Internet of Things machine room control method and device

Abstract

The invention discloses an Internet of Things machine room control method and device.An Internet of Things machine room is provided with a plurality of temperature sensors and air conditioner assemblys.The method comprises the steps that the real-time detection values of the temperature sensors in the current detection period are obtained, and the temperature average value is calculated; based on the temperature average value of the current detection period, calculating a temperature prediction value of the next detection period by combining the temperature average values of the plurality of historical detection periods; and based on the temperature prediction value, an air conditioner assembly is controlled to adjust the indoor temperature of the Internet of Things machine room according to the indoor constant temperature value of the Internet of Things machine room, and the indoor temperature average value of the next detection period is adjusted to the indoor constant temperature value. The temperature average value of the current period is monitored and calculated in real time, the temperature change of the next period is predicted through historical data, then the air conditioning system is intelligently adjusted, it is ensured that the temperature in the machine room is maintained at the preset constant value, efficient and accurate temperature control is achieved, and the stability of the machine room environment and the reliability of equipment operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer room control, and in particular to a method and device for controlling an Internet of Things computer room. Background Art

[0002] The Internet of Things technology is currently in a stage of rapid development. With the diversification of smart devices and the improvement of network infrastructure, hundreds of millions of devices and systems are interconnected through the network, realizing real-time collection, exchange and analysis of data, and promoting innovative applications in smart homes, smart cities, industrial automation and other fields. At the same time, with the integration of artificial intelligence and big data technologies, the Internet of Things is becoming more intelligent and autonomous, but this also brings new challenges such as data security and privacy protection. The industry needs to continuously explore solutions to achieve sustainable and safe development.

[0003] IoT technology plays a vital role in the temperature control of computer rooms. By real-time monitoring of temperature changes in the computer room and using advanced sensor and actuator networks, combined with intelligent algorithms and automated control systems, precise temperature regulation can be achieved to ensure that the equipment operates under optimal environmental conditions, thereby improving the energy efficiency of the entire computer room, ensuring equipment stability and extending its service life, and avoiding equipment failures and performance degradation due to overheating. This plays an irreplaceable role in maintaining the high availability and reliability of data centers.

[0004] Servers and network equipment in the computer room are very sensitive to temperature. Too high or too low a temperature may cause equipment failure or performance degradation. Existing computer room temperature control methods, such as traditional constant temperature and humidity air conditioning systems, often have problems such as slow response, high energy consumption, and inability to accurately control the local microenvironment, resulting in the inability to respond to rapidly changing temperature requirements in a timely manner. At the same time, a single temperature control strategy is difficult to adapt to the diverse temperature requirements of different areas and equipment in the computer room, increasing the risk of equipment failure and operation and maintenance costs, and limiting the improvement of the overall energy efficiency of the computer room. Summary of the invention

[0005] The purpose of the embodiment of the present invention is to provide an Internet of Things computer room control method and device, which deploys multiple temperature sensors and air-conditioning components, monitors and calculates the temperature average of the current cycle in real time, uses historical data to predict the temperature change of the next cycle, and then intelligently adjusts the air-conditioning system to ensure that the temperature in the computer room is maintained at a preset constant value, thereby achieving efficient and accurate temperature control and improving the stability of the computer room environment and the reliability of equipment operation.

[0006] To solve the above technical problems, a first aspect of an embodiment of the present invention provides an Internet of Things computer room control method, wherein the Internet of Things computer room is provided with a plurality of temperature sensors and air conditioning components, comprising the following steps:

[0007] Obtaining real-time detection values ​​of the temperature sensors in the current detection cycle and calculating the average temperature value;

[0008] Calculate the predicted temperature value for the next detection cycle based on the average temperature value of the current detection cycle and the average temperature values ​​of several historical detection cycles;

[0009] Based on the temperature prediction value, the air conditioning component is controlled to adjust the indoor temperature of the Internet of Things computer room according to the indoor constant temperature value of the Internet of Things computer room, and the average indoor temperature of the next detection cycle is adjusted to the indoor constant temperature value.

[0010] Furthermore, the temperature prediction value of the next detection cycle is calculated by combining the temperature average values ​​of several historical detection cycles, including:

[0011] Obtain the average temperature of the current detection period of the day;

[0012] Obtaining the temperature average value of a first preset number of historical detection cycles before the current detection cycle of the day;

[0013] Obtaining an average temperature of the detection cycles of a second preset number of dates before the current day, which are in the same period as the current detection cycle;

[0014] The predicted temperature value for the next detection cycle is calculated based on the temperature average value of the current detection cycle on the day, the temperature average value of the first preset number of historical detection cycles on the day, and the temperature average value of the corresponding detection cycles of the second preset number of dates before the day.

[0015] Furthermore, the temperature prediction value T of the next detection cycle D,k+1 The calculation formula is:

[0016]

[0017] Among them, T D,k+1 is the predicted temperature value of the next detection cycle, T D,k is the average temperature of the current detection period on that day, T D,k-i is the average temperature of the kith historical detection cycle before the current detection cycle on that day, T D-j,k is the temperature average value of the corresponding detection periods of the Dj dates before the current day, m is the first preset number value, n is the second preset number value, a, b, c are the weight coefficients of each type of temperature average value, η, λ, σ are the exponential coefficients of each type of temperature average value, D is the date of the current day, k is the current detection period of the current day, and δ is the attenuation rate.

[0018] Further, the calculation of the temperature prediction value of the next detection cycle includes:

[0019] Obtaining a temperature prediction value of the next detection cycle calculated in the current detection cycle;

[0020] Obtain the predicted temperature value of the current detection cycle calculated in the previous detection cycle and the average temperature value actually detected in the current detection cycle;

[0021] Based on the temperature prediction value of the current detection cycle calculated in the previous detection cycle and the temperature average value actually detected in the current detection cycle, the temperature prediction value of the next detection cycle obtained in the current calculation cycle is calibrated to obtain the temperature prediction calibration value of the next detection cycle.

[0022] Furthermore, the temperature prediction calibration value T of the next detection cycle D ” ,k+1 The calculation formula is:

[0023]

[0024] Among them, T D,k+1 is the temperature prediction value of the next detection cycle calculated in the current detection cycle, T D,k is the actual average temperature of the current detection period, T D ' ,k The temperature prediction value of the current detection cycle calculated in the previous detection cycle.

[0025] Furthermore, the temperature prediction calibration value T of the next detection cycle D ” ,k+1 The calculation formula is:

[0026]

[0027]

[0028] Where ψ is the seasonal adjustment factor, T (D,k),seasonal It is the actual temperature detection value of the detection period corresponding to the current detection period on the date of the same season.

[0029] Furthermore, the Internet of Things room further comprises: a humidity detector and a ventilation component, wherein the ventilation component performs ventilation and dehumidification according to an actual humidity detection value of the humidity detector and an indoor humidity setting value;

[0030] The method further comprises:

[0031] When the outdoor humidity value of the Internet of Things computer room is greater than the indoor humidity value, the ventilation component is stopped and the air conditioning component is controlled to operate in a dehumidification mode.

[0032] Accordingly, a second aspect of an embodiment of the present invention provides an Internet of Things computer room control device, which controls the Internet of Things computer room based on the above-mentioned Internet of Things computer room control method. The Internet of Things computer room is provided with a plurality of temperature sensors and air conditioning components, including:

[0033] A temperature acquisition module, which is used to currently detect and acquire the real-time detection values ​​of the current detection cycle of the plurality of temperature sensors, and calculate the temperature average value;

[0034] A temperature prediction module, which is used to calculate a temperature prediction value for the next detection cycle based on the temperature average value of the current detection cycle and the temperature average values ​​of several historical detection cycles;

[0035] The temperature control module is used to control the air-conditioning component to adjust the indoor temperature of the Internet of Things computer room based on the temperature prediction value and the indoor constant temperature value of the Internet of Things computer room, and adjust the average indoor temperature of the next detection cycle to the indoor constant temperature value.

[0036] Accordingly, a third aspect of an embodiment of the present invention provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the above-mentioned Internet of Things computer room control method.

[0037] Correspondingly, a fourth aspect of an embodiment of the present invention provides a computer-readable storage medium on which computer instructions are stored, and when the instructions are executed by a processor, the above-mentioned Internet of Things computer room control method is implemented.

[0038] The above technical solution of the embodiment of the present invention has the following beneficial technical effects:

[0039] 1. By combining historical data to calculate temperature forecasts, we can identify trends and patterns in temperature change, thereby improving the accuracy of forecasts. By combining the correlation of time series data, the forecast results can be closer to actual changes.

[0040] 2. Using the error between the actual value and the predicted value of the previous detection cycle to adjust the current predicted value can dynamically calibrate the prediction model, reduce the cumulative error, ensure that the predicted values ​​of consecutive cycles are more accurate, and enhance the adaptability and reliability of the prediction model;

[0041] 3. Temperature calibration combined with seasonal factors can take into account the impact of climate characteristics of different seasons on temperature, so that the prediction model can adapt to seasonal temperature changes and improve the applicability and effectiveness of predictions under different seasonal conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of the Internet of Things computer room control method provided by an embodiment of the present invention;

[0043] Figure 2 It is a module block diagram of an Internet of Things computer room control device provided by an embodiment of the present invention;

[0044] Figure 3 is a block diagram of a temperature prediction module provided by an embodiment of the present invention;

[0045] Figure 4 It is a block diagram of a prediction value calibration unit provided in an embodiment of the present invention.

[0046] Reference numerals:

[0047] 1. Temperature acquisition module, 2. Temperature prediction module, 21. First data acquisition unit, 22. Second data acquisition unit, 23. Third data acquisition unit, 24. Prediction value calculation unit, 25. Prediction value calibration unit, 251. First data acquisition sub-unit, 252. Second data acquisition sub-unit, 253. Prediction value calibration sub-unit, 3. Temperature control module. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0049] Servers and network equipment in the computer room are extremely sensitive to temperature conditions because they generate a lot of heat during operation. If the temperature is too high, the equipment will overheat, which may cause performance degradation, system crashes, or even hardware damage, while if the temperature is too low, it may cause difficulty in starting the equipment or unstable operation. Therefore, maintaining the constant temperature of the computer room is crucial to ensuring the stable operation of key equipment. It can not only extend the service life of the equipment and reduce maintenance costs, but also ensure the continuity and reliability of data processing, avoid data loss or service interruption caused by temperature fluctuations, and play an irreplaceable role in ensuring the efficient operation and data security of the entire computer room.

[0050] Please refer to Figure 1 In order to achieve the above-mentioned purpose, a first aspect of an embodiment of the present invention provides an Internet of Things computer room control method, wherein the Internet of Things computer room is provided with a plurality of temperature sensors and air conditioning components, comprising the following steps:

[0051] Step S100, obtaining real-time detection values ​​of a plurality of temperature sensors in a current detection cycle, and calculating an average temperature value.

[0052] Several key devices in the IoT room are scattered on various equipment cabinets in the room. Maintaining the overall temperature balance and constant temperature inside the room is crucial for the stable operation of all devices. Therefore, multiple temperature sensors installed inside the room are used to obtain the real-time value of the indoor temperature and calculate the average indoor temperature. Based on the average temperature of the current detection cycle and combined with historical data, the indoor temperature of the next detection cycle is predicted.

[0053] Step S200, based on the temperature average value of the current detection cycle and in combination with the temperature average values ​​of several historical detection cycles, a temperature prediction value for the next detection cycle is calculated.

[0054] Historical temperature data provides trends and patterns of temperature changes, helping to identify periodic fluctuations and potential nonlinear behavior of ambient temperature, making forecasts more accurate.

[0055] Step S300, based on the temperature prediction value, the air conditioning component is controlled to adjust the indoor temperature of the Internet of Things room according to the indoor constant temperature value of the Internet of Things room, and the average indoor temperature of the next detection cycle is adjusted to the indoor constant temperature value.

[0056] The results obtained based on historical temperature data prediction can not only help adjust air conditioning systems and other cooling equipment in advance to adapt to expected temperature changes, but also play a significant role in optimizing energy use, reducing energy consumption, and improving energy efficiency. Accurate temperature prediction is crucial to preventing equipment failures and performance degradation caused by abnormal temperatures, ensuring the stability of the computer room environment and the reliability of key equipment, and providing a strong guarantee for maintaining the continuous operation of the data center and data security.

[0057] Specifically, in a specific implementation of the embodiment of the present invention, the step S200 of calculating the temperature prediction value of the next detection cycle by combining the temperature average values ​​of several historical detection cycles includes:

[0058] Step S210, obtaining the average temperature of the current detection period of the day.

[0059] Step S220, obtaining the temperature average value of a first preset number of historical detection cycles before the current detection cycle of the day.

[0060] Step S230, obtaining the average temperature of the detection cycles of a second preset number of dates before the current day and in the same period as the current detection cycle.

[0061] Step S240, calculate the predicted temperature value for the next detection cycle based on the temperature average value of the current detection cycle on the day, the temperature average value of a first preset number of historical detection cycles on the day, and the temperature average value of corresponding detection cycles of a second preset number of dates before the day.

[0062] The temperature of the next cycle is predicted by combining the average temperature of the current detection cycle of the day, the average temperature of multiple historical detection cycles of the day, and the average temperature of the detection cycles of the previous corresponding dates, so as to capture the immediacy, periodicity and long-term trends of temperature changes, thereby making the prediction more accurate. The above method can not only adapt to short-term temperature fluctuations, but also take into account seasonal and historical temperature patterns of similar dates, enhancing the adaptability and reliability of the prediction model. It realizes fine control of the computer room environment, optimizes energy management, prevents equipment from overheating or insufficient cooling, and ensures stable operation of key equipment and data security.

[0063] Furthermore, the temperature prediction value T of the next detection cycle is D,k+1 The calculation formula is:

[0064]

[0065] Among them, T D,k+1 is the predicted temperature value of the next detection cycle, T D,k is the average temperature of the current detection period on that day, T D,k-i is the average temperature of the kith historical detection cycle before the current detection cycle of the day, T D-j,k is the temperature average value of the corresponding detection period of Dj dates before the current day, m is the first preset number value, n is the second preset number value, a, b, c are the weight coefficients of the average temperature of each type, η, λ, σ are the exponential coefficients of the average temperature of each type, D is the date of the current day, k is the current detection period of the current day, and δ is the decay rate.

[0066] Specifically, the number of the first preset quantity value m can generally be selected as 20, that is, in order to improve the accuracy of the temperature prediction value, the temperature average value of 20 historical detection cycles is selected for calculation. In order to reduce the amount of calculation, it can also be selected as 10 or 5 historical detection cycles. The weight coefficients a, b, and c of each type of temperature average value can be determined by summarizing the rules of historical data within a certain period. The second preset quantity value n is generally selected from 3-5 days. Taking into account the periodic change rules of the weather, 3 days are generally selected to ensure the correlation and accuracy of the temperature change rules of several days before the current date and the current date. η, λ, and σ are respectively the exponential coefficient values ​​of each type of temperature average value. Reverse calculation can be performed based on the rules of historical data to determine the numerical range of the change rules of most historical data of the load.

[0067] Furthermore, the calculation of the temperature prediction value of the next detection cycle in step S200 also includes:

[0068] Step S251, obtaining the temperature prediction value of the next detection cycle calculated in the current detection cycle.

[0069] Step S252, obtaining the temperature prediction value of the current detection cycle calculated in the previous detection cycle and the average temperature value actually detected in the current detection cycle.

[0070] Step S253, based on the temperature prediction value of the current detection cycle calculated in the previous detection cycle and the temperature average value actually detected in the current detection cycle, calibrate the temperature prediction value of the next detection cycle obtained in the current calculation cycle to obtain the temperature prediction calibration value of the next detection cycle.

[0071] By combining the predicted value of the previous detection cycle with the actual temperature average value of the current cycle to calibrate the temperature predicted value of the next detection cycle obtained in the current calculation cycle, the accuracy and reliability of temperature prediction are significantly improved. This calibration method takes into account the possible deviations in the prediction model and adjusts it through feedback from actual data, reducing the prediction error and making the predicted value closer to the actual temperature change, thereby optimizing the room environment control strategy and improving energy efficiency.

[0072] Specifically, the temperature prediction calibration value T for the next detection cycle D ” ,k+1 The calculation formula is:

[0073]

[0074] Among them, T D,k+1 is the temperature prediction value of the next detection cycle calculated from the current detection cycle, T D,k is the actual average temperature of the current detection cycle, T D ' ,k The temperature prediction value for the current detection cycle calculated from the previous detection cycle.

[0075] Furthermore, the temperature prediction calibration value T of the next detection cycle is D ” ,k+1 The calculation formula is:

[0076]

[0077] Where ψ is the seasonal adjustment factor, T (D,k ) ,seasonal It is the actual temperature detection value of the detection period corresponding to the current detection period on the date of the same season.

[0078] Combining the seasonal adjustment factor to calculate the temperature prediction calibration value for the next detection cycle can accurately capture and adapt to the regular fluctuations of temperature with seasonal changes. By identifying and applying the temperature patterns of different seasons in a year, the prediction model's ability to respond to seasonal temperature changes is enhanced. The above calculation method not only improves the prediction accuracy, but also enables the temperature control system of the computer room to more intelligently respond to seasonal extreme climate conditions such as high temperatures in summer and low temperatures in winter, optimizes energy efficiency, ensures the stability of the computer room environment and the reliability of key equipment such as servers, and reduces the risk of equipment failure caused by temperature fluctuations.

[0079] In addition, the IoT computer room also includes: a humidity detector and a ventilation component, and the ventilation component performs ventilation and dehumidification according to the actual humidity detection value of the humidity detector and the indoor humidity setting value. The IoT computer room control method also includes: when the outdoor humidity value of the IoT computer room is greater than the indoor humidity value, stopping the ventilation component from running and controlling the air conditioning component to run in a dehumidification mode.

[0080] The IoT computer room achieves precise control of the environment in the computer room by integrating humidity detectors and intelligent ventilation components. When the outdoor humidity is detected to be higher than the indoor humidity, the control method will intelligently stop ventilation to prevent humid air from entering, and at the same time control the air conditioning system to switch to dehumidification mode to reduce the humidity in the computer room to the set value. It not only effectively avoids the risk of equipment corrosion and data loss caused by excessive humidity, but also improves energy efficiency and environmental stability, ensuring that key equipment in the computer room operates in the best condition, reflecting the important value of IoT technology in improving the intelligence and automation of data center environmental management.

[0081] Accordingly, please refer to Figure 2 The second aspect of the embodiment of the present invention provides an Internet of Things computer room control device, which controls the Internet of Things computer room based on the above-mentioned Internet of Things computer room control method. The Internet of Things computer room is provided with a plurality of temperature sensors and air conditioning components, including:

[0082] The temperature acquisition module 1 is used to obtain the real-time detection values ​​of several temperature sensors in the current detection cycle and calculate the temperature average value;

[0083] Temperature prediction module 2, which is used to calculate the temperature prediction value of the next detection cycle based on the temperature average value of the current detection cycle and the temperature average values ​​of several historical detection cycles;

[0084] The temperature control module 3 is used to control the air conditioning component to adjust the indoor temperature of the Internet of Things room based on the temperature prediction value and the indoor constant temperature value of the Internet of Things room, and adjust the indoor temperature average value of the next detection cycle to the indoor constant temperature value.

[0085] For further information, please refer to Figure 3, the temperature prediction module 2 includes:

[0086] A first data acquisition unit 21, which is used to obtain the average temperature of the current detection period of the day;

[0087] A second data acquisition unit 22, which is used to acquire the temperature average value of a first preset number of historical detection cycles before the current detection cycle of the day;

[0088] A third data acquisition unit 23, which is used to obtain the temperature average value of the detection cycle in the same period as the current detection cycle on a second preset number of dates before the current day;

[0089] The prediction value calculation unit 24 is used to calculate the temperature prediction value of the next detection cycle based on the temperature average value of the current detection cycle on the day, the temperature average value of the first preset number of historical detection cycles on the day, and the temperature average value of the corresponding detection cycles of the second preset number of dates before the day.

[0090] For further information, please refer to Figure 4 The temperature prediction module 2 further includes: a prediction value calibration unit 25, and the prediction value calibration unit 25 includes:

[0091] A first data acquisition subunit 251, which is used to obtain the temperature prediction value of the next detection cycle calculated in the current detection cycle;

[0092] A second data acquisition subunit 252, which is used to acquire the temperature prediction value of the current detection cycle calculated in the previous detection cycle and the average temperature value actually detected in the current detection cycle;

[0093] The prediction value calibration subunit 253 is used to calibrate the temperature prediction value of the next detection cycle obtained in the current calculation cycle based on the temperature prediction value of the current detection cycle calculated in the previous detection cycle and the temperature average value actually detected in the current detection cycle, so as to obtain the temperature prediction calibration value of the next detection cycle.

[0094] The functional modules in the above-mentioned spatial data analysis and processing system correspond to the steps in the spatial data analysis and processing method. Each functional module can be further defined as each functional unit according to the detailed steps of the analysis and processing method to be consistent with the method, and will not be described in detail here.

[0095] Accordingly, a third aspect of an embodiment of the present invention provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the above-mentioned Internet of Things computer room control method.

[0096] Correspondingly, a fourth aspect of an embodiment of the present invention provides a computer-readable storage medium on which computer instructions are stored, and when the instructions are executed by a processor, the above-mentioned Internet of Things computer room control method is implemented.

[0097] The embodiment of the present invention aims to protect a method and device for controlling an Internet of Things computer room, wherein the Internet of Things computer room is provided with a number of temperature sensors and air conditioning components, wherein the method comprises: obtaining the real-time detection values ​​of the current detection cycle of a number of temperature sensors, and calculating the temperature average value; calculating the temperature prediction value of the next detection cycle based on the temperature average value of the current detection cycle, combined with the temperature average values ​​of a number of historical detection cycles; based on the temperature prediction value, controlling the air conditioning component to adjust the indoor temperature of the Internet of Things computer room according to the indoor constant temperature value of the Internet of Things computer room, and adjusting the indoor temperature average value of the next detection cycle to the indoor constant temperature value. The above technical solution has the following effects:

[0098] 1. By combining historical data to calculate temperature forecasts, we can identify trends and patterns in temperature change, thereby improving the accuracy of forecasts. By combining the correlation of time series data, the forecast results can be closer to actual changes.

[0099] 2. Using the error between the actual value and the predicted value of the previous detection cycle to adjust the current predicted value can dynamically calibrate the prediction model, reduce the cumulative error, ensure that the predicted values ​​of consecutive cycles are more accurate, and enhance the adaptability and reliability of the prediction model;

[0100] 3. Temperature calibration combined with seasonal factors can take into account the impact of climate characteristics of different seasons on temperature, so that the prediction model can adapt to seasonal temperature changes and improve the applicability and effectiveness of predictions under different seasonal conditions.

[0101] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0102] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for controlling an Internet of Things computer room, characterized in that: The IoT room is equipped with several temperature sensors and air conditioning components, including the following steps: Obtaining real-time detection values ​​of the temperature sensors in the current detection cycle and calculating the average temperature value; Calculate the predicted temperature value for the next detection cycle based on the average temperature value of the current detection cycle and the average temperature values ​​of several historical detection cycles; Based on the temperature prediction value, the air conditioning component is controlled to adjust the indoor temperature of the Internet of Things computer room according to the indoor constant temperature value of the Internet of Things computer room, and the average indoor temperature of the next detection cycle is adjusted to the indoor constant temperature value.

2. The Internet of Things computer room control method according to claim 1, characterized in that: The temperature prediction value of the next detection cycle is calculated by combining the temperature average values ​​of several historical detection cycles, including: Obtain the average temperature of the current detection period of the day; Obtaining the temperature average value of a first preset number of historical detection cycles before the current detection cycle of the day; Obtaining an average temperature of the detection cycles of a second preset number of dates before the current day, which are in the same period as the current detection cycle; The predicted temperature value for the next detection cycle is calculated based on the temperature average value of the current detection cycle on the day, the temperature average value of the first preset number of historical detection cycles on the day, and the temperature average value of the corresponding detection cycles of the second preset number of dates before the day.

3. The Internet of Things computer room control method according to claim 2, characterized in that: The temperature prediction value T of the next detection cycle D,k+1 The calculation formula is: Among them, T D,k+1 is the predicted temperature value of the next detection cycle, T D,k is the average temperature of the current detection period on that day, T D,k-i is the average temperature of the kith historical detection cycle before the current detection cycle on that day, T D-j,k is the temperature average value of the corresponding detection periods of the Dj dates before the current day, m is the first preset number value, n is the second preset number value, a, b, c are the weight coefficients of each type of temperature average value, η, λ, σ are the exponential coefficients of each type of temperature average value, D is the date of the current day, k is the current detection period of the current day, and δ is the decay rate.

4. The method for controlling an Internet of Things computer room according to any one of claims 1 to 3, characterized in that: The step of calculating the predicted temperature value for the next detection cycle includes: Obtaining a temperature prediction value of the next detection cycle calculated in the current detection cycle; Obtain the temperature prediction value of the current detection cycle calculated in the previous detection cycle and the average temperature value actually detected in the current detection cycle; Based on the temperature prediction value of the current detection cycle calculated in the previous detection cycle and the temperature average value actually detected in the current detection cycle, the temperature prediction value of the next detection cycle obtained in the current calculation cycle is calibrated to obtain the temperature prediction calibration value of the next detection cycle.

5. The Internet of Things computer room control method according to claim 4, characterized in that: The temperature prediction calibration value T" of the next detection cycle D,k+1 The calculation formula is: Among them, T D,k+1 is the temperature prediction value of the next detection cycle calculated in the current detection cycle, T D,k is the actual average temperature of the current detection period, T D ' ,k The temperature prediction value of the current detection cycle calculated in the previous detection cycle.

6. The Internet of Things computer room control method according to claim 5, characterized in that: The temperature prediction calibration value T of the next detection cycle D ” ,k+1 The calculation formula is: Where ψ is the seasonal adjustment factor, T (D,k),seasonal It is the actual temperature detection value of the detection period corresponding to the current detection period on the date of the same season.

7. The Internet of Things computer room control method according to claim 1, characterized in that: The Internet of Things room also includes: a humidity detector and a ventilation component, wherein the ventilation component performs ventilation and dehumidification according to an actual humidity detection value of the humidity detector and an indoor humidity setting value; The method further comprises: When the outdoor humidity value of the Internet of Things computer room is greater than the indoor humidity value, the ventilation component is stopped and the air conditioning component is controlled to operate in a dehumidification mode.

8. An Internet of Things computer room control device, characterized in that: Based on the Internet of Things computer room control method according to any one of claims 1 to 7, the Internet of Things computer room is controlled, and the Internet of Things computer room is provided with a plurality of temperature sensors and air conditioning components, including: A temperature acquisition module, which is used to currently detect and acquire the real-time detection values ​​of the current detection cycle of the plurality of temperature sensors, and calculate the temperature average value; A temperature prediction module, which is used to calculate a temperature prediction value for the next detection cycle based on the temperature average value of the current detection cycle and the temperature average values ​​of several historical detection cycles; The temperature control module is used to control the air-conditioning component to adjust the indoor temperature of the Internet of Things computer room based on the temperature prediction value and the indoor constant temperature value of the Internet of Things computer room, and adjust the average indoor temperature of the next detection cycle to the indoor constant temperature value.

9. An electronic device, characterized in that: include: at least one processor; And a memory connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the Internet of Things computer room control method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the instructions are executed by a processor, an Internet of Things computer room control method as described in any one of claims 1-7 is implemented.

Images