A HVAC energy-saving optimization system for data centers

Abstract

The present invention belongs to the technical field of HVAC equipment energy-saving control, and discloses a HVAC energy-saving optimization system for a data center, including a monitoring area division module, an external environment monitoring module, a personnel data monitoring module, an equipment data monitoring module, an allowable temperature analysis module, a temperature control judgment module, an air quality analysis module, and a data storage library. When performing HVAC energy-saving optimization analysis, the present invention divides the target location into monitoring areas, and then analyzes different monitoring areas, can accurately identify energy-saving potential areas, and formulate accurate energy-saving strategies based on the characteristics of different areas. When performing temperature control analysis, the present invention corrects abnormal temperature conditions based on external environment evaluation conditions, personnel behavior evaluation conditions, and equipment operation evaluation conditions, and then determines temperature control needs. It can accurately perceive changes in multiple factors in the external environment and dynamically adjust strategies to improve the accuracy and energy saving of temperature control.

Description

Technical Field

[0001] The invention belongs to the technical field of energy-saving control of HVAC equipment and relates to a HVAC energy-saving optimization system for a data center. Background Art

[0002] Data centers are the foundation of cloud computing services and are places where key information system facilities such as servers, storage devices, and network equipment are centrally stored, managed, and operated. Excessive ambient temperature and excessive dust in the data center room will seriously damage equipment performance, shorten equipment life, and increase energy consumption. Therefore, it is necessary to perform temperature control, cleanliness control, and ventilation operations on the HVAC equipment in the data center. HVAC energy-saving optimization of data centers can reduce energy consumption and operating costs, improve energy utilization, and reduce environmental carbon emissions. At the same time, it ensures that the equipment is in a suitable operating environment to enhance reliability and stability and extend service life, which is conducive to the sustainable and efficient operation of data centers. Therefore, research based on HVAC energy-saving optimization in data centers is of great significance.

[0003] In the prior art, there are also related solutions for energy-saving control of HVAC equipment in data centers, for example, a Chinese invention patent application for a building HVAC system control method with publication number CN112460741B, which includes: establishing a knowledge model for a HVAC system with variable air volume and constant air supply temperature, and pre-training the agent based on deep deterministic policy gradient according to the knowledge model. The pre-trained agent is subjected to online iterative learning based on the hybrid reinforcement learning algorithm of the knowledge model and the deep deterministic policy gradient in a real environment, and the data-driven environment model is updated in real time.

[0004] In addition, a Chinese invention patent application for an indoor HVAC control method, system and medium with publication number CN117346285B includes: obtaining indoor video data and environmental parameters. Inputting the video data into a trained indoor window segmentation model IWS-Net to obtain a window segmentation mask image, and calculating the degree of opening of the indoor windows based on the window segmentation mask image. Calculating the inverse light flow sequence and the human skeleton key point sequence corresponding to the video data, and inputting the video data, the inverse light flow sequence and the human skeleton key point sequence into a trained indoor personnel behavior recognition model IDARM to obtain the thermal comfort behavior of the indoor personnel. Calculating the indoor HVAC control adjustment strategy based on the obtained indoor window opening degree, indoor personnel thermal comfort behavior and the environmental parameters. The present invention controls and adjusts the HVAC system based on the indoor environmental parameters, the window opening degree and the behavior of the indoor personnel to achieve energy saving and emission reduction and improve indoor thermal comfort.

[0005] Although the above two schemes have proposed some solutions for the energy-saving control technology of HVAC equipment, there are still certain limitations: on the one hand, the existing schemes usually only analyze the temperature parameters of the target location, ignoring the correction analysis based on external influencing parameters. This analysis method may cause HVAC equipment to over-operate when it is not necessary, unable to adapt to complex external environmental changes, over-reliance on internal regulation leading to high energy consumption, unable to make full use of the natural environment to adjust the temperature, and unable to dynamically optimize energy-saving strategies. Since the allowable temperature is not taken into account, HVAC equipment may continue to operate under unreasonable conditions, increasing the probability of failure and operating energy consumption.

[0006] On the other hand, existing solutions usually analyze the comprehensive air quality of the target location when conducting air quality analysis, and lack targeted analysis of the air quality of different monitoring areas. This analysis method cannot accurately locate air quality problems in specific areas of the data center. For example, the server-intensive areas and personnel operation areas have different requirements. A general analysis is likely to miss dust or harmful gas exceeding the standard in certain areas; it is difficult to formulate personalized improvement strategies based on the actual needs of each area; it is also not conducive to timely detection of sudden changes in air quality in local areas caused by equipment failure or personnel activities, thereby affecting the stability of equipment operation and personnel health, and unable to achieve refined management of the air quality of the data center. Summary of the invention

[0007] In view of this, in order to solve the problems raised in the above background technology, a data center HVAC energy-saving optimization system is now proposed.

[0008] The purpose of the present invention can be achieved through the following technical solutions: A data center HVAC energy-saving optimization system, including: a monitoring area division module, which is used to divide the monitoring area of ​​a target place into several monitoring areas.

[0009] The external environment monitoring module is used to obtain the external temperature and external wind speed during the current monitoring period, and analyze the external environment evaluation of each monitoring area during the current monitoring period.

[0010] The personnel data monitoring module is used to obtain the personnel data of each monitoring area during the current monitoring period, including the number of personnel, average stay time and average movement distance, and analyze the personnel behavior evaluation of each monitoring area during the current monitoring period.

[0011] The equipment data monitoring module is used to obtain the number of operating equipment and the power consumption of operating equipment in each monitoring area during the current monitoring period, and analyze the equipment operation evaluation status of each monitoring area during the current monitoring period.

[0012] The allowable temperature analysis module is used to analyze the allowable temperature of each monitoring area within the current monitoring period based on the external environment evaluation, personnel behavior evaluation and standby operation evaluation of each monitoring area within the current monitoring period.

[0013] The temperature control judgment module is used to collect the monitoring temperature of each monitoring area during the current monitoring period, analyze the temperature abnormality of each monitoring area during the current monitoring period, and then judge whether each monitoring area needs temperature control, and perform temperature control if necessary.

[0014] The air quality analysis module is used to set dust concentration sensors and harmful gas concentration sensors in each monitoring area, obtain the dust concentration and harmful gas concentration of each monitoring area during the current monitoring period, analyze the air quality abnormalities in each monitoring area during the current monitoring period, and determine whether ventilation control is required in each monitoring area. If necessary, ventilation control is performed.

[0015] A data repository is used to store a two-dimensional distribution map of target places.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) When performing HVAC energy-saving optimization analysis, the present invention monitors the target location and then analyzes different monitoring areas. This analysis method can accurately identify energy-saving potential areas and formulate more accurate energy-saving strategies based on the characteristics of different areas. Dividing the monitoring areas can refine the overall monitoring data and improve the accuracy and reliability of the analysis data, providing reliable data support for further optimizing energy-saving strategies.

[0017] (2) When performing temperature control analysis, the present invention corrects abnormal temperature conditions based on the external environment evaluation, equipment operation evaluation and personnel behavior evaluation, and then makes a temperature control demand judgment. This analysis method can fully adapt to changes in natural factors and equipment factors, avoid damage to equipment caused by fluctuations in the computer room environment, accurately respond to the influence of human factors to improve operational efficiency and personnel convenience, and adjust temperature control according to equipment status to stabilize performance, reduce energy consumption and failure risks, thereby constructing a comprehensive temperature control strategy system that is multi-dimensional, accurate, efficient, energy-saving and conducive to equipment maintenance, providing a scientific solution for temperature management in related places.

[0018] (3) The present invention analyzes the abnormal air quality in each monitoring area during the current monitoring period, and then determines whether ventilation control is required in each monitoring area. If necessary, ventilation control is performed. This analysis method can clearly grasp the air quality of each area and quickly identify abnormalities. It is more accurate than traditional general analysis and can achieve targeted regulation. Ventilation is performed on demand according to the characteristics of different areas, avoiding waste of resources. Only areas with demand are ventilated, reducing energy consumption and the operating time of ventilation equipment, improving energy utilization efficiency, reducing operating costs, and achieving a win-win situation of efficient air quality management and energy saving in data centers. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention.

[0021] Figure 2 A judgment flow chart corresponding to a temperature control demand judgment embodiment provided by the present invention.

[0022] Figure 3 A judgment flow chart corresponding to a ventilation control demand judgment embodiment provided by the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1 As shown, the present invention provides a data center HVAC energy-saving optimization system, including a monitoring area division module, an external environment monitoring module, an equipment data monitoring module, a personnel data monitoring module, an allowable temperature analysis module, a temperature control judgment module, an air quality analysis module and a data storage library, wherein the monitoring area division module is connected to the external environment monitoring module, the personnel data monitoring module and the equipment data monitoring module respectively, the external environment monitoring module, the personnel data monitoring module and the equipment data monitoring module are all connected to the allowable temperature analysis module, the allowable temperature analysis module is connected to the temperature control judgment module, the temperature control judgment module is connected to the air quality analysis module, and the data storage library is connected to the monitoring area division module.

[0025] The monitoring area division module is used to divide the monitoring area of ​​the target place into a plurality of monitoring areas.

[0026] It should be noted that the specific method of dividing the target place into monitoring areas is: obtaining a two-dimensional distribution image of the target place, and then dividing the target place into monitoring areas based on a preset basic unit area to obtain a number of monitoring areas, each monitoring area corresponding to a number of cabinets or racks.

[0027] It should be noted that the present invention monitors the target area when performing HVAC energy-saving optimization analysis, and then analyzes different monitoring areas. This analysis method can accurately identify energy-saving potential areas, and formulate more accurate energy-saving strategies based on the characteristics of different areas. Dividing the monitoring area can refine the overall monitoring data to improve the accuracy and reliability of the analysis data, and provide reliable data support for further optimizing energy-saving strategies.

[0028] The external environment monitoring module is used to obtain the external temperature and external wind speed in the current monitoring period, and analyze the external environment evaluation of each monitoring area in the current monitoring period.

[0029] It should be explained that the reason for choosing outside temperature and outside wind speed as the factors affecting the external environment evaluation is that different combinations of outside temperature and wind speed will greatly affect the energy consumption of HVAC equipment. Taking the cooling tower as an example, when the outside temperature is high and the wind speed is low, the cooling tower needs to consume more electricity to overcome the influence of the outdoor high temperature and realize cooling water cooling. Because the heat exchange efficiency between the outdoor hot air and the cooling tower is relatively low at this time, the cooling tower needs to work harder to discharge the indoor heat. When the outside temperature is low and the wind speed is high, cooling water cooling can be achieved to a certain extent through natural ventilation, and the energy consumption of the cooling tower can be greatly reduced.

[0030] In a preferred embodiment of the present invention, the analysis of the external environment evaluation of each monitoring area in the current monitoring period requires the construction of an external environment evaluation index for each monitoring area in the current monitoring period, and the specific method is as follows: extract the external temperature and external wind speed of each monitoring area in the current monitoring period, and record them as , ,in Indicates the number of the monitoring area. , Indicates the number of monitoring areas.

[0031] Using the formula Analyze and obtain the external environment evaluation index of each monitoring area during the current monitoring period ,in Indicates the preset reference temperature. Indicates the preset reference wind speed. They represent the influence weights of the preset outside temperature and outside wind speed respectively. Represents a natural constant.

[0032] For example, .

[0033] It should be noted that when analyzing the external environment evaluation index of each monitoring area, the setting basis of the influence weights corresponding to the external temperature and external wind speed is: the external wind speed is the "behind-the-scenes promoter" that accelerates the exchange of indoor and outdoor heat and water vapor. Strong wind will accelerate the loss or intrusion of heat in the gaps of walls, doors and windows, affecting the indoor temperature and humidity balance. Although the influence of wind speed is slightly less than that of external temperature, it is of great significance in the scenes of ventilation, heat dissipation and air cooling effect.

[0034] The personnel data monitoring module is used to obtain the personnel data of each monitoring area in the current monitoring period, specifically including the number of personnel, average stay time and average movement distance, and analyze the personnel behavior evaluation of each monitoring area in each monitoring period.

[0035] It should be noted that the reasons for selecting the number of people, average residence time and average moving distance as factors affecting the evaluation of personnel behavior are as follows: 1. The number of people is one of the important sources of indoor heat load. The human body will continuously generate heat through metabolism, and will also dissipate heat to the surrounding environment through convection, radiation and other means during activities. When the number of people increases, the total heat load in the room will increase significantly, thereby reducing the workload of the heating equipment. 2. The average residence time reflects the length of time that people stay in a specific area. When the average residence time of people in an area is long, the heat continuously generated by the human body will gradually accumulate in the area. 3. The frequent movement of people will drive the flow of surrounding air and accelerate the transfer and diffusion of heat in the space. When people shuttle in the indoor space, the hot air around their bodies is driven and mixed with the air in other areas, and the originally relatively stable temperature field is broken. This air disturbance allows heat to spread over a wider range and reduces the accumulation of heat energy in some areas.

[0036] In a preferred embodiment of the present invention, the analysis of the personnel behavior evaluation of each monitoring area in each monitoring period requires the construction of a personnel behavior evaluation index for each monitoring area in the current monitoring period, and the specific method is as follows: extract the number of personnel, average stay time and average moving distance in each monitoring area in the current monitoring period, and record them as , , .

[0037] Using the formula Analyze and obtain the personnel behavior evaluation index of each monitoring area during the current monitoring period ,in represents the number of reference personnel in each monitoring area, Indicates the length of time that reference personnel stay in each monitoring area, Indicates the reference personnel movement distance in each monitoring area.

[0038] The analysis method for the reference number of personnel, reference personnel retention time and reference personnel moving distance in each monitoring area is as follows: obtain the number of personnel, average retention time and average moving distance in each monitoring area in the current monitoring period corresponding to the previous monitoring period, and then use them as the reference number of personnel, reference personnel retention time and reference personnel moving distance in each monitoring area corresponding to the current monitoring period.

[0039] The equipment data monitoring module is used to obtain the number of running equipment and the power consumption of running equipment in each monitoring area during the current monitoring period, and analyze the equipment operation evaluation status of each monitoring area during the current monitoring period.

[0040] It should be noted that the reasons for selecting the number of running devices and the power consumption of running devices as the factors affecting the equipment operation evaluation of each monitoring area during the current monitoring period are: 1. The number of devices in each monitoring area of ​​the data center directly reflects the equipment density of the area. Differences in equipment density in different areas may lead to different operating conditions. When the number of devices exceeds a certain limit, it may cause local power overload or poor heat dissipation. 2. Equipment power consumption is closely related to heat dissipation requirements. Devices with high power consumption will generate more heat during operation, which requires stronger heat dissipation capabilities to maintain the normal operating temperature of the equipment.

[0041] In a preferred embodiment of the present invention, the analysis of the equipment operation evaluation of each monitoring area in the current monitoring period requires the construction of the equipment operation evaluation index of each monitoring area in the current monitoring period, and the specific method is as follows: extract the number of operating equipment and the power consumption of the operating equipment in each monitoring area in the current monitoring period, respectively recorded as , .

[0042] Using the formula Analyze and obtain the equipment operation evaluation index of each monitoring area during the current monitoring period ,in Indicates the number of reference operating devices in each monitoring area during the current monitoring period. Indicates the reference operating equipment power consumption of each monitoring area during the current monitoring period. They respectively represent the impact weights corresponding to the preset number of running devices and the power consumption of the running devices.

[0043] For example, .

[0044] It should be noted that when analyzing the equipment operation evaluation index of each monitoring area during the current monitoring period, the basis for setting the impact weights corresponding to the number of operating equipment and the power consumption of operating equipment is: the basis for setting the weight of the number of operating equipment mainly includes its close relationship with the bearing pressure of the infrastructure, its significant impact on business continuity, and its key position in considering the rationality of resource allocation. The basis for setting the weight of the power consumption of operating equipment focuses on the high proportion of energy costs in operating costs, the high correlation with the pressure of the cooling system, and the important impact on the stability of equipment performance.

[0045] The analysis method of the reference number of operating devices and the reference power consumption of operating devices in each monitoring area during the current monitoring period is as follows: the number of operating devices and the power consumption of operating devices in each monitoring area in the current monitoring period corresponding to the previous monitoring period are obtained, and then used as the reference number of operating devices and the reference power consumption of operating devices in each monitoring area in the current monitoring period.

[0046] The allowable temperature analysis module is used to analyze the allowable temperature of each monitoring area in the current monitoring period based on the external environment evaluation, personnel behavior evaluation and standby operation evaluation of each monitoring area in the current monitoring period.

[0047] The external environment evaluation index, personnel behavior evaluation index and equipment operation evaluation index of each monitoring area in the current monitoring period are extracted, and then the required temperature correction coefficient of each monitoring area in the current monitoring period is obtained by summing them up according to the weights.

[0048] For example, the weights corresponding to the external environment evaluation index, the personnel behavior evaluation index, and the equipment operation evaluation index are: .

[0049] It should be noted that the weights of the external environment evaluation index, personnel behavior evaluation index and equipment operation evaluation index are set based on the following: first, the impact of equipment operation plays a leading role, and then based on the relative importance of indoor temperature control, the external environment plays a certain role in extreme weather. Finally, the actual data and simulation analysis results are combined, and historical temperature control, energy consumption and other data are referred to. With the help of building environment simulation software, the degree of influence of changes in various factors on indoor temperature is analyzed, and scientific and reasonable weights are comprehensively determined to achieve accurate and effective temperature control.

[0050] The required temperature correction value of each monitoring area in the current monitoring period is obtained by multiplying the preset required temperature of each monitoring area in the current monitoring period with the corresponding required temperature correction coefficient, and then the required temperature of each monitoring area is summed with the corresponding required temperature of each monitoring area to obtain the allowable temperature of each monitoring area in the current monitoring period.

[0051] The temperature control judgment module is used to collect the monitoring temperature of each monitoring area in the current monitoring period, analyze the temperature abnormality of each monitoring area in the current monitoring period, and then judge whether each monitoring area needs temperature control, and perform temperature control if necessary.

[0052] In a preferred embodiment of the present invention, the analysis of the temperature anomaly of each monitoring area in the current monitoring period requires constructing a temperature anomaly index for each monitoring area in the current monitoring period, and the specific method is as follows: extracting the monitoring temperature of each monitoring area in the current monitoring period, and then performing a difference calculation between the monitoring temperature of each monitoring area in the current monitoring period and the corresponding allowable temperature to obtain the temperature deviation value of each monitoring area in the current monitoring period, and then performing a ratio calculation with the corresponding allowable temperature to obtain the temperature anomaly index of each monitoring area in the current monitoring period.

[0053] In a preferred embodiment of the present invention, please refer to Figure 2 As shown, the specific method for judging whether each monitoring area needs temperature control is as follows: the temperature anomaly index of each monitoring area in the current monitoring period is compared with the pre-set temperature anomaly index threshold value. If the temperature anomaly index of a monitoring area in the current monitoring period is greater than the temperature anomaly index threshold value, it is judged that the monitoring area needs temperature correction; otherwise, it is judged that the monitoring area does not need temperature correction.

[0054] In a preferred embodiment of the present invention, the specific method of temperature control is: when it is determined that temperature control is required, the power of the HVAC equipment in the corresponding monitoring area is adjusted and the ventilation equipment is turned on at the same time, so that the temperature of the corresponding monitoring area is reduced to the allowable temperature of the corresponding monitoring area; when the temperature of the corresponding monitoring area is equal to the preset reference temperature, the power of the HVAC equipment in the corresponding monitoring area is adjusted to the preset standby power.

[0055] It should be noted that the present invention corrects abnormal temperature conditions based on external environment evaluation, personnel behavior evaluation and equipment operation evaluation when conducting temperature control analysis, and then determines the temperature control needs. This analysis method can fully adapt to changes in natural factors, avoid damage to equipment caused by fluctuations in the computer room environment, accurately respond to the influence of human factors to improve operational efficiency and personnel convenience, and adjust temperature control according to equipment status to stabilize performance, reduce energy consumption and failure risks, thereby constructing a comprehensive temperature control strategy system that is multi-dimensional, precise, efficient, energy-saving and conducive to equipment maintenance, providing scientific solutions for temperature management in related places.

[0056] The air quality analysis module is used to set dust concentration sensors and harmful gas concentration sensors in each monitoring area, obtain the dust concentration and harmful gas concentration of each monitoring area during the current monitoring period, analyze the air quality abnormalities in each monitoring area during the current monitoring period, and determine whether ventilation control is required in each monitoring area, and perform ventilation control if necessary.

[0057] It should be noted that the reason for choosing dust concentration and harmful gas concentration as the influencing factors of abnormal air quality is that excessive dust concentration will bring risks to the operation of data center equipment, increase maintenance costs, and lead to an overall decline in air quality. As for the concentration of harmful gases, it will cause equipment corrosion and damage, endanger personnel health, and some harmful gases even have fire and explosion hazards. Both have a significant impact on the normal operation of the data center and personnel safety.

[0058] Exemplarily, the harmful gases may be hydrogen sulfide and hydrogen, and the corresponding sensors are a hydrogen sulfide concentration sensor and a hydrogen concentration sensor, respectively.

[0059] What needs to be explained is the reason why harmful gases such as hydrogen sulfide and hydrogen appear in data centers: hydrogen sulfide appears in data centers, mostly due to failure of lead-acid batteries in UPS systems, such as plate sulfidation, overcharging, etc., which cause the decomposition of sulfur-containing compounds, or due to microbial activity in high humidity and poor ventilation environments, which reduces sulfate to hydrogen sulfide. Data center equipment will produce hydrogen during the charging process.

[0060] In a preferred embodiment of the present invention, the analysis of the abnormal air quality in each monitoring area requires constructing an abnormal air quality index for each monitoring area in the current monitoring period, and the specific method is as follows: extract the dust concentration and harmful gas concentration in each monitoring area in the current monitoring period, and then perform difference calculations with the pre-set dust concentration threshold and harmful gas concentration threshold to obtain the abnormal dust concentration amount and the abnormal harmful gas concentration amount in each monitoring area in the current monitoring period, and then perform ratio calculations with the dust concentration threshold and the harmful gas concentration threshold to obtain the abnormal dust concentration index and the abnormal harmful gas concentration index in each monitoring area in the current monitoring period.

[0061] The dust concentration anomaly index and harmful gas concentration anomaly index of each monitoring area in the current monitoring period are summed up according to the weights to obtain the air quality anomaly index of each monitoring area in the current monitoring period.

[0062] For example, the weights corresponding to the dust concentration abnormality index and the harmful gas concentration abnormality index are respectively .

[0063] It should be noted that the weights of the dust concentration abnormality index and the harmful gas concentration abnormality index are set based on: the dust concentration abnormality index weight is set based on its close relationship with equipment failure, its key impact on maintenance work, and its importance at the basic level of air quality. The weight of the harmful gas concentration abnormality index is set based on the severity of damage to equipment caused by harmful gases, the high risk to personnel health, and the potential safety hazards. These factors are combined to determine the weights of the two in order to accurately measure the impact of abnormal air quality in the data center on equipment, personnel, and overall operational safety.

[0064] In a preferred embodiment of the present invention, please refer to Figure 3 As shown, the specific method for judging whether ventilation control is needed in each monitoring area is as follows: extract the air quality anomaly index of each monitoring area in the current monitoring period, and then compare them with the pre-set air quality anomaly index thresholds respectively. If the air quality anomaly index of a monitoring area is greater than the air quality anomaly index threshold, it is judged that ventilation control is needed for the monitoring area; otherwise, it is judged that ventilation control is not needed for the monitoring area.

[0065] For example, the air quality abnormality index threshold is .

[0066] In a preferred embodiment of the present invention, the specific method of ventilation control is: when it is determined that ventilation control is needed, the ventilation equipment of the corresponding monitoring area is turned on, and the air quality abnormality index of the corresponding monitoring area is analyzed in real time; when the air quality abnormality index of a certain monitoring area is less than the preset reference air quality abnormality index, the ventilation control is stopped and the ventilation equipment is turned off.

[0067] It should be noted that the present invention analyzes the abnormal air quality in each monitoring area during the current monitoring period, and then determines whether ventilation control is needed in each monitoring area. If necessary, ventilation control is performed. This analysis method can clearly grasp the air quality of each area and quickly lock in abnormalities. It is more accurate than traditional general analysis and can achieve targeted regulation and control. Ventilation is performed on demand according to the characteristics of different areas, avoiding waste of resources. Only areas with demand are ventilated, reducing energy consumption and the operating time of ventilation equipment, improving energy utilization efficiency, reducing operating costs, and achieving a win-win situation of efficient air quality management and energy saving in data centers.

[0068] The data storage repository is used to store a two-dimensional distribution map of the target location.

[0069] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A data center HVAC energy-saving optimization system, characterized in that: include: The monitoring area division module divides the monitoring area of ​​the target place into several monitoring areas; The external environment monitoring module obtains the external temperature and external wind speed during the current monitoring period, and analyzes the external environment evaluation of each monitoring area during the current monitoring period; The personnel data monitoring module obtains the personnel data of each monitoring area during the current monitoring period, including the number of personnel, average stay time and average movement distance, and analyzes the personnel behavior evaluation of each monitoring area during the current monitoring period; The equipment data monitoring module obtains the number of running equipment and the power consumption of running equipment in each monitoring area during the current monitoring period, and analyzes the equipment operation evaluation status of each monitoring area during the current monitoring period; The allowable temperature analysis module analyzes the allowable temperature of each monitoring area within the current monitoring period based on the external environment evaluation, personnel behavior evaluation and standby operation evaluation of each monitoring area within the current monitoring period; The temperature control judgment module collects the monitored temperature of each monitoring area during the current monitoring period, analyzes the temperature anomalies of each monitoring area during the current monitoring period, and then determines whether each monitoring area needs temperature control, and performs temperature control if necessary; The air quality analysis module sets dust concentration sensors and harmful gas concentration sensors in each monitoring area to obtain the dust concentration and harmful gas concentration of each monitoring area during the current monitoring period, analyze the air quality abnormalities of each monitoring area during the current monitoring period, and determine whether ventilation control is required in each monitoring area. If necessary, ventilation control is performed; A data repository for storing a two-dimensional distribution map of target places; To analyze the external environment evaluation of each monitoring area during the current monitoring period, it is necessary to construct the external environment evaluation index of each monitoring area during the current monitoring period. The specific method is as follows: Extract the outside temperature and outside wind speed of each monitoring area during the current monitoring period and record them as , ; Using the formula Analyze and obtain the external environment evaluation index of each monitoring area during the current monitoring period ,in Indicates the preset reference temperature. Indicates the preset reference wind speed. They represent the influence weights of the preset outside temperature and outside wind speed respectively. represents a natural constant; To analyze the personnel behavior evaluation of each monitoring area during the current monitoring period, it is necessary to construct the personnel behavior evaluation index of each monitoring area during the current monitoring period. The specific method is as follows: Extract the number of people, average stay time and average moving distance in each monitoring area during the current monitoring period and record them as , , ; Using the formula Analyze and obtain the personnel behavior evaluation index of each monitoring area during the current monitoring period ,in represents the number of reference personnel in each monitoring area, Indicates the reference retention time of each monitoring area, Indicates the reference moving distance of each monitoring area; The analysis method for the reference number of personnel, reference detention time and reference moving distance in each monitoring area is as follows: obtain the number of personnel, average detention time, and average moving distance in each monitoring area in the current monitoring period corresponding to the previous monitoring period, and then use them as the reference number of personnel, reference detention time and reference moving distance in each monitoring area corresponding to the current monitoring period.

2. A data center HVAC energy-saving optimization system according to claim 1, characterized in that: The analysis of the equipment operation evaluation of each monitoring area in the current monitoring period requires the construction of the equipment operation evaluation index of each monitoring area in the current monitoring period, and the specific method is as follows: Extract the number of running devices and the power consumption of running devices in each monitoring area during the current monitoring period, and record them as , ; Using the formula Analyze and obtain the equipment operation evaluation index of each monitoring area during the current monitoring period ,in Indicates the number of reference operating devices in each monitoring area during the current monitoring period. Indicates the reference operating equipment power consumption of each monitoring area during the current monitoring period. They respectively represent the impact weights corresponding to the preset number of running devices and the power consumption of the running devices; The analysis method of the reference number of operating devices and the reference power consumption of operating devices in each monitoring area during the current monitoring period is as follows: the number of operating devices and the power consumption of operating devices in each monitoring area in the current monitoring period corresponding to the previous monitoring period are obtained, and then used as the reference number of operating devices and the power consumption of operating devices in each monitoring area in the current monitoring period.

3. A data center HVAC energy-saving optimization system as claimed in claim 2, characterized in that: The specific method of analyzing the allowable temperature of each monitoring area within the current monitoring period is as follows: Extract the external environment evaluation index, personnel behavior evaluation index and equipment operation evaluation index of each monitoring area in the current monitoring period, and then sum them up according to the weights to obtain the required temperature correction coefficient of each monitoring area in the current monitoring period; The required temperature correction value of each monitoring area in the current monitoring period is obtained by multiplying the preset required temperature of each monitoring area in the current monitoring period with the corresponding required temperature correction coefficient, and then the required temperature of each monitoring area is summed with the corresponding required temperature of each monitoring area to obtain the allowable temperature of each monitoring area in the current monitoring period.

4. A data center HVAC energy-saving optimization system as claimed in claim 3, characterized in that: The analysis of the temperature anomaly of each monitoring area in the current monitoring period requires the construction of the temperature anomaly index of each monitoring area in the current monitoring period, and the specific method is as follows: The monitored temperature of each monitoring area in the current monitoring period is extracted, and then the monitored temperature of each monitoring area in the current monitoring period is calculated by difference with the corresponding allowable temperature to obtain the temperature deviation value of each monitoring area in the current monitoring period, and then the ratio is calculated with the corresponding allowable temperature to obtain the temperature anomaly index of each monitoring area in the current monitoring period.

5. A data center HVAC energy-saving optimization system as claimed in claim 4, characterized in that: The specific method of determining whether each monitoring area needs to be temperature controlled is as follows: The temperature anomaly index of each monitoring area in the current monitoring period is compared with the pre-set temperature anomaly index threshold. If the temperature anomaly index of a monitoring area in the current monitoring period is greater than the temperature anomaly index threshold, it is judged that the monitoring area needs temperature correction. Otherwise, it is judged that the monitoring area does not need temperature correction.

6. A data center HVAC energy-saving optimization system according to claim 1, characterized in that: The analysis of the abnormal air quality in each monitoring area during the current monitoring period requires the construction of an abnormal air quality index for each monitoring area during the current monitoring period, and the specific method is as follows: Extract the dust concentration and harmful gas concentration of each monitoring area in the current monitoring period, and then perform difference calculation with the pre-set dust concentration threshold and harmful gas concentration threshold to obtain the abnormal dust concentration and harmful gas concentration of each monitoring area in the current monitoring period, and then perform ratio calculation with the dust concentration threshold and harmful gas concentration threshold to obtain the abnormal dust concentration index and harmful gas concentration index of each monitoring area in the current monitoring period; The dust concentration anomaly index and harmful gas concentration anomaly index of each monitoring area in the current monitoring period are summed up according to the weights to obtain the air quality anomaly index of each monitoring area in the current monitoring period.

7. A data center HVAC energy-saving optimization system according to claim 6, characterized in that: The specific method of judging whether ventilation control is required in each monitoring area is as follows: The air quality anomaly index of each monitoring area during the current monitoring period is extracted, and then compared with the pre-set air quality anomaly index threshold. If the air quality anomaly index of a monitoring area is greater than the air quality anomaly index threshold, it is judged that ventilation control is required for the monitoring area. Otherwise, it is judged that ventilation control is not required for the monitoring area.

8. A data center HVAC energy-saving optimization system according to claim 1, characterized in that: The specific methods of temperature control and ventilation control are as follows: When it is determined that temperature control is required, the power of the HVAC equipment in the corresponding monitoring area is adjusted and the ventilation equipment is turned on at the same time, so that the temperature in the corresponding monitoring area drops to the allowable temperature in the corresponding monitoring area, and then the power of the HVAC equipment in the corresponding monitoring area is reduced; When it is determined that ventilation control is needed, the ventilation equipment in the corresponding monitoring area is turned on, and the air quality abnormality index of the corresponding monitoring area is analyzed in real time. When the air quality abnormality index of a monitoring area is less than the preset reference air quality abnormality index, ventilation control is stopped and the ventilation equipment is turned off.

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