Prefabricated data center AI generation method and display system based on module standardization

Abstract

The invention provides a prefabricated data center AI generation method based on module standardization and a display system. The generation method of the prefabricated data center comprises the following steps: acquiring a demand input by a user; analyzing the demands, determining whether the demands meet set integrity and rationality conditions or not, and generating one or more preliminary module configuration schemes based on the demands meeting the integrity and rationality conditions; wherein the module configuration scheme is a set of various standardized modules capable of meeting requirements; analyzing the one or more preliminary module configuration schemes by using a pre-configured algorithm, and generating an optimal solution of the data center model; and outputting a configuration file corresponding to the optimal solution based on scene requirements. Therefore, by introducing a standardized module design and an artificial intelligence technology, intelligent design and optimization of the data center can be realized, and the design efficiency and the operation efficiency of the data center are improved.

Description

Technical Field

[0001] The present application relates to a prefabricated data center AI generation method and display system based on module standardization, which belongs to the field of data center technology. Background Art

[0002] As the global digital transformation accelerates, the demand for data centers as the infrastructure of the information age continues to increase. The problems of long construction period, high cost and poor flexibility of traditional data centers are gradually emerging. In order to meet these challenges, prefabricated data centers came into being. Prefabricated data centers can achieve rapid deployment and flexible expansion through modular design and factory prefabrication, which greatly improves construction efficiency and reduces costs.

[0003] However, existing prefabricated data centers still face some problems in the design and deployment process. For example, due to the lack of standardized module design, a lot of customized design and adjustment are required in actual deployment. In addition, with the rapid development of artificial intelligence technology, the demand for computing power and data processing capabilities in data centers continues to increase, and traditional design methods are difficult to meet these new demands. Summary of the invention

[0004] The present application provides a prefabricated data center AI generation method and display system based on module standardization to solve the problem that existing prefabricated data centers lack standardized module design and are difficult to meet the ever-increasing computing power and data processing capacity requirements.

[0005] In a first aspect, an embodiment of the present application provides a method for generating AI for a prefabricated data center based on module standardization, including:

[0006] Obtaining user input requirements;

[0007] Analyze the requirements to determine whether they meet the set integrity and rationality conditions, and generate one or more preliminary module configuration schemes based on the requirements that meet the integrity and rationality conditions; wherein the module configuration scheme is a collection of multiple standardized modules that can meet the requirements;

[0008] Analyze the one or more preliminary module configuration solutions using a pre-configured algorithm and generate an optimal solution for the data center model;

[0009] Based on the scenario requirements, the configuration file corresponding to the optimal solution is output.

[0010] Based on the above method, optionally, the requirements are analyzed to determine whether they meet the set integrity and rationality conditions, and one or more preliminary module configuration solutions are generated based on the requirements that meet the integrity and rationality conditions, including:

[0011] Based on the natural language processing model, semantic analysis is performed on the input requirements to ensure accurate understanding of the requirements;

[0012] Analyze the completeness and rationality of the requirements, and output feedback if the completeness or rationality conditions are not met;

[0013] If the completeness and rationality conditions are met, the parsed requirements are mapped to predefined standardized module categories to generate a preliminary module configuration plan.

[0014] Based on the above method, optionally, the using of a pre-configured algorithm to analyze the one or more preliminary module configuration solutions and generate an optimal solution for the data center model includes:

[0015] The constraint satisfaction problem framework is used to handle the compatibility, dependency and constraints between various standardized modules, and the standardized modules are reasonably combined to generate a data center model;

[0016] Through the rule engine and multi-criteria decision-making algorithm, the generated data center model is analyzed and verified to see if it meets the corresponding specifications, and weights are assigned through different scoring criteria to determine the optimal solution.

[0017] Based on the above method, optionally, outputting a configuration file corresponding to the optimal solution based on scenario requirements includes:

[0018] The determined optimal solution is generated through a generative adversarial network and the corresponding drawing file, model file or list file is output to cope with different application scenarios.

[0019] Based on the above method, the generated data center model is optionally used in the following scenarios:

[0020] Design generation: In the pre-sales stage, the data center that meets the input conditions is visually displayed and the corresponding technical parameter files are generated;

[0021] Production empowerment: The generated data center model can form corresponding drawing files and list files for production empowerment;

[0022] Twin base: The generated data center model serves as the base for the intelligent operation and maintenance software.

[0023] Based on the above method, optionally, the standardized module is designed in a standardized manner, adopts a unified module, is presented in the form of a three-dimensional model, and supports independent configuration and expansion; and each standardized module is provided with a predefined label to mark the parameters of the standardized module, including type, function and specification.

[0024] In a second aspect, the embodiment of the present application further provides a display system of a prefabricated data center based on module standardization, which includes:

[0025] A display device based on mixed reality technology, wherein the display device is used to display to a user, through an immersive interactive environment, a data center model generated by the prefabricated data center AI generation method based on module standardization described in any of the above items.

[0026] In the AI ​​generation method and display system of prefabricated data centers based on module standardization provided by the present application, the generation method of the prefabricated data center includes: obtaining the requirements input by the user; analyzing the requirements to determine whether they meet the set integrity and rationality conditions, and generating one or more preliminary module configuration schemes based on the requirements that meet the integrity and rationality conditions; wherein the module configuration scheme is a collection of multiple standardized modules that can meet the requirements; using a pre-configured algorithm to analyze one or more preliminary module configuration schemes and generate the optimal solution for the data center model; based on the scenario requirements, outputting the configuration file corresponding to the optimal solution. In this way, by introducing standardized module design and artificial intelligence technology, the intelligent design and optimization of the data center can be realized, and the design efficiency and operational effectiveness of the data center can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. In addition, these drawings and text descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application for those skilled in the art by referring to specific embodiments.

[0028] Figure 1 A flowchart of a method for generating an AI prefabricated data center based on module standardization provided in one embodiment of the present application;

[0029] Figure 2 This is a schematic diagram of the application process of the AI ​​generation method for prefabricated data centers based on module standardization in this application. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0031] In order to solve the problem that existing prefabricated data centers lack standardized module design and are difficult to meet the ever-increasing computing power and data processing capacity requirements, this application provides a prefabricated data center AI generation solution based on module standardization. By introducing standardized module design and artificial intelligence technology, the intelligent design and optimization of data centers can be realized, and the design efficiency and operational efficiency of data centers can be improved. The following non-limiting description of the specific implementation scheme is given through several examples or embodiments.

[0032] Reference Figure 1 , Figure 1 A flowchart of a method for generating a prefabricated data center AI based on module standardization provided in one embodiment of the present application. The method of this embodiment can be executed by a computer or a server. Figure 1 As shown, the method comprises at least the following steps:

[0033] Step S101: Obtaining the requirements input by the user.

[0034] Specifically, the user's needs refer to the user's requirements for the data center that he wants to design, such as the power, quantity, power distribution architecture, and cooling form of a single cabinet, etc. The user's input form can be manual input or voice input, etc., which is not specifically limited.

[0035] Step S102: Analyze the requirements to determine whether they meet the set integrity and rationality conditions, and generate one or more preliminary module configuration schemes based on the requirements that meet the integrity and rationality conditions; wherein the module configuration scheme is a collection of multiple standardized modules that can meet the requirements.

[0036] Specifically, before executing this step, various standardized modules need to be designed in advance, each of which is a component or device that can independently realize a specific function, such as IT room module, power distribution module, MEP (mechanical and electrical pipeline) module, refrigeration module and corridor module.

[0037] More specifically, in some embodiments, the standardized modules are standardized in design, adopt a unified module, are presented in the form of a three-dimensional model, and support independent configuration and expansion; and each standardized module is provided with a predefined label to mark the parameters of the standardized module, including type, function, and specification, etc. This makes it convenient for users to view intuitively.

[0038] In addition, after obtaining the user's needs, we first conduct a completeness and rationality analysis to ensure that a complete and reasonable data center model can be generated based on the obtained needs. Multiple input conditions, namely requirements, are required to generate a complete solution. These requirements include, for example, IT capacity, power distribution architecture, cooling form, project location, etc. When the requirements are missing, for example, when the input conditions only provide information such as IT capacity, power distribution architecture, and geographic location to generate a solution, the completeness requirements are not met. Rationality is similar to this, and it is necessary to ensure that multiple demand conditions are reasonable and there are no conflicts between them. For example, when there is an obvious unreasonableness between the cooling form and the geographic location information in the requirements, the cooling form needs to be adjusted to meet the rationality conditions.

[0039] When the requirements meet the conditions of completeness and rationality, one or more module configuration schemes are generated based on these requirements. The module configuration scheme is a collection of multiple standardized modules that can meet the requirements, but does not include information such as the connection or positional relationship between the mutual combination or coordination of the multiple standardized modules.

[0040] Furthermore, the requirements are analyzed to determine whether they meet the set integrity and rationality conditions, and one or more preliminary module configuration solutions are generated based on the requirements that meet the integrity and rationality conditions, including:

[0041] Based on the natural language processing model, the input requirements are semantically parsed to ensure accurate understanding of the requirements; the completeness and rationality of the requirements are analyzed, and if the completeness conditions or rationality conditions are not met, feedback is output; if the completeness conditions and rationality conditions are met, the parsed requirements are mapped to predefined standardized module categories to generate a preliminary module configuration plan.

[0042] That is, firstly, the input requirements are processed and analyzed through natural language processing (NLP) technology to ensure that the meaning of the requirements is accurately obtained. Then, the completeness and rationality of the requirements are analyzed, and feedback is provided, that is, prompt information is output to inform the user to add more necessary requirements or modify the input requirements. When these requirements meet the completeness and rationality conditions, the parsed requirements are mapped to the predefined standardized module categories to generate a preliminary module configuration plan. For example, according to the refrigeration form conditions in the requirements, a refrigeration module that meets the requirements is selected from a variety of refrigeration modules.

[0043] Step S103: Analyze one or more preliminary module configuration solutions using a pre-configured algorithm and generate an optimal solution for the data center model.

[0044] Specifically, the above steps obtain a variety of module configuration solutions that can meet user needs, but in practice, different connection relationships or different position relationships between multiple standardized modules will have different effects on the structure of the data center model, that is, data center models with different structures will be formed. These data center models with different structures will eventually have different parameters and have optimal solutions based on their focus. Therefore, in this step, the optimal solution can be determined as needed.

[0045] Furthermore, in some embodiments, using a pre-configured algorithm to analyze one or more preliminary module configuration solutions and generate an optimal solution for the data center model may specifically include:

[0046] The constraint satisfaction problem framework is used to handle the compatibility, dependency and constraints among the standardized modules, and the standardized modules are reasonably combined to generate a data center model. The rule engine and multi-criteria decision-making algorithm are used to analyze and verify whether the generated data center model meets the corresponding specifications, and weights are allocated through different scoring criteria to determine the optimal solution.

[0047] In step S102, different types of standardized modules that match the requirements are determined. This step is to combine the modules to form a complete data center model by satisfying various constraints such as compatibility and dependency. For example, the IT room module should be arranged in the central area of ​​the whole, the cooling module should be combined close to the IT room module according to the requirements of the constraints, and the power distribution module should be combined around the IT room because of the need to be close to the IT room module set in the constraints.

[0048] In addition, there are many evaluation criteria in the rule engine, such as area size, PUE (data center energy efficiency) value, building plane length and width, etc. According to the model generated in the previous step, there will be multiple possibilities, so it is necessary to constrain and screen the multiple possibilities through the above rules and decision algorithms. For example, among the solutions that meet all the necessary conditions, the possibility of smaller PUE or smaller area can be selected as the optimal solution.

[0049] It is understandable that the optimal solution may be an optimal solution focusing on a certain parameter (such as the smallest floor space), or may be a comprehensive optimal solution that comprehensively considers multiple parameters (through weights).

[0050] Step S104: Based on the scenario requirements, output the configuration file corresponding to the optimal solution.

[0051] Specifically, the optimal data center model generated in this embodiment can be applied to a variety of scenarios, and different scenarios require different configuration file formats. Therefore, in this step, the configuration file corresponding to the optimal solution can be output according to the scenario requirements for users to view or use.

[0052] Among them, the configuration file may include drawing files, model files, and list files, etc., to cope with different application scenarios.

[0053] In some embodiments, the generated data center model can be used in the following scenarios:

[0054] Design generation: In the pre-sales stage, the data center that meets the input conditions is visually displayed and the corresponding technical parameter files are generated;

[0055] Production empowerment: The generated data center model can form corresponding drawing files and list files for production empowerment;

[0056] Twin base: The generated data center model is used as the base for intelligent operation and maintenance software, thereby reducing secondary development costs.

[0057] For better understanding, refer to Figure 2 , Figure 2 This is a schematic diagram of the application process of the AI ​​generation method for prefabricated data centers based on module standardization in this application.

[0058] Before application, various standardized modules are configured first. The standardized modules are composed of different functional spaces in the data center, including IT modules, power distribution modules, MEP modules, corridor modules, etc. Each module completely contains all system content and data within the function it represents. These contents and data will exist in the form of labels. The label content includes the following information: module name, module type, function description, specification parameters (size, weight, power demand, heat dissipation demand, etc.), cost information, etc.

[0059] Based on this, users can input demand conditions into the interactive system. The demand conditions are input into the system in the form of natural language, including the conditions required for designing a data center, such as: single cabinet power, number of cabinets, power distribution architecture, cooling form, etc. Through natural language processing technology, the accurate requirements in the language are extracted and parsed into keyword parameters to determine whether the necessary conditions for generating a data center are met. If not, the required input conditions are guided to be supplemented. When it is determined that the required basic conditions are met, the labels that match the input conditions are sought in the standardized module, and the corresponding standardized modules are locked. Through multiple algorithms, the standardized modules are combined into multiple solutions that meet the corresponding rules and specifications, and the model with a better comprehensive score is weighed according to multiple scoring criteria. At the same time, the corresponding drawing files, model files, list files, etc. can be selectively output to cope with different application scenarios.

[0060] The generated data center model can be used in multiple scenarios: when it is in the pre-sales stage, the generated model can be used for pre-sales display, giving users an intuitive display through MR (mixed reality) equipment, and generating relevant parameter files to assist in decision-making; when it is in the production stage, the generated model and the label information summarized by the standard module can be used to generate construction drawings and production lists to empower production and delivery; when it comes to the operation and maintenance stage, the generated model can be used as the base model of the intelligent operation and maintenance software to avoid secondary development and achieve rapid deployment of operation and maintenance software.

[0061] In summary, based on the above solution, by introducing standardized module design and artificial intelligence technology, the intelligent design and optimization of data centers can be realized, and the design efficiency and operational effectiveness of data centers can be improved.

[0062] In addition, an embodiment of the present application provides a display system for a prefabricated data center based on module standardization, the system including a display device based on mixed reality technology, the display device being used to display a data center model generated by the method described in any of the above embodiments to a user through an immersive interactive environment.

[0063] Users can use MR technology to intuitively view the overall and detailed information of the generated data center and interact with it.

[0064] Specifically, the system mainly includes the following key components: (1) using smart glasses, projection technology, spatial computing and other technologies for display; (2) allowing users to view the details and parameters of the data center through an interactive controller; (3) the interactive data is connected to the main control system through a high-speed data interface to ensure real-time data transmission and synchronization.

[0065] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0066] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0067] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0068] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0069] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0070] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0071] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0072] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for generating AI prefabricated data centers based on module standardization, characterized in that: include: Obtaining user input requirements; Analyze the requirements to determine whether they meet the set integrity and rationality conditions, and generate one or more preliminary module configuration schemes based on the requirements that meet the integrity and rationality conditions; wherein the module configuration scheme is a collection of multiple standardized modules that can meet the requirements; Analyze the one or more preliminary module configuration solutions using a pre-configured algorithm and generate an optimal solution for the data center model; Based on the scenario requirements, the configuration file corresponding to the optimal solution is output.

2. The method according to claim 1, characterized in that The requirements are analyzed to determine whether they meet the set integrity and rationality conditions, and one or more preliminary module configuration solutions are generated based on the requirements that meet the integrity and rationality conditions, including: Based on the natural language processing model, semantic analysis is performed on the input requirements to ensure accurate understanding of the requirements; Analyze the completeness and rationality of the requirements, and output feedback if the completeness or rationality conditions are not met; If the completeness and rationality conditions are met, the parsed requirements are mapped to predefined standardized module categories to generate a preliminary module configuration plan.

3. The method according to claim 1, characterized in that: The method of analyzing the one or more preliminary module configuration solutions using a pre-configured algorithm and generating an optimal solution for the data center model includes: The constraint satisfaction problem framework is used to handle the compatibility, dependency and constraints between various standardized modules, and the standardized modules are reasonably combined to generate a data center model; Through the rule engine and multi-criteria decision-making algorithm, the generated data center model is analyzed and verified to see if it meets the corresponding specifications, and weights are assigned through different scoring criteria to determine the optimal solution.

4. The method according to claim 1, characterized in that The outputting of the configuration file corresponding to the optimal solution based on the scenario requirements includes: The determined optimal solution is generated through a generative adversarial network and the corresponding drawing file, model file or list file is output to cope with different application scenarios.

5. The method according to claim 4, characterized in that The generated data center model is used in the following scenarios: Design generation: In the pre-sales stage, the data center that meets the input conditions is visually displayed and the corresponding technical parameter files are generated; Production empowerment: The generated data center model can form corresponding drawing files and list files for production empowerment; Twin base: The generated data center model serves as the base for the intelligent operation and maintenance software.

6. The method according to claim 1, characterized in that The standardized modules are designed in a standardized manner, adopt a unified module, are presented in the form of a three-dimensional model, and support independent configuration and expansion; and each standardized module is provided with a predefined label to mark the parameters of the standardized module, including type, function and specification.

7. A display system for a prefabricated data center based on modular standardization, characterized in that: include: A display device based on mixed reality technology, wherein the display device is used to display to a user through an immersive interactive environment a data center model generated by the prefabricated data center AI generation method based on module standardization according to any one of claims 1 to 6.

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