CN112113314A - Real-time temperature data acquisition system and temperature adjusting method based on learning model - Google Patents
Real-time temperature data acquisition system and temperature adjusting method based on learning model Download PDFInfo
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- CN112113314A CN112113314A CN202011003256.XA CN202011003256A CN112113314A CN 112113314 A CN112113314 A CN 112113314A CN 202011003256 A CN202011003256 A CN 202011003256A CN 112113314 A CN112113314 A CN 112113314A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
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Abstract
The invention relates to the technical field of temperature acquisition and control, in particular to a real-time temperature data acquisition system, which comprises: the system comprises a plurality of machine room air conditioners, an upper computer, a main controller, a bus module and a sensor module; the upper computer is used for issuing a control instruction according to the temperature data in the machine room; the main controller is used for receiving a control instruction sent by the upper computer and controlling the air supply temperature of the machine room air conditioner according to the control instruction; the bus module is provided with a plurality of bus modules; the sensor module is in communication connection with the bus module and used for collecting temperature data in the machine room. According to the temperature adjusting method based on the learning model, the air supply temperature of the machine room air conditioner is adjusted through the real-time temperature data acquisition system. According to the invention, the parameter setting of the machine room air conditioner is predicted through the neural network learning model, and the machine room air conditioner is subjected to centralized control, so that the energy consumption of an air conditioning system is greatly reduced, and the accurate control of the refrigeration requirement of the machine room is realized.
Description
Technical Field
The invention relates to the technical field of temperature acquisition and control, in particular to a real-time temperature data acquisition system and a temperature adjusting method based on a learning model.
Background
Along with the rapid application of new technologies such as artificial intelligence, 5G, cloud computing and the Internet of things, the data flow is increased rapidly, the machine room market of a data center is continuously and rapidly developed, more and more servers are required to be arranged, more and more machine rooms are also required, a machine room air conditioning system is composed of a group of air conditioners, each air conditioner is formed by assembling and combining a compressor, a fan and other devices, and the coordination control between the number of running air conditioners and the cooling capacity of each device is closely related to the energy-saving running of the system.
The problem of energy consumption of the air conditioning system of the machine room becomes the focus of the whole industry, the problem of overheating of equipment cannot be thoroughly solved by increasing the number of air conditioners or reducing the set temperature of the air conditioners blindly, and the energy consumption of the machine room can be greatly increased.
At present, traditional computer lab air conditioner centralized control system computer lab is based on fortune dimension personnel experience, and the air conditioner is opened the number and parameter setting is relatively independent, can't accomplish high-efficient in coordination, and the energy-conserving effect of system is limited, because the factor that influences the air conditioner cold air and carry is numerous, can't test one by one in actual operation, otherwise can consume a large amount of manpower and materials to along with uncontrollable risk, this method that makes traditional computer lab air conditioner centralized control system meets the bottleneck. In view of this, we propose a real-time temperature data acquisition system and a temperature adjustment method based on a learning model.
Disclosure of Invention
The invention aims to provide a real-time temperature data acquisition system and a temperature adjusting method based on a learning model, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a real-time temperature data acquisition system, the system comprising: the system comprises a plurality of machine room air conditioners, an upper computer, a main controller, a bus module and a sensor module;
the upper computer is used for issuing a control instruction according to the temperature data in the machine room;
the main controller is in communication connection with the upper computer and is used for receiving a control instruction sent by the upper computer and controlling the air supply temperature of the machine room air conditioner according to the control instruction;
a plurality of bus modules are arranged and are in communication connection with the main controller;
the sensor module with bus module communication is connected for gather the temperature data in the computer lab, the sensor module includes: the temperature sensor of the air conditioner air supply outlet, the temperature sensor of the air conditioner air return inlet, the temperature sensor of the machine room cold channel, the temperature sensor of the machine room hot channel and the temperature sensor of the machine cabinet air inlet side.
Preferably, the system also comprises a backup controller, and the backup controller is in communication connection with the main controller, the bus module and the machine room air conditioner.
The invention also provides a temperature adjusting method based on the learning model, which adjusts the air supply temperature of the air conditioner in the machine room through the real-time temperature data acquisition system and is characterized by comprising the following steps:
a: gather the temperature data in the computer lab in real time through the sensor module, temperature data includes: the temperature of an air conditioner air supply outlet, the temperature of an air conditioner air return inlet, the temperature of a machine room cold channel, the temperature of a machine room hot channel and the temperature of a machine cabinet air inlet side;
b: establishing a neural network model with multiple data sources, taking the temperature of an air supply outlet of an air conditioner, the temperature of an air return inlet of the air conditioner and the temperature of the air inlet side of a cabinet as input layers of the neural network model, taking the set temperature of return air of an air conditioner of a machine room and the maximum air inlet temperature of the cabinet at a certain time interval as output layers, and training to obtain a neural network learning model;
c: predicting the set return air temperature of the air conditioner in the machine room by utilizing the neural network learning model;
d: and the upper computer controls the air supply temperature of the machine room air conditioner according to the set return air temperature obtained by the neural network learning model prediction.
Preferably, in the step B, when the highest inlet air temperature of the cabinet is used as an input layer of the neural network model, the selected time interval is not more than 3 hours.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, by building the real-time acquisition system of the temperature data of the machine room, the thermodynamic environment in the machine room can be known in time, a large amount of environment temperature data is recorded, the neural network learning model is trained by utilizing massive historical environment temperature data, the parameter setting of the most energy-saving machine room air conditioner under different working conditions can be predicted, the upper computer sends a control command to carry out centralized control on the machine room air conditioner, the set parameter or the starting state of the machine room air conditioner is adjusted in real time, the energy consumption of the air conditioning system is greatly reduced, the accurate control on the refrigeration requirement of the machine room is realized, and the use is convenient.
Drawings
FIG. 1 is a block diagram showing the overall structure of embodiment 1 of the present invention;
fig. 2 is a block diagram of a sensor module according to embodiment 1 of the present invention;
FIG. 3 is a flowchart of the method of embodiment 2 of the present invention.
In the figure: 100. air conditioning in the machine room; 200. an upper computer; 300. a main controller; 400. a bus module; 500. the sensor module 501 is a temperature sensor of an air conditioner air supply outlet; 502. an air conditioner return air inlet temperature sensor; 503. a machine room cold channel temperature sensor; 504. a machine room hot channel temperature sensor; 505. a temperature sensor at the air inlet side of the cabinet; 600. a backup controller.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of this patent, it is noted that unless otherwise specifically stated or limited, the terms "connected," "connected," and "disposed" are to be construed broadly and may for example be fixedly connected, disposed, detachably connected, disposed, or integrally connected and disposed. The specific meaning of the above terms in this patent may be understood by those of ordinary skill in the art as appropriate.
In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Example 1
As shown in fig. 1 and 2, a real-time temperature data acquisition system, the system comprising: the system comprises a plurality of machine room air conditioners 100, an upper computer 200, a main controller 300, a bus module 400 and a sensor module 500;
the upper computer 200 is used for issuing a control instruction according to the temperature data in the machine room;
the main controller 300 is in communication connection with the upper computer 200, and is used for receiving a control instruction sent by the upper computer 200 and controlling the air supply temperature of the machine room air conditioner according to the control instruction;
a plurality of bus modules 400 are arranged and are all in communication connection with the main controller;
In this embodiment, the system further includes a backup controller 600, the backup controller 600 is in communication connection with the main controller 300, the bus module 400 and the air conditioner 100 in the machine room, and as can be seen from the above, the temperature data acquired by the system is massive, so that the backup controller 600 is configured to perform data backup in order to prevent data loss.
It should be noted that the main controller 300 has a communication interface (RS485) with the upper computer 200 and the backup controller 600, and the backup controller 600 and the upper computer 200 directly communicate with the main controller 300 through the communication interface.
Further, the main controller 300 has a communication interface (RS485) with the room air conditioner 100, and adopts a Modbus communication protocol for reading/writing operation of the operating state and operating parameters of the room air conditioner 100, so that the control command sent by the upper computer 200 is transmitted to the room air conditioner 100 through the main controller 300 to implement the command.
Example 2
As shown in fig. 3, a temperature adjusting method based on a learning model, which adjusts the air supply temperature of a machine room air conditioner through the real-time temperature data acquisition system, includes the following steps:
a: gather the temperature data in the computer lab in real time through the sensor module, temperature data includes: the temperature of an air conditioner air supply outlet, the temperature of an air conditioner air return inlet, the temperature of a machine room cold channel, the temperature of a machine room hot channel and the temperature of a machine cabinet air inlet side;
b: establishing a neural network model with multiple data sources, taking the temperature of an air supply outlet of an air conditioner, the temperature of an air return inlet of the air conditioner and the temperature of the air inlet side of a cabinet as input layers of the neural network model, taking the set temperature of return air of an air conditioner of a machine room and the maximum air inlet temperature of the cabinet at a certain time interval as output layers, and training to obtain a neural network learning model;
c: predicting the set return air temperature of the air conditioner in the machine room by utilizing the neural network learning model;
d: and the upper computer controls the air supply temperature of the machine room air conditioner according to the set return air temperature obtained by the neural network learning model prediction.
It is worth noting that in the step B, when the highest inlet air temperature of the cabinet is used as an input layer of the neural network model, the selected time interval is not more than 3 hours, in the embodiment, because the heating efficiency of the server in the machine room is difficult to control manually when the server runs normally, the highest inlet air side temperature of the cabinet is used as an output layer by taking one hour as the interval time, the robustness of the neural network learning model is improved, the predicted return air set temperature of the air conditioner of the machine room is more accurate, the cooling efficiency of the machine room is improved, and the neural network learning model is convenient to popularize and popularize.
As can be seen from the above, by setting up the real-time collection system of the temperature data of the machine room, the real-time temperature of the air inlet side, the air supply temperature and the air return temperature of the air conditioner, and the temperatures of the cold channel and the hot channel of the machine room of the server cabinet are obtained, so that the thermodynamic environment in the machine room can be known in time, a large amount of environmental temperature data is recorded, and a neural network learning model is trained by using a large amount of historical environmental temperature data, so that the parameter setting of the most energy-saving machine room air conditioner 100 under different working conditions can be predicted, the upper computer 200 sends a control instruction to perform centralized control on the machine room air conditioner 100, the set parameter or the starting state of the machine room air conditioner 100 is adjusted in real time, the energy consumption of the air conditioning system is greatly reduced.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. A real-time temperature data acquisition system, the system comprising: the system comprises a plurality of machine room air conditioners, an upper computer, a main controller, a bus module and a sensor module;
the upper computer is used for issuing a control instruction according to the temperature data in the machine room;
the main controller is in communication connection with the upper computer and is used for receiving a control instruction sent by the upper computer and controlling the air supply temperature of the machine room air conditioner according to the control instruction;
a plurality of bus modules are arranged and are in communication connection with the main controller;
the sensor module with bus module communication is connected for gather the temperature data in the computer lab, the sensor module includes: the temperature sensor of the air conditioner air supply outlet, the temperature sensor of the air conditioner air return inlet, the temperature sensor of the machine room cold channel, the temperature sensor of the machine room hot channel and the temperature sensor of the machine cabinet air inlet side.
2. The real-time temperature data acquisition system of claim 1, wherein: the system also comprises a backup controller, wherein the backup controller is in communication connection with the main controller, the bus module and the machine room air conditioner.
3. A temperature adjusting method based on a learning model, which adjusts the temperature of the air supply of a machine room air conditioner through the real-time temperature data acquisition system of any one of claims 1-2, characterized by comprising the following steps:
a: gather the temperature data in the computer lab in real time through the sensor module, temperature data includes: the temperature of an air conditioner air supply outlet, the temperature of an air conditioner air return inlet, the temperature of a machine room cold channel, the temperature of a machine room hot channel and the temperature of a machine cabinet air inlet side;
b: establishing a neural network model with multiple data sources, taking the temperature of an air supply outlet of an air conditioner, the temperature of an air return inlet of the air conditioner and the temperature of the air inlet side of a cabinet as input layers of the neural network model, taking the set temperature of return air of an air conditioner of a machine room and the maximum air inlet temperature of the cabinet at a certain time interval as output layers, and training to obtain a neural network learning model;
c: predicting the set return air temperature of the air conditioner in the machine room by utilizing the neural network learning model;
d: and the upper computer controls the air supply temperature of the machine room air conditioner according to the set return air temperature obtained by the neural network learning model prediction.
4. The learning-model-based temperature adjustment method according to claim 3, characterized in that: and in the step B, when the highest inlet air temperature of the cabinet is used as an input layer of the neural network model, the selected time interval is not more than 3 hours.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112926807A (en) * | 2021-04-15 | 2021-06-08 | 德州欧瑞电子通信设备制造有限公司 | Ultra-short-term prediction method and system for heat productivity of cabinet equipment by considering prediction error |
| CN113654200A (en) * | 2021-07-29 | 2021-11-16 | 中国水利水电第六工程局有限公司 | Temperature control system for steel pipe production workshop |
| CN113932351A (en) * | 2021-11-05 | 2022-01-14 | 上海理工大学 | Non-uniform temperature field real-time regulation and control system and method based on artificial intelligence algorithm |
| CN116193819A (en) * | 2023-01-19 | 2023-05-30 | 中国长江三峡集团有限公司 | Energy-saving control method, system and device for data center machine room and electronic equipment |
-
2020
- 2020-09-22 CN CN202011003256.XA patent/CN112113314A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112926807A (en) * | 2021-04-15 | 2021-06-08 | 德州欧瑞电子通信设备制造有限公司 | Ultra-short-term prediction method and system for heat productivity of cabinet equipment by considering prediction error |
| CN113654200A (en) * | 2021-07-29 | 2021-11-16 | 中国水利水电第六工程局有限公司 | Temperature control system for steel pipe production workshop |
| CN113932351A (en) * | 2021-11-05 | 2022-01-14 | 上海理工大学 | Non-uniform temperature field real-time regulation and control system and method based on artificial intelligence algorithm |
| CN116193819A (en) * | 2023-01-19 | 2023-05-30 | 中国长江三峡集团有限公司 | Energy-saving control method, system and device for data center machine room and electronic equipment |
| CN116193819B (en) * | 2023-01-19 | 2024-02-02 | 中国长江三峡集团有限公司 | Energy-saving control method, system and device for data center machine room and electronic equipment |
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