corresponding to the pipeline model in the standard module design is identified to perform economic module design; when performing the economic module design, the following steps are performed:

[0124] Identifying selection parameters of a surplus material pipeline model based on the coding of the pipeline model in the standard module design;

[0125] Identify and call a residual material pipeline model that meets the selected parameters based on the residual material pipeline model code;

[0126] Comparing the lengths of the surplus material pipeline model and the corresponding pipeline model in the standard module design;

[0127] The residual material pipeline model whose comparison results meet the comparison rules is replaced with the corresponding pipeline model to obtain an economical module design.

[0128] In an embodiment of the present application, first, by comparing the model code of the pipeline model in the current standard module design, the selected specifications in the surplus pipeline model are identified, the length of the pipeline model is cut section by section during the standard module design, and the selected length of the surplus model is identified.

[0129] Return the surplus model code identification of the model code management list to the surplus material warehouse through the surplus model, and verify the surplus model codes that meet the surplus model selection specifications one by one. The verification content is whether the small and medium category codes in the coding rules, that is, whether the specifications of the surplus model are met, the surplus model that does not meet the surplus model selection specifications will continue to be retained in the surplus material warehouse for use, and the surplus model that meets the surplus model selection specifications will be transferred out of the surplus material warehouse for use.

[0130] Finally, the length of each section of the pipeline model designed by the standard design of the spare material model and the module is called out for rotation comparison. The spare material model that meets the comparison rules is used to replace the pipe segment model to form the module. The replaced pipe segment model returns to the pipeline model section-by-section cutting process, and the cutting of this section of the corresponding pipeline model is skipped.

[0131] The comparison process is as follows: compare the length of the first residual material model with the cut length of the first pipe model, then compare the length of the first residual material model with the cut length of the second pipe model, until the length of the first residual material model completes the comparison of all pipe models, then rotate the length of the second residual material model to compare with the cut length of the first pipe model, until the comparison of all pipe models is completed.

[0132] (1) If the length of the spare stock model to be used is the same as the cut-off length of the pipeline model, it is directly replaced, and the corresponding code of the spare stock model is deleted from the model code management list, and the cut-off length of the current pipeline model is exited from the comparison;

[0133] (2) If the length of the unused residual material model is less than the intercepted length of all pipeline models, the current unused residual material model is returned to the residual material library of the library management module, and the model code management list is not operated;

[0134] (3) If the length of the spare stock model to be used is longer than the cut-off length of the pipeline model, and the length of the extended part is less than 10 cm, the cut-off length of the pipeline model is cut off from the spare stock model to replace the pipeline, and the corresponding code of the spare stock model is deleted from the model code management list. At the same time, the remaining section of the spare stock model is returned to the spare stock library of the library management module as the spare stock model. By coding and identifying the spare stock pipeline model and entering it into the model code management list, the cut-off length of the current pipeline model exits the comparison;

[0135] (4) If the length of the spare stock model to be used is longer than the cut-off length of the pipeline model, and the length of the extended part is greater than or equal to 10 cm and less than or equal to 20 cm, then the current spare stock model length is compared with the cut-off lengths of other pipeline models, and the cut-off length of the pipeline model closest to the current spare stock model length is selected. The cut-off length of the pipeline model is cut from the spare stock model to replace the pipeline, and the corresponding code of the spare stock model is deleted from the model code management list. At the same time, the remaining section of the spare stock model is returned to the spare stock library of the library management module as the spare stock model, and the spare stock pipeline model is coded and identified and entered into the model code management list. The current pipeline model cut-off length exits the comparison;

[0136] (5) If the length of the standby stock model is longer than the cut-off length of the pipeline model, and the length of the protruding part is greater than 20 cm, the length of the current stock model is compared with the cut-off lengths of other pipeline models. The execution priority of the comparison process is as follows: if the comparison result shows that the length of the standby stock model is the same as the cut-off length of the pipeline model, execute (1); if the comparison result shows that the length of the protruding part is less than 10 cm, execute (3); if the comparison result shows that the length of the protruding part is greater than or equal to 10 cm and less than or equal to 20 cm, execute (4); if the comparison result shows that the protruding part is greater than 20 cm, the current standby stock model is returned to the stock library of the library management module, and the model coding management list is not operated.

[0137] The economic module design is optimized based on machine learning to obtain a modular design of the computer room.

[0138] In this embodiment, in order to optimize the economic module to obtain the final modular design of the computer room, this application collects module design case models from multiple projects and multiple fields through various methods such as pre-built, exchanged, and purchased, collects various modular layout design case models of computer rooms through the company's accumulated project documents, and collects various connection design case models between computer room modules through the company's accumulated project documents, etc., further uses machine learning to learn the acquired module design case models, layout design case models, and connection design case models, and performs optimization on the economic module based on the learning results. Specifically,

[0139] Optimizing the module design of the economic module design by learning the collected design case models;

[0140] Optimizing the layout design of the economic module design by learning the collected layout design case models;

[0141] The pipeline connection designed by the economic module is optimized after learning the collected connection design case models.

[0142] Before the step of optimizing the economic module design based on machine learning to obtain a modular design of a computer room, the step further includes:

[0143] Perform module performance analysis on economic design modules based on scientific simulation verification;

[0144] Verify assembly cost of economic module design that has passed module performance analysis;

[0145] When the assembly cost is verified, the economic module design is optimized to obtain a modular design of the computer room.

[0146] In the embodiment of the present application, when performing module performance analysis, scientific simulation verification is adopted, and the formula preset by the algorithm module is automatically executed and the verification result is output, wherein the preset formula is as follows:

[0147]

[0148] Where z is the module flow resistance loss, in meters. y is the module pipe resistance loss, in meters. j is the resistance loss of the module components, in meters. f is the pipeline resistance coefficient, which is 0.03 during simulation verification. 001 is the detailed category code of the model coding as described in step 4, that is, the length. ∑001 is the sum of the detailed category codes, that is, the sum of the lengths, in cm. v is the liquid flow velocity in the module, and its value is 2.0 m / s. Z is the subcategory code of the model coding as described in step 4, that is, the specification, in mm. Y is the middle category code of the model coding as described in step 4, that is, the type. ∑Y is the sum of the corresponding values ​​of the middle category codes, that is, the sum of the corresponding values ​​of the types. The corresponding values ​​are: SB, that is, the equipment value is 32, GJ, that is, the pipe fitting value is 1.5, and FM, that is, the valve value is 9.

[0149] Among them, when conducting scientific simulation verification, according to the model codes corresponding to the component models, pipeline models and residual material models contained in the current economic module design, the information contained in the model codes is extracted, such as: the detailed category code of the pipeline model and the residual material model, that is, the length, and the total length of the pipeline model and the residual material model used by the module is obtained by adding the detailed category codes; the subcategory code of the pipeline model and the residual material model, that is, the specification, and the diameter of the pipeline model and the residual material model is obtained by the subcategory code; the middle category code corresponding to the equipment, pipe fittings, and valves, that is, the type

[0150] The sum of the values ​​corresponding to the class code is obtained by adding the corresponding values ​​one by one. The module flow resistance loss is the sum of the module pipeline resistance loss and the module component resistance loss. For example, the flow resistance loss of the economic module is set to no more than 100. If it is greater than 100, the algorithm module will issue an alarm. If it is less than or equal to 100, the analysis is passed, thus completing the module performance analysis.

[0151] When verifying the assembly cost of the economic module that has passed the module performance analysis, in the embodiment of the present application, the assembly cost verification adopts the method of embedding a manual quota, that is, embedding a preset manual quota into the algorithm module to verify the module assembly cost. The details are as follows:

[0152] First, the model codes for the component, piping, and stock models of the current module are retrieved, and the information contained in the model codes is extracted. This information includes: obtaining the number of equipment, pipe fittings, and valves using the detailed category code; obtaining the length and number of pipes and stock from the number of codes corresponding to the detailed category code and the medium category code; and obtaining the number of assembly joints based on the total number of equipment, pipe fittings, valves, pipes, and stock. The algorithm module then inputs the labor quota rules, which are as follows: installer cost: 400 yuan / person / day, welder cost: 750 yuan / person / day, equipment assembly efficiency: 4 people * 0.5 days / unit, pipe fitting assembly efficiency: 1 person * 0.1 day / unit, valve assembly efficiency: 2 people * 0.2 days / unit, pipe assembly efficiency: 2 people * 1 day / 120 meters, stock assembly efficiency: 2 people * 1 day / 120 meters, and joint connection efficiency: 1 person * 0.4 day / unit. Equipment, pipe fittings, valves, pipes, and stock are assembled by installers, while joint connection is done by welders. Finally, the module assembly cost calculation formula is established: Module Assembly Cost = Equipment Assembly Cost + Pipe Fitting Assembly Cost + Valve Assembly Cost + Pipe Assembly Cost + Residual Material Assembly Cost + Joint Connection Cost. Equipment Assembly Cost = Installation Labor Cost * Equipment Assembly Efficiency * Number of Equipment; Pipe Fitting Assembly Cost = Installation Labor Cost * Pipe Fitting Assembly Efficiency * Number of Pipe Fittings; Valve Assembly Cost = Installation Labor Cost * Valve Assembly Efficiency * Number of Valves; Pipe Assembly Cost = Installation Labor Cost * Pipe Assembly Efficiency * Pipe Length; Residual Material Assembly Cost = Installation Labor Cost * Residual Material Assembly Efficiency * Residual Material Length; Joint Connection Cost = Welding Labor Cost * Joint Connection Efficiency * Number of Joints. For example, the economic module assembly cost must not exceed 160,000. If it exceeds 160,000, the algorithm module will issue an alarm. If it is less than or equal to 160,000, the analysis will pass. This completes the assembly cost verification and ultimately results in an economic module design for the computer room.

[0153] To sum up, the present application scheme realizes the electromechanical system functions of the computer room by splicing multiple independent modules, reduces the complexity and risk of on-site construction, forms a customized and scalable design scheme, and improves the flexibility and reusability of the computer room design; responds to the actual product component requirements of the construction to achieve seamless connection between design and construction, improves the material utilization efficiency of the computer room, and evaluates the labor consumption of computer room installation and module design performance; directly links accumulated cases for intelligent adjustment and optimization, improves the pertinence and practicality of the design scheme, and further reduces material waste and labor costs.

[0154] The present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of any one of the above-mentioned methods for modular design of a computer room are implemented.

[0155] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0156] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0157] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0158] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0159] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0160] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0161] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0162] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A modular design system for a computer room, characterized in that: The modular design system of the computer room includes: The warehouse management module is used to build a computer room material model and establish model coding rules to encode and manage the computer room material model. The computer room material model includes: a component model, a pipeline model, and a residual material pipeline model; An algorithm module is used to execute a design algorithm to obtain a computer room material model from the warehouse management module for modular design according to design requirements; A learning optimization module is used to learn the collected case models and optimize the economic module design of the computer room based on the learning results to obtain a modular design of the computer room; The warehouse management module includes: A component library is used to process the acquired sample data to establish a data form, and to construct a component model consistent with the data in the data form; A pipeline library is used to process the obtained bill of materials to form a pipeline bill of materials, and to construct a corresponding pipeline model based on the pipeline parameters of the pipeline bill of materials; A surplus material library is used to manage the pipeline models that do not meet the use requirements or the surplus pipeline models of the remaining sections after the algorithm module completes the modular design of the computer room; An inventory model management unit, configured to encode the models in the component library, the pipeline library, and the residual material library to form a model code list; Wherein, the algorithm module includes: A standard module design unit is used to execute a standard connection algorithm to call the corresponding component model and pipeline model from the library management module to design a standard module design; An economical module design unit, configured to execute a surplus material replacement algorithm to identify surplus material pipeline models corresponding to pipeline models in the standard module design from the warehouse management module and replace them to obtain an economical module design; a module performance analysis unit, configured to perform module performance analysis on the economic module design; An assembly cost verification unit is used to verify the assembly cost of the economic module design that passes the module performance analysis.

2. The modular design system for a computer room according to claim 1, characterized in that: The learning optimization module includes: A module design optimization unit, used for learning the collected module design case models to optimize the module design of the economic module design; A module layout optimization unit is used to learn the collected layout design case models to optimize the layout design of the economic module design; The module connection optimization unit is used to learn the collected connection design case models to optimize the pipeline connection of the economic module design.

3. A modular design method for a computer room, characterized in that: The modular design method for a computer room includes: Select corresponding component models and pipeline models based on received design requirements for standard module design; Identifying and obtaining a surplus pipe model corresponding to the pipe model in the standard module design to perform economical module design; Optimizing the economic module design based on machine learning to obtain a modular design for a computer room; The step of selecting corresponding component models and pipeline models for standard module design based on the received design function requirements includes: Analyze the design requirements and determine the corresponding computer room material model; Based on the parameters of the computer room material model, the corresponding component model and pipeline model are called from the warehouse management module; Executing a standard connection algorithm to arrange the component model and the pipeline model in a linear path; Arranging the component models and pipeline models one by one according to specifications based on the linear path arrangement to obtain a standard module design; The step of identifying and obtaining the surplus pipe model corresponding to the pipe model in the standard module design to perform economic module design includes: Identifying selection parameters of a residual material pipeline model based on a model code of the pipeline model in the standard module design; Identify and call a residual material pipeline model that meets the selected parameters based on the model code of the residual material pipeline model; Comparing the lengths of the surplus material pipeline model and the corresponding pipeline model in the standard module design; The residual material pipeline model whose comparison results meet the comparison rules is replaced with the corresponding pipeline model to obtain an economical module design.

4. The modular design method for a computer room according to claim 3, characterized in that: The step of optimizing the economic module design based on machine learning to obtain a modular design of a computer room includes: After learning the collected design case models, optimizing the module design of the economic module design; After learning the collected layout design case models, optimizing the layout design of the economic module design; After studying the collected connection design case models, the pipeline connection designed by the economic module is optimized.

5. The modular design method for a computer room according to any one of claims 3 or 4, characterized in that: Before the step of optimizing the economic module design based on machine learning to obtain a modular design of a computer room, the following steps are further included: Conduct module performance analysis for economic module design; Verify assembly cost of economic module design that has passed module performance analysis; When the assembly cost is verified, the economic module design is optimized to obtain a modular design of the computer room.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the computer room modular design method according to any one of claims 3 to 5 are implemented.

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