AI-based centralized air conditioner efficient machine room control method and system

Abstract

The invention relates to the technical field of self-adaptive control, in particular to an AI-based centralized air-conditioning efficient machine room control method and system, which comprises the following steps: acquiring temperature distribution of a machine room, calling temperature data of an air supply outlet, an air outlet and equipment, identifying temperature gradient, calculating airflow direction and wind speed, screening temperature abnormal areas, and analyzing the relationship between temperature fluctuation and equipment operation. And acquiring a temperature flow state. According to the invention, through fine monitoring of local temperature changes and air flow vectors in the machine room, the recognition capability of a heat accumulation area, accurate analysis of a cold and hot air flow intersection and dynamic adjustment of an air supply angle and an air volume are improved, cold air backflow is effectively reduced, the conveying efficiency is improved, exhaust control is combined with heat diffusion path optimization, and the heat extraction efficiency is improved; real-time calculation of the short-time temperature change rate ensures accurate matching of the air supply amount and the equipment load, energy consumption is reduced, optimization of an air supply system and an air exhaust system is achieved through comprehensive state analysis, and the self-adaptive adjusting capacity and energy efficiency of the air conditioning system are improved.

Description

Technical Field

[0001] The present invention relates to the field of adaptive control technology, and in particular to an AI-based high-efficiency centralized air-conditioning room control method and system. Background Art

[0002] The field of adaptive control technology encompasses control systems based on real-time feedback and dynamic adjustment, capable of automatically optimizing control strategies based on environmental changes or system status. The core elements of adaptive control include model reference adaptive control, parameter adaptive control, and intelligent adaptive control based on neural networks or machine learning. This technical field is primarily used in scenarios such as industrial automation, robotics, aerospace, and intelligent transportation. By monitoring system status in real time, identifying external interference, and dynamically adjusting control parameters, efficient and stable system operation is achieved. With the development of artificial intelligence and big data technologies, adaptive control is gradually evolving toward intelligence, combining deep learning, reinforcement learning, and other methods to enhance the autonomous learning and optimization capabilities of control systems, enabling them to maintain precise control in complex and changing environments.

[0003] Among them, the AI-based centralized air-conditioning efficient computer room control method refers to the use of artificial intelligence technology to dynamically adjust the centralized air-conditioning system to achieve efficient computer room temperature and humidity management and energy consumption optimization. The subject of this patent involves collecting computer room environmental information based on sensor data, including parameters such as temperature, humidity, and air flow rate, and adjusting the air-conditioning operating parameters through an adaptive control method in combination with historical operating data and current load status. The method uses a neural network model to extract features from the computer room environment and uses an optimization algorithm to calculate the optimal temperature control strategy, so that the air-conditioning system can adapt to changes in the internal load of the computer room in real time. By combining fuzzy control with a deep reinforcement learning model, key variables such as air-conditioning wind speed, cooling capacity, and supply air temperature are dynamically adjusted to achieve precise control. The control method further combines an energy consumption prediction model to analyze the energy consumption distribution under different operating modes of the computer room, and optimizes the air-conditioning start and stop strategy based on the prediction results, thereby forming an adaptive intelligent air-conditioning control solution.

[0004] Existing technologies do not adequately monitor local temperature fluctuations within the computer room, and air flow vector calculations are not incorporated into the temperature control optimization system. This makes it difficult to accurately identify areas of heat accumulation, impacting the response speed of temperature regulation. The temperature difference calculation at the intersection of hot and cold air flows lacks detailed analysis, and the adjustment of air supply angles and air volume relies on static parameters. The cold air flow path is difficult to accurately match the equipment's heat dissipation requirements, increasing the probability of cold air backflow and reducing cooling efficiency. Exhaust control fails to effectively integrate the heat diffusion path for dynamic adjustment, and wind direction adjustment relies on a fixed strategy. Heat emissions from high-temperature areas are uneven, impacting the overall heat removal effect. Cooling demand calculations are based on long-term operating data and fail to fully consider the rate of temperature change over short periods of time, resulting in insufficient cold air coverage or excessive air supply in some areas, increasing system energy consumption. The coordinated optimization capabilities of supply and exhaust air are insufficient, and the adjustment of hot and cold air flow distribution lacks a real-time feedback mechanism, impacting the overall temperature control accuracy and energy-saving effect of the computer room. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose an AI-based centralized air-conditioning efficient computer room control method and system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an AI-based centralized air-conditioning high-efficiency computer room control method, comprising the following steps: S1: Obtain the temperature distribution in the equipment room, retrieve the temperature data of the air supply and exhaust vents and equipment, identify the temperature gradient, calculate the airflow direction and wind speed, screen out abnormal temperature areas, analyze the relationship between temperature fluctuations and equipment operation, and obtain the temperature flow status; S2: Based on the temperature flow state, call the cold and hot air flow intersection data, calculate the temperature difference, identify the heat return path, compare the cold air flow offset, screen abnormal air outlets, adjust the air direction and air volume, and obtain the cold air delivery adjustment result; S3: Based on the cooling air delivery adjustment result, call the exhaust vent data, calculate the air volume and heat emission rate, screen the heat retention area, analyze the impact of the exhaust mode on heat removal efficiency, adjust the air volume and wind direction, optimize heat removal, and obtain the exhaust adjustment result; S4: Based on the exhaust adjustment result, call the equipment load data, calculate the temperature change rate, compare the cold air coverage, screen the insufficient coverage areas, adjust the air supply volume and the operation of the refrigeration equipment, optimize the cold air flow, and obtain the cooling load matching result; S5: Based on the cooling load matching result, call the supply air, exhaust air and compressor data, analyze the temperature trend, adjust the air supply mode, adjust the cooling capacity and air flow distribution, and obtain the air conditioning operation adjustment result.

[0007] As a further solution of the present invention, the temperature flow state includes temperature gradient distribution, air flow velocity, air flow direction, heat accumulation area and abnormal temperature change rate area; the cold air delivery adjustment result includes the intersection temperature difference, heat return path, air supply angle adjustment range and air supply volume adjustment range; the exhaust adjustment result includes exhaust outlet air volume, heat emission rate, heat retention area, exhaust wind direction adjustment and exhaust volume adjustment; the refrigeration load matching result includes short-time temperature change rate, current refrigeration demand, cold air flow coverage, air supply volume adjustment range and cold air flow path optimization; the air conditioning operation adjustment result includes temperature change trend, air supply mode adjustment, exhaust system operation adjustment, cooling capacity output adjustment and air flow distribution optimization.

[0008] As a further solution of the present invention, the specific steps of S1 are: S101: Obtain temperature data of the air supply outlet, air exhaust outlet, and surrounding area of the equipment in the equipment room, calculate the temperature gradient and temperature change rate, and obtain a temperature distribution map; S102: Based on the temperature distribution map and the temperature change rate data, identify the air flow speed and direction in the area, obtain the air flow vector through the relationship between the temperature difference and the spatial position, analyze the air flow direction and wind speed changes in adjacent areas, and establish the air flow state; S103: Calling the air flow state, comparing the airflow direction and wind speed changes in adjacent areas, identifying the heat accumulation area where the temperature change rate is higher than the set range, and obtaining the temperature flow state.

[0009] As a further solution of the present invention, the specific steps of S2 are: S201: Acquire the temperature flow state, call the cold and hot air flow intersection data, calculate the difference value according to the temperature difference of the intersection and the time change, and filter the area where the temperature difference exceeds the set range to obtain the temperature difference exceeding the limit area; S202: Based on the temperature difference exceeding the limit area, identifying the heat return path in the area, comparing the cold air flow direction with the offset of the return path, screening the air outlets whose offset exceeds the set wind direction range, adjusting the air supply angle of the air outlets, and comparing the direction difference before and after the offset adjustment to obtain the offset correction range; S203: calling the offset correction range and the air volume data of the air outlet, calculating the air volume adjustment range, and adjusting the air volume of the air outlet accordingly to obtain a cold air delivery adjustment result.

[0010] As a further solution of the present invention, the adjusted air volume calculation formula is specifically: ; in, Indicates the adjusted air volume. Indicates the current air volume of the air outlet. represents the reference wind speed at the i-th measuring point, represents the actual wind speed at the i-th measuring point, represents the weight coefficient of the i-th measuring point, n represents the total number of measuring points, Indicates the current pressure loss in the system. Indicates the current total pressure of the system.

[0011] As a further solution of the present invention, the specific steps of S3 are: S301: Calling the cooling air delivery adjustment result and the exhaust vent operation data, calculating the exhaust vent air volume and heat emission rate, comparing the changes in the exhaust vent air volume and heat emission rate, screening areas where the heat retention time is higher than a set threshold, and obtaining heat retention areas; S302: Based on the heat retention area, compare the heat exhaust efficiency under the differentiated exhaust modes, calculate the degree of matching between the exhaust outlet wind direction and the heat diffusion path, determine the difference in the matching degree and the influencing factors, adjust the air volume of the exhaust outlet and change the exhaust direction, obtain the range of change of the exhaust outlet wind direction and air volume, and obtain the exhaust direction adjustment range; S303: calling the exhaust direction adjustment range, calculating the exhaust effect of the exhaust port under the conditions of differentiated air volume and wind direction, comparing the exhaust efficiency changes, and obtaining the exhaust adjustment result.

[0012] As a further solution of the present invention, the specific steps of S4 are: S401: Calculate the temperature change rate of the area in a short period of time by calling the exhaust adjustment result and the equipment load data, obtain the cooling demand based on the temperature change rate data, and compare the cold air flow coverage of the area to obtain the cooling demand status; S402: Based on the cooling demand state, the degree of matching between the air supply volume and the heat load is analyzed, and areas with insufficient cooling coverage are screened. Based on the air supply volume and the temperature change rate in the insufficiently cooled areas, the difference between the equipment load and the air supply volume is compared, and the air supply volume and path to be adjusted are calculated to obtain an air supply volume adjustment range. S403: Calling the air supply volume adjustment range, adjusting the operating state of the air supply device, allocating the cold air flow path, and obtaining a cooling load matching result based on the adjusted air supply volume and path data.

[0013] As a further solution of the present invention, the temperature change rate calculation formula of the region within a short period of time is specifically: ; in, Represents the temperature change rate of the region in a short period of time, represents the final temperature of the i-th measuring point, represents the initial temperature of the i-th measuring point, t represents the measurement time interval, and m represents the total number of measuring points.

[0014] As a further solution of the present invention, the specific steps of S5 are: S501: Calling the cooling load matching result and the operating status data of the air supply, exhaust and compressor, analyzing the temperature change trend of the area, adjusting the air supply mode according to the data of the heat accumulation area, and comparing the temperature change trends under the differentiated air supply modes to obtain the air supply mode adjustment result; S502: Based on the air supply mode adjustment result, the exhaust outlet air volume data is retrieved to calculate the heat retention index of the area, the operation mode of the exhaust system is adjusted according to the degree of heat retention, the matching between the exhaust outlet air volume and the heat diffusion path is analyzed, the exhaust volume adjustment range and wind direction change are calculated, and the exhaust system adjustment range is obtained; S503: Calling the exhaust system adjustment range and cooling capacity output data, adjusting the operating status of the air supply and exhaust systems, adjusting the cold air flow distribution and cooling capacity output, and obtaining the air conditioning operation adjustment result.

[0015] An AI-based centralized air conditioning high-efficiency computer room control system, including: The temperature monitoring and analysis module obtains the temperature distribution in the computer room, accesses the temperature data of the supply and exhaust vents and equipment, calculates the temperature change rate, identifies the regional temperature gradient, uses the airflow sensing data to calculate the wind speed and direction, screens out temperature anomalies based on the changes in the airflow vectors in adjacent areas, calculates the correlation between equipment load and temperature fluctuations, and obtains the temperature flow status; Based on the temperature flow state, the hot and cold air flow control module calls the hot and cold air flow intersection data to calculate the intersection temperature difference, calls the wind direction and airflow data to analyze the heat return path, calculates the offset between the cold air flow direction and the return path, filters the air outlets whose offset exceeds the set range, adjusts the wind direction and air volume, and obtains the cold air delivery adjustment result; Based on the cooling air delivery adjustment result, the exhaust path optimization module uses the exhaust vent data to calculate the air volume and heat emission rate, uses the equipment operation data to analyze the heat retention area, uses the exhaust mode data to calculate the impact of differentiated air volume and wind direction on heat exhaust efficiency, adjusts the exhaust air volume and direction, and obtains the exhaust adjustment result; Based on the exhaust adjustment result, the cooling demand matching module uses the equipment load data to calculate the temperature change rate in a short period of time, uses the air supply volume data to calculate the cold air coverage, compares the cold air flow path with the heat load distribution, screens the areas with insufficient cold air coverage, calculates the air supply volume adjustment range, uses the refrigeration equipment operation data to calculate the current power output status, adjusts the operating parameters based on the equipment's refrigeration capacity, and obtains the cooling load matching result; Based on the cooling load matching result, the air conditioning overall operation adjustment module calls the supply air, exhaust air and compressor data to analyze the temperature change trend, calls the supply air mode data to adjust the cold air flow direction, calls the exhaust air volume data to calculate the heat retention degree, adjusts the exhaust system operation mode, and obtains the air conditioning operation adjustment result.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by precisely monitoring the local temperature changes and air flow vectors in the computer room, the ability to identify heat accumulation areas is improved, the accurate analysis of the intersection of cold and hot air flows and the dynamic adjustment of the air supply angle and air volume can effectively reduce the cold air backflow and improve the transmission efficiency. The exhaust control is combined with the optimization of the heat diffusion path to improve the heat exhaust efficiency. The real-time calculation of the short-term temperature change rate ensures the accurate matching of the air supply volume and the equipment load, reduces energy consumption, and the comprehensive state analysis realizes the optimization of the air supply and exhaust systems, thereby improving the adaptive adjustment capability and energy efficiency of the air-conditioning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 Schematic diagram of the steps of the present invention; Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0020] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0021] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0022] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0023] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0024] See also Figure 1 , an AI-based centralized air-conditioning efficient computer room control method, comprising the following steps: S1: Obtain the regional temperature distribution within the equipment room, access temperature data for air supply and exhaust vents, and around equipment, identify temperature gradients based on point temperature change rates, obtain air velocity and direction, calculate air flow vectors within differentiated areas, compare air flow direction and speed changes in adjacent areas, identify heat accumulation areas, screen areas where the temperature change rate exceeds the set range, analyze the correlation between temperature fluctuations and equipment operation, and obtain temperature flow status; S2: Based on the temperature flow status, call the data of the intersection of cold and hot air flows, calculate the temperature difference of the intersection, screen areas where the temperature difference exceeds the set range, identify the heat return path, compare the offset between the cold air flow direction and the return path, screen air outlets where the offset exceeds the set wind direction range, adjust the air supply angle, call the air supply outlet air volume data, calculate the air volume adjustment range, and obtain the cold air delivery adjustment result; S3: Based on the cooling air delivery adjustment results, the exhaust vent operating data is retrieved to calculate the exhaust vent air volume and heat emission rate. Areas where the heat retention time exceeds the set threshold are screened. The heat emission efficiency under differentiated exhaust modes is analyzed. The matching degree between the exhaust vent wind direction and the heat diffusion path is compared. The exhaust air volume and direction are adjusted to obtain the exhaust adjustment results. S4: Based on the exhaust adjustment results, the equipment load data is called to calculate the temperature change rate within a short period of time to obtain the current cooling demand. The cooling air flow coverage of different areas in the computer room is compared, and the matching degree between the supply air volume and the heat load is analyzed. Areas with insufficient cooling air coverage are screened, the supply air volume is adjusted, the operating status of the cooling equipment is controlled, the cooling air flow path is allocated, and the cooling load matching result is obtained. S5: Based on the cooling load matching result, call the supply air, exhaust air and compressor operating status data, analyze the temperature change trend, adjust the supply air mode according to the heat accumulation area, call the exhaust air volume data, and adjust the exhaust system operation mode according to the heat retention situation. Optimize the overall operation of the air conditioner, adjust the cooling capacity output and air flow distribution, and obtain the air conditioner operation adjustment results.

[0025] The temperature flow state includes temperature gradient distribution, air flow velocity, air flow direction, heat accumulation area and abnormal temperature change rate area; the cold air delivery adjustment results include the intersection temperature difference, heat return path, air supply angle adjustment range and air supply volume adjustment range; the exhaust adjustment results include exhaust outlet air volume, heat emission rate, heat retention area, exhaust wind direction adjustment and exhaust volume adjustment; the cooling load matching results include short-term temperature change rate, current cooling demand, cold air flow coverage, air supply volume adjustment range and cold air flow path optimization; the air conditioning operation adjustment results include temperature change trend, air supply mode adjustment, exhaust system operation adjustment, cooling capacity output adjustment and air flow distribution optimization.

[0026] The specific steps of S1 are: S101: Obtain temperature data of the air supply outlet, air exhaust outlet, and surrounding area of the equipment in the equipment room, calculate the temperature gradient and temperature change rate, and obtain a temperature distribution map; By deploying an array of temperature sensors at the air supply and exhaust vents, as well as around the equipment, real-time temperature data is collected at each location. Temperature data at the air supply vent is collected by sensors installed near the air supply vent, while temperature data at the exhaust vent is collected by sensors evenly distributed around the equipment, above and below it. All sensors are sampled once per second to ensure data synchronization. Collected data is transmitted to a data processing module, which preprocesses all temperature data, including data cleaning, missing value imputation, and outlier removal. Missing values are imputed using linear interpolation between adjacent time points, while outliers are identified and removed using the triple standard deviation method. After data cleaning, a three-dimensional coordinate system is established based on the spatial coordinates of the sensors, and the collected temperature data is mapped to the spatial model. The temperature gradient at each measurement point is then calculated by differentially processing the temperature data collected by sensors at different locations. For example, if the temperature difference between the air supply vent and the equipment surface is 8°C at a distance of 4 meters, the temperature gradient is 2°C per meter. In this way, the temperature gradient is calculated for all measuring points within the computer room. The temperature change rate is calculated by comparing the temperature difference between adjacent time points with the time interval. For example, if the temperature rises from 28°C to 30°C in 5 seconds, the temperature change rate is 0.4°C per second. The temperature gradients and temperature change rates of all measuring points are summarized to generate a three-dimensional temperature distribution map within the computer room, visually displaying the temperature distribution and changes within the room.

[0027] S102: Based on the temperature distribution map and combined with the temperature change rate data, identify the air flow speed and direction in the area, obtain the air flow vector through the relationship between the temperature difference and the spatial position, analyze the air flow direction and wind speed changes in adjacent areas, and establish the air flow status; First, identify and screen areas with large temperature differences. Determine the direction and speed of air flow by comparing the temperature differences between adjacent measuring points. For example, between two adjacent measuring points A and B, the temperature at point A is 32°C and the temperature at point B is 28°C, and the spatial distance between the two points is 2 meters, so the temperature difference is 4°C. The side with the higher temperature is the starting point of the air flow, and the side with the lower temperature is the end point of the flow. In this case, the air flows from point A to point B. The air velocity calculation process compares and analyzes the ratio of the temperature difference to the spatial distance, and combines the data from multiple adjacent measuring points for continuity verification to ensure the accuracy and consistency of the calculation. After obtaining the data on air velocity and flow direction, these data are integrated into an air flow vector diagram to display the air flow status in different areas. When drawing the air flow vector diagram, the air flow direction and flow velocity of each measuring point can be represented by arrows, and the flow velocity can be displayed by combining color depth. During the analysis process, it's also necessary to compare changes in air velocity and direction across different areas to determine airflow stability and fluctuations. For example, if the air velocity within a particular area fluctuates by more than 0.5 m / s, it can be determined that there is significant air flow instability in that area. This information can be used to construct a model of the air flow within the computer room and analyze the air exchange capacity of different areas.

[0028] S103: Calling the air flow status, comparing the airflow direction and wind speed changes in adjacent areas, identifying the heat accumulation area where the temperature change rate is higher than the set range, and obtaining the temperature flow status; By comparing airflow direction and wind speed changes in adjacent areas, heat accumulation areas with a temperature change rate exceeding a set range are identified. For heat accumulation areas within a computer room, a temperature change rate threshold of 0.5°C per second is typically defined: an area where the temperature rises by more than 0.5°C within one second is considered to have heat accumulation. This threshold is set based on the normal operating environment parameters of the equipment in the computer room and the upper limit of the equipment's surface heat dissipation capacity. For example, if the surface temperature of a device rises from 30°C to 35°C within 10 seconds, the temperature change rate is 0.5°C per second, meeting the heat accumulation criteria. After identifying the heat accumulation area, the system further analyzes the air flow direction and wind speed changes within the area. By comparing the air flow direction with the relative position of the heat accumulation center, it determines whether air stagnation or reverse flow is occurring. For example, if the air flow rate detected in the heat accumulation area is 0.2 m / s, while the air flow rate in the adjacent area is 0.8 m / s, air stagnation can be detected in that area. Combined with the characteristics of air flow velocity and direction, the air flow state in the heat accumulation area is described in detail, and a temperature flow state diagram in the computer room is generated to show the dynamic characteristics of temperature changes in graphical form.

[0029] The specific steps of S2 are: S201: Acquire the temperature flow state, call the intersection point data of the cold and hot air flows, calculate the difference value based on the temperature difference of the intersection point and the time change, and filter the areas where the temperature difference exceeds the set range to obtain the temperature difference exceeding the limit area; First, the data for the intersection of hot and cold airflows is retrieved. These intersections are determined by the intersection of the airflow paths in the temperature distribution map and the airflow vector diagram. Specifically, the system iterates through all the measurement point data and identifies combinations of adjacent measurement points with significant temperature differences and opposite airflow directions. For example, if the temperatures at measurement points A and B are 32°C and 26°C, respectively, and the relative distance is 1 meter, with air flowing from A to B and from B to A, respectively, the area between them is considered the intersection of hot and cold airflows. After determining the intersection points, the temperature data and time points of each intersection are recorded. The temperature difference at each intersection is then calculated by calculating the temperature difference between adjacent measurement points. For example, the temperature difference between measurement points A and B is 32°C - 26°C = 6°C, and the time difference is recorded. Data from all intersection points is continuously collected, with the difference recorded every 1 second. The rate of change of the difference is then compared with a set threshold. The process for screening areas with excessive temperature differences involves traversing the data of all intersection points and determining whether the temperature difference exceeds a set threshold. Typically, the threshold is set at 5°C. If the temperature difference between measurement points A and B is 6°C, the area is considered to be an excessive temperature difference area. All areas that meet the excessive temperature difference criteria are marked and their locations and corresponding temperature differences are recorded to complete the identification of excessive temperature difference areas.

[0030] S202: Based on the temperature difference exceeding the limit area, the heat return path in the area is identified. The cold air flow direction is compared with the offset of the return path. Air outlets whose offset exceeds the set wind direction range are selected. The air supply angle of the air outlet is adjusted. The direction difference before and after the offset adjustment is compared to obtain the offset correction range. First, an air flow vector diagram is used to compare the air flow direction within the intersection of hot and cold airflows. The direction and velocity of the cold airflow are recorded. For example, in a certain area, the cold airflow direction is downward from the air outlet at a velocity of 0.8 m / s. Next, by analyzing the changes in airflow direction in areas where the temperature difference exceeds the limit, heat return paths are identified—paths where the airflow direction is inconsistent with the expected cold airflow direction. For example, a reverse airflow velocity of 0.3 m / s is detected in the same area. The return flow path is compared with the cold airflow direction, and the offset between the two is calculated. For example, if the cold airflow direction is vertically downward, while the heat return direction is diagonally upward at a 45-degree angle, the offset is 45 degrees. The criterion for an offset exceeding the set wind direction range is defined as a deviation exceeding 30 degrees. When the offset exceeds 30 degrees, it is marked as a supply air direction deviation. Air outlets with an offset exceeding 30 degrees from the return flow path are then identified, and their locations and offset magnitude are recorded. Next, adjust the air outlet angles, changing their orientation to align with the desired direction of the cooling airflow. Recollect data and calculate the adjusted offset. For example, if the offset decreases from 45 degrees to 15 degrees after adjusting the outlet angle, the correction range is 30 degrees. Record the offset correction ranges for all outlets and the differences in orientation before and after adjustment to create a dataset of outlet offset correction ranges.

[0031] S203: Calling the offset correction range and the air volume data of the air outlet to calculate the adjusted air volume, and adjusting the air volume of the air outlet accordingly to obtain the cold air delivery adjustment result; The adjusted air volume calculation formula is as follows: ; in, Indicates the adjusted air volume. Indicates the current air volume of the air outlet. represents the reference wind speed at the i-th measuring point, represents the actual wind speed at the i-th measuring point, represents the weight coefficient of the i-th measuring point, n represents the total number of measuring points, Indicates the current pressure loss in the system. Indicates the total pressure of the current system; Formula used to calculate the adjusted air supply volume , to adapt to changes in actual air flow rate and reduce system pressure loss. This formula incorporates the difference between actual and reference wind speeds, takes into account the importance of different measurement points, and adjusts the air supply volume based on system efficiency.

[0032] Assume that the system has three measuring points, the current air volume of the air outlet The reference wind speed at each measuring point is 500m3 / h. Set to 5m / s, 4m / s and 6m / s; the actual measured wind speed The corresponding weight factors are 4.5m / s, 4.5m / s and 5.5m / s. Set to 1.0, 1.5 and 1.0, indicating that the middle measurement point is given more importance. The pressure loss is 100Pa. It is 20Pa.

[0033] First, calculate the wind speed difference at each measuring point and multiply it by the weight: - For the first measuring point, the difference is m / s, and the weighted value is -For the second measuring point, the difference is m / s, and the weighted value is -For the third measuring point, the difference is m / s, and the weighted value is

[0034] Add up these values and divide by the total number of measurement points , and the average weighted wind speed difference is obtained: ; Next, calculate the square root of the pressure loss ratio: ; Substitute these values into the formula to calculate the adjusted air volume: ; The adjusted air volume is approximately 500.261m3 / h.

[0035] This calculation shows the need to make subtle adjustments to airflow based on the difference between actual and reference air speeds, as well as system efficiency. This adjustment helps ensure the air distribution system more precisely meets the ventilation needs of the space, reducing energy waste and improving overall system efficiency.

[0036] The specific steps of S3 are: S301: Retrieving the cooling air delivery adjustment results and exhaust vent operation data, calculating the exhaust vent air volume and heat emission rate, comparing the changes in the exhaust vent air volume and heat emission rate, and screening areas where the heat retention time exceeds a set threshold to obtain the heat retention area; First, obtain the current air volume and heat emission rate of all exhaust vents. The exhaust vent air volume is monitored in real time by an air volume sensor installed at the exhaust vent. For example, the current air volume of a certain exhaust vent is 700 cubic meters per hour. The heat emission rate is calculated based on the relationship between temperature difference and air volume. By recording the temperature data and air flow rate of the exhaust vent, the heat emission rate data at different time points are obtained. For example, if the temperature of a certain exhaust vent drops from 40°C to 30°C within 10 minutes and the air volume is 700 cubic meters per hour, the heat emission rate for this time period is recorded. The air volume and heat emission rate of different exhaust vents are compared to determine the heat emission efficiency of each exhaust vent and record their respective change trends. Filter out areas where the heat retention time is higher than the set threshold. The threshold is usually set to 5 minutes. That is, if the temperature change in a certain area does not exceed 2°C within 5 minutes, it is determined to be a heat retention area. The specific screening method involves traversing the data of all exhaust vents, comparing the change in heat emission rate at different time points, and recording areas where the temperature change rate is less than 0.4°C per minute. For example, if the temperature at a particular exhaust vent only drops from 32°C to 30°C within 5 minutes, the temperature change rate is 0.4°C per minute, and it is identified as a heat retention area. The final output of the heat retention area information includes the area location, heat retention time, and the corresponding exhaust vent data.

[0037] S302: Based on the heat retention area, compare the heat removal efficiency under the differentiated exhaust modes, calculate the degree of match between the exhaust outlet wind direction and the heat diffusion path, determine the difference in the degree of match and the influencing factors, adjust the exhaust outlet air volume and change the exhaust direction, obtain the range of change of the exhaust outlet wind direction and air volume, and obtain the exhaust direction adjustment range; First, the current exhaust vent operating mode and exhaust efficiency are recorded and classified, such as normal exhaust mode and enhanced exhaust mode, where the air volume of the normal exhaust mode is set to 700 cubic meters / hour and the air volume of the enhanced exhaust mode is set to 1000 cubic meters / hour. Next, the heat emission rates in the two modes are compared. For example, in normal exhaust mode, it takes 15 minutes for the exhaust vent temperature to drop from 40°C to 30°C, while in enhanced exhaust mode, it only takes 10 minutes to drop the temperature to the same range. Comparing the exhaust efficiency of the two modes, the degree of matching between the wind direction and the heat diffusion path is calculated. By monitoring the correspondence between the temperature change rate and wind direction in different areas, for example, if the flow direction of cold air is opposite to the diffusion direction of hot air, it is judged that the matching degree is poor. The threshold value of the matching degree is set to a wind direction difference of less than 20 degrees. When the wind direction difference exceeds 20 degrees, it is judged that the matching degree is poor. Adjust the exhaust vent's air volume and change the exhaust direction. Use the control module to modify the vent's angle and air volume output. For example, change the exhaust vent's wind direction from its original straight-on discharge to a 30-degree angle, while increasing the air volume from 700 cubic meters per hour to 1,000 cubic meters per hour. Re-record the data and determine the change in exhaust efficiency after the adjustment. Record the range of wind direction and air volume changes for all exhaust vents and output a dataset showing the exhaust direction adjustment range.

[0038] S303: Calling the exhaust direction adjustment range, calculating the exhaust effect of the exhaust port under different air volume and wind direction conditions, comparing the exhaust efficiency changes, and obtaining the exhaust adjustment result; First, air volume and direction data are collected for different exhaust vents before and after adjustment. For example, before adjustment, the air volume at a particular exhaust vent was 700 cubic meters per hour with a wind direction of 0 degrees. After adjustment, the air volume was 1000 cubic meters per hour with a wind direction of 30 degrees. The improvement in exhaust efficiency is then determined by comparing the temperature change rates within the corresponding areas before and after the adjustment. For example, before the adjustment, it took 15 minutes for the area temperature to drop from 35°C to 30°C. After the adjustment, the same temperature change was achieved in just 10 minutes. The changes in exhaust efficiency within each area are recorded, and the efficiency of each exhaust vent under different air volume and direction conditions is calculated. The efficiency change results for all exhaust vents are summarized and categorized to generate a dataset of exhaust adjustment results. The final exhaust adjustment results are output as data on the air volume, wind direction, and corresponding temperature change rates for each exhaust vent, forming a complete exhaust adjustment report.

[0039] The specific steps of S4 are: S401: Calculate the temperature change rate of the area within a short period of time by using the exhaust adjustment results and equipment load data. Calculate the cooling demand based on the temperature change rate data and compare the cooling air flow coverage of the area to determine the cooling demand status. The specific formula for calculating the temperature change rate of a region in a short period of time is: ; in, Represents the temperature change rate of the region in a short period of time, represents the final temperature of the i-th measuring point, represents the initial temperature of the i-th measuring point, t represents the measurement time interval, and m represents the total number of measuring points; This formula calculates the temperature change rate of a region within a given time at multiple measuring points within the region within a short period of time. To the final temperature The temperature change rate of the area in a short period of time is calculated based on the changes in the temperature, so as to evaluate the overall cooling demand.

[0040] Assume that there are three temperature measurement points in an area, and the measurement time interval t is 60 minutes. The initial temperature of each measurement point is 22°C, 23°C, and 24°C respectively; final temperature 25°C, 25°C, and 26°C respectively.

[0041] First calculate the temperature change of each measuring point: -The temperature change of the first measuring point is -The temperature change of the second measuring point is -The temperature change of the third measuring point is

[0042] Add up these temperature changes and divide by the total number of measurement points and time , and the temperature change rate of the region in a short period of time is obtained: ; During the 60 minutes of monitoring, the short-term temperature change rate of the area was 0.1167°C / minute. This information is critical for evaluating and adjusting the performance of the cooling system to ensure that cooling demand is met and the temperature within the area is maintained within the ideal range.

[0043] S402: Based on the cooling demand status, the matching degree between the air supply volume and the heat load is analyzed, and areas with insufficient cooling coverage are screened. Based on the air supply volume and temperature change rate in these areas, the difference between the equipment load and the air supply volume is compared, and the required air supply volume and path are calculated to determine the air supply volume adjustment range. First, compare the degree of matching between air supply volume and heat load. By accessing air supply volume and equipment load data for different zones, calculate the difference between air supply volume and load. For example, if the equipment load in a zone is 70%, while the air supply in that zone is 400 cubic meters / hour, calculate the difference between load and air supply volume as 70% - 60% = 10%, where 60% is the load percentage that the current air supply can meet. Filter the data for all zones to identify areas with insufficient cooling coverage and record the air supply volume and temperature change rate data for those areas. Then, compare the difference between equipment load and air supply volume in these areas with insufficient cooling coverage. For example, if the load in a zone is 80%, while the current air supply can only meet 50%, the difference is 30%. Based on this difference, calculate the required air supply volume adjustment. Assuming the current air supply is 400 cubic meters / hour, the additional air supply volume is 400 cubic meters / hour x (30% / 50%) = 240 cubic meters / hour, for a total air supply volume adjustment of 640 cubic meters / hour. Next, the airflow path is optimized, calculating the optimal airflow path based on the relative position of the air outlets and the areas with insufficient cooling coverage. If the airflow path deviates by more than 30 degrees, the outlet orientation is adjusted to reduce the deviation. All airflow and path adjustment values and corresponding area data are recorded, and the airflow adjustment range is output.

[0044] S403: Invoking the air supply volume adjustment range, adjusting the operating status of the air supply equipment, allocating the cooling air flow path, and obtaining the cooling load matching result based on the adjusted air supply volume and path data; After obtaining the air supply volume adjustment range, the adjustment result is called to control the operating status of the air supply equipment. According to the adjusted air supply volume and path data, the parameters of the air supply equipment are modified. For example, the air supply volume of a certain area is adjusted from 400 cubic meters / hour to 640 cubic meters / hour, and the air supply direction is changed to reduce path deviation. The monitoring system collects the adjusted temperature change rate and equipment load data in real time, and compares the changes before and after the adjustment. By recording the difference in temperature change rate before and after the adjustment, for example, the regional temperature decreases by 0.2°C per minute before the adjustment and decreases by 0.4°C per minute after the adjustment, the effectiveness of the adjustment and the extent of improvement are determined. The adjusted temperature change rate and air supply volume parameters of all areas are recorded, and a data set of cooling load matching results is generated.

[0045] The specific steps of S5 are: S501: Analyze the cooling load matching results and the operating status data of the air supply, exhaust, and compressor to analyze the temperature change trend of the area. Adjust the air supply mode based on the data of the heat accumulation area. Compare the temperature change trends under the differentiated air supply modes to obtain the air supply mode adjustment results. First, analyze the temperature trends in different areas. Temperature sensor data is continuously collected in each area, recording the temperature value every 1 second and comparing data from adjacent time points. For example, in a certain area, the temperature gradually rises from 30°C to 33°C, increasing by 0.3°C per minute. Simultaneously, device load data is used to identify the relationship between temperature trends and device load. For example, at 80% device load, the temperature change rate is 0.3°C per minute. Then, based on the data from identified heat accumulation areas, identify areas where the temperature change rate exceeds a threshold. The threshold is set at 0.5°C per minute; when the temperature change rate exceeds this threshold, it is identified as a heat accumulation area. Next, adjust the air supply mode by comparing temperature trends under different air supply modes. For example, in normal air supply mode with an air volume of 500 cubic meters per hour, the temperature change rate is 0.4°C per minute. Switching to enhanced air supply mode increases the air volume to 700 cubic meters per hour, and the temperature change rate decreases to 0.2°C per minute. The temperature change rates under the two air supply modes are compared and recorded to obtain the air supply mode adjustment result.

[0046] S502: Based on the air supply mode adjustment result, the exhaust outlet air volume data is retrieved to calculate the heat retention index of the area. The operation mode of the exhaust system is adjusted according to the degree of heat retention. The matching between the exhaust outlet air volume and the heat diffusion path is analyzed. The exhaust volume adjustment range and wind direction change are calculated to obtain the exhaust system adjustment range. The system compares the temperature change rate in each area with the exhaust air volume data. For example, if the exhaust air volume in a certain area is 800 cubic meters per hour and the current temperature change rate is 0.6°C per minute, the system compares the matching of the heat diffusion paths between standard and enhanced exhaust modes based on the efficiency differences between the different exhaust modes. For example, in standard exhaust mode, the difference between the heat diffusion path and the exhaust air volume is 20 degrees, while in enhanced exhaust mode, the difference is reduced to 10 degrees. A valid match is considered if the wind direction difference is less than 15 degrees; otherwise, it is considered invalid. Based on the matching results, the system adjusts the exhaust system's operating mode to increase the exhaust volume or change the exhaust direction. Assuming the current exhaust volume is 800 cubic meters per hour and the wind direction difference in the heat diffusion path is 20 degrees, the adjusted air volume is 800 cubic meters per hour x 1.2 = 960 cubic meters per hour. The exhaust direction is also adjusted by 10 degrees to reduce the wind direction difference. Record all adjusted exhaust vent air volume and direction data, and output the exhaust system adjustment range.

[0047] S503: Calling the exhaust system adjustment range and cooling capacity output data, adjusting the operating status of the air supply and exhaust systems, adjusting the cold air flow distribution and cooling capacity output, and obtaining the air conditioning operation adjustment results; Based on the adjusted exhaust volume and wind direction data, the air supply equipment's air volume and cooling air flow paths are optimized. First, the relationship between the temperature change rate and air supply volume in different areas is compared. For example, if the current air supply volume is 700 cubic meters per hour and the temperature change rate is 0.3°C per minute, the adjusted air supply volume is 900 cubic meters per hour, and the temperature change rate is reduced to 0.2°C per minute. Next, the exhaust system's exhaust performance under different air volume and wind direction conditions is compared. For example, if the current exhaust volume is 800 cubic meters per hour and the adjusted volume is 960 cubic meters per hour, changes in the temperature change rate and heat diffusion path are recorded in real time through the monitoring system. The parameter adjustment results of all air supply and exhaust equipment are summarized, and a report is generated to match the cooling air flow distribution with the cooling capacity output. The final matching of the supply and exhaust volumes and the temperature change rate is recorded, and the air conditioning operation adjustment results are output.

[0048] See also Figure 2 , an AI-based centralized air-conditioning high-efficiency computer room control system, including: The temperature monitoring and analysis module obtains the temperature distribution in the computer room, accesses the temperature data of the supply and exhaust vents and equipment, calculates the temperature change rate, identifies the regional temperature gradient, uses the airflow sensing data to calculate the wind speed and direction, screens out temperature anomalies based on the changes in the airflow vectors in adjacent areas, calculates the correlation between equipment load and temperature fluctuations, and obtains the temperature flow status; The hot and cold air flow control module uses the data of the intersection of hot and cold air flows to calculate the intersection temperature difference based on the temperature flow state. It also uses the wind direction and airflow data to analyze the heat return path, calculates the offset between the cold air flow direction and the return path, filters out air outlets with offsets outside the set range, adjusts the wind direction and air volume, and obtains the cold air delivery adjustment results. The exhaust path optimization module uses exhaust vent data to calculate air volume and heat emission rate based on the cold air delivery adjustment results. It also uses equipment operation data to analyze heat retention areas. It also uses exhaust mode data to calculate the impact of differentiated air volume and direction on heat removal efficiency. It then adjusts the exhaust volume and direction to obtain the exhaust adjustment results. Based on the exhaust adjustment results, the cooling demand matching module uses equipment load data to calculate the temperature change rate within a short period of time, uses air outlet volume data to calculate the cooling coverage, compares the cooling air flow path with the heat load distribution, identifies areas with insufficient cooling coverage, calculates the air supply volume adjustment range, uses cooling equipment operating data to calculate the current power output status, adjusts operating parameters based on the equipment's cooling capacity, and obtains the cooling load matching results. Based on the cooling load matching results, the air conditioning overall operation adjustment module calls the supply air, exhaust air and compressor data to analyze the temperature change trend, calls the supply air mode data to adjust the cold air flow direction, calls the exhaust air volume data to calculate the heat retention degree, adjusts the exhaust system operation mode, and obtains the air conditioning operation adjustment results.

[0049] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An AI-based high-efficiency central air-conditioning room control method, characterized in that: The following steps are involved: S1: Obtain the temperature distribution in the equipment room, retrieve the temperature data of the air supply and exhaust vents and equipment, identify the temperature gradient, calculate the airflow direction and wind speed, screen out abnormal temperature areas, analyze the relationship between temperature fluctuations and equipment operation, and obtain the temperature flow status; S2: Based on the temperature flow state, call the cold and hot air flow intersection data, calculate the temperature difference, identify the heat return path, compare the cold air flow offset, screen abnormal air outlets, adjust the air direction and air volume, and obtain the cold air delivery adjustment result; S3: Based on the cooling air delivery adjustment result, call the exhaust vent data, calculate the air volume and heat emission rate, screen the heat retention area, analyze the impact of the exhaust mode on heat removal efficiency, adjust the air volume and direction, optimize heat removal, and obtain the exhaust adjustment result; S4: Based on the exhaust adjustment result, call the equipment load data, calculate the temperature change rate, compare the cold air coverage, screen the insufficient coverage areas, adjust the air supply volume and the operation of the refrigeration equipment, optimize the cold air flow, and obtain the cooling load matching result; S5: Based on the cooling load matching result, call the supply air, exhaust air and compressor data, analyze the temperature trend, adjust the air supply mode, adjust the cooling capacity and air flow distribution, and obtain the air conditioning operation adjustment result.

2. The AI-based high-efficiency central air-conditioning room control method according to claim 1 is characterized in that: The temperature flow state includes temperature gradient distribution, air flow velocity, air flow direction, heat accumulation area and abnormal temperature change rate area; the cold air delivery adjustment result includes intersection temperature difference, heat return path, air supply angle adjustment range and air supply volume adjustment range; the exhaust adjustment result includes exhaust outlet air volume, heat emission rate, heat retention area, exhaust wind direction adjustment and exhaust volume adjustment; the cooling load matching result includes short-term temperature change rate, current cooling demand, cold air flow coverage, air supply volume adjustment range and cold air flow path optimization; the air conditioning operation adjustment result includes temperature change trend, air supply mode adjustment, exhaust system operation adjustment, cooling capacity output adjustment and air flow distribution optimization.

3. The AI-based high-efficiency central air-conditioning room control method according to claim 1 is characterized in that: The specific steps of S1 are: S101: Obtain temperature data of the air supply outlet, air exhaust outlet, and surrounding area of the equipment in the equipment room, calculate the temperature gradient and temperature change rate, and obtain a temperature distribution map; S102: Based on the temperature distribution map and the temperature change rate data, identify the air flow speed and direction in the area, obtain the air flow vector through the relationship between the temperature difference and the spatial position, analyze the air flow direction and wind speed changes in adjacent areas, and establish the air flow state; S103: Calling the air flow state, comparing the airflow direction and wind speed changes in adjacent areas, identifying the heat accumulation area where the temperature change rate is higher than the set range, and obtaining the temperature flow state.

4. The AI-based high-efficiency central air-conditioning room control method according to claim 1 is characterized in that: The specific steps of S2 are: S201: Acquire the temperature flow state, call the cold and hot air flow intersection data, calculate the difference value according to the temperature difference of the intersection and the time change, and filter the area where the temperature difference exceeds the set range to obtain the temperature difference exceeding the limit area; S202: Based on the temperature difference exceeding the limit area, identifying the heat return path in the area, comparing the cold air flow direction with the offset of the return path, screening the air outlets whose offset exceeds the set wind direction range, adjusting the air supply angle of the air outlets, and comparing the direction difference before and after the offset adjustment to obtain the offset correction range; S203: calling the offset correction range and the air volume data of the air outlet, calculating the air volume adjustment range, and adjusting the air volume of the air outlet accordingly to obtain a cold air delivery adjustment result.

5. The AI-based high-efficiency central air-conditioning room control method according to claim 1 is characterized in that: The adjusted air volume calculation formula is specifically: ； in, Indicates the adjusted air volume. Indicates the current air volume of the air outlet. represents the reference wind speed at the i-th measuring point, represents the actual wind speed at the i-th measuring point, represents the weight coefficient of the i-th measuring point, n represents the total number of measuring points, Indicates the current pressure loss in the system. Indicates the current total pressure of the system.

6. The AI-based high-efficiency central air-conditioning room control method according to claim 1 is characterized in that: The specific steps of S3 are: S301: Calling the cooling air delivery adjustment result and the exhaust vent operation data, calculating the exhaust vent air volume and heat emission rate, comparing the changes in the exhaust vent air volume and heat emission rate, screening areas where the heat retention time is higher than a set threshold, and obtaining heat retention areas; S302: Based on the heat retention area, compare the heat exhaust efficiency under the differentiated exhaust modes, calculate the degree of matching between the exhaust outlet wind direction and the heat diffusion path, determine the difference in the matching degree and the influencing factors, adjust the air volume of the exhaust outlet and change the exhaust direction, obtain the range of change of the exhaust outlet wind direction and air volume, and obtain the exhaust direction adjustment range; S303: calling the exhaust direction adjustment range, calculating the exhaust effect of the exhaust port under the conditions of differentiated air volume and wind direction, comparing the exhaust efficiency changes, and obtaining the exhaust adjustment result.

7. The AI-based high-efficiency central air-conditioning room control method according to claim 1 is characterized in that: The specific steps of S4 are: S401: Calculate the temperature change rate of the area in a short period of time by calling the exhaust adjustment result and the equipment load data, obtain the cooling demand based on the temperature change rate data, and compare the cold air flow coverage of the area to obtain the cooling demand status; S402: Based on the cooling demand state, the degree of matching between the air supply volume and the heat load is analyzed, and areas with insufficient cooling coverage are screened. Based on the air supply volume and the temperature change rate in the insufficiently cooled areas, the difference between the equipment load and the air supply volume is compared, and the air supply volume and path to be adjusted are calculated to obtain an air supply volume adjustment range. S403: Calling the air supply volume adjustment range, adjusting the operating state of the air supply device, allocating the cold air flow path, and obtaining a cooling load matching result based on the adjusted air supply volume and path data.

8. The AI-based high-efficiency central air-conditioning room control method according to claim 1 is characterized in that: The specific calculation formula for the temperature change rate of the region within a short period of time is: ； in, Represents the temperature change rate of the region in a short period of time, represents the final temperature of the i-th measuring point, represents the initial temperature of the i-th measuring point, t represents the measurement time interval, and m represents the total number of measuring points.

9. The AI-based high-efficiency central air-conditioning room control method according to claim 1 is characterized in that: The specific steps of S5 are: S501: Calling the cooling load matching result and the operating status data of the air supply, exhaust and compressor, analyzing the temperature change trend of the area, adjusting the air supply mode according to the data of the heat accumulation area, and comparing the temperature change trends under the differentiated air supply modes to obtain the air supply mode adjustment result; S502: Based on the air supply mode adjustment result, the exhaust outlet air volume data is retrieved to calculate the heat retention index of the area, the operation mode of the exhaust system is adjusted according to the degree of heat retention, the matching between the exhaust outlet air volume and the heat diffusion path is analyzed, the exhaust volume adjustment range and wind direction change are calculated, and the exhaust system adjustment range is obtained; S503: Calling the exhaust system adjustment range and cooling capacity output data, adjusting the operating status of the air supply and exhaust systems, adjusting the cold air flow distribution and cooling capacity output, and obtaining the air conditioning operation adjustment result.

10. An AI-based centralized air-conditioning high-efficiency computer room control system, characterized in that: According to the method according to any one of claims 1 to 9, the system comprises: The temperature monitoring and analysis module obtains the temperature distribution in the computer room, accesses the temperature data of the supply and exhaust vents and equipment, calculates the temperature change rate, identifies the regional temperature gradient, uses the airflow sensing data to calculate the wind speed and direction, screens out temperature anomalies based on the changes in the airflow vectors in adjacent areas, calculates the correlation between equipment load and temperature fluctuations, and obtains the temperature flow status; Based on the temperature flow state, the hot and cold air flow control module calls the hot and cold air flow intersection data to calculate the intersection temperature difference, calls the wind direction and airflow data to analyze the heat return path, calculates the offset between the cold air flow direction and the return path, filters the air outlets whose offset exceeds the set range, adjusts the wind direction and air volume, and obtains the cold air delivery adjustment result; Based on the cooling air delivery adjustment result, the exhaust path optimization module uses the exhaust vent data to calculate the air volume and heat emission rate, uses the equipment operation data to analyze the heat retention area, uses the exhaust mode data to calculate the impact of differentiated air volume and wind direction on heat exhaust efficiency, adjusts the exhaust air volume and direction, and obtains the exhaust adjustment result; Based on the exhaust adjustment result, the cooling demand matching module uses the equipment load data to calculate the temperature change rate in a short period of time, uses the air supply volume data to calculate the cold air coverage, compares the cold air flow path with the heat load distribution, screens the areas with insufficient cold air coverage, calculates the air supply volume adjustment range, uses the refrigeration equipment operation data to calculate the current power output status, adjusts the operating parameters based on the equipment's refrigeration capacity, and obtains the cooling load matching result; Based on the cooling load matching result, the air conditioning overall operation adjustment module calls the supply air, exhaust air and compressor data to analyze the temperature change trend, calls the supply air mode data to adjust the cold air flow direction, calls the exhaust air volume data to calculate the heat retention degree, adjusts the exhaust system operation mode, and obtains the air conditioning operation adjustment result.

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