Machine room equipment management method and device, electronic equipment and storage medium
By collecting and analyzing computer room resource data, predicting load demand and generating equipment shelving solutions, the problem of unreasonable allocation of equipment shelving in traditional methods is solved, and efficient resource utilization and operational efficiency optimization is achieved.
Patent Information
- Application Number
- CN202510076488.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-17
- Publication Date
- 2025-05-06
AI Technical Summary
The traditional method of shelving cabinet equipment ignores the rational allocation of power and refrigeration resources, resulting in the problem of unreasonable allocation of equipment on-shelf.
By collecting resource data from the computer room, including power consumption data, refrigeration data, airflow data and temperature data, we predict the load requirements in the future preset time period, generate the equipment shelf plan, and carry out the equipment shelf.
It realizes efficient utilization of resources, optimizes overall operational efficiency and reliability, and solves the problem of unreasonable allocation of equipment on-the-shelf equipment.
Smart Images
Figure CN119941175A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of equipment management, and in particular to a method, device, electronic equipment and storage medium for computer room equipment management. Background Art
[0002] With the continuous expansion of data center scale and the sharp increase in computing demand, rack management faces increasing challenges. Traditional rack management methods mainly rely on manual experience and static planning, using simple zoning strategies and basic thermal management technologies such as hot and cold channel layout. It operates data centers through fixed cooling settings, manual monitoring and passive maintenance, and power distribution and capacity planning are often rough.
[0003] With the rapid development of information technology, the scale of data centers continues to expand, and the number and density of equipment in computer rooms continue to increase. Traditional rack equipment racking methods often ignore the reasonable allocation of power and cooling resources, resulting in unreasonable equipment racking allocation. Summary of the invention
[0004] In view of this, the present application provides a method, device, electronic device and storage medium for managing equipment in a computer room to solve the problem of unreasonable equipment shelf allocation in the prior art.
[0005] To achieve the above objectives, this application provides the following technical solutions:
[0006] The first aspect of the present application discloses a method for managing equipment in a computer room, comprising:
[0007] Collect resource data of the computer room; wherein the resource data includes power consumption data, cooling data, air flow data, and temperature data;
[0008] Predicting the load demand of the computer room within a preset time period in the future based on the resource data; wherein the load demand includes power demand, heat dissipation demand, and refrigeration demand;
[0009] Generate a plan for equipment placement in the computer room based on the load demand;
[0010] The equipment is put on the shelves based on the equipment putting on the shelves plan.
[0011] Optionally, in the above method, predicting the load demand of the computer room within a future preset time period based on the resource data includes:
[0012] Performing data preprocessing on the resource data to obtain processed data;
[0013] Acquire historical resource data, and use the historical resource data to train a preset machine learning model to obtain a prediction model;
[0014] The processed data is input into the prediction model for processing to obtain the load demand of the computer room within a preset time period in the future.
[0015] Optionally, the above method further includes:
[0016] The power consumption of the computer room is adjusted based on the resource data.
[0017] Optionally, the above method further includes:
[0018] Based on the resource data, a computer room visualization interface is generated; wherein the computer room visualization interface is used to display the resource distribution and heat map of the computer room;
[0019] Based on the computer room visualization interface, a virtual shelf-loading simulation test of the equipment is performed, and an interface after the shelf-loading simulation is generated.
[0020] The second aspect of the present application discloses a device for managing equipment in a computer room, comprising:
[0021] A collection unit, used for collecting resource data of the computer room; wherein the resource data includes power consumption data, cooling data, air flow data, and temperature data;
[0022] A prediction unit, configured to predict the load demand of the computer room within a preset time period in the future based on the resource data; wherein the load demand includes power demand, heat dissipation demand, and refrigeration demand;
[0023] A solution generating unit, used for generating a device racking solution for the computer room based on the load demand;
[0024] An execution unit is used to implement device placement based on the device placement plan.
[0025] Optionally, in the above device, the prediction unit includes:
[0026] A preprocessing subunit, used for performing data preprocessing on the resource data to obtain processed data;
[0027] A training subunit, used to obtain historical resource data, and use the historical resource data to train a preset machine learning model to obtain a prediction model;
[0028] The prediction subunit is used to input the processed data into the prediction model for processing to obtain the load demand of the computer room within a preset time period in the future.
[0029] Optionally, the above device further includes:
[0030] An adjustment unit is used to adjust the power consumption of the computer room based on the resource data.
[0031] Optionally, the above device further includes:
[0032] A visualization unit, used to generate a computer room visualization interface based on the resource data; wherein the computer room visualization interface is used to display the resource distribution and heat map of the computer room;
[0033] The simulation unit is used to perform a virtual shelf-loading simulation test of the equipment based on the visual interface of the computer room, and generate an interface after the shelf-loading simulation.
[0034] The third aspect of the present application discloses an electronic device, comprising:
[0035] one or more processors;
[0036] a storage device having one or more programs stored thereon;
[0037] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of the first aspects of the present invention.
[0038] A fourth aspect of the present application discloses a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method described in any one of the first aspects of the present invention is implemented.
[0039] It can be seen from the above technical scheme that the present application provides a method for managing equipment in a computer room, which first collects resource data of the computer room; wherein the resource data includes power consumption data, cooling data, airflow data, and temperature data. Then, based on the resource data, the load demand of the computer room in a preset time period in the future is predicted; wherein the load demand includes power demand, heat dissipation demand, and cooling demand. Then, based on the load demand, an equipment shelving plan for the computer room is generated. Finally, the equipment is shelved based on the equipment shelving plan. It can be seen that by using the method of the present application, by comprehensively considering the resource data of the computer room, the load demand of the computer room in a preset time period in the future is predicted, thereby generating an equipment shelving plan for the computer room, and systematically recommending cabinet equipment shelving. It is possible to achieve efficient utilization of resources and optimization of overall operational efficiency and reliability. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.
[0041] Figure 1A flowchart of a method for managing equipment in a computer room disclosed in an embodiment of the present application;
[0042] Figure 2 This is a flowchart of an implementation of step S102 disclosed in an embodiment of the present application;
[0043] Figure 3 A system framework diagram of a computer room equipment management system disclosed in an embodiment of the present application;
[0044] Figure 4 This is an architecture diagram of the resource monitoring module disclosed in the embodiment of the present application;
[0045] Figure 5 This is an architecture diagram of the optimization decision module disclosed in the embodiment of the present application;
[0046] Figure 6 This is an architecture diagram of the execution control module disclosed in the embodiment of the present application;
[0047] Figure 7 A schematic diagram of a device for managing equipment in a computer room disclosed in an embodiment of the present application;
[0048] Figure 8 A schematic diagram of an electronic device disclosed in an embodiment of the present application. DETAILED DESCRIPTION
[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0050] In this application, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.
[0051] Furthermore, in this document, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.
[0052] As can be seen from the background technology, with the rapid development of information technology, the scale of data centers continues to expand, and the number and density of equipment in computer rooms continue to increase. Traditional cabinet equipment racking methods often ignore the reasonable allocation of power and cooling resources, resulting in unreasonable equipment racking allocation problems.
[0053] In view of this, the present application provides a method, device, electronic device and storage medium for managing equipment in a computer room to solve the problem of unreasonable equipment shelf allocation in the prior art.
[0054] The present application embodiment provides a method for managing equipment in a computer room, such as Figure 1 As shown, specifically including:
[0055] S101. Collect resource data of a computer room; wherein the resource data includes power consumption data, cooling data, air flow data, and temperature data.
[0056] It should be noted that the intelligent sensor network is used to monitor and collect resource data such as power consumption data, cooling data, airflow data, and temperature data of each cabinet in the computer room in real time. Among them, the intelligent power distribution unit (PDU) is used to collect accurate cabinet-level power consumption data. Distributed temperature sensors and airflow monitoring equipment are used to collect airflow data and temperature data. Through the intelligent interface of the precision air conditioner, the cooling data of the precision air conditioner is monitored and controlled in real time. The communication protocols used when collecting data include: using SNMP (Simple Network Management Protocol) protocol to collect network device data; communicating with the intelligent power distribution unit and temperature sensor through Modbus protocol (serial communication protocol). Finally, a time series database is used to store high-frequency collected data.
[0057] S102. Predicting the load demand of the computer room within a preset time period in the future based on the resource data; wherein the load demand includes power demand, heat dissipation demand, and refrigeration demand.
[0058] It should be noted that a prediction model is established by analyzing historical resource data using a machine learning algorithm. The collected resource data is input into the prediction model for processing to predict the load demand of the computer room within a preset time period in the future, where the load demand includes power demand, heat dissipation demand, and cooling demand. The preset time period can be set according to actual conditions, for example, 6 hours.
[0059] Optionally, in another embodiment of the present application, an implementation of the above step S102 is as follows: Figure 2 As shown, it may include:
[0060] S201. Preprocess resource data to obtain processed data.
[0061] It should be noted that the collected resource data is subjected to data preprocessing, including missing value processing, outlier detection and feature engineering, to obtain processed data.
[0062] S202: Obtain historical resource data, and use the historical resource data to train a preset machine learning model to obtain a prediction model.
[0063] It should be noted that a machine learning model, such as the LSTM (Long Short-Term Memory, a special type of recurrent neural network) model, is constructed. Historical resource data is obtained and input into the machine learning model to generate short-term and long-term forecasts. The forecast accuracy is then evaluated through methods such as cross-validation. If the accuracy is poor, the model is retrained until it is qualified and then the model is output to obtain a forecast model.
[0064] After a predictive model is built, it can be periodically updated with new data to maintain predictive accuracy.
[0065] S203: Input the processed data into a prediction model for processing to obtain the load demand of the computer room within a preset time period in the future.
[0066] It should be noted that the processed data is input into the prediction model for processing to obtain the load demand of the computer room in a future preset time period (for example, the next 6 hours).
[0067] S103. Generate a plan for placing equipment in the computer room based on load demand.
[0068] It should be noted that NSGA-II (Non-dominated Sorting Genetic Algorithm II) is used to perform multi-objective optimization on multiple factors in load demand, such as power demand, heat dissipation demand, and cooling demand. Multi-objective decision-making theory is applied to balance various indicators and generate the optimal equipment racking plan.
[0069] S104: Put the equipment on the shelves based on the equipment putting on the shelves solution.
[0070] It should be noted that after the equipment placement plan is generated, the equipment placement plan is converted into a specific execution instruction, and the execution instruction is sent through the equipment control interface to perform the equipment placement operation.
[0071] A method for managing equipment in a computer room provided in an embodiment of the present application first collects resource data of the computer room; wherein the resource data includes power consumption data, cooling data, airflow data, and temperature data. Then, based on the resource data, the load demand of the computer room in a preset time period in the future is predicted; wherein the load demand includes power demand, heat dissipation demand, and cooling demand. Then, based on the load demand, an equipment shelving plan for the computer room is generated. Finally, equipment is shelved based on the equipment shelving plan. It can be seen that by using the method of the present application, by comprehensively considering the resource data of the computer room, the load demand of the computer room in a preset time period in the future is predicted, thereby generating an equipment shelving plan for the computer room, and systematically recommending cabinet equipment shelving. It is possible to achieve efficient utilization of resources and optimization of overall operational efficiency and reliability.
[0072] Optionally, in another embodiment of the present application, the method for managing equipment in a computer room may further include:
[0073] Adjust the power consumption of the computer room based on resource data.
[0074] It should be noted that according to the resource data of each cabinet in the computer room, the power consumption of the equipment in the computer room is monitored and adjusted dynamically in real time to balance the load and energy consumption. For example, the heat flow simulation optimization is performed through algorithms, and the airflow and temperature distribution in the computer room are simulated using computational fluid dynamics (CFD) software to optimize the equipment layout to improve the heat dissipation effect. The cooling output of the precision air conditioner (CRAC, Computer Room Air Conditioning) is controlled in real time, and the cooling capacity is automatically adjusted according to the real-time load to achieve accurate cooling capacity distribution and improve energy efficiency. The PUE value is calculated and optimized in real time to continuously improve the energy efficiency of the data center.
[0075] Optionally, in another embodiment of the present application, the method for managing equipment in a computer room may further include:
[0076] Generate a computer room visualization interface based on resource data; the computer room visualization interface is used to display the resource distribution and heat map of the computer room;
[0077] Based on the visualization interface of the computer room, a virtual shelf simulation test of the equipment is carried out, and the interface after the shelf simulation is generated.
[0078] It should be noted that based on the resource data, WebGL (Web Graphics Library, 3D drawing protocol) technology is used to realize 3D computer room visualization, generate a computer room visualization interface, and intuitively display the resource distribution and heat map of the computer room. Among them, the React framework (Web development framework) is used to develop a responsive Web interface, and the D3.js library (JavaScript function library) is used to create interactive data visualization charts. In this visualization interface, users can realize virtual shelf simulation, allowing administrators to perform shelf simulation operations on the visualization interface to simulate the shelf effect.
[0079] In another embodiment of the present application, based on the above-mentioned method for managing equipment in a computer room, a device management system is developed, and its system framework is as follows: Figure 3 As shown, it mainly includes the following modules:
[0080] Resource monitoring module: real-time collection of computer room power consumption, cooling data, airflow, temperature data and DCIM system data.
[0081] Data processing module: cleans, standardizes and preliminarily analyzes the collected data.
[0082] Intelligent prediction module: predict future electricity demand and cooling demand trends based on historical data.
[0083] Optimization decision module: Generate the optimal cabinet racking plan.
[0084] Execution control module: implement the listing plan and make real-time adjustments.
[0085] User interface module: provides visual interface and interactive functions.
[0086] These modules are interconnected through a central data bus to achieve real-time data sharing and collaborative work, providing a comprehensive, efficient and intelligent solution for the data center.
[0087] Among them, the architecture of the resource monitoring module is as follows Figure 4 As shown, the key components include:
[0088] Data collection layer: including intelligent PDU, precision air conditioner, differential pressure sensor, temperature sensor and other related dynamic environment, DCIM, BA system, etc. Obtain the equipment data needed by the algorithm.
[0089] Data processing layer: The acquisition gateway collects, cleans, standardizes and preliminarily analyzes the device data through RS485, SUMP, API and other interfaces. Then the MQTT (Message Queuing Telemetry Transport) protocol is used to transmit the data to the upper platform.
[0090] Data service layer: Use time series database to store processed data.
[0091] System application layer: The system forms a unified platform that integrates the management functions of multiple resources such as power, refrigeration, and computer room environment. It realizes the coordinated optimization among various systems and improves the overall resource utilization efficiency. It realizes real-time monitoring, intelligent prediction, model algorithm, dynamic resource allocation, recommended shelf decision-making, and energy consumption PUE tuning functions. At the same time, it provides API interfaces and third-party platforms for data interaction.
[0092] The main steps of the intelligent prediction module include:
[0093] Data preprocessing: including missing value processing, outlier detection and feature engineering.
[0094] Model training: Use historical data to train machine learning models such as LSTM.
[0095] Forecast Generation: Generate short-term and long-term forecasts using trained models.
[0096] Result verification: Evaluate the prediction accuracy through cross-validation and other methods. If the accuracy is poor, go back and retrain the data until it is qualified before outputting the model for update.
[0097] Model updating: Regularly update the model with new data to maintain prediction accuracy.
[0098] Optimize the architecture of the decision module Figure 5 As shown, it includes the following main components:
[0099] Target definition component: defines multiple optimization targets, such as energy efficiency, heat dissipation, etc.
[0100] Constraint component: Set optimization constraints, such as power capacity, temperature threshold, etc.
[0101] Optimization algorithm engine: implements multi-objective optimization algorithms such as power load, heat dissipation efficiency, cooling resources, computer room hot spots, and airflow direction.
[0102] Solution evaluation component: Evaluate the feasibility and effectiveness of the generated solution.
[0103] Decision support component: Provides administrators with plan comparison and selection recommendations.
[0104] The architecture of the execution control module is as follows Figure 6 As shown, the key components include:
[0105] Instruction parser: Converts optimization plans into specific execution instructions.
[0106] Device control interface: A standardized interface for communicating with various hardware devices.
[0107] Execution Scheduler: coordinates and sorts the execution tasks.
[0108] Feedback Processor: Processes real-time feedback during execution and makes necessary adjustments.
[0109] Logger: Detailed logging of all execution operations and results.
[0110] Another embodiment of the present application also discloses a device for managing equipment in a computer room, such as Figure 7 As shown, specifically including:
[0111] The collection unit 701 is used to collect resource data of the computer room; wherein the resource data includes power consumption data, cooling data, air flow data, and temperature data.
[0112] The prediction unit 702 is used to predict the load demand of the computer room within a preset time period in the future based on the resource data; wherein the load demand includes power demand, heat dissipation demand, and refrigeration demand.
[0113] The solution generation unit 703 is used to generate a device placement solution for the computer room based on load demand.
[0114] The execution unit 704 is used to implement device placement based on the device placement plan.
[0115] In this embodiment, the specific execution process of the collection unit 701, the prediction unit 702, the solution generation unit 703 and the execution unit 704 can be found in the corresponding Figure 1 The content of the method embodiment will not be repeated here.
[0116] An embodiment of the present application provides a device for managing equipment in a computer room. First, a collection unit 701 collects resource data of the computer room; wherein the resource data includes power consumption data, cooling data, airflow data, and temperature data. Then, a prediction unit 702 predicts the load demand of the computer room within a preset time period in the future based on the resource data; wherein the load demand includes power demand, heat dissipation demand, and cooling demand. Then, a solution generation unit 703 generates an equipment shelving solution for the computer room based on the load demand. Finally, an execution unit 704 shelving equipment based on the equipment shelving solution. It can be seen that by using the method of the present application, by comprehensively considering the resource data of the computer room, the load demand of the computer room within a preset time period in the future is predicted, thereby generating an equipment shelving solution for the computer room, and systematically recommending cabinet equipment shelving. It is possible to achieve efficient utilization of resources and optimization of overall operational efficiency and reliability.
[0117] Optionally, in another embodiment of the present application, an implementation of the prediction unit 702 includes:
[0118] The preprocessing subunit is used to perform data preprocessing on the resource data to obtain processed data.
[0119] The training subunit is used to obtain historical resource data and use the historical resource data to train a preset machine learning model to obtain a prediction model.
[0120] The prediction subunit is used to input the processed data into the prediction model for processing to obtain the load demand of the computer room within a preset time period in the future.
[0121] In this embodiment, the specific execution process of the preprocessing subunit, the training subunit and the prediction subunit can be found in the corresponding Figure 2 The content of the method embodiment will not be repeated here.
[0122] Optionally, in another embodiment of the present application, the above-mentioned device for managing equipment in a computer room may further include:
[0123] The adjustment unit is used to adjust the power consumption of the computer room based on the resource data.
[0124] In this embodiment, the specific execution process of the adjustment unit can refer to the corresponding method embodiment content mentioned above, which will not be repeated here.
[0125] Optionally, in another embodiment of the present application, the above-mentioned device for managing equipment in a computer room may further include:
[0126] The visualization unit is used to generate a computer room visualization interface based on resource data; wherein the computer room visualization interface is used to display the resource distribution and heat map of the computer room.
[0127] The simulation unit is used to perform virtual shelf simulation testing of equipment based on the computer room visualization interface and generate an interface after shelf simulation.
[0128] In this embodiment, the specific execution process of the visualization unit and the simulation unit can refer to the corresponding method embodiment content mentioned above, which will not be repeated here.
[0129] Another embodiment of the present application further provides an electronic device, such as Figure 8 As shown, specifically including:
[0130] One or more processors 801.
[0131] The storage device 802 stores one or more programs.
[0132] When one or more programs are executed by one or more processors 801 , the one or more processors 801 implement any one of the methods in the above embodiments.
[0133] Another embodiment of the present application further provides a computer storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, any one of the methods in the above embodiments is implemented.
[0134] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.
[0135] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.
[0136] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for managing equipment in a computer room, characterized in that: include: Collect resource data of the computer room; wherein the resource data includes power consumption data, cooling data, air flow data, and temperature data; Predicting the load demand of the computer room within a preset time period in the future based on the resource data; wherein the load demand includes power demand, heat dissipation demand, and refrigeration demand; Generate a plan for equipment placement in the computer room based on the load demand; The equipment is put on the shelves based on the equipment putting on the shelves solution.
2. The method according to claim 1, characterized in that The predicting the load demand of the computer room within a future preset time period based on the resource data includes: Performing data preprocessing on the resource data to obtain processed data; Acquire historical resource data, and use the historical resource data to train a preset machine learning model to obtain a prediction model; The processed data is input into the prediction model for processing to obtain the load demand of the computer room within a preset time period in the future.
3. The method according to claim 1, characterized in that Also includes: The power consumption of the computer room is adjusted based on the resource data.
4. The method according to claim 1, characterized in that Also includes: Based on the resource data, a computer room visualization interface is generated; wherein the computer room visualization interface is used to display the resource distribution and heat map of the computer room; Based on the computer room visualization interface, a virtual shelf-loading simulation test of the equipment is performed, and an interface after the shelf-loading simulation is generated.
5. A device for managing equipment in a computer room, characterized in that: include: A collection unit, used for collecting resource data of the computer room; wherein the resource data includes power consumption data, cooling data, air flow data, and temperature data; A prediction unit, configured to predict the load demand of the computer room within a preset time period in the future based on the resource data; wherein the load demand includes power demand, heat dissipation demand, and refrigeration demand; A solution generating unit, used for generating a device racking solution for the computer room based on the load demand; An execution unit is used to implement device placement based on the device placement plan.
6. The device according to claim 5, characterized in that The prediction unit comprises: A preprocessing subunit, used for performing data preprocessing on the resource data to obtain processed data; A training subunit, used to obtain historical resource data, and use the historical resource data to train a preset machine learning model to obtain a prediction model; The prediction subunit is used to input the processed data into the prediction model for processing to obtain the load demand of the computer room within a preset time period in the future.
7. The device according to claim 5, characterized in that Also includes: An adjustment unit is used to adjust the power consumption of the computer room based on the resource data.
8. The device according to claim 5, characterized in that Also includes: A visualization unit, used to generate a computer room visualization interface based on the resource data; wherein the computer room visualization interface is used to display the resource distribution and heat map of the computer room; The simulation unit is used to perform a virtual shelf-loading simulation test of the equipment based on the visual interface of the computer room, and generate an interface after the shelf-loading simulation.
9. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method according to any one of claims 1 to 4.
10. A computer storage medium, characterized in that: A computer program is stored thereon, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.