Terminal air conditioner energy-saving regulation and control method and device, electronic equipment, readable storage medium and computer program product

Abstract

The invention discloses a terminal air conditioner energy-saving regulation and control method and device, electronic equipment, a readable storage medium and a computer program product, and belongs to the technical field of intelligent control. The method comprises the steps that historical air supply temperatures of M air conditioners and historical cold channel temperatures of N cold channel temperature sensors in a preset time period are obtained, corresponding related cold channels are selected for all the air conditioners based on historical temperature collection data, and the related cold channels comprise the multiple cold channel temperature sensors; the real-time cold channel temperatures of the N cold channel temperature sensors are obtained, and the real-time cold channel temperature mean value of the related cold channel corresponding to each air conditioner is calculated based on the real-time cold channel temperatures of the N cold channel temperature sensors; an air conditioner to be regulated and controlled is screened out from the M air conditioners according to a preset screening condition, and the real-time cold channel temperature mean value of the related cold channels corresponding to the air conditioner to be regulated and controlled at least meets the preset screening condition; and performing temperature rise regulation and control on the air conditioner to be regulated and controlled.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of intelligent control technology, and in particular to a terminal air-conditioning energy-saving control method, device, electronic device, readable storage medium and computer program product. Background Art

[0002] With the rapid development of information technology, data center energy consumption is becoming increasingly prominent, especially in cooling systems, which consume a significant portion of the energy. Traditional cooling methods often suffer from overcooling or undercooling, leading to energy waste and equipment overheating. Therefore, achieving energy savings through precise temperature control has become a focus of industry attention.

[0003] To address this issue, existing machine learning algorithms are as follows: Consider a scenario with 20 precision air conditioners, two rows of cold aisles, eight cold aisles per row, and four temperature sensors per cold aisle, for a total of 64 cold aisle temperatures. To ensure that all 64 cold aisle temperatures do not exceed the specified temperature when controlling the precision air conditioners, prediction models must be built that combine the control parameters of the 20 precision air conditioners with the temperatures of these 64 cold aisles, requiring a total of 64 models. The models are then invoked to determine whether the cold aisle temperatures exceed the specified temperature by increasing the air supply temperature of the air conditioners.

[0004] The above existing solutions have the following problems: (1) Too many models: 64 independent models are required to predict the temperature of each cold channel, which greatly increases the complexity of model management and maintenance. Each model requires separate training, validation, and tuning, which not only increases the consumption of computing resources but also makes it difficult to ensure consistency between models. In addition, it is difficult to ensure that the accuracy of each model meets the accuracy requirements.

[0005] (2) Data management and processing complexity: Each model needs to process a large amount of input data, including the control parameters of 20 precision air conditioners and the temperature data of 64 cold channels. The process of data preprocessing, feature engineering, and model training becomes very cumbersome, increasing the complexity of data management and processing.

[0006] (3) Difficulty in model training and updating: Due to the large number of models, training and updating these models requires a lot of time and computing resources. In practical applications, it may not be possible to update the model in time to reflect the latest environmental changes, resulting in a decrease in prediction accuracy.

[0007] (4) Complex system integration: Integrating 64 models into a unified control system creates high system integration complexity. This can increase the difficulty of system development and debugging, and extend the project implementation cycle. Summary of the Invention

[0008] The embodiments of the present application provide a terminal air-conditioning energy-saving control method, device, electronic device, readable storage medium and computer program product, which can solve the problems in the prior art that the excessive number of models not only increases the consumption of computing resources, but also increases the complexity of data management and processing, as well as the problems of reduced prediction accuracy and increased difficulty in complex system integration and development due to difficulties in model training and updating.

[0009] In order to solve the above technical problems, this application is implemented as follows: In a first aspect, a terminal air conditioner energy-saving control method is provided, comprising: Acquire historical temperature data collected during a preset time period, the historical temperature data including: historical supply air temperatures of M air conditioners and historical cold aisle temperatures of N cold aisle temperature sensors, where M and N are positive integers; Selecting a corresponding cold aisle for each air conditioner based on the historical temperature collection data, wherein the relevant cold aisle includes a plurality of cold aisle temperature sensors, and a historical cold aisle temperature of the relevant cold aisle is correlated with a historical supply air temperature of the air conditioner corresponding to the relevant cold aisle; Obtaining the real-time cold aisle temperatures of the N cold aisle temperature sensors, and calculating the average real-time cold aisle temperature of the relevant cold aisle corresponding to each air conditioner based on the real-time cold aisle temperatures of the N cold aisle temperature sensors; Filtering an air conditioner to be regulated from the M air conditioners according to a preset screening condition, wherein the real-time cold channel temperature average of the relevant cold channel corresponding to the air conditioner to be regulated at least meets the preset screening condition; The air conditioner to be regulated is subjected to temperature-raising regulation.

[0010] In a second aspect, a terminal air-conditioning energy-saving control device is provided, comprising: A data acquisition module is used to acquire historical temperature data collected during a preset time period, wherein the historical temperature data includes: historical supply air temperatures of M air conditioners and historical cold aisle temperatures of N cold aisle temperature sensors; a correlation analysis module, configured to select a corresponding cold aisle for each of the air conditioners based on the historical temperature collection data, wherein the relevant cold aisle includes a plurality of the cold aisle temperature sensors, and a historical cold aisle temperature of the relevant cold aisle is correlated with a historical supply air temperature of the air conditioner corresponding to the relevant cold aisle; an average calculation module, configured to obtain the real-time cold aisle temperatures of the N cold aisle temperature sensors, and calculate the average of the real-time cold aisle temperatures of the relevant cold aisles corresponding to each of the air conditioners based on the real-time cold aisle temperatures of the N cold aisle temperature sensors; an air conditioner screening module to be regulated, configured to select an air conditioner to be regulated from the M air conditioners according to a preset screening condition, wherein the real-time cold channel temperature average of the relevant cold channel corresponding to the air conditioner to be regulated at least meets the preset screening condition; The control module is used to control the temperature of the air conditioner to be controlled.

[0011] In a third aspect, an electronic device is provided, which includes a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor to implement the above-mentioned terminal air-conditioning energy-saving control method.

[0012] In a fourth aspect, a readable storage medium is provided, in which at least one computer program is stored. The computer program is loaded and executed by a processor to implement the above-mentioned terminal air-conditioning energy-saving control method.

[0013] In a fifth aspect, a computer program product is provided, which includes at least one computer program, and the computer program is loaded and executed by a processor to implement the terminal air-conditioning energy-saving control method provided in the above-mentioned various optional implementation methods.

[0014] The terminal air-conditioning energy-saving control method, device, electronic device, readable storage medium and computer program product provided in the embodiments of the present application screen out the cold channel temperature most relevant to each air conditioner based on the cold channel temperature correlation analysis and distance priority principle, ensuring that the selected relevant cold channel temperature sensor can more accurately reflect the influence of the outlet air temperature of each air conditioner. Then, the precision air conditioner that needs to be controlled is accurately located through the real-time cold channel temperature and safety temperature threshold of the relevant cold channel, thereby avoiding waste of resources. In addition, the embodiments of the present application calculate the real-time cold channel temperature mean of the relevant cold channel corresponding to the air conditioner, and replace multiple models with a single indicator, thereby simplifying the complex system that originally required 64 independent models into a unified logic or a small number of models, thereby reducing the complexity of the system and reducing the difficulty of model management and maintenance.

[0015] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] Figure 1 A flow chart of a terminal air-conditioning energy-saving control method provided by an exemplary embodiment of the present application is shown; Figure 2A flowchart of selecting a corresponding cold channel for each air conditioner based on historical temperature collection data provided by an exemplary embodiment of the present application is shown; Figure 3 A flowchart of obtaining the real-time average cold channel temperature of the corresponding cold channel corresponding to each air conditioner provided by an exemplary embodiment of the present application is shown; Figure 4 A flowchart of selecting an air conditioner to be regulated from M air conditioners provided by an exemplary embodiment of the present application is shown; Figure 5 A block diagram of a terminal air-conditioning energy-saving control device provided by an exemplary embodiment of the present application is shown; Figure 6 A structural block diagram of an electronic device provided by an exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0018] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0019] In order to solve the problems in the existing technology that the excessive number of models not only increases the consumption of computing resources, but also increases the complexity of data management and processing, as well as the problems of reduced prediction accuracy and increased difficulty in complex system integration and development due to the difficulty in model training and updating, the embodiments of the present application provide a terminal air-conditioning energy-saving control method and device.

[0020] The method can be executed by various types of computing devices, or by applications (APPs) installed on the computing devices. The computing devices can be user terminals such as mobile phones, tablet computers, and smart wearable devices, or servers. The servers can be single network servers, server groups consisting of multiple network servers, or cloud computing-based clouds consisting of a large number of computers and network servers.

[0021] For ease of description, the present invention describes the method using a server as an example. Those skilled in the art will appreciate that the description of the method using the server as an example is merely illustrative and does not limit the scope of protection of the claims corresponding to this solution.

[0022] The terminal air-conditioning energy-saving control method provided in the embodiment of the present application is a terminal energy-saving solution based on cold channel temperature correlation analysis and distance priority principle. It dynamically adjusts the operating parameters of the cooling system by real-time monitoring and analysis of the temperature distribution of the cold channel to achieve precise cooling control. Specifically, the method uses temperature sensors arranged in the cold channel to collect temperature data in real time, and identifies areas or equipment with abnormal temperatures through data analysis algorithms. Based on this data, the control system of the data center computer room can automatically adjust the temperature setting value of the air-conditioning equipment to ensure that cold air can be accurately delivered to the equipment that needs cooling, thereby avoiding unnecessary energy consumption.

[0023] Figure 1 FIG. 1 shows a flow chart of a terminal air conditioning energy-saving control method according to an exemplary embodiment of the present application. Figure 1 As shown, the terminal air-conditioning energy-saving control method mainly includes the following steps (S101-S105): S101. Acquire historical temperature data collected during a preset time period, the historical temperature data including: historical supply air temperatures of M air conditioners and historical cold aisle temperatures of N cold aisle temperature sensors, where M and N are positive integers. In this example, a scenario with 20 precision air conditioners, two rows of cold aisles, eight cold aisles per row, and four temperature sensors per cold aisle, for a total of 64 cold aisle temperature sensors, is used as an example. In this scenario, M = 20 and N = 64.

[0024] The preset time period is six months or longer to obtain richer research data. Historical temperature data for more than six months is collected, including the historical supply air temperatures (control parameters) of 20 air conditioners and the historical cold aisle temperatures of 64 cold aisle temperature sensors.

[0025] In specific implementation, these historical temperature collection data are stored in a DataFrame data structure. Each row represents a data record at a time point, and the columns are the historical supply air temperatures of 20 air conditioners and the historical cold aisle temperatures of 64 cold aisle temperature sensors.

[0026] These historical temperature collection data provide the basis for subsequent analysis and model building. The rich data helps to more accurately explore the relationship between air conditioning control parameters and cold channel temperature.

[0027] S102: selecting a corresponding cold aisle for each air conditioner based on the historical temperature collection data, wherein the relevant cold aisle includes a plurality of cold aisle temperature sensors, and a historical cold aisle temperature of the relevant cold aisle is correlated with a historical supply air temperature of the air conditioner corresponding to the relevant cold aisle; As an optional implementation in this embodiment, Pearson correlation analysis is used to analyze the correlation between the historical cold aisle temperature of the relevant cold aisle and the historical supply air temperature of the air conditioner corresponding to the relevant cold aisle.

[0028] like Figure 2 As shown, in an exemplary embodiment of the present application, a terminal air-conditioning energy-saving control method is shown, in which a corresponding cold channel is selected for each air-conditioner based on historical temperature collection data, including the following steps (S201-S202): S201. Calculate the correlation coefficient between the historical supply air temperature of each air conditioner and the historical cold aisle temperature of each cold aisle temperature sensor using Pearson correlation analysis. In this example, Pearson correlation analysis is used to analyze the correlation coefficient between the historical cold aisle temperatures of 64 cold aisle temperature sensors and the historical supply air temperatures of 20 air conditioners. To implement this, call df.corr(method='pearson') to return a symmetric DataFrame where each element represents the Pearson correlation coefficient between two columns (a column for historical cold aisle temperatures and a column for historical supply air temperatures of air conditioners). The correlation coefficient ranges from -1 to 1, with 1 indicating a perfect positive correlation, -1 indicating a perfect negative correlation, and 0 indicating no linear correlation.

[0029] S202: Selecting, for each air conditioner, a preset number of cold aisle temperature sensors with the highest correlation coefficient as the relevant cold aisles corresponding to each air conditioner.

[0030] The correlation coefficients between each air conditioner and the 64 historical cold aisle temperatures are ranked, and the top-ranked cold aisle temperature sensors are selected as the relevant cold aisle for each air conditioner. The first preset number can be set based on actual control needs and is not limited in this application. For example, based on the correlation coefficient ranking, the top four cold aisle temperature sensors with the highest correlation coefficient are selected for each air conditioner.

[0031] like Figure 2 As shown, in another exemplary embodiment of the present application, a terminal air-conditioning energy-saving control method is shown, in which a corresponding cold channel is selected for each air-conditioner based on historical temperature collection data, and further includes: S203, obtaining the second preset number of cold aisles closest to each air conditioner; S204: Remove overlaps from the first preset number of cold aisles and merge the second preset number of cold aisles as the relevant cold aisles corresponding to each air conditioner.

[0032] In this embodiment, to ensure service security, a preset number of cold aisle temperature sensors closest to each air conditioner are selected based on the distance-priority principle, after deduplication, as the corresponding relevant cold aisle. The second preset number can be set based on actual control needs and is not limited by this application. For example, if the two closest cold aisle temperature sensors are selected for an air conditioner, it is possible that the closest cold aisle temperature sensor overlaps with the top four cold aisle temperature sensors selected in step S202. In this case, the remaining cold aisle temperature sensors after deduplication are used as the final relevant cold aisle for that air conditioner. This screening method comprehensively considers correlation and distance factors, preventing missed detection of critical areas and screening out the most relevant cold aisle temperature for each air conditioner, ensuring that the selected cold aisle temperature sensor more accurately reflects the impact of each air conditioner's outlet air temperature. Through cold aisle temperature correlation analysis and the distance-priority principle, a complex system that originally required 64 independent models is simplified into a unified logic or a small number of models, thereby reducing system complexity and simplifying model management and maintenance.

[0033] In one application example, a Pearson correlation analysis of the historical temperature data revealed that the correlation coefficients between air conditioner A and cold aisle temperature sensors C1, C2, C3, and C4 were 0.8, 0.75, 0.7, and 0.65, respectively, ranking them in the top four. Furthermore, the two cold aisles closest to air conditioner A were C5 and C6, with C5 overlapping with C2. Therefore, C1, C2, C3, C4, and C6 were ultimately selected as the cold aisle temperatures associated with air conditioner A. The same method was used to screen the other 19 air conditioners.

[0034] S103: Obtain real-time cold aisle temperatures from N cold aisle temperature sensors, and calculate an average of the real-time cold aisle temperatures of the cold aisles corresponding to each air conditioner based on the real-time cold aisle temperatures from the N cold aisle temperature sensors. like Figure 3 As shown, in another exemplary embodiment of the present application, a terminal air-conditioning energy-saving control method is shown, in which the real-time cold channel temperatures of N cold channels are obtained, and based on the real-time cold channel temperatures of the N cold channels, the real-time cold channel temperature average of the relevant cold channels corresponding to each air conditioner is calculated, including the following steps: S301: Acquire real-time cold aisle temperatures of N cold aisle temperature sensors within a first preset time period, and calculate an average cold aisle temperature of each cold aisle temperature sensor within the first preset time period, wherein the average cold aisle temperature is obtained by calculating an average of the real-time cold aisle temperatures within the first preset time period; In one application example, the first preset duration is one hour, and real-time cold aisle temperature data from 64 cold aisle temperature sensors is pulled for one hour. For example, assume that real-time cold aisle temperature data for cold aisle temperature sensor C1 is recorded at 60 time points within one hour. The overall data is averaged to obtain the average cold aisle temperature for C1. The average cold aisle temperatures for the other 63 cold aisle temperature sensors are obtained in the same manner. Therefore, by averaging the overall data, data fluctuations can be reduced.

[0035] Specifically, in an application example, the data averaging method is df.mean(), which returns data in Series format, where the index is the column name (64 cold aisle temperature sensors) and the value is the average cold aisle temperature of the corresponding sensor.

[0036] S302. Calculate the real-time average cold aisle temperature of the corresponding cold aisle of each air conditioner based on the average cold aisle temperature of each cold aisle within the first preset time period, wherein the real-time average cold aisle temperature of the air conditioner is obtained by calculating the average of the average cold aisle temperatures of the corresponding cold aisles of the air conditioner.

[0037] In an application example, it is assumed that the relevant cold channels of air conditioner A are C2, C3, C4, and C6. In step S301, the average cold channel temperatures of C2, C3, C4, and C6 within the first preset time period are obtained, and the average of these four values ​​is taken to obtain the real-time cold channel temperature mean k of the relevant cold channels corresponding to air conditioner A. The real-time cold channel temperature mean k of the relevant cold channels corresponding to 20 air conditioners are all obtained in this way. In the embodiment of the present application, a comprehensive value k is used to represent the cold channel temperature situation related to each air conditioner, and a single indicator is used instead of multiple models, reducing the number of models from 64 to 20 comprehensive indicators. For example, the real-time cold channel temperature mean k of the four associated cold channel temperature sensors of air conditioner A replaces four independent models, thereby simplifying the model and reducing the complexity of data processing.

[0038] S104: Filtering an air conditioner to be regulated from the M air conditioners according to a preset screening condition, wherein the real-time average cold aisle temperature of the corresponding cold aisle corresponding to the air conditioner to be regulated at least meets the preset screening condition; In this embodiment, as an optional implementation method of this embodiment, before screening out the air conditioner to be regulated from M air conditioners, the method provided in the embodiment of the present application also includes: obtaining the real-time air supply temperature of the M air conditioners within the second preset time length, and calculating the average air supply temperature of each air conditioner within the second preset time length.

[0039] In one application example, the second preset duration can be one hour, and real-time supply air temperature data for 20 air conditioners is pulled for one hour. For example, suppose the supply air temperature data for air conditioner A is recorded at 60 time points within one hour. The average of this data is calculated to obtain the average supply air temperature of air conditioner A. The average supply air temperatures of the other 19 air conditioners are obtained in the same manner. Therefore, by averaging the overall data, data fluctuations can be reduced.

[0040] like Figure 4 As shown, as an optional implementation in the embodiment of the present application, the air conditioner to be regulated is screened out from M air conditioners according to a preset screening condition, including the following steps: S401. Mark the air conditioners whose average air supply temperature is lower than the safety temperature threshold among the M air conditioners, and use the marked air conditioners as S air conditioners to be screened.

[0041] In this embodiment, the preset screening conditions include whether the average supply air temperature is less than a safety temperature threshold. For example, the safety temperature threshold can be set to 23.5°C. This temperature setting ensures a safe margin during regulation to prevent excessive regulation leading to excessive temperatures. First, S air conditioners with average supply air temperatures less than the safety temperature threshold are initially screened out. This narrows the scope for subsequent selection of air conditioners to be regulated, improving screening efficiency.

[0042] Specifically, in an application example, the data averaging method is df.mean(), which returns data in a Series format, where the index is the column name (20 air conditioners) and the value is the average air supply temperature of the corresponding air conditioner.

[0043] S402, sorting the real-time cold channel temperature averages of the relevant cold channels corresponding to S air conditioners to be screened in ascending order, where S is a positive integer and S≤M; S403: The air conditioners whose real-time cold channel temperature average values ​​are ranked in the top third of the preset number are selected as the air conditioners to be regulated.

[0044] In this embodiment of the present application, the preset screening criteria also include selecting air conditioners whose real-time average cold aisle temperature values ​​rank in the top three preset numbers. This third preset number can be set based on actual control needs and is not a limitation in this application. For example, three air conditioners to be controlled may be selected based on business requirements (the actual number may be determined based on the specific business scenario). In step S302, the comprehensive real-time average cold aisle temperature k for each air conditioner is obtained and these k values ​​are sorted in ascending order. Specifically, the selection principle is to select the three air conditioners corresponding to the top three k values ​​in ascending order based on the real-time average cold aisle temperature k, and ensure that the air conditioner supply air temperature is less than 23.5°C. If this condition is not met, the search continues until three air conditioners that meet the requirements are found. This screening logic combines the air conditioner supply air temperature and the real-time average cold aisle temperature to accurately identify the air conditioners most in need of control, thereby improving the effectiveness of control.

[0045] In one application example, at the current moment, the average air supply temperature of 10 air conditioners is found to be less than 23.5°C. After sorting the real-time cold aisle temperature averages k corresponding to these 10 air conditioners in ascending order, select the three air conditioners with the top real-time cold aisle temperature averages k and an average air supply temperature less than 23.5°C. Assume that these three air conditioners are air conditioner A, air conditioner B, and air conditioner C.

[0046] The embodiment of the present application uses correlation analysis and distance priority principle to select the relevant cold channel for each air conditioner, and then accurately locates the precision air conditioner that needs to be regulated through the real-time cold channel temperature and safety temperature threshold of the relevant cold channel, thereby avoiding waste of resources.

[0047] S105, performing temperature control on the air conditioner to be controlled.

[0048] As an optional implementation in this embodiment, temperature control of the controlled air conditioner includes: controlling the temperature of the controlled air conditioner according to a preset safety temperature control value. Specifically, the safety temperature control value can be set according to actual control needs, and this application does not limit this. For example, the safety temperature control value can be set to 0.5°C, and air conditioners A, B, and C can each be controlled by an additional 0.5°C. For example, the supply air temperature of air conditioner A can be adjusted from 23°C to 23.5°C, the supply air temperature of air conditioner B can be adjusted from 22°C to 22.5°C, and the supply air temperature of air conditioner C can be adjusted from 22.5°C to 23°C. In specific implementations, the cold aisle temperatures of 64 cold aisle temperature sensors are monitored in real time to ensure that they do not exceed the safety temperature threshold. If, during the control process, the temperature of a cold aisle approaches or exceeds the preset safety temperature threshold, the air conditioner's supply air temperature is promptly adjusted to ensure a stable and safe temperature environment throughout the data center. Through precise control and real-time monitoring, this embodiment ensures that the cold aisle temperature does not exceed the specified temperature threshold while avoiding over-control, thereby achieving energy savings.

[0049] Through the terminal air-conditioning energy-saving control method provided by the embodiment of the present application, the cold channel temperature most relevant to each air conditioner is screened out based on the cold channel temperature correlation analysis and the distance priority principle, ensuring that the selected relevant cold channel temperature sensor can more accurately reflect the influence of the outlet air temperature of each air conditioner. Then, the precision air conditioner that needs to be regulated is accurately located through the real-time cold channel temperature and safety temperature threshold of the relevant cold channel, thereby avoiding waste of resources. In addition, the embodiment of the present application calculates the real-time cold channel temperature mean of the relevant cold channel corresponding to the air conditioner, replaces multiple models with a single indicator, and simplifies the complex system that originally required 64 independent models into a unified logic or a small number of models, thereby reducing the complexity of the system and reducing the difficulty of model management and maintenance.

[0050] An exemplary embodiment of the present application provides a terminal air-conditioning energy-saving control device 10 . Figure 5 The structure block diagram of the terminal air conditioning energy-saving control device 10 provided by an exemplary embodiment of the present application is shown. The terminal air conditioning energy-saving control device 10 is applied to the control system of the computer room data center, which can achieve the following Figures 1-4 All or part of the contents of any of the illustrated embodiments. The following is only a brief description of the structure and function of the terminal air-conditioning energy-saving control device 10. For other matters not covered, please refer to the relevant description in the above-mentioned terminal air-conditioning energy-saving control method. The embodiment of the terminal air-conditioning energy-saving control device 10 corresponds to the embodiment of the above-mentioned terminal air-conditioning energy-saving control method. The various implementation processes and implementation methods of the above-mentioned method embodiments can be applied to the embodiment of the terminal air-conditioning energy-saving control device 10 and can achieve the same technical effects.

[0051] like Figure 5 As shown, the terminal air conditioner energy-saving control device 10 includes: a data acquisition module 100, a correlation analysis module 200, a mean calculation module 300, an air conditioner screening module 400 to be controlled, and a control module 500, wherein: The data acquisition module 100 is used to acquire historical temperature data collected during a preset time period, wherein the historical temperature data includes: historical supply air temperatures of M air conditioners and historical cold aisle temperatures of N cold aisle temperature sensors; A correlation analysis module 200 is configured to select a corresponding cold aisle for each air conditioner based on the historical temperature collection data, wherein the relevant cold aisle includes a plurality of cold aisle temperature sensors, and the historical cold aisle temperature of the relevant cold aisle is correlated with the historical supply air temperature of the air conditioner corresponding to the relevant cold aisle; The mean calculation module 300 is used to obtain the real-time cold aisle temperatures of N cold aisle temperature sensors and calculate the mean of the real-time cold aisle temperatures of the relevant cold aisle corresponding to each air conditioner based on the real-time cold aisle temperatures of the N cold aisle temperature sensors; The air conditioner screening module 400 is configured to screen the air conditioner to be regulated from the M air conditioners according to a preset screening condition, wherein the real-time cold aisle temperature average of the cold aisle corresponding to the air conditioner to be regulated at least meets the preset screening condition; The control module 500 is used to control the temperature of the air conditioner to be controlled.

[0052] In this embodiment, a scenario with 20 precision air conditioners, two rows of cold aisles, eight cold aisles per row, and four temperature sensors per cold aisle, for a total of 64 cold aisle temperature sensors, is used as an example. In this scenario, M = 20 and N = 64. The preset time period is six months or longer to obtain richer research data. The data acquisition module 100 collects historical temperature data for more than six months, including the historical supply air temperatures (control parameters) of the 20 air conditioners and the historical cold aisle temperatures of the 64 cold aisle temperature sensors.

[0053] As an optional implementation in this embodiment, the correlation analysis module 200 uses Pearson correlation analysis to analyze the correlation between the historical cold aisle temperature of the relevant cold aisle and the historical supply air temperature of the air conditioner corresponding to the relevant cold aisle.

[0054] As an optional implementation in this embodiment, the correlation analysis module 200 selects a corresponding relevant cold channel for each air conditioner based on the historical temperature collection data in the following manner: using Pearson correlation analysis to calculate the correlation coefficient between the historical supply air temperature of each air conditioner and the historical cold channel temperature of each cold channel temperature sensor; for each air conditioner, selecting the cold channel temperature sensors with the first preset number of correlation coefficient rankings as the relevant cold channel corresponding to each air conditioner.

[0055] In addition, the correlation analysis module 200 also selects the corresponding relevant cold channel for each air conditioner based on the historical temperature collection data in the following manner: obtaining the second preset number of cold channel temperature sensors closest to each air conditioner; de-overlapping and merging the first preset number of cold channel temperature sensors and the second preset number of cold channel temperature sensors as the relevant cold channel corresponding to each air conditioner.

[0056] The embodiment of the present application takes into account the correlation and distance factors to prevent missed detection of key areas, screen out the cold aisle temperature most relevant to each air conditioner, and ensure that the selected cold aisle temperature sensor can more accurately reflect the impact of the air outlet temperature of each air conditioner.

[0057] As an optional implementation in this embodiment, the mean calculation module 300 obtains the real-time cold aisle temperatures of N cold aisles in the following manner, and calculates the real-time cold aisle temperature mean of the relevant cold aisle corresponding to each air conditioner based on the real-time cold aisle temperatures of the N cold aisles: Obtaining real-time cold aisle temperatures of N cold aisle temperature sensors within a first preset time period, and calculating an average cold aisle temperature of each cold aisle temperature sensor within the first preset time period, wherein the average cold aisle temperature is obtained by calculating an average of the real-time cold aisle temperatures within the first preset time period; Based on the average cold channel temperature of each cold channel within a first preset time period, the real-time average cold channel temperature of the relevant cold channel corresponding to each air conditioner is calculated, wherein the real-time average cold channel temperature of the air conditioner is obtained by calculating the average of the average cold channel temperatures of the relevant cold channels corresponding to the air conditioner.

[0058] In one application example, the first preset duration is one hour, and real-time cold aisle temperature data from 64 cold aisle temperature sensors is pulled for one hour. For example, assume that real-time cold aisle temperature data for cold aisle temperature sensor C1 is recorded at 60 time points within one hour. The overall data is averaged to obtain the average cold aisle temperature for C1. The average cold aisle temperatures for the other 63 cold aisle temperature sensors are obtained in the same manner. Therefore, by averaging the overall data, data fluctuations can be reduced.

[0059] In one application example, assume that the cold aisles associated with air conditioner A are C2, C3, C4, and C6. In step S301, the average cold aisle temperatures of C2, C3, C4, and C6 over a first preset time period are obtained. These four values ​​are then averaged to obtain the real-time average cold aisle temperature k for the cold aisles associated with air conditioner A. The real-time average cold aisle temperature k for the cold aisles associated with all 20 air conditioners is obtained in this manner.

[0060] In an embodiment of the present application, by calculating the real-time cold channel temperature average of the corresponding relevant cold channels of the air conditioner, a single indicator is used instead of multiple models, and the complex system that originally required 64 independent models is simplified into a unified logic or a small number of models, thereby reducing the complexity of the system and reducing the difficulty of model management and maintenance.

[0061] In this embodiment, as an optional implementation in this embodiment, the mean calculation module 300 is also used to obtain the real-time air supply temperature of M air conditioners within the second preset time period, and calculate the average air supply temperature of each air conditioner within the second preset time period.

[0062] In one application example, the second preset duration can be one hour, and real-time supply air temperature data for 20 air conditioners is pulled for one hour. For example, suppose the supply air temperature data for air conditioner A is recorded at 60 time points within one hour. The average of this data is calculated to obtain the average supply air temperature of air conditioner A. The average supply air temperatures of the other 19 air conditioners are obtained in the same manner. Therefore, by averaging the overall data, data fluctuations can be reduced.

[0063] As an optional implementation in the embodiment of the present application, the air conditioner screening module 400 screens out the air conditioner to be regulated from the M air conditioners according to the preset screening conditions in the following manner: Mark the air conditioners whose average air supply temperature is lower than the safety temperature threshold among the M air conditioners, and use the marked air conditioners as the S air conditioners to be screened; sort the real-time cold channel temperature means of the relevant cold channels corresponding to the S air conditioners to be screened in ascending order, where S is a positive integer and S≤M; and select the air conditioners whose real-time cold channel temperature means are ranked in the top third preset number as the air conditioners to be regulated.

[0064] In this embodiment, the preset screening conditions include whether the average supply air temperature is less than a safety temperature threshold. For example, the safety temperature threshold can be set to 23.5°C. This temperature setting ensures a safe margin during regulation to prevent excessive regulation leading to excessive temperatures. First, S air conditioners with average supply air temperatures less than the safety temperature threshold are initially screened out. This narrows the scope for subsequent selection of air conditioners to be regulated, improving screening efficiency.

[0065] In an embodiment of the present application, the preset screening conditions also include: air conditioners ranked in the top third of a preset number by real-time cold aisle temperature average. The third preset number can be set based on actual control needs and is not restricted by this application. For example, three air conditioners to be controlled can be selected based on business needs (the actual number can be determined based on the specific business scenario). The selection principle is to select the three air conditioners corresponding to the top three k values ​​in ascending order according to the real-time cold aisle temperature average k, and ensure that the air conditioner supply air temperature is less than 23.5°C. If this condition is not met, the search continues until three air conditioners that meet the requirements are found. This screening logic combines the air conditioner supply air temperature and the real-time cold aisle temperature average to accurately identify the air conditioners that need the most control, thereby improving the effectiveness of the control.

[0066] The embodiment of the present application uses correlation analysis and distance priority principle to select the relevant cold channel for each air conditioner, and then accurately locates the precision air conditioner that needs to be regulated through the real-time cold channel temperature and safety temperature threshold of the relevant cold channel, thereby avoiding waste of resources.

[0067] As an optional implementation in this embodiment, the control module 500 performs temperature control on the controlled air conditioner in the following manner: The temperature of the controlled air conditioner is controlled according to a preset safety temperature control value. Specifically, the safety temperature control value can be set according to actual control needs, and this application does not impose any restrictions on this. For example, the safety temperature control value can be set to 0.5°C, and the temperature of air conditioners A, B, and C can be controlled by increasing the temperature by 0.5°C.

[0068] Through the terminal air-conditioning energy-saving control device provided by the embodiment of the present application, the cold channel temperature most relevant to each air conditioner is screened out based on the cold channel temperature correlation analysis and the distance priority principle, ensuring that the selected relevant cold channel temperature sensor can more accurately reflect the influence of the outlet air temperature of each air conditioner. Then, the precision air conditioner that needs to be regulated is accurately located through the real-time cold channel temperature and safety temperature threshold of the relevant cold channel, thereby avoiding waste of resources. In addition, the embodiment of the present application calculates the real-time cold channel temperature mean of the relevant cold channel corresponding to the air conditioner, replaces multiple models with a single indicator, and simplifies the complex system that originally required 64 independent models into a unified logic or a small number of models, thereby reducing the complexity of the system and reducing the difficulty of model management and maintenance.

[0069] Figure 6 The following is a block diagram of an electronic device 1000 according to an exemplary embodiment of the present application. The electronic device 1000 can be implemented as the aforementioned terminal air conditioner energy-saving control device, which can be configured in electronic devices such as smartphones, tablet computers, laptops, desktop computers, smart watches, televisions, or servers.

[0070] Typically, the electronic device 1000 includes a processor 1001 and a memory 1002 .

[0071] Processor 1001 may include one or more processing cores, such as a quad-core processor or a dec-core processor. Processor 1001 may be implemented in hardware using at least one of the following: a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), or a PLA (Programmable Logic Array). Processor 1001 may also include a main processor and a coprocessor. The main processor is used to process data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 1001 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing content required for display. In some embodiments, processor 1001 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0072] The memory 1002 may include one or more computer-readable storage media, which may be non-transitory. The memory 1002 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 1002 is used to store at least one instruction, which is used to be executed by the processor 1001 to implement all or part of the steps in the terminal air conditioner energy-saving control method shown in the method embodiment of the present application.

[0073] Those skilled in the art will understand that Figure 6 The structure shown in the figure does not constitute a limitation on the electronic device 1000, and the electronic device 1000 may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0074] In an exemplary embodiment, a computer-readable storage medium is also provided, storing a program or instruction that, when executed by a processor, implements all or part of the steps in the above-described terminal air conditioner energy-saving control method. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, or an optical data storage device.

[0075] In an exemplary embodiment, a computer program product is further provided, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, which, when executed by a computer, causes the computer to perform the above-mentioned Figures 1 to 5 All or part of the steps of the terminal air conditioning energy-saving control method shown in any embodiment.

[0076] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered merely as exemplary, and the true scope and spirit of the present application are indicated by the claims.

[0077] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A terminal air conditioning energy-saving control method, characterized in that: include: Acquire historical temperature data collected during a preset time period, the historical temperature data including: historical supply air temperatures of M air conditioners and historical cold aisle temperatures of N cold aisle temperature sensors, where M and N are positive integers; Selecting a corresponding cold aisle for each air conditioner based on the historical temperature collection data, wherein the relevant cold aisle includes a plurality of cold aisle temperature sensors, and a historical cold aisle temperature of the relevant cold aisle is correlated with a historical supply air temperature of the air conditioner corresponding to the relevant cold aisle; Obtaining the real-time cold aisle temperatures of the N cold aisle temperature sensors, and calculating the average real-time cold aisle temperature of the relevant cold aisle corresponding to each air conditioner based on the real-time cold aisle temperatures of the N cold aisle temperature sensors; Filtering an air conditioner to be regulated from the M air conditioners according to a preset screening condition, wherein the real-time cold channel temperature average of the relevant cold channel corresponding to the air conditioner to be regulated at least meets the preset screening condition; The air conditioner to be regulated is subjected to temperature-raising regulation.

2. The method according to claim 1, characterized in that The selecting a corresponding cold channel for each air conditioner based on the historical temperature collection data includes: Pearson correlation analysis was used to calculate the correlation coefficient between the historical supply air temperature of each air conditioner and the historical cold aisle temperature of each cold aisle temperature sensor; For each of the air conditioners, a preset number of cold channel temperature sensors with the highest correlation coefficients are selected as the relevant cold channels corresponding to each of the air conditioners.

3. The method according to claim 2, characterized in that The selecting a corresponding cold channel for each of the air conditioners based on the historical temperature collection data further includes: Obtaining a second preset number of cold aisle temperature sensors that are closest to each of the air conditioners; The first preset number of cold aisle temperature sensors and the second preset number of cold aisle temperature sensors are combined after de-duplication to serve as the relevant cold aisle corresponding to each air conditioner.

4. The method according to claim 1, wherein The acquiring the real-time cold aisle temperatures of the N cold aisle temperature sensors and calculating the average real-time cold aisle temperature of the relevant cold aisle corresponding to each air conditioner based on the real-time cold aisle temperatures of the N cold aisle temperature sensors includes: Obtaining real-time cold aisle temperatures of N cold aisle temperature sensors within a first preset time period, and calculating an average cold aisle temperature of each cold aisle temperature sensor within the first preset time period, wherein the average cold aisle temperature is obtained by calculating an average of the real-time cold aisle temperatures within the first preset time period; Based on the average cold aisle temperature of each of the cold aisle temperature sensors within the first preset time period, the real-time average cold aisle temperature of the corresponding cold aisle corresponding to each of the air conditioners is calculated, wherein the real-time average cold aisle temperature of the air conditioner is obtained by calculating the average of the average cold aisle temperatures of the corresponding cold aisles corresponding to the air conditioners.

5. The method according to claim 1, wherein The step of selecting the air conditioner to be regulated from the M air conditioners according to the preset screening conditions includes: Sort the real-time cold channel temperature averages of the relevant cold channels corresponding to the S air conditioners to be screened in ascending order, where S is a positive integer and S≤M; The air conditioners whose real-time cold channel temperature average values ​​are ranked in the top third preset number are used as the air conditioners to be regulated.

6. The method according to claim 5, characterized in that The average air supply temperature of the air conditioner to be regulated meets the preset screening condition; Before selecting the air conditioner to be regulated from the M air conditioners, the method further includes: obtaining the real-time air supply temperatures of the M air conditioners within a second preset time period, and calculating the average air supply temperature of each air conditioner within the second preset time period; Before sorting the real-time cold channel temperature averages of the relevant cold channels corresponding to the S air conditioners to be screened in ascending order, the method includes: The air conditioners whose average air supply temperature is lower than the safety temperature threshold among the M air conditioners are marked, and the marked air conditioners are used as the S air conditioners to be screened.

7. The method according to claim 1, characterized in that The temperature of the air conditioner to be regulated is controlled by increasing the temperature, including: controlling the temperature of the air conditioner to be regulated by increasing the temperature according to a preset safety temperature control value.

8. A terminal air conditioning energy-saving control device, characterized in that: include: A data acquisition module is used to acquire historical temperature data collected during a preset time period, wherein the historical temperature data includes: historical supply air temperatures of M air conditioners and historical cold aisle temperatures of N cold aisle temperature sensors; a correlation analysis module, configured to select a corresponding cold aisle for each of the air conditioners based on the historical temperature collection data, wherein the relevant cold aisle includes a plurality of the cold aisle temperature sensors, and a historical cold aisle temperature of the relevant cold aisle is correlated with a historical supply air temperature of the air conditioner corresponding to the relevant cold aisle; an average calculation module, configured to obtain the real-time cold aisle temperatures of the N cold aisle temperature sensors, and calculate the average of the real-time cold aisle temperatures of the relevant cold aisles corresponding to each of the air conditioners based on the real-time cold aisle temperatures of the N cold aisle temperature sensors; an air conditioner screening module to be regulated, configured to select an air conditioner to be regulated from the M air conditioners according to a preset screening condition, wherein the real-time cold channel temperature average of the relevant cold channel corresponding to the air conditioner to be regulated at least meets the preset screening condition; The control module is used to control the temperature of the air conditioner to be controlled.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the terminal air conditioning energy-saving control method as described in any one of claims 1 to 7 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the terminal air-conditioning energy-saving control method according to any one of claims 1 to 7 are implemented.

11. A computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions, and when the program instructions are executed by a computer, the computer executes the steps of the terminal air conditioning energy-saving control method according to any one of claims 1 to 7.

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