Air conditioner energy consumption management and control method, device and equipment for machine room base station and storage medium

Abstract

The invention relates to the technical field of energy consumption management and control, and discloses an air conditioner energy consumption management and control method, device and equipment for a machine room base station and a storage medium. The method comprises the steps that air conditioner operation parameters of a machine room base station are set, and heat balance parameters are calculated; calculating a first air conditioner control parameter according to the heat balance parameter; and executing the first air conditioner control parameter, and adjusting the air conditioner refrigerating capacity of the machine room base station in real time to obtain a second air conditioner control parameter, so that accurate adjustment of the temperature of the machine room is realized, and intelligent management of the air conditioner system of the machine room base station is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy consumption management and control, and in particular to an air conditioner energy consumption management and control method, device, equipment and storage medium for a machine room base station. BACKGROUND

[0002] Among various types of equipment in a machine room base station, the energy consumption of an air conditioning system accounts for the largest proportion, about 40-50% of the total energy consumption. At present, the existing air conditioning system of a machine room base station cannot effectively utilize the time-of-use electricity price policy for energy saving and cost reduction, cannot flexibly adjust the operation strategy according to the electricity price peak and valley period, and in the case of large temperature fluctuation, the air conditioning system often adopts an excessive refrigeration or heating mode to maintain the temperature of the machine room, which not only causes energy waste, but also may affect the normal operation of the equipment. SUMMARY

[0003] The present application provides an air conditioner energy consumption management and control method, device, equipment and storage medium for a machine room base station, which realizes accurate adjustment of the temperature of the machine room and intelligent management of the air conditioning system of the machine room base station.

[0004] In a first aspect, the present application provides an air conditioner energy consumption management and control method for a machine room base station, which comprises: setting air conditioner operation parameters of the machine room base station and calculating heat balance parameters; calculating first air conditioner control parameters according to the heat balance parameters; executing the first air conditioner control parameters and adjusting the refrigeration capacity of the air conditioner of the machine room base station in real time to obtain second air conditioner control parameters.

[0005] In combination with the first aspect, in a first implementation manner of the first aspect of the present application, the setting of the air conditioner operation parameters of the machine room base station and the calculation of the heat balance parameters comprise: defining time-of-use electricity price parameters of the machine room base station; defining PID self-control label parameters of a PID controller of the air conditioner in the machine room base station; setting temperature threshold parameters of the air conditioner in the machine room base station; taking the time-of-use electricity price parameters, the PID self-control label parameters and the temperature threshold parameters as air conditioner operation parameters; calculating heat balance parameters of the machine room base station based on the air conditioner operation parameters.

[0006] In combination with the first aspect, in a second implementation manner of the first aspect of the present application, the calculation of the heat balance parameters of the machine room base station based on the air conditioner operation parameters comprises: collecting air conditioner operation data of the machine room base station according to the PID self-control label parameters, and classifying the air conditioner operation data to obtain a classified data set; calculate heat difference data between heat generation of equipment in the machine room base station and refrigeration capacity of the air conditioner in the machine room base station based on the classification data set and the time-of-use electricity price parameter; perform time series analysis on the heat difference data to obtain heat change data, and calculate a heat balance parameter according to the heat change data and the temperature threshold parameter.

[0007] In a third implementation manner of the first aspect, the method further includes: construct an AIoT edge computing model based on the heat balance parameter and energy-saving PID self-control tag data, and perform time period calculation on the AIoT edge computing model to obtain time period calculation data; perform duty cycle threshold calculation on heat generation of equipment and refrigeration capacity of the air conditioner based on the time period calculation data, and generate the first air conditioner control parameter.

[0008] In a fourth implementation manner of the first aspect, the method further includes: execute the first air conditioner control parameter, and monitor environmental temperature change data; calculate a duty cycle adjustment parameter according to the environmental temperature change data and a temperature threshold parameter, and perform air conditioner air supply mode control based on the duty cycle adjustment parameter to obtain air supply control data; perform time period matching operation on the air supply control data and the time-of-use electricity price parameter to generate temperature setting data; perform boundary check calculation on the temperature setting data to obtain temperature correction data, and perform refrigeration capacity optimization according to the temperature correction data to obtain the second air conditioner control parameter.

[0009] In a fifth implementation manner of the first aspect, the method further includes: calculate real-time energy consumption data of the air conditioner in the machine room base station according to the second air conditioner control parameter, and generate energy consumption evaluation data based on the real-time energy consumption data; set alarm conditions of voltage overrun, current overrun and temperature overrun based on the energy consumption evaluation data, and perform comparison calculation on the alarm conditions and an energy-saving rate threshold to generate an energy consumption early warning strategy; perform air conditioner running state early warning on the machine room base station according to the energy consumption early warning strategy to generate a target response instruction.

[0010] In conjunction with the first aspect, in the sixth implementation of the first aspect of the present invention, the step of issuing an early warning of the air conditioning operation status of the computer room base station according to the energy consumption early warning strategy and generating a target response command includes: Based on the energy consumption early warning strategy, abnormal events are classified into categories in the operation cycle data of the equipment room base station to obtain abnormal classification data. Based on the anomaly classification data, control strategy adjustments and backup equipment switching analyses are performed to obtain early warning response data, which is then converted into target response commands.

[0011] Secondly, the present invention provides an air conditioning energy consumption control device for a computer room base station, the air conditioning energy consumption control device for the computer room base station comprising: The configuration module is used to set the air conditioning operating parameters of the base station in the computer room and calculate the heat balance parameters. The calculation module is used to calculate the first air conditioning control parameters based on the heat balance parameters; The real-time adjustment module is used to execute the first air conditioning control parameters and adjust the air conditioning cooling capacity of the computer room base station in real time to obtain the second air conditioning control parameters.

[0012] A third aspect of the present invention provides an air conditioning energy consumption management device for a data center base station, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the air conditioning energy consumption management device for the data center base station to execute the above-described air conditioning energy consumption management method for the data center base station.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described method for controlling the air conditioning energy consumption of a computer room base station.

[0014] The technical solution provided by this invention achieves intelligent control of the air conditioning system by constructing an AIoT edge computing model and a PID self-control tag system, thereby reducing the operating costs of the data center base station. By adopting time-of-use electricity pricing parameters and a heat balance strategy, the operating hours of the air conditioning are rationally allocated, making full use of off-peak electricity pricing periods for pre-cooling and avoiding peak-hour electricity consumption, effectively reducing electricity costs. Based on a dynamic temperature adjustment model and duty cycle parameter control, precise temperature regulation of the data center is achieved, avoiding over-cooling and over-heating and improving energy efficiency. A multi-level early warning analysis mechanism and a backup equipment switching scheme are set up to improve the reliability and security of the data center base station operation, effectively preventing equipment failures and abnormal situations. This invention supports an independent operation mode, maintaining basic control functions even in the event of communication interruptions, ensuring the continuous and stable operation of the data center base station. Through automated report generation and early warning processing mechanisms, manual intervention is reduced, operation and maintenance efficiency is improved, and intelligent management of the data center base station air conditioning system is achieved. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the air conditioning energy consumption control method for a computer room base station provided in this application embodiment; Figure 2 A schematic block diagram of the structure of the air conditioning energy consumption control device for a computer room base station provided in this application embodiment; Figure 3 A schematic block diagram of the structure of the air conditioning energy consumption control equipment for the computer room base station provided in the embodiments of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the described order. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change based on the actual situation.

[0019] It should also be understood that the terminology used in this specification is for the purpose of describing preferred embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0020] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0021] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features described herein can be combined with each other.

[0022] Please see Figure 1 , Figure 1 This is a flowchart illustrating the air conditioning energy consumption control method for a computer room base station provided in this application embodiment, as shown below. Figure 1 As shown, the air conditioning energy consumption control method for computer room base stations provided in this application includes steps S100 to S300.

[0023] Step S100: Set the air conditioning operating parameters of the computer room base station and calculate the heat balance parameters; Specifically, 5:00-7:00 and 17:00-21:00 are designated as peak hours, during which the power grid load is high and electricity prices are high, resulting in strict control over air conditioning use to reduce operating costs. 7:00-10:00, 15:00-17:00, and 21:00-5:00 the next day are designated as normal hours, where electricity prices are relatively stable, and air conditioning is dynamically adjusted based on the temperature requirements of the data center. 10:00-15:00 is designated as off-peak hours, where the power grid load is low and electricity prices are lowest, allowing air conditioning to increase cooling capacity in advance to support reduced cooling demand during peak hours. Next, the PID control tag parameters for the air conditioning PID controller in the data center base station are defined. Commercial electricity price off-peak / peak hour tags, ambient temperature tags, air conditioning operating status tags, air conditioning cooling capacity tags, air conditioning temperature setpoint tags, air conditioning heating tags, and air conditioning cooling tags are input into the controller. Commercial electricity price peak / off-peak period tags enable the PID control algorithm to automatically adjust the air conditioning operation strategy based on electricity price fluctuations. Ambient temperature tags monitor the internal temperature of the data center in real time to ensure that the air conditioning operation matches environmental demands. Air conditioning operating status tags record the operating mode, including cooling, heating, ventilation, or standby. An upper limit for air conditioning operating temperature is set, defining the highest permissible temperature inside the data center, ensuring the controller automatically increases cooling capacity when the temperature exceeds this threshold. A lower limit for air conditioning operating temperature is set, defining the lowest permissible temperature inside the data center, ensuring the air conditioning automatically reduces cooling or switches to ventilation mode when the temperature falls below this value. Temperature trigger values ​​and temperature response values ​​are input to set the temperature conditions for starting or stopping cooling and the response speed to temperature changes, respectively. Based on the PID control tag parameters, ambient temperature, return air temperature, outlet air temperature, three-phase input voltage, three-phase operating current, and power data of the data center base station are collected to form air conditioning operation data, which is then classified to obtain a categorized dataset. The TeleDB data storage engine is used to perform standard encoding processing on the categorized dataset, and the data is fused with time-of-use electricity price parameters to evaluate the heat generation of data center equipment and the cooling capacity of the air conditioning, calculating the heat difference data. Time-series analysis of heat difference data during peak, normal, and off-peak periods is performed. Heat balance correction calculations are then performed using temperature threshold parameters to obtain the final heat balance parameters.

[0024] Step S200: Calculate the first air conditioning control parameters based on the heat balance parameters; Specifically, heat balance parameters and energy-saving PID self-control tag data are input into the edge computing unit to construct an AIoT edge computing model. This model can dynamically adapt to changes in the data center load, dividing the calculation cycle according to the time-of-use electricity pricing rules for peak, normal, and off-peak periods. This allows the AIoT computing unit to adopt optimal energy consumption control strategies in different time periods, ensuring that the air conditioning's on / off times can adapt to electricity price fluctuations and changes in the data center's internal heat load, thus obtaining time-period calculation data. Based on the time-period calculation data, dynamic matching calculations are performed on the heat generation of data center equipment and the cooling capacity of the air conditioning. The actual heat generation of data center equipment in each time period includes the power consumption of IT equipment, the impact of the external environment on the data center temperature, and the heat dissipation of other equipment in the data center. Simultaneously, the cooling capacity of the air conditioning is calculated for each time period to ensure that the cooling capacity matches the heat generation, avoiding over-cooling or under-cooling. The heat balance equation is used to calculate the dynamic changes in data center temperature. If the data center heat load exceeds the air conditioning cooling capacity, the operating time is increased; if the cooling capacity is excessive, the operating time is reduced, resulting in air conditioning operation strategy data. Duty cycle thresholds are then calculated based on the air conditioning operation strategy data. Duty cycle refers to the proportion of time the air conditioner runs within a calculation cycle, and it is adjusted according to the real-time load of the computer room. When the duty cycle exceeds 95%, it means that the air conditioner needs to run almost continuously to maintain a stable temperature in the computer room, and this period is set to full-cycle on. When the duty cycle is below 5%, it means that the heat load inside the computer room is low, and the air conditioner hardly needs to run, so this period is set to full-cycle off to avoid unnecessary energy waste. The air conditioner operation control command is input into the PID closed-loop control unit to optimize the air conditioner operation parameters. Based on real-time temperature data, computer room load data, and electricity cost data, the optimal operating time of the air conditioner is calculated and adjusted according to the duty cycle threshold. If the current temperature is higher than the set upper temperature limit, the PID control unit increases the air conditioner operating time; if the current temperature is lower than the lower temperature limit, the operating time is reduced or the air supply mode is switched. At the same time, dynamic adjustments are made based on changes in electricity costs, reducing the operating time during peak hours and appropriately increasing the operating time during off-peak hours to achieve overall energy-saving optimization, and finally generating the first air conditioner control parameters.

[0025] Step S300: Execute the first air conditioning control parameters and adjust the air conditioning cooling capacity of the computer room base station in real time to obtain the second air conditioning control parameters.

[0026] Specifically, the first air conditioning control parameter is executed, and ambient temperature change data is monitored. The dynamic temperature adjustment model calculates the changes in the computer room ambient temperature. By continuously sampling the computer room ambient temperature, return air temperature, and supply air temperature, combined with the air conditioning operating status, the heat generation of computer room equipment, and external environmental factors, it calculates the temperature change trend and generates a continuous temperature change curve, reflecting the impact of air conditioning cooling capacity on the internal temperature of the computer room and identifying the main causes of temperature fluctuations. Duty cycle adjustment parameters are calculated based on ambient temperature change data and temperature threshold parameters. Trend judgment is performed on the ambient temperature change data to analyze the direction and rate of temperature change. If the temperature continues to rise and approaches the upper limit, it indicates that the current air conditioning cooling capacity is insufficient, and the duty cycle needs to be increased to extend the air conditioning operating time. If the temperature drops and approaches the lower limit, it indicates that the cooling capacity is too large, and the duty cycle should be reduced to decrease the air conditioning operating time to avoid over-cooling. The trend judgment algorithm calculates based on the rate of change of historical temperature data, using a moving average or first-order derivative method to evaluate the rate of temperature increase or decrease, and generates duty cycle adjustment parameters based on the temperature threshold parameter setting range. Air conditioning air supply mode control is performed based on the duty cycle adjustment parameters. The air supply mode selection is optimized based on duty cycle adjustment parameters and the current heat load of the computer room. A high-speed mode is used when the computer room temperature is high and significant cooling is required; a medium-speed mode is used when the temperature is close to the set value but still requires some cooling; and a low-speed mode is used when the temperature is relatively stable and the air conditioner only needs to maintain the temperature. In some cases, an automatic air supply mode is used, allowing the air conditioner to automatically adjust the fan speed according to temperature changes, thus obtaining air supply control data. The air supply control data is matched with time-of-use electricity price parameters to generate the optimal temperature setting strategy for different electricity price periods, producing temperature setting data. Boundary checks are performed on the temperature setting data, and safety verification is conducted based on the trigger and response values ​​of the temperature threshold parameters to ensure that the temperature setting will not cause excessively high or low temperatures that could affect the normal operation of the computer room equipment. After safety verification, cooling capacity optimization calculations are performed based on the temperature correction data, adjusting the air conditioner's compressor operating mode, fan running time, and refrigerant circulation control strategy to achieve optimal cooling effect, resulting in the second air conditioning control parameters.

[0027] In one specific embodiment, the process of performing step S100 may specifically include the following steps: Define the time-of-use electricity price parameters for the base station in the data center; define the PID self-control tag parameters for the PID controller of the air conditioner in the base station in the data center; set the temperature threshold parameters for the air conditioner in the base station in the data center. The time-of-use electricity price parameter, the PID self-control tag parameter, and the temperature threshold parameter are used as the air conditioning operation parameters; Based on the air conditioning operating parameters, the heat balance parameters of the base station in the computer room are calculated.

[0028] Specifically, the 24 hours of a day are divided into three distinct time periods: peak, normal, and off-peak, allowing for the implementation of corresponding air conditioning energy consumption control strategies during different electricity price periods. Based on time-of-use pricing rules, 5:00-7:00 and 17:00-21:00 are designated as peak periods. During these times, the grid load is high and electricity prices are expensive; therefore, air conditioning power consumption should be minimized or cooling methods optimized to reduce operating costs. 7:00-10:00, 15:00-17:00, and 21:00-5:00 the next day are designated as normal periods. During these times, air conditioning operates moderately according to actual demand, ensuring stable room temperature while controlling energy consumption. For 10:00-15:00, since this period represents the lowest grid load, it is designated as an off-peak period. During this time, air conditioning power consumption is appropriately increased or temperature adjustments are made earlier to complete cooling tasks during periods of low electricity prices, reducing the load during peak periods. After completing the time-of-use electricity pricing parameter allocation, the PID controllers of the air conditioning systems in the data center base station are tagged with over 50 energy-saving PID control labels, including commercial electricity price peak / valley time labels, ambient temperature labels, air conditioning operating status labels, air conditioning cooling capacity labels, air conditioning temperature setpoint labels, air conditioning heating labels, and air conditioning cooling labels. This ensures that the controllers can dynamically adjust based on the AIoT edge computing model across different time periods. The commercial electricity price peak / valley time labels mark whether the current time is peak, normal, or off-peak, enabling the air conditioning controllers to adjust their operating status in real time. During peak hours, they automatically reduce cooling power or increase airflow time to reduce compressor running time, and during off-peak hours, they pre-cool to lower the data center temperature during periods of low electricity prices. The ambient temperature label monitors temperature changes within the data center and serves as an input parameter for PID control. The air conditioning operating status label records the air conditioning's operating modes in real time, including cooling, airflow, heating, and standby. The air conditioning cooling capacity label monitors the current cooling capacity of the air conditioning and dynamically adjusts it based on the data center load to ensure that the cooling capacity matches the data center's heat demand. Simultaneously, temperature threshold parameters for the air conditioners in the data center base station are set, including the upper limit of the air conditioner's operating temperature, the lower limit of its temperature, the temperature trigger value, and the temperature response value, establishing a temperature control boundary to ensure that the air conditioner's operation is always within a safe range. Based on these air conditioner operating parameters, a controller with an open modular structure is adopted, incorporating a built-in PID self-control AIoT edge computing model. It collects real-time monitoring data of the air conditioner's operation from the data center base station according to the PID self-control tag parameters, including ambient temperature, return air temperature, outlet air temperature, three-phase input voltage, three-phase operating current, and power. The air conditioner operating data is then classified into temperature, voltage, current, and power categories to obtain a categorized dataset. The categorized dataset is then processed using the TeleDB data storage engine for standard encoding to ensure a unified data format and improve data query and retrieval efficiency.Based on a categorized dataset and time-of-use electricity pricing parameters, the heat difference data between the heat generated by equipment in a computer room base station and the cooling capacity of the air conditioner is used to assess the heat load status of the computer room through a heat balance equation, determining whether the current cooling capacity matches the heat generated. Time-series analysis of the heat difference data during peak, normal, and off-peak periods is performed to uncover patterns in heat changes in the computer room and optimize air conditioning operation strategies, yielding heat change data. This heat change data is then used to perform heat balance correction calculations with temperature threshold parameters. Combining upper and lower temperature limits, temperature trigger values, and temperature response values, the air conditioning operation strategy is optimized, ultimately calculating the heat balance parameters for the computer room base station. This provides fundamental data support for subsequent AIoT edge computing model construction and intelligent air conditioning control, ensuring that an energy saving rate of over 15% can be achieved while guaranteeing the safe operation of the computer room equipment.

[0029] In one specific embodiment, the process of calculating the heat balance parameters of the computer room base station based on the air conditioning operating parameters may specifically include the following steps: The air conditioning operation data of the base station in the computer room is collected according to the PID self-control tag parameters, and the air conditioning operation data is classified to obtain a classified dataset; Based on the classification dataset and the time-of-use electricity price parameters, calculate the heat difference data between the heat generated by the equipment in the computer room and the cooling capacity of the air conditioner in the computer room base station; A time-series analysis is performed on the heat difference data to obtain heat change data, and heat balance parameters are calculated based on the heat change data and the temperature threshold parameters.

[0030] Specifically, the data acquisition module of the data center base station is activated based on the PID self-control tag parameters. Through the sensor network deployed in the data center air conditioning system, all key operating parameters are acquired to form air conditioning operation data. Ambient temperature data reflects the overall temperature state inside the data center; return air temperature data and outlet air temperature data describe the air temperatures drawn in and exhausted by the air conditioner, directly affecting the evaluation of the air conditioner's cooling efficiency. Simultaneously, the A-phase, B-phase, and C-phase input voltage data of the air conditioner are collected to monitor the power supply stability of the air conditioning system in the three-phase power grid. Three-phase operating current data reflects the current consumption of the air conditioner in different phases, helping to assess the equipment's load balance and operating status. Power data is calculated through a combination of voltage and current to measure the actual energy consumption level of the air conditioner. The air conditioning operation data is categorized to construct a classification dataset. Temperature data, including ambient temperature, return air temperature, and outlet air temperature, are grouped into one category for analyzing the heat distribution in the data center and the cooling effect of the air conditioner. Voltage data, including three-phase input voltage, is grouped into another category for monitoring the stability and abnormal fluctuations of the data center's power supply system. Current-related data, including three-phase operating current, is grouped together to analyze air conditioner load conditions and avoid overload or phase imbalance. Power-related data is categorized separately to measure the actual energy consumption level of the air conditioner. After data classification, the categorized dataset is standardized using the TeleDB data storage engine with standard code logic adaptation capabilities. This ensures data format consistency and improves data query and retrieval speed. All data is formatted and stored according to a preset database structure, and a timestamp is added to each record for subsequent time-series analysis. Based on the categorized dataset and the time-of-use electricity price parameters, data fusion calculations are performed to evaluate the relationship between the heat generation of equipment in the data center base station and the cooling capacity of the air conditioner. The heat generation of the equipment is mainly determined by factors such as the power consumption of IT equipment, the impact of environmental factors on the data center temperature, and the energy consumption of the air conditioner itself. The total heat generation is calculated by comprehensively analyzing the equipment operating status, load changes, and environmental conditions. The cooling capacity of the air conditioner is determined by analyzing the difference between the air conditioner's outlet and return air temperatures, combined with the airflow parameters and operating mode. By comparing and analyzing the heat output of the equipment with the cooling capacity of the air conditioning, the heat difference between the two is calculated to determine whether the current heat load of the computer room is in a dynamic equilibrium state. When the heat output of the equipment is higher than the cooling capacity of the air conditioning, it indicates that the temperature in the computer room will rise, and the air conditioning operation needs to be adjusted to increase cooling capacity. When the cooling capacity of the air conditioning is higher than the heat output of the equipment, it indicates over-cooling, and the air conditioning operating time should be reduced or the cooling power should be lowered to avoid unnecessary energy waste. Time-series analysis is performed on the heat difference data, with independent analysis of data during peak, normal, and off-peak periods to observe the changing trends and patterns of heat in the computer room across different time periods. During peak periods, due to higher electricity costs, the air conditioning operating time should be minimized. The study calculates how to utilize the advanced cooling strategy from previous periods with lower electricity prices to reduce energy consumption during peak periods.During off-peak hours, due to lower electricity prices, the operating time of air conditioners can be appropriately increased to ensure reduced load during peak hours. This time-series analysis uncovers patterns in heat changes in the data center and provides data support for intelligent control, yielding heat change data reflecting the characteristics of heat changes at different times. Heat change data is then used to perform heat balance correction calculations with temperature threshold parameters, optimizing air conditioner operation strategies by combining upper and lower temperature limits, temperature trigger values, and temperature response values. When heat change data shows a long-term upward trend in data center temperature approaching the upper temperature limit, the operating time of air conditioners is automatically increased or the air supply mode is adjusted to improve heat exchange efficiency. When heat change data shows a long-term excess cooling capacity and the temperature approaches the lower limit, the cooling capacity of air conditioners is reduced or the target temperature setpoint is appropriately increased to reduce unnecessary energy consumption. Simultaneously, time-of-use pricing strategies ensure that the adjusted air conditioner operating time complies with the principle of minimizing electricity costs. By comprehensively considering heat balance requirements and electricity price fluctuations, the final calculated heat balance parameters for the data center base station can guide intelligent control by the AIoT edge computing model.

[0031] Optionally, when calculating the heat difference between the heat generation of the equipment in the base station and the cooling capacity of the air conditioner based on the classification dataset and the time-of-use electricity price parameters, historical air conditioning data processed by the TeleDB data storage engine is input into the temperature sensitivity calculation unit. By analyzing the correlation between the changing trends of the ambient temperature, return air temperature, and outlet air temperature in the equipment room and the cooling capacity of the air conditioner, a temperature response model is established and a temperature response sensitivity matrix reflecting the sensitivity of different air conditioning devices to temperature changes is generated. Based on the temperature response sensitivity matrix, a group control collaborative control model supporting the coordinated operation of multiple air conditioning devices is constructed. The cooling capacity parameters, power consumption characteristics, current operating status data, and PID self-control label parameters of each air conditioning device are input into the collaborative calculation unit. By comprehensively evaluating the cooling efficiency, energy consumption level, and response speed of each device, an air conditioning group control priority sequence reflecting the priority order of device use is generated. The control timing of each air conditioning unit is divided according to the priority sequence of the air conditioning group control. Combined with the real-time heat load distribution of the data center base station, the start-up and shutdown sequence, runtime, and cooling capacity allocation of each air conditioning unit are optimized to ensure that high-efficiency units are started first and low-efficiency units are used as supplementary units, while avoiding mutual interference between units, generating equipment coordination strategy data. This equipment coordination strategy data is then matched with the time-of-use electricity price parameters. Based on the electricity price variation patterns during peak, normal, and off-peak periods, the operating mode of the air conditioning group is dynamically adjusted. During off-peak periods, cooling capacity is appropriately increased for pre-cooling; during peak periods, high-efficiency units are prioritized and overall operating time is reduced; and during normal periods, balancing adjustments are made according to actual load demand, generating group control timing strategy data. The group control timing strategy data undergoes comprehensive constraint verification. The upper and lower limits of the data center ambient temperature, the maximum power limit of each air conditioning unit, operational stability requirements, and temperature threshold parameters are input into the constraint check unit to verify whether the formulated control strategy meets the equipment safety operation conditions and data center environmental requirements, ensuring that reliability is not affected by over-optimization, and obtaining strategy verification results. Based on the strategy verification results, the operating parameters of the air conditioning group are monotonically converged and optimized. Following the order of temperature response sensitivity from high to low, the cooling capacity setpoints and operating times of each air conditioning unit are adjusted step-by-step. An iterative optimization algorithm ensures optimal energy consumption while meeting temperature control requirements, generating an optimized control sequence. This optimized control sequence is input into the load distribution unit. Based on the heat distribution characteristics of IT equipment in the computer room, the installation location of the air conditioning units, and their cooling coverage, the cooling load of each air conditioner is dynamically allocated to ensure temperature uniformity across the computer room and avoid localized overheating or undercooling, generating load balancing data.The load balancing data and the real-time temperature monitoring data of the base station in the computer room are used to perform closed-loop correction calculation. By continuously monitoring the changes in ambient temperature, the control parameters of each air conditioning device are dynamically updated. When the actual temperature deviates from the target set value, the cooling capacity distribution and operation strategy are adjusted in a timely manner to ensure that the temperature of the computer room is always kept within the set range. Finally, the heat difference data reflecting the dynamic balance between the heat generation of the computer room equipment and the cooling capacity of the air conditioning is obtained.

[0032] In one specific embodiment, the process of performing step S200 may specifically include the following steps: An AIoT edge computing model is constructed based on the heat balance parameters and energy-saving PID self-control tag data, and the AIoT edge computing model is calculated in time periods to obtain time period calculation data. Based on the time period calculation data, the duty cycle threshold of the heat generation of the computer room equipment and the cooling capacity of the air conditioner is calculated to generate the first air conditioning control parameters.

[0033] Specifically, the heat balance parameters of the base station in the data center are input as the main input variables to the AIoT edge computing unit. These heat balance parameters include data on the heat generation of the data center equipment, air conditioning cooling capacity, ambient temperature trends, the difference between return and outlet air temperatures, and key operational data such as three-phase input voltage, three-phase operating current, and power consumption, all calculated through a heat balance strategy model. Simultaneously, energy-saving PID self-control tag data is input to the edge computing unit. This PID self-control tag data includes more than 50 control tag parameters, such as commercial electricity price peak / off-peak period tags, ambient temperature tags, air conditioning operating status tags, air conditioning cooling capacity tags, air conditioning temperature setpoint tags, air conditioning heating tags, and air conditioning cooling tags, ensuring that the AIoT edge computing model can make intelligent decisions based on real-time data. This AIoT edge computing model adopts an open modular structure design with scalability, executing computing tasks locally in the data center without relying on a remote cloud computing platform. This reduces response latency and ensures real-time data processing and independent operation capabilities, maintaining basic control functions even in the event of communication interruptions. In constructing the AIoT edge computing model, input data is categorized, stored, and parsed, and a calculation cycle is set. Based on time-of-use electricity pricing parameters, a 24-hour day is divided into three main calculation periods: peak, normal, and off-peak. This ensures that the computing unit can dynamically adjust according to electricity price fluctuations, employing differentiated energy consumption control strategies to obtain time-period calculation data. During peak hours, due to higher electricity costs, the air conditioning load is reduced; during off-peak hours, due to lower electricity costs, the cooling capacity is appropriately increased for pre-cooling; and during normal hours, a balance adjustment is made based on actual heat load demand. This time-period calculation method allows the AIoT edge computing model to select the optimal control strategy under different electricity price environments. Based on the time-period calculation data, the AIoT edge computing model dynamically matches the heat generation of the data center equipment with the cooling capacity of the air conditioning to ensure that the data center temperature is always maintained within the set temperature threshold parameters. By analyzing factors such as the power consumption of the IT equipment in the data center, equipment heat dissipation efficiency, and the influence of external ambient temperature, the total heat generation of the data center equipment is calculated. Simultaneously, based on parameters such as the air supply volume, the difference between return and outlet air temperatures, and cooling efficiency, the actual cooling capacity of the air conditioning is calculated. By comparing the numerical relationship between heat generation and cooling capacity, the trend of temperature change in the computer room is determined. When heat generation exceeds cooling capacity, it indicates that the computer room temperature is trending upward, requiring increased cooling capacity or extended air conditioning operating time. When cooling capacity exceeds heat generation, it indicates over-cooling, requiring reduced air conditioning operating time or reduced air supply power to avoid energy waste. After completing the heat matching calculation, a duty cycle threshold is calculated for the air conditioning operation strategy to ensure a reasonable allocation of air conditioning operating time. The duty cycle reflects the proportion of air conditioning operating time within a calculation cycle, and is obtained by calculating the ratio of the actual air conditioning operating time to the total calculation cycle duration.To optimize energy efficiency and ensure stable temperature control, when the calculated duty cycle exceeds 95%, the system is set to full-cycle on for that period to ensure continuous operation and maintain stable temperature, avoiding temperature fluctuations caused by frequent start-stop cycles. When the duty cycle is below 5%, the system is set to full-cycle off for that period to avoid unnecessary energy consumption. This threshold setting mechanism ensures stable operation of the air conditioner under high load and reduces unnecessary operation under low load, thereby optimizing overall energy consumption. The calculated duty cycle parameter is input into the PID closed-loop control unit, which uses a proportional-integral-derivative (PID) control algorithm to precisely optimize the air conditioner's operating parameters. Based on the error between the set temperature and the actual ambient temperature, the PID control unit uses a proportional gain coefficient to control the response intensity of the error, an integral gain coefficient to eliminate steady-state errors, and a derivative gain coefficient to predict temperature change trends. It dynamically adjusts the air conditioner's cooling power and fan mode to ensure the accuracy and stability of temperature control. The air conditioning control optimization data is obtained by calculating through PID closed-loop control. This data includes air conditioning operation strategies for different temperature error conditions, ensuring that the air conditioner can automatically adjust its operating status to achieve precise temperature control and energy use optimization. Finally, the first air conditioning control parameters are generated, which include key control information such as air conditioning duty cycle parameters, operating mode settings, and cooling capacity distribution.

[0034] Optionally, before constructing the AIoT edge computing model based on the heat balance parameters, the heat balance parameters are input into the strategy generation unit. Based on the temperature change trends and heat load characteristics of the base station in the data center during peak, normal, and off-peak periods, and combined with control information such as commercial electricity price peak / off-peak period tags, ambient temperature tags, and air conditioning operating status tags in the PID self-control tag parameters, multiple sets of air conditioning control strategy schemes for different operating scenarios are generated, resulting in an air conditioning control strategy set containing multiple operating modes and parameter configurations. A comprehensive energy consumption cost calculation is performed on each strategy in the air conditioning control strategy set. The air conditioning energy consumption data during peak, normal, and off-peak periods are weighted according to time-of-use electricity price parameters. Taking into account the electricity price differences and operating costs at different times, the weighted calculation results are input into the cost evaluation model for economic analysis, obtaining strategy cost data reflecting the economic benefits of each strategy. The strategy cost data is sorted in ascending order of cost from low to high to generate selectable menu options. Each menu option is detailed with its corresponding temperature control range, expected energy-saving effect, electricity cost estimate, and usage of temperature threshold parameters, forming air conditioning control menu data that is easy for automatic selection and user reference. Based on the air conditioning control menu data, a real-time selection model is built that can dynamically select according to real-time operating conditions. The current temperature threshold parameters of the computer room, the real-time load status of the equipment, the time-of-use electricity price parameters, and the trend of ambient temperature change are input into the selection model for comprehensive calculation and analysis. Through a multi-objective optimization algorithm, the optimal balance point between temperature control accuracy, energy consumption level, and operational stability is found to obtain the optimal menu option suitable for the current operating conditions. The optimal menu option is compared and analyzed with strategy selection data during historical operation. By establishing a user preference model that reflects operational preferences and optimization tendencies, statistical analysis is performed on strategy selection tendencies under different operating scenarios to identify the balance preference between temperature control accuracy and energy-saving effect, obtaining strategy preference data that can guide subsequent strategy optimization. The strategy preference data is input into the verification unit to comprehensively evaluate and verify the rationality and feasibility of the selected strategy. This includes checking whether the strategy meets the requirements for safe operation of the data center equipment, whether it satisfies temperature control accuracy requirements, and whether it can achieve the control objectives within the expected timeframe. Strategy verification data, including estimated execution time and performance commitments, is generated. A detailed feasibility analysis is then performed on the strategy verification data. Temperature control accuracy requirements, energy-saving target settings, response time constraints, and equipment operational stability requirements are input into the feasibility assessment model for comprehensive calculations. This verifies the feasibility of the selected strategy in the actual operating environment and the attainability of the expected results, yielding strategy feasibility data.By comparing and analyzing the feasibility data of the strategy with the optimal control effect calculated based on theory, the potential space and improvement direction of the strategy optimization are identified by calculating the performance difference between the actual strategy and the theoretical optimal solution. Targeted optimization suggestions are generated, including PID parameter adjustment suggestions, control cycle optimization suggestions, and temperature setpoint fine-tuning suggestions. The strategy optimization parameters are obtained and input as important preprocessing information into the AIoT edge computing model to provide data support for the model's precise control and continuous optimization.

[0035] In one specific embodiment, the process of performing step S300 may specifically include the following steps: The first air conditioning control parameters are executed, and ambient temperature change data is monitored; The duty cycle adjustment parameters are calculated based on the ambient temperature change data and temperature threshold parameters, and the air conditioning air supply mode is controlled based on the duty cycle adjustment parameters to obtain air supply control data. The air supply control data is matched with the time-of-use electricity price parameters to generate temperature setting data. Boundary check calculations are performed on the temperature set data to obtain temperature correction data, and cooling capacity optimization is performed based on the temperature correction data to obtain the second air conditioning control parameters.

[0036] Specifically, the first air conditioning control parameters are executed. These parameters, including key control information such as the air conditioning duty cycle, operating mode settings, and cooling capacity allocation, along with temperature threshold parameters, are input into the temperature dynamic adjustment model. The air conditioning system then operates according to the calculated optimal operating strategy. The temperature dynamic adjustment model continuously samples and monitors the ambient temperature, return air temperature, and outlet air temperature of the computer room. It combines the air conditioning operating status, the heat generation of the computer room equipment, and the influence of external environmental factors to calculate the temperature change trend in real time and generate a continuous temperature change curve using a fitting algorithm. This curve reflects the actual impact of the air conditioning cooling capacity on the internal temperature of the computer room. Simultaneously, it identifies the main causes of temperature fluctuations, including increased equipment heat dissipation, changes in air conditioning cooling efficiency, or sudden changes in ambient temperature, obtaining ambient temperature change data that accurately reflects the thermal environment of the computer room. Based on the ambient temperature change data and temperature threshold parameters, the duty cycle adjustment parameters are calculated, and an in-depth trend analysis of the ambient temperature change data is performed, focusing on evaluating the directionality and rate of temperature change. When a sustained temperature rise is detected and it gradually approaches the upper temperature limit set in the temperature threshold parameter, it indicates that the current cooling capacity of the air conditioner is insufficient to meet the heat load demand of the computer room. The duty cycle parameter is automatically calculated to increase the operating time of the air conditioner in the next control cycle, ensuring sufficient cooling capacity to control the temperature rise. Conversely, when a temperature decline is detected and it gradually approaches the lower temperature limit, it indicates that the current cooling capacity exceeds actual demand, resulting in over-cooling. The duty cycle parameter is calculated to decrease the operating time of the air conditioner to avoid unnecessary energy waste. The calculation process of the duty cycle adjustment parameter uses a proportional-integral-derivative (PID) control algorithm. By analyzing the magnitude, accumulation, and trend of temperature errors, the proportion of air conditioner operating time to the total cycle time is dynamically adjusted to ensure the accuracy of temperature control and the timeliness of response. Based on the duty cycle adjustment parameter, the air conditioner's air supply mode is controlled. According to the calculated duty cycle adjustment parameter and the current heat load status of the computer room, the most suitable air supply mode is intelligently selected to optimize the cooling effect and energy consumption level. When the computer room temperature is high and significant cooling is required, the system automatically selects high fan speed mode. This enhances heat exchange efficiency by increasing fan speed and airflow, rapidly lowering the room temperature. When the temperature approaches the target value but still requires some cooling, medium fan speed mode is selected to maintain cooling effectiveness while appropriately controlling energy consumption. When the computer room temperature is relatively stable and the air conditioner only needs to maintain the current temperature, the system switches to low fan speed mode to reduce overall energy consumption by decreasing fan power consumption. Under preset conditions, an automatic air supply mode can also be selected, allowing the air conditioner to automatically adjust the fan speed based on temperature changes. This further improves control precision and responsiveness, providing air supply control data that includes fan speed settings, airflow angle, and operating mode.The air supply control data is matched with time-of-use electricity price parameters for different time periods. Based on the electricity price variation patterns during peak, normal, and off-peak periods, the temperature setting strategy is dynamically optimized and adjusted. During peak electricity periods, due to higher electricity prices, the temperature setpoint is appropriately increased while ensuring the safe operation of the data center equipment, thereby reducing electricity costs by decreasing the cooling load. During off-peak periods, due to lower electricity prices, the temperature setpoint is appropriately reduced to provide advance cooling, leveraging the cost advantage of low electricity prices to reserve cooling capacity for subsequent peak periods. During normal electricity periods, a balance adjustment is made based on actual heat load demand to ensure the optimal balance between temperature control effectiveness and economy, generating temperature setting data adapted to different electricity price environments. Boundary checks are performed on the temperature setting data. Based on the temperature trigger value and temperature response value in the temperature threshold parameters, a comprehensive safety verification of the generated temperature setting data is conducted to ensure that the set temperature range does not exceed the safe operating boundaries of the data center equipment. The temperature setpoint is checked to see if it might cause equipment overheating and affect stability, or if it might cause excessive cooling leading to energy waste and temperature fluctuations. Boundary checks ensure the rationality and feasibility of the temperature control strategy, resulting in safety-verified temperature correction data. Based on the temperature correction data, cooling capacity optimization is performed, and key operating parameters such as the compressor working mode, fan operating parameters, and circulating refrigerant control strategy of the air conditioner are finely adjusted. By optimizing the coordination of each subsystem, the optimal cooling effect is achieved, ensuring the best energy utilization efficiency while meeting the temperature control accuracy requirements. Finally, a second air conditioner control parameter containing optimized operating parameters, control strategies, and expected effects is generated.

[0037] In one specific embodiment, the method for controlling the air conditioning energy consumption of a computer room base station further includes the following steps: Based on the real-time energy consumption data of the air conditioner in the computer room base station according to the second air conditioning control parameters, energy consumption assessment data is generated based on the real-time energy consumption data. Based on the energy consumption assessment data, alarm conditions for voltage over-limit, current over-limit, and temperature over-limit are set, and the alarm conditions are compared with the energy saving rate threshold to generate an energy consumption early warning strategy. According to the energy consumption early warning strategy, the air conditioning operation status of the base station in the computer room is given an early warning, and a target response command is generated.

[0038] Specifically, based on the real-time energy consumption data of the air conditioner in the computer room base station according to the second air conditioning control parameters, the accuracy and real-time performance of data acquisition are ensured through intelligent sensors and power monitoring modules deployed inside the computer room. During data acquisition, voltage data includes the three-phase input voltages (A-phase, B-phase, and C-phase) used to monitor the stability of the power supply voltage to the computer room air conditioner on the three phases; current data includes the three-phase operating current used to assess the operating load and phase balance of the air conditioner; and power data is calculated through comprehensive analysis of the three-phase voltage, three-phase current, and power factor, reflecting the actual power consumption level of the air conditioner. Simultaneously, temperature monitoring data of the computer room is collected, including ambient temperature, return air temperature, and outlet air temperature. The ambient temperature reflects the overall temperature change trend of the computer room, while the return air temperature and outlet air temperature are used to assess the cooling efficiency and heat exchange effect of the air conditioner. Combining electrical and temperature parameters, the total energy consumption of the computer room air conditioner per unit time is calculated through a set sampling time interval, forming real-time energy consumption data that accurately reflects the operating status. Based on the real-time energy consumption data, energy consumption assessment data is generated, and a comprehensive assessment is performed, including PUE value calculation, current and voltage trend analysis, and electricity cost statistics. Power Usage Effectiveness (PUE), a key indicator for measuring the energy efficiency of data centers, is assessed by the ratio of total power consumption in the computer room to the actual power consumption of IT equipment. Ideally, a PUE closer to 1.0 indicates higher energy efficiency. Current and voltage trend analysis uses time-series analysis to evaluate fluctuations in three-phase current and voltage to determine the stability and abnormal fluctuations of the data center's power supply system. Electricity cost statistics are compiled using time-of-use pricing parameters, showing electricity consumption during peak, normal, and off-peak hours. Quantitative analysis of energy consumption levels across different time periods is used to classify energy consumption levels. Based on PUE values, load rates, and unit power consumption, a comprehensive energy consumption assessment is generated, including temperature trend analysis, PUE change statistics, and electricity cost analysis. Based on the energy consumption assessment data, alarm conditions for voltage, current, and temperature over-limits are set. According to equipment safety operation requirements and computer room environmental standards, the voltage alarm trigger condition is set to generate an alarm message when the current effective voltage value exceeds the set voltage safety range. The current alarm trigger condition is activated when any phase current exceeds the rated operating range or the phase current imbalance exceeds the allowable deviation. The temperature alarm trigger condition is triggered when the ambient temperature exceeds the upper or lower limit value set in the temperature threshold parameter. The alarm conditions are compared with preset energy-saving rate thresholds. When the current energy-saving rate is detected to be lower than the set energy-saving rate threshold, it indicates that the air conditioning operation strategy needs optimization and adjustment. The system automatically analyzes energy-saving potential and proposes improvement suggestions, such as adjusting the temperature setpoint, optimizing the air supply mode, adding a nighttime pre-cooling strategy, or adjusting the time-segmented operation plan. Targeted energy consumption early warning strategies are generated to guide subsequent energy-saving optimization work.According to the energy consumption early warning strategy, the air conditioning operation status of the base station in the data center is given early warning. Based on the severity and impact of abnormal events, the early warning levels are classified and managed as follows: low-level early warning is used to handle minor energy consumption exceedances and is for reference only, requiring no manual intervention; medium-level early warning is used to handle temperature or energy consumption exceedances and suggests adjusting operating parameters; high-level early warning is used to handle severe current, voltage, or temperature exceedances and suggests immediate equipment inspection; and emergency early warning is used to handle severe situations such as abnormal shutdowns or power supply failures, requiring the triggering of emergency procedures. Intelligent algorithms analyze the impact range and processing priority of abnormal events, and control strategy adjustments and backup equipment switching analyses are performed based on the abnormality classification data. When a failure or performance degradation of the main air conditioning equipment is detected, the availability and switching conditions of backup equipment are automatically assessed, and an equipment switching plan is formulated to ensure the continuity of the data center's cooling capacity. Simultaneously, based on the specific characteristics of the abnormal situation, the air conditioning operating parameters, including key control parameters such as cooling capacity distribution, air supply mode selection, and operating time setting, are dynamically adjusted to ensure the stability of the computer room environment under abnormal conditions. Early warning response data is obtained and converted into executable target response instructions, including specific operation instructions such as automatically adjusting air conditioning operating parameters, starting backup cooling equipment, sending remote alarm notifications, and generating operation and maintenance work orders, to ensure that the computer room air conditioning system can maintain a stable and reliable operating state under various operating conditions.

[0039] Optionally, when setting alarm conditions for voltage over-limit, current over-limit, and temperature over-limit based on the energy consumption assessment data, a comprehensive analysis model is established based on the real-time energy consumption data. Time series analysis and feature extraction are performed on the three-phase input voltage data, three-phase operating current data, ambient temperature data, return air temperature data, outlet air temperature data, and power data collected from the computer room base station according to different data types. By analyzing the changing patterns and interrelationships of various data types over time, voltage fluctuation patterns, current load change trends, temperature change characteristics, and power consumption patterns are identified, resulting in comprehensive time-series characteristic data reflecting the multi-dimensional operating status of the air conditioning system. This comprehensive time-series characteristic data is input into an intelligent analysis network, where deep learning algorithms are used to calculate and analyze the interdependencies and influence mechanisms between different types of data. This identifies the impact of voltage changes on the air conditioning operating status, the correlation between current load and cooling capacity, the correspondence between temperature changes and PID control tag parameter responses, and the degree of matching between power consumption and time-of-use electricity price parameters, resulting in intermodal correlation data reflecting the complex relationships between various parameters. Deep feature processing is performed on the intermodal correlation data to extract the spatial distribution and temporal evolution features of the air conditioning system during operation. This includes the spatial correlation of temperature distribution in different areas, the collaborative operation characteristics among multiple air conditioning units, and the evolutionary patterns of operating parameters over time. By comprehensively analyzing these features, a spatiotemporal feature vector is formed that comprehensively describes the operating status of the air conditioning system. Based on this spatiotemporal feature vector, a dynamic threshold model with multi-level detection capabilities is constructed. Energy consumption assessment data is divided into a global detection layer and a local detection layer according to the scope of influence and detection accuracy requirements. The global detection layer is responsible for monitoring the overall operating status and energy consumption level of the entire base station in the computer room, while the local detection layer is responsible for monitoring the detailed operating parameters of individual air conditioning units or preset areas. Through this layered detection mechanism, accurate identification of abnormal situations at different levels is achieved, resulting in layered detection parameters adapted to different detection needs. Adaptive threshold calculations are performed on the layered detection parameters. Based on historical operating data of the base station in the equipment room, current load status, changes in environmental conditions, and fluctuations in time-of-use electricity prices, the threshold range settings for different detection levels are dynamically adjusted to ensure that the threshold settings can both promptly detect anomalies and avoid false alarms. Corresponding dynamic adjustment strategies are set for different parameter types such as voltage, current, and temperature, resulting in adaptive threshold adjustment strategies. These threshold adjustment strategies are applied to the alarm judgment process for voltage over-limit, current over-limit, and temperature over-limit. When a voltage parameter is detected to exceed the dynamically set safety range, a voltage anomaly alarm is triggered; when a current parameter is detected to exceed the rated operating range or phase imbalance occurs, a current anomaly alarm is triggered; and when a temperature parameter is detected to exceed the temperature threshold parameter setting range, a temperature anomaly alarm is triggered. Multi-level comprehensive analysis verifies the accuracy and reliability of the anomaly detection results, avoiding false alarms caused by fluctuations in a single parameter, and obtaining anomaly detection results that have undergone multiple verifications.Based on the anomaly detection results, alarm levels are classified, and a global alarm rule covering minor, general, severe, and emergency anomalies is established. Simultaneously, corresponding local alarm rules are established for the specific circumstances of different devices and areas. By comprehensively evaluating factors such as the severity, scope of impact, duration, and urgency of handling the anomaly, the anomaly event is scientifically classified, resulting in graded alarm data containing information such as alarm level, impact assessment, and handling suggestions. This graded alarm data is input into the alarm processing unit, which generates corresponding handling strategies based on the characteristics and handling requirements of different alarm levels. For minor anomalies, an automatic adjustment of control parameters is used; for general anomalies, a notification is sent and manual inspection is recommended; for severe anomalies, backup equipment is automatically activated and maintenance personnel are immediately notified; and for emergency anomalies, an emergency handling procedure is triggered and emergency shutdown protection is implemented. Finally, energy consumption alarm data containing complete information such as handling strategy, response time, and execution steps is obtained.

[0040] In one specific embodiment, the process of executing the step of providing an early warning of the air conditioning operation status of the computer room base station according to the energy consumption early warning strategy and generating a target response command may specifically include the following steps: Based on the energy consumption early warning strategy, abnormal events are classified into categories in the operation cycle data of the equipment room base station to obtain abnormal classification data. Based on the anomaly classification data, control strategy adjustments and backup equipment switching analyses are performed to obtain early warning response data, which is then converted into target response commands.

[0041] Specifically, a time-series data acquisition and storage mechanism is established. A database with a TeleDB data storage engine records air conditioning operation cycle data for each calculation period. This operation cycle data includes air conditioning duty cycle parameters, three-phase input voltage data, three-phase operating current data, air conditioning power data, computer room ambient temperature data, air conditioning return air temperature data, air conditioning outlet air temperature data, and air conditioning operating status data. Real-time energy consumption data is calculated based on the second air conditioning control parameters. Statistical analysis of daily, weekly, and monthly energy consumption data generates energy consumption assessment data, which is used to evaluate the energy-saving effect of the air conditioning system and long-term energy consumption trend changes. After acquiring the operation cycle data, abnormal events are identified and analyzed, with a focus on monitoring three types of events: abnormal power supply status, abnormal operating status, and abnormal temperature parameters. Abnormal power supply status is mainly determined by monitoring three-phase voltage fluctuations, three-phase operating current overload, and abnormal air conditioning power. When the root mean square value of the three-phase input voltage exceeds the voltage safety range set in the temperature threshold parameters or falls below the minimum operating voltage, it is automatically marked as a voltage abnormality event. Abnormal air conditioning operation is identified by analyzing the frequency of changes in the duty cycle parameter during air conditioning operation. If the duty cycle parameter changes too frequently within a unit of time or the number of air conditioner start-stop cycles exceeds the normal range, it indicates a potential load problem or improper configuration of the PID control tag parameters, requiring adjustment of the control strategy. Abnormal temperature parameters are determined by detecting whether the ambient temperature in the computer room exceeds the upper and lower limits set by the temperature threshold parameters, while simultaneously monitoring whether the difference between the air conditioner's return air temperature and outlet air temperature falls within the normal cooling efficiency range. After comprehensive analysis, these abnormal data are classified into three levels—low-risk, medium-risk, and high-risk—based on the degree of abnormality and the scope of impact, forming an abnormality classification data. Based on this classification data, multi-level warning rules are set for voltage over-limit warnings, current over-limit warnings, and temperature over-limit warnings. These warning rules are compared with energy-saving rate thresholds to generate specific warning trigger conditions. When a warning rule is triggered, control strategy adjustments and backup equipment switching analyses are performed to ensure the air conditioning system maintains stable operation. For low-risk abnormal events, the operating status is optimized by fine-tuning the PID control tag parameters and duty cycle parameters to prevent the abnormality from escalating. For medium-risk anomalies, the implementation strategy for time-of-use electricity pricing parameters will be adjusted. For example, during voltage anomalies, air conditioning load during peak hours will be reduced, and cooling will be initiated earlier during off-peak hours to alleviate pressure on the power system. For high-risk anomalies, emergency response mechanisms will be activated immediately, including switching to backup air conditioning equipment, adjusting temperature threshold parameters to expand the safe operating range, or activating the UPS power supply system to ensure the normal operation of critical equipment. Through these tiered response measures, early warning response data containing specific handling instructions will be generated.Event statistical analysis is performed on the early warning response data. By analyzing the frequency, severity, and impact of abnormal events over a period of time, early warning optimization data is generated. This data is used to continuously optimize air conditioning operating parameters and improve the early warning mechanism. The analysis of early warning optimization data focuses on identifying the root causes of high-frequency abnormal events, evaluating the effectiveness of current control strategies, and predicting potential risk points. For example, if statistics show that temperature exceedance events occur frequently during peak hours of time-of-use electricity pricing parameters, the control strategy of the AIoT edge computing model will be automatically adjusted to increase cooling capacity in advance during off-peak hours to reduce the base temperature of the data center, thereby reducing the cooling load demand during peak hours. At the same time, the triggering conditions of PID automatic control tag parameters will be optimized according to the distribution pattern of abnormal events to improve adaptability to environmental changes. Based on the early warning optimization data and real-time anomaly classification data, the early warning response data is finally converted into target response instructions. These instructions include specific equipment control commands, parameter adjustment instructions, early warning notification information, and emergency handling plans, ensuring that maintenance personnel can take timely measures to maintain the safe and stable operation of the data center base station air conditioning system, while ensuring the achievement of energy saving and consumption reduction goals.

[0042] Please see Figure 2 , Figure 2 This is a schematic block diagram of the structure of the air conditioning energy consumption control device 200 for a computer room base station provided in this application embodiment, as shown below. Figure 2 As shown, the air conditioning energy consumption control device 200 for the computer room base station includes: The setting module 210 is used to set the air conditioning operation parameters of the computer room base station and calculate the heat balance parameters; Calculation module 220 is used to calculate the first air conditioning control parameters based on the heat balance parameters; The real-time adjustment module 230 is used to execute the first air conditioning control parameters and adjust the air conditioning cooling capacity of the computer room base station in real time to obtain the second air conditioning control parameters.

[0043] Through the collaborative efforts of the aforementioned components, and by constructing an AIoT edge computing model and a PID self-control tag system, intelligent control of the air conditioning system was achieved, resulting in energy savings of over 15% and reducing the operating costs of the data center base station. By adopting time-of-use electricity pricing parameters and a heat balance strategy, the operating hours of the air conditioning system were rationally allocated, fully utilizing off-peak electricity periods for pre-cooling and avoiding peak-hour power consumption, effectively reducing electricity costs. Based on a dynamic temperature adjustment model and duty cycle parameter control, precise temperature regulation of the data center was achieved, avoiding over-cooling and over-heating and improving energy efficiency. A complete data acquisition and analysis system was established through the TeleDB data storage engine and standard coding processing, providing data support for the optimized operation of the air conditioning system. A multi-level early warning analysis mechanism and a backup equipment switching scheme were implemented, improving the reliability and security of system operation and effectively preventing equipment failures and abnormal situations. The system supports independent operation mode, maintaining basic control functions even in the event of communication interruptions, ensuring the continuous and stable operation of the data center base station. Automated report generation and early warning processing mechanisms reduced manual intervention, improved operational efficiency, and enabled intelligent management of the data center base station air conditioning system.

[0044] Please see Figure 3 , Figure 3 This is a schematic block diagram of the structure of the air conditioning energy consumption control device 300 for the computer room base station provided in this application embodiment. The air conditioning energy consumption control device 300 for the computer room base station includes a processor 301 and a memory 302. The processor 301 and the memory 302 are connected through a device bus 303. The memory 302 may include a non-volatile storage medium and internal memory.

[0045] The non-volatile storage medium can store a computer program. The computer program includes program instructions, which, when executed by the processor 301, cause the processor 301 to perform any of the above-mentioned methods for controlling the air conditioning energy consumption of a computer room base station.

[0046] The processor 301 provides computing and control capabilities to support the operation of the air conditioning energy consumption management equipment 300 for the entire computer room base station.

[0047] The internal memory provides an environment for the operation of computer programs in non-volatile storage media. When the computer program is executed by the processor 301, the processor 301 can execute any of the above-mentioned methods for controlling the air conditioning energy consumption of the computer room base station.

[0048] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the solution of this application and does not constitute a limitation on the air conditioning energy consumption control device 300 of the computer room base station involved in the solution of this application. The specific air conditioning energy consumption control device 300 of the computer room base station may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0049] It should be understood that processor 301 can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0050] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the air conditioning energy consumption control equipment 300 for the computer room base station described above can be referred to the corresponding process of the aforementioned air conditioning energy consumption control method for the computer room base station, and will not be repeated here.

[0051] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by one or more processors, it enables the one or more processors to implement the air conditioning energy consumption control method for a computer room base station as provided in this application.

[0052] The computer-readable storage medium can be an internal storage unit of the air conditioning energy consumption management device 300 of the computer room base station in the aforementioned embodiment, such as a hard disk or memory of the air conditioning energy consumption management device 300. The computer-readable storage medium can also be an external storage device of the air conditioning energy consumption management device 300 of the computer room base station, such as a pluggable hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped with the air conditioning energy consumption management device 300 of the computer room base station.

[0053] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0054] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0055] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for controlling the energy consumption of air conditioning in a computer room base station, characterized in that, include: Set the air conditioning operating parameters for the base station in the computer room and calculate the heat balance parameters; Calculate the first air conditioning control parameters based on the heat balance parameters; The first air conditioning control parameters are executed, and the air conditioning cooling capacity of the base station in the computer room is adjusted in real time to obtain the second air conditioning control parameters.

2. The method for controlling air conditioning energy consumption in a computer room base station according to claim 1, characterized in that, The process of setting the air conditioning operating parameters for the computer room base station and calculating the heat balance parameters includes: Define the time-of-use electricity price parameters for the base station in the data center; define the PID self-control tag parameters for the PID controller of the air conditioner in the base station in the data center; set the temperature threshold parameters for the air conditioner in the base station in the data center. The time-of-use electricity price parameter, the PID self-control tag parameter, and the temperature threshold parameter are used as the air conditioning operation parameters; Based on the air conditioning operating parameters, the heat balance parameters of the base station in the computer room are calculated.

3. The method for controlling air conditioning energy consumption in a computer room base station according to claim 2, characterized in that, The calculation of the heat balance parameters of the base station in the computer room based on the air conditioning operating parameters includes: The air conditioning operation data of the base station in the computer room is collected according to the PID self-control tag parameters, and the air conditioning operation data is classified to obtain a classified dataset; Based on the classification dataset and the time-of-use electricity price parameters, calculate the heat difference data between the heat generated by the equipment in the computer room and the cooling capacity of the air conditioner in the computer room base station; A time-series analysis is performed on the heat difference data to obtain heat change data, and heat balance parameters are calculated based on the heat change data and the temperature threshold parameters.

4. The method for controlling air conditioning energy consumption in a computer room base station according to claim 1, characterized in that, The calculation of the first air conditioning control parameter based on the heat balance parameter includes: An AIoT edge computing model is constructed based on the heat balance parameters and energy-saving PID self-control tag data, and the AIoT edge computing model is calculated in time periods to obtain time period calculation data. Based on the time period calculation data, the duty cycle threshold of the heat generation of the computer room equipment and the cooling capacity of the air conditioner is calculated to generate the first air conditioning control parameters.

5. The method for controlling air conditioning energy consumption in a computer room base station according to claim 1, characterized in that, The step of executing the first air conditioning control parameters and adjusting the air conditioning cooling capacity of the base station in the computer room in real time to obtain the second air conditioning control parameters includes: The first air conditioning control parameters are executed, and ambient temperature change data is monitored; The duty cycle adjustment parameters are calculated based on the ambient temperature change data and temperature threshold parameters, and the air conditioning air supply mode is controlled based on the duty cycle adjustment parameters to obtain air supply control data. The air supply control data is matched with the time-of-use electricity price parameters to generate temperature setting data. Boundary check calculations are performed on the temperature set data to obtain temperature correction data, and cooling capacity optimization is performed based on the temperature correction data to obtain the second air conditioning control parameters.

6. The method for controlling air conditioning energy consumption in a computer room base station according to claim 1, characterized in that, The method for controlling the air conditioning energy consumption of the computer room base station also includes: Based on the real-time energy consumption data of the air conditioner in the computer room base station according to the second air conditioning control parameters, energy consumption assessment data is generated based on the real-time energy consumption data. Based on the energy consumption assessment data, alarm conditions for voltage over-limit, current over-limit, and temperature over-limit are set, and the alarm conditions are compared with the energy saving rate threshold to generate an energy consumption early warning strategy. According to the energy consumption early warning strategy, the air conditioning operation status of the base station in the computer room is given an early warning, and a target response command is generated.

7. The method for controlling air conditioning energy consumption in a computer room base station according to claim 6, characterized in that, The step of issuing an early warning of the air conditioning operation status of the computer room base station according to the energy consumption early warning strategy and generating a target response command includes: Based on the energy consumption early warning strategy, abnormal events are classified into categories in the operation cycle data of the equipment room base station to obtain abnormal classification data. Based on the anomaly classification data, control strategy adjustments and backup equipment switching analyses are performed to obtain early warning response data, which is then converted into target response commands.

8. An air conditioning energy consumption control device for a computer room base station, characterized in that, The method for controlling the air conditioning energy consumption of a base station in a computer room as described in any one of claims 1-7 includes: The configuration module is used to set the air conditioning operating parameters of the base station in the computer room and calculate the heat balance parameters. The calculation module is used to calculate the first air conditioning control parameters based on the heat balance parameters; The real-time adjustment module is used to execute the first air conditioning control parameters and adjust the air conditioning cooling capacity of the computer room base station in real time to obtain the second air conditioning control parameters.

9. An air conditioning energy consumption control device for a computer room base station, characterized in that, The air conditioning energy consumption control equipment of the computer room base station includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor invokes the instructions in the memory to cause the air conditioning energy consumption management device of the computer room base station to execute the air conditioning energy consumption management method of the computer room base station as described in any one of claims 1-7.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instruction is executed by the processor, it implements the air conditioning energy consumption control method for the computer room base station as described in any one of claims 1-7.

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