A high-efficiency machine room intelligent control system suitable for intelligent buildings

Abstract

The application provides a high-efficiency machine room intelligent control system suitable for intelligent buildings, relates to the technical field of energy saving, and collects the environmental state information of the intelligent building machine room through a collection module; obtains the temperature control amount of the airflow channel of the intelligent building machine room and the machine room environment airflow channel through a processing module adjustment module, constructs a temperature characteristic identification model of the intelligent building machine room during operation according to the temperature control amount, adjusts the thermal response deviation in the temperature characteristic identification model of the intelligent building machine room during operation through a temperature correction coefficient; corrects the adaptive compensation value during energy consumption adjustment in the cooling side energy consumption model of the intelligent building machine room through a correction module; and performs feedback control on the working state of the machine room of the intelligent building through a feedback control module. The application can improve the dynamic adaptability of the intelligent control of the intelligent building machine room under the influence of the deviation over-limit of the control response during the intelligent control of the intelligent building machine room.

Description

Technical Field

[0001] This application relates to the field of energy-saving technology, and more specifically, to a high-efficiency intelligent control system for computer rooms suitable for smart buildings. Background Technology

[0002] Energy conservation refers to a series of technologies and methods aimed at reducing energy consumption and improving energy efficiency. By optimizing energy use and improving equipment efficiency, energy consumption and related costs are reduced, while environmental impact is also reduced. Common forms include high-efficiency equipment, intelligent control systems, heat recovery technologies and energy management systems. These technologies not only help reduce energy costs but also reduce greenhouse gas emissions and promote sustainable development.

[0003] In existing intelligent building systems, high-efficiency intelligent control systems for computer rooms integrate advanced monitoring, control, and management technologies to achieve efficient operation of equipment within the room. The system first utilizes sensors and IoT technology to monitor environmental parameters in real time, such as temperature, humidity, and equipment load. This data is analyzed through a centralized management platform to identify energy consumption patterns and equipment status. Based on the analysis results, the intelligent control system can automatically adjust the operating modes of air conditioning, lighting, and other equipment to adapt to actual needs, thereby avoiding unnecessary energy consumption. Furthermore, the system employs load management technology to optimize equipment operation and ensure optimal energy efficiency under different load conditions. Simultaneously, heat recovery technology is applied to recover waste heat generated by the equipment for other heating or warming needs. Further improving energy efficiency is crucial. However, in the process of intelligent control of high-efficiency computer rooms in smart buildings, the dynamic changes in the computer room environment have not been fully considered, and the feedback control mechanism often relies on simple threshold judgments, resulting in a lag in control response. This lag leads to the computer room's inability to respond promptly to changes in load and environment, resulting in insufficient coordination of feedback control in the intelligent control of computer rooms in smart buildings. This causes deviations in the control response to exceed limits when the computer room is intelligently controlled, thereby reducing the dynamic adaptability of intelligent control in smart building computer rooms. Therefore, how to improve the dynamic adaptability of intelligent control in smart building computer rooms under the influence of deviations exceeding limits is a problem faced by the industry. Summary of the Invention

[0004] This application provides a high-efficiency intelligent control system for computer rooms suitable for intelligent buildings, which can improve the dynamic adaptability of intelligent control in intelligent building computer rooms under the influence of excessive deviation in control response when performing intelligent control.

[0005] This application provides a high-efficiency intelligent control system for computer rooms suitable for intelligent buildings. The intelligent control system includes: The data acquisition module is used to monitor the operating status of the intelligent building computer room and collect environmental status information of the intelligent building computer room; The processing module is used to decompose the environmental state information to obtain the steady-state confidence vector of the intelligent building equipment room during operation, and to determine the temperature correction coefficient for intelligent control of the operating temperature of the intelligent building equipment room based on the steady-state confidence vector. The adjustment module is used to acquire the temperature control values ​​of the dedicated airflow channel and the airflow channel of the intelligent building computer room, construct a temperature characteristic identification model for the operation of the intelligent building computer room based on the temperature control values, and adjust the thermal response deviation in the temperature characteristic identification model for the operation of the intelligent building computer room through the temperature correction coefficient. The calibration module is used to determine the system load characteristics during the operation of the intelligent building computer room, determine the energy consumption fluctuation during the operation of the intelligent building computer room based on the system load characteristics, and correct the adaptation compensation value in the energy consumption adjustment of the cooling side energy consumption model of the intelligent building computer room through the energy consumption fluctuation. The feedback control module is used to perform feedback control on the working status of the intelligent building's computer room based on the thermal response deviation in the temperature characteristic identification model after adjustment and the adaptation compensation value during energy consumption adjustment in the energy consumption model of the cooling side of the intelligent building's computer room after correction.

[0006] In this embodiment, the environmental status information of the intelligent building computer room is obtained by reading the database.

[0007] In this embodiment, the decomposition of the environmental state information to obtain the steady-state confidence vector of the intelligent building computer room during operation specifically includes: The environmental state information is weighted to obtain the environmental weight vector during the operation of the intelligent building computer room. The environmental weight vector is mapped onto the steady-state space of the intelligent building computer room during operation to obtain a steady-state operation sample set; The steady-state confidence vector of the intelligent building computer room during operation is determined based on the steady-state operation sample set.

[0008] In this embodiment, the dedicated airflow channel for intelligent building computer rooms refers to an airflow channel specifically designed to provide cooling air for the equipment in the computer room.

[0009] In this embodiment, the airflow channel in the computer room refers to the airflow channel formed naturally or artificially within the computer room, which is mainly used for air circulation and ventilation.

[0010] In this embodiment, determining the system load characteristics during the operation of the intelligent building computer room specifically includes: Determine the controlled parameters of the load regulation system during the operation of the intelligent building computer room; Obtain the predicted target value of energy consumption during the operation of the intelligent building computer room; The system load characteristics of the building computer room during operation are determined based on the adjusted parameters and the predicted target values.

[0011] In this embodiment, determining the energy consumption fluctuation during the operation of the intelligent building computer room based on the system load characteristics specifically includes: The output heat balance parameters corresponding to the energy consumption changes during the operation of the intelligent building computer room are extracted from the system load characteristics. Determine the energy consumption control strategy for the operation of the intelligent building computer room; The energy consumption fluctuation during the operation of the intelligent building computer room is determined based on the output heat balance parameters and the energy consumption control strategy.

[0012] In this embodiment, the specific steps of correcting the energy consumption adjustment value in the energy consumption model of the intelligent building computer room cooling side using the energy consumption fluctuation amount include: The energy consumption model of the cooling side of the intelligent building computer room outputs the adaptation compensation value when adjusting the energy consumption. Determine the adjustment target information when adjusting the energy consumption of intelligent building computer rooms; The adaptation compensation value is coordinated and corrected using the adjusted target information.

[0013] In this embodiment, the adaptation compensation value refers to the value used to correct the difference between model prediction and actual energy consumption during the energy consumption adjustment process.

[0014] In this embodiment, energy consumption fluctuation refers to the difference between actual energy consumption and predicted energy consumption.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The intelligent building equipment room's operational status is monitored by a data acquisition module, which also collects environmental status information. A processing module decomposes this environmental status information to obtain a steady-state confidence vector for the intelligent building equipment room's operation. Based on this steady-state confidence vector, a temperature correction coefficient is determined for intelligent temperature control. An adjustment module acquires temperature control parameters for the dedicated airflow channel and the ambient airflow channel within the intelligent building equipment room. Based on these temperature control parameters, a temperature characteristic identification model for the intelligent building equipment room's operation is constructed. The intelligent building equipment room's temperature is then adjusted using the temperature correction coefficient. The system identifies thermal response deviations in the temperature characteristic identification model of the building's computer room during operation; determines the system load characteristics of the intelligent building's computer room during operation through a correction module; determines the energy consumption fluctuations of the intelligent building's computer room during operation based on the system load characteristics; corrects the energy consumption adjustment adaptation compensation value in the cooling-side energy consumption model of the intelligent building's computer room based on the energy consumption fluctuations; and performs feedback control on the working status of the intelligent building's computer room based on the thermal response deviations in the temperature characteristic identification model of the intelligent building's computer room after adjustment and the adaptation compensation value in the cooling-side energy consumption model of the intelligent building's computer room after correction.

[0016] Therefore, this application demonstrates that intelligent control of a smart building's computer room can be achieved even when the control response deviation exceeds limits. Specifically, it involves real-time monitoring of the computer room's operational status and environmental information, collecting key data (such as temperature, humidity, and airflow) through sensor technology to achieve comprehensive perception of the computer room environment. This data-driven approach enables the system to promptly identify anomalies, reduce potential failure risks, and thus improve equipment operational stability and safety. By decomposing environmental state information into a steady-state confidence vector and utilizing mathematical modeling and data analysis techniques, the system can quantitatively assess the stability and reliability of the computer room's operation. This assessment provides a basis for temperature control, allowing the cooling system to dynamically adjust based on real-time data, ensuring equipment operates within the optimal temperature range and reducing energy consumption and failure rates. Furthermore, by establishing a temperature feature identification model… By incorporating a temperature correction coefficient, the system can accurately identify and adjust thermal response deviations. This intelligent adjustment mechanism not only improves cooling efficiency but also optimizes energy use, avoiding over- or under-cooling and enhancing energy economy and environmental friendliness. By analyzing system load characteristics, the system can predict fluctuations in data center energy consumption and dynamically adjust using energy management algorithms. This process achieves precise correction of the adaptation compensation value in the cooling-side energy consumption model, thereby optimizing energy allocation when the load changes, reducing overall energy consumption, and improving energy efficiency ratio. The feedback control mechanism enables the system to automatically adjust the data center's operating status based on real-time data and the adjusted model results. This closed-loop control system continuously optimizes the data center's temperature and energy consumption through intelligent algorithms, ensuring the data center maintains optimal operating conditions under different loads and environmental conditions, guaranteeing efficient resource utilization and equipment safety.

[0017] In summary, the technical solution adopted in this application can improve the dynamic adaptability of intelligent control in intelligent building computer rooms under the influence of excessive deviation in control response during intelligent control. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a modular structure diagram of a high-efficiency intelligent control system for computer rooms suitable for intelligent buildings, provided in this application. Figure 2 This is a flowchart illustrating the determination of the temperature correction factor provided in this application; Figure 3This is a flowchart illustrating the process of adjusting thermal response deviation provided in this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] This application provides a high-efficiency intelligent control system for computer rooms suitable for intelligent buildings. Its core is to monitor the operational status of the intelligent building's computer room and collect environmental status information through a data acquisition module; decompose the environmental status information through a processing module to obtain a steady-state confidence vector for the computer room's operation; determine a temperature correction coefficient for intelligent temperature control based on the steady-state confidence vector; and acquire temperature control parameters for the dedicated airflow channel and the ambient airflow channel of the computer room through an adjustment module, constructing a temperature feature identification model for the computer room's operation based on these temperature control parameters. The system employs a temperature correction coefficient to adjust the thermal response deviation in the temperature characteristic identification model of the intelligent building equipment room during operation. A correction module determines the system load characteristics of the intelligent building equipment room during operation, and based on these characteristics, determines the energy consumption fluctuation. This energy consumption fluctuation is then used to correct the energy consumption adjustment compensation value in the cooling-side energy consumption model of the intelligent building equipment room. A feedback control module then performs feedback control on the operating status of the intelligent building equipment room based on the adjusted thermal response deviation in the temperature characteristic identification model and the corrected energy consumption adjustment compensation value in the cooling-side energy consumption model. This approach improves the dynamic adaptability of intelligent control in the intelligent building equipment room under the influence of excessive control response deviations.

[0022] To better understand the above technical solutions, a detailed description of the technical solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. (Refer to...) Figure 1 As shown in the figure, this is a modular structure diagram of a high-efficiency intelligent control system for a smart building, suitable for a smart building, according to this embodiment of the present application. The intelligent control system includes: a data acquisition module 100, a processing module 200, an adjustment module 300, a correction module 400, and a feedback control module 500, which are described below: The data acquisition module 100 is used to monitor the operating status of the intelligent building computer room and collect environmental status information of the intelligent building computer room.

[0023] In practice, multiple sensors are installed in the computer room, including temperature sensors, humidity sensors, and airflow sensors. These sensors should be evenly distributed in different areas of the computer room, especially in locations with dense equipment and airflow channels, to ensure comprehensive monitoring.

[0024] In terms of specific implementation, firstly, a data acquisition device or gateway can be used to transmit the information collected by each sensor to the central control system. This can be achieved via wired (such as RS-485, Ethernet) or wireless (such as Zigbee, Wi-Fi) methods. Then, the collected data is preprocessed, and the preprocessed data is used as the environmental status information of the intelligent building equipment room and stored in a database. A time series database (such as InfluxDB) can be used. Then, the environmental status information of the intelligent building equipment room can be obtained by reading the database, which will not be elaborated here.

[0025] It should be noted that the environmental status information in this application refers to various physical and chemical parameter data collected under specific environmental conditions (such as intelligent building computer rooms). These data directly affect the operating efficiency and safety of the equipment, and specifically include temperature, humidity, airflow speed, air pressure, noise level, etc., which can reflect the actual environmental conditions in the computer room.

[0026] The processing module 200 is used to decompose the environmental state information to obtain the steady-state confidence vector of the intelligent building equipment room during operation, and to determine the temperature correction coefficient for intelligent control of the operating temperature of the intelligent building equipment room based on the steady-state confidence vector.

[0027] In this embodiment, the decomposition of the environmental state information to obtain the steady-state confidence vector of the intelligent building computer room during operation can be achieved through the following steps: The environmental state information is weighted to obtain the environmental weight vector during the operation of the intelligent building computer room. The environmental weight vector is mapped onto the steady-state space of the intelligent building computer room during operation to obtain a steady-state operation sample set; The steady-state confidence vector of the intelligent building computer room during operation is determined based on the steady-state operation sample set.

[0028] In practical implementation, firstly, environmental parameters affecting the operation of the computer room are identified, such as temperature, humidity, airflow speed, and air pressure. Each parameter is then scored using an expert rating method, with scores ranging from 1 to 5 (1 being the lowest and 5 the highest). Combined with historical data, regression or correlation analysis is used to determine the actual impact of each parameter. For example, temperature fluctuations may have a high correlation with equipment failure, thus receiving a larger weight. All weights are normalized to ensure the sum of the weights is 1. The normalized weights are then used as the environmental weight vector for the intelligent building computer room during operation. Next, the environmental weight vector is used to map the environmental state information to a steady-state space. This space can be constructed using multidimensional data analysis methods (such as principal component analysis or t-SNE). The environmental state information at each time point is then encoded and used as part of a sample set, which consists of multiple time points. The environmental states at various points are combined to form a high-dimensional data matrix. An algorithm is then used to map this data matrix into a low-dimensional steady-state space, highlighting the relationships between environmental variables. For example, each point in the two-dimensional space represents the environmental state at a given time point, and its coordinates are obtained after weight adjustment, thus yielding the steady-state operating sample set. Finally, statistical analysis is performed using the steady-state operating sample set to calculate the mean and variance of each sample in the steady-state space, reflecting the concentration and volatility of the environmental states. Combined with environmental weights, a steady-state confidence vector is calculated. For example, a weighted average method is used to combine the weight of each sample with its corresponding environmental state to obtain the steady-state confidence. The steady-state confidence vector is defined as: [weight 1 * state 1, weight 2 * state 2, weight 3 * state 3]. In other embodiments, the method for determining the steady-state confidence vector is not limited to this, and will not be elaborated upon in this embodiment.

[0029] It should be noted that, in this application, the environmental weight vector refers to the quantitative representation of the influence of various environmental parameters (such as temperature, humidity, airflow speed, etc.) on the operation of the intelligent building computer room; the steady-state operation sample set refers to the set of environmental data samples collected within a specific time period that reflect the operating status of the intelligent building computer room; and the steady-state confidence vector refers to the confidence level obtained by analyzing the operating status of the computer room under specific environmental conditions, reflecting the reliability and confidence level of the system in a steady state.

[0030] Preferably, in this embodiment, the temperature correction coefficient for intelligent control of the intelligent building equipment room is determined based on the steady-state confidence vector, with reference to... Figure 2 The diagram is a flowchart illustrating the determination of the temperature correction coefficient in some embodiments of this application. In this embodiment, the temperature correction coefficient can be determined using the following steps: In step S21, the predicted parameters for intelligent control of the operating temperature of the intelligent building computer room are set. In step S22, the control constraint quantity for intelligent control of the operating temperature of the intelligent building equipment room is determined based on the steady-state confidence vector; In step S23, the temperature correction reference value is obtained when the operating temperature of the intelligent building equipment room is used for intelligent control; In step S24, the temperature correction coefficient for intelligent control of the operating temperature of the intelligent building computer room is determined based on the predicted parameters, the control constraints, and the temperature correction reference value.

[0031] In practice, firstly, based on the historical operating data of the equipment in the computer room, the target temperature to be controlled is set. For example, by analyzing temperature data from the past few weeks, a target range (e.g., 22°C to 24°C) can be determined, and a center value (e.g., 23°C) can be selected as the prediction parameter. Next, based on the steady-state confidence vector analysis, the impact of various environmental factors on the operating temperature is determined, including setting the maximum and minimum temperature limits to ensure the temperature remains within a safe range. For example, if the steady-state confidence vector indicates that temperature has the most significant impact on the equipment, the upper limit of the temperature at a specific confidence level can be set to 25°C, and the lower limit to 25°C can be set to 25°C. The temperature is limited to 20°C, thus obtaining the control constraint. Then, by analyzing historical operating data and real-time monitoring information, a reference value for temperature correction is obtained. This value can be calculated based on the equipment's cooling capacity, ambient temperature changes, and equipment load. For example, the equipment's cooling system efficiency can be used as a reference to obtain a correction reference value, such as -1°C, indicating that additional cooling is needed to maintain the target temperature. Finally, the final temperature correction coefficient can be calculated using methods such as weighted average or linear regression. For example, the temperature correction coefficient = prediction parameter * (control constraint / temperature correction reference value), which will not be elaborated upon in this application.

[0032] It should be noted that, in this application, the prediction parameter represents the target temperature value or range set in the intelligent control; the control constraint refers to the upper and lower limits of temperature change during the temperature control process to ensure that the equipment operates in a safe and effective environment; the temperature correction reference value represents the benchmark value used to adjust the operating temperature during the temperature control process, reflecting the degree of temperature adjustment required under the current environmental conditions; and the temperature correction coefficient represents the value that needs to be adjusted to the actual temperature in order to achieve the preset target temperature in the intelligent control system. This coefficient reflects the degree of correction for deviations in the target temperature based on real-time environmental conditions, equipment load, and operating conditions. By applying the temperature correction coefficient, the system can effectively adjust the cooling or heating output to ensure that the equipment in the computer room operates within the optimal operating temperature range.

[0033] The adjustment module 300 is used to acquire the temperature control values ​​of the dedicated airflow channel and the airflow channel of the intelligent building computer room, construct a temperature characteristic identification model for the operation of the intelligent building computer room based on the temperature control values, and adjust the thermal response deviation in the temperature characteristic identification model for the operation of the intelligent building computer room through the temperature correction coefficient.

[0034] In specific implementation, the temperature control parameters of the dedicated airflow channel and the ambient airflow channel in the intelligent building's computer room can be obtained in the following way: First, temperature sensors are installed at appropriate locations in the dedicated airflow channel and the ambient airflow channel. The sensors should be placed at the airflow inlet and outlet to obtain comprehensive temperature data. For example, a sensor can be set at different heights and positions in the air handling unit (AHU) and the computer room cooling channel to capture temperature changes at different levels. Then, a data acquisition system is used to periodically read the data from each sensor and transmit the temperature information to the central control system in real time. The sensors need to have high accuracy and fast response capabilities to ensure accurate capture of temperature changes. The data acquisition frequency can be set to once per minute. The central control system then preprocesses the collected temperature data to eliminate noise and outliers. Smoothing can be performed using moving average or weighted average methods. Finally, based on the processed temperature data, the temperature control parameters of the dedicated airflow channel and the ambient airflow channel are calculated. For example, if the target temperature is 22°C and the current temperature of the dedicated airflow channel is 24°C, the temperature control parameter can be set to -2°C, indicating that the temperature needs to be reduced. This embodiment will not elaborate further.

[0035] It should be noted that, in this application, the temperature control quantity refers to the value required to adjust the current ambient temperature in order to achieve the target temperature in an intelligent control system. It reflects the difference between the actual temperature and the set target temperature and is used to guide the operation of cooling or heating equipment. The role of the temperature control quantity is to ensure that the computer room or other controlled environment is kept within the optimal temperature range in order to improve the operating efficiency and safety of the equipment.

[0036] In addition, in this application, the dedicated airflow channel for intelligent building computer rooms refers to an airflow channel specifically designed to provide cooling air for the equipment in the computer room. These channels are usually connected to air conditioning systems or cooling equipment to ensure that cold air flows effectively to heat sources (such as server racks) to maintain the normal operating temperature of the equipment. The design of the dedicated airflow channel aims to optimize airflow, reduce the loss of cold air, and improve cooling efficiency. The environmental airflow channel for computer rooms refers to the airflow channels formed naturally or artificially within the computer room. It is mainly used for air circulation and ventilation, helping to distribute the temperature evenly within the computer room, preventing dead corners and heat accumulation, and maintaining the overall comfort and stability of the environment. The environmental airflow channel may involve structures such as windows, doors, and vents, and is designed to improve air quality and regulate humidity.

[0037] In specific implementation, the temperature feature identification model for the operation of the intelligent building computer room, based on the temperature control parameters, can be constructed in the following way: First, collect historical data related to the computer room temperature, including temperature control parameters, ambient temperature, equipment load, cooling system output, etc., and then clean the collected data to remove outliers and missing data to ensure data integrity; then, through correlation analysis and feature importance assessment, select features that significantly affect temperature changes. For example, random forest or LASSO regression analysis can be used to determine the relationship between input features (such as external ambient temperature, equipment load) and temperature changes; finally, select an appropriate model algorithm, such as linear regression, support vector machine (SVM), neural network, etc., according to the characteristics and requirements of the data, and then use a training set (such as 70... The model is trained using 20% ​​of historical data. The model parameters are adjusted by minimizing the prediction error. For example, a linear regression model can be used, where temperature = β0 + β1 * ambient temperature + β2 * equipment load + ..., and β0, β1, and β2 are the weight parameters in the linear regression model, which can be obtained through expert experience or experimental simulation. Cross-validation can be used during training to ensure the model's generalization ability. The model performance is then evaluated using a validation set (such as 20% of historical data), and error indices such as root mean square error (RMSE) or mean absolute error (MAE) are calculated. The model is then tuned based on the validation results. If necessary, the model parameters are adjusted or different model algorithms are selected to obtain the temperature feature identification model. This application will not elaborate further. In other embodiments, other methods can also be used to construct the temperature feature identification model.

[0038] It should be noted that, in this application, the temperature feature identification model refers to a mathematical model established in the intelligent control system by analyzing and processing temperature-related data in the computer room. This model is used to predict and identify the characteristics of temperature changes in the computer room and their influencing factors. The model integrates multiple parameters such as ambient temperature, equipment load, and cooling system output, aiming to provide accurate predictions of temperature change trends in order to optimize temperature control strategies and improve the energy efficiency and stability of the computer room. Through real-time application, the model can help the decision-making system achieve precise temperature regulation and ensure that the equipment operates under optimal working conditions.

[0039] Preferably, in this embodiment, the thermal response deviation in the temperature characteristic identification model during the operation of the intelligent building computer room is adjusted by the temperature correction coefficient, with reference to... Figure 3 The diagram is a flowchart illustrating the adjustment of thermal response deviation in some embodiments of this application. In this embodiment, the adjustment of thermal response deviation can be achieved using the following steps: In step S31, the total thermal balance of the computer room environment is output from the temperature feature identification model; In step S32, the thermal response deviation is output from the temperature feature identification model; In step S33, the adjustment feedback value is determined when the temperature characteristic identification model of the intelligent building computer room is adjusted during operation; In step S34, the thermal response deviation in the temperature characteristic identification model during the operation of the intelligent building computer room is adjusted according to the total heat balance, the temperature correction coefficient, and the adjustment feedback value.

[0040] In specific implementation, firstly, based on the temperature characteristic identification model, the total heat balance of the computer room includes the sum of all heat inputs and heat outputs. Heat inputs can include equipment heat dissipation, external environmental heat (such as direct sunlight), and heat brought by personnel, while heat outputs include the cooling capacity dissipated through the air conditioning system, ventilation, and other cooling equipment. The total heat balance is calculated as: Total Heat Input - Total Heat Output. Next, the difference between the current actual temperature and the model-predicted temperature is obtained from the temperature characteristic identification model, i.e., the thermal response deviation. The thermal response deviation is calculated as: Current Temperature - Predicted Temperature. Then, based on the output thermal response deviation, the current state of the system is evaluated, and an adjustment feedback value is determined. The feedback value can be set based on the magnitude of the thermal response deviation. For example, when the deviation is large, the feedback value is set high, indicating that more adjustment is needed; when the deviation is within an acceptable range, the feedback value can be set low. Finally, the new thermal response deviation can be calculated using the following formula: New Thermal Response Deviation = Original Thermal Response Deviation - (Temperature Correction Coefficient × Adjustment Feedback Value). Further details are omitted in this application.

[0041] It should be noted that, in this application, the total heat balance quantity refers to the total amount that reflects the relationship between all heat inputs and outputs in the computer room, characterizing the thermal state of the computer room; the thermal response deviation refers to the difference between the current actual temperature and the model predicted temperature, reflecting the temperature control effect of the system; the adjustment feedback value is a parameter used to guide the degree of adjustment of the temperature control system; the thermal response deviation adjustment is a process of real-time adjustment of the thermal response deviation based on the total heat balance quantity, the temperature correction coefficient, and the adjustment feedback value.

[0042] The calibration module 400 is used to determine the system load characteristics during the operation of the intelligent building computer room, determine the energy consumption fluctuation during the operation of the intelligent building computer room based on the system load characteristics, and correct the adaptation compensation value in the energy consumption adjustment of the cooling side energy consumption model of the intelligent building computer room through the energy consumption fluctuation.

[0043] In this embodiment, determining the system load characteristics of the intelligent building computer room during operation can be achieved through the following steps: Determine the controlled parameters of the load regulation system during the operation of the intelligent building computer room; Obtain the predicted target value of energy consumption during the operation of the intelligent building computer room; The system load characteristics of the building computer room during operation are determined based on the adjusted parameters and the predicted target values.

[0044] In practical implementation, firstly, key parameters are set for monitoring and adjustment in the load regulation system, and these parameters are used as the regulated parameters of the load regulation system during the operation of the intelligent building computer room. These parameters include: equipment operating status, such as server load and running time; environmental conditions, such as temperature and humidity, which affect equipment performance; and power supply, such as voltage and frequency, which affect equipment energy efficiency. Then, based on historical data and operating models, the target energy consumption value of the intelligent building computer room within a specific time period is predicted. This can be achieved by using statistical analysis, machine learning algorithms, or historical trend analysis to generate an energy consumption prediction model. For example, if historical data indicates that the energy consumption under similar load conditions is 500 kWh, this is used as the target value. Finally, the relationship between the actual energy consumption of the system and the predicted target value under the current regulated parameters is calculated to determine the system's response characteristics, such as the trend of energy consumption changes when the load increases or decreases. Charts, models, or data analysis tools can be used to display the system load characteristics. For example, by plotting the relationship curve between load and energy consumption, the system load characteristics during the operation of the building computer room are determined, which will not be elaborated upon in this application.

[0045] It should be noted that, in this application, the adjusted parameter refers to the key variable that needs to be monitored and adjusted in the load regulation system, which is used to affect the system's energy efficiency and operating status; the energy consumption prediction target value is the expected energy consumption level calculated based on historical data and operating models, which serves as the benchmark for load regulation; the system load characteristics are features that describe the relationship between energy consumption and load in the intelligent building computer room under different conditions, which are used to guide load regulation and optimization.

[0046] In this embodiment, determining the energy consumption fluctuation of the intelligent building computer room during operation based on the system load characteristics can be achieved through the following steps: The output heat balance parameters corresponding to the energy consumption changes during the operation of the intelligent building computer room are extracted from the system load characteristics. Determine the energy consumption control strategy for the operation of the intelligent building computer room; The energy consumption fluctuation during the operation of the intelligent building computer room is determined based on the output heat balance parameters and the energy consumption control strategy.

[0047] In practice, firstly, output heat balance parameters related to energy consumption changes are obtained from the system load characteristics. These include: input heat (heat generated by equipment in the data center, including servers and network devices); cooling capacity (cooling capacity provided by air conditioners or cooling equipment); and environmental heat impact (the influence of the external environment on the temperature inside the data center, such as heat input caused by weather changes). These parameters are then integrated using a heat balance equation to generate output heat balance parameters. Next, based on the actual operating conditions of the data center and the heat balance parameters, corresponding energy consumption control strategies are formulated. These include: dynamic adjustment (automatically adjusting the operating status of the cooling system according to real-time load changes, such as changing fan speed and temperature settings); and peak-hour management (managing during periods of high electricity demand). During peak periods, the operation of certain equipment can be restricted, or energy-saving modes can be adopted; predictive control uses predictive models to adjust equipment operation in advance to adapt to expected load changes; for example, the output of cooling equipment can be increased under high load conditions to ensure that the temperature remains within a safe range; finally, by comparing historical data with the energy consumption predicted by the model, the fluctuation value between actual energy consumption and expected energy consumption is calculated, that is, the energy consumption fluctuation is equal to the absolute value of the difference between actual energy consumption and predicted energy consumption, where actual energy consumption is real-time monitoring data, and predicted energy consumption is a target value preset according to the control strategy. Data can be collected in real time through the monitoring system to ensure that the calculation of energy consumption fluctuation reflects the current status of the computer room, which will not be elaborated in this application.

[0048] It should be noted that, in this application, the output heat balance parameter represents a parameter describing the relationship between the heat generated by the equipment in the computer room and the cooling system capacity, used to analyze energy consumption changes; the energy consumption control strategy refers to the adjustment scheme formulated for the operation of the computer room, which aims to optimize energy consumption and equipment performance; the energy consumption fluctuation refers to the difference between actual energy consumption and predicted energy consumption, reflecting the stability and control effect of the computer room's energy consumption.

[0049] In this embodiment, the adaptation compensation value for energy consumption adjustment in the energy consumption model of the intelligent building computer room cooling side is corrected by the energy consumption fluctuation amount using the following steps: The energy consumption model of the cooling side of the intelligent building computer room outputs the adaptation compensation value when adjusting the energy consumption. Determine the adjustment target information when adjusting the energy consumption of intelligent building computer rooms; The adaptation compensation value is coordinated and corrected using the adjusted target information.

[0050] In practice, firstly, the adaptation compensation value for energy consumption adjustment is extracted from the energy consumption model of the intelligent building's computer room cooling side. This compensation value is used to compensate for the deviation between the model's prediction and actual energy consumption. Based on historical operating data and real-time monitoring data, the model calculates the adaptation compensation value under current conditions through heat balance analysis. That is, adaptation compensation value = actual energy consumption - predicted energy consumption. This typically requires the use of algorithms or software tools, such as linear regression or neural networks, to improve the model's prediction accuracy. Then, based on the actual operating conditions and management needs of the computer room, target information for energy consumption adjustment is set, including the target energy consumption level, the desired upper or lower limit of energy consumption, and cooling requirements under specific loads. Cooling requirements under certain conditions, time window: the energy consumption target to be achieved within certain specific time periods; for example, the energy consumption of the cooling system can be set not to exceed a certain value during peak hours to avoid excessive power consumption; finally, according to the adjustment target information, the output adaptation compensation value is coordinated and corrected, the compensation value is compared with the target information, and it is determined whether the current compensation value meets the set adjustment target. If the compensation value deviates from the target, corresponding adjustments are made. For example, the new compensation value = the old compensation value ± the adjustment coefficient. The adjustment coefficient can be dynamically set according to the actual deviation, and then iterative calculation is performed to ensure that the final compensation value meets the energy consumption target. This will not be elaborated in this application.

[0051] It should be noted that, in this application, the adaptation compensation value represents the value used to correct the difference between the model prediction and the actual energy consumption during the energy consumption adjustment process, ensuring that the model adapts to the current operating conditions; the adjustment target information represents the specific target data used to guide the energy consumption adjustment, including energy consumption level, cooling requirements and time constraints; the coordination correction is a dynamic adjustment process of the adaptation compensation value based on the adjustment target information, to ensure the achievement of the energy consumption control target.

[0052] The feedback control module 500 is used to perform feedback control on the working status of the intelligent building's computer room based on the thermal response deviation in the temperature characteristic identification model of the intelligent building's computer room after adjustment and the adaptation compensation value during energy consumption adjustment in the energy consumption model of the cooling side of the intelligent building's computer room after correction.

[0053] In practice, feedback control of the intelligent building's computer room operating status, based on the thermal response deviation in the adjusted intelligent building computer room operating temperature characteristic identification model and the adaptive compensation value for energy consumption adjustment in the corrected intelligent building computer room cooling-side energy consumption model, can be achieved in the following way: First, monitor the computer room's temperature and energy consumption data in real time to ensure that the collected values ​​are up-to-date. Based on this, extract the thermal response deviation from the temperature characteristic identification model to reflect the difference between the current temperature and the desired temperature. Simultaneously, obtain the adaptive compensation value from the cooling-side energy consumption model to identify the necessity of energy consumption adjustment. Next, based on the extracted data, set the feedback control target. For example, if the thermal response deviation indicates that the temperature is too high, the control target can be set to reduce the temperature to a safe range; if the energy consumption adaptive compensation value indicates that the energy consumption is higher than the target, the control target can be set to reduce energy consumption. Then, adjust the cooling system, such as increasing the fan speed or reducing the airflow. Adjusting temperature settings directly addresses thermal response deviations. Furthermore, by regulating equipment operating conditions, such as reducing load or shutting down non-critical equipment during off-peak hours, overall energy consumption is optimized. During implementation, the control system continuously feeds back current temperature and energy consumption to the model, monitoring and analyzing thermal response deviations and compensation values. The system can set thresholds; when deviations exceed the set range, control measures are automatically triggered. Through real-time data and feedback, the system can iterate and optimize control strategies to achieve optimal energy efficiency and temperature control. Machine learning algorithms improve model prediction accuracy and make feedback control more intelligent. By combining the adjustment results of temperature feature identification models and cooling-side energy consumption models, intelligent building computer rooms can achieve effective feedback control, ensuring optimal operation under dynamic loads and environmental changes, improving energy efficiency, and guaranteeing safe equipment operation. Further details are omitted here.

[0054] Therefore, this application demonstrates that intelligent control of a smart building's computer room can be achieved even when the control response deviation exceeds limits. Specifically, it involves real-time monitoring of the computer room's operational status and environmental information, collecting key data (such as temperature, humidity, and airflow) through sensor technology to achieve comprehensive perception of the computer room environment. This data-driven approach enables the system to promptly identify anomalies, reduce potential failure risks, and thus improve equipment operational stability and safety. By decomposing environmental state information into a steady-state confidence vector and utilizing mathematical modeling and data analysis techniques, the system can quantitatively assess the stability and reliability of the computer room's operation. This assessment provides a basis for temperature control, allowing the cooling system to dynamically adjust based on real-time data, ensuring equipment operates within the optimal temperature range and reducing energy consumption and failure rates. Furthermore, by establishing a temperature feature identification model… By incorporating a temperature correction coefficient, the system can accurately identify and adjust thermal response deviations. This intelligent adjustment mechanism not only improves cooling efficiency but also optimizes energy use, avoiding over- or under-cooling and enhancing energy economy and environmental friendliness. By analyzing system load characteristics, the system can predict fluctuations in data center energy consumption and dynamically adjust using energy management algorithms. This process achieves precise correction of the adaptation compensation value in the cooling-side energy consumption model, thereby optimizing energy allocation when the load changes, reducing overall energy consumption, and improving energy efficiency ratio. The feedback control mechanism enables the system to automatically adjust the data center's operating status based on real-time data and the adjusted model results. This closed-loop control system continuously optimizes the data center's temperature and energy consumption through intelligent algorithms, ensuring the data center maintains optimal operating conditions under different loads and environmental conditions, guaranteeing efficient resource utilization and equipment safety.

[0055] In summary, the technical solution adopted in this application can improve the dynamic adaptability of intelligent control in intelligent building computer rooms under the influence of excessive deviation in control response during intelligent control.

[0056] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0057] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compactdisc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0058] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A high-efficiency intelligent control system for computer rooms suitable for intelligent buildings, characterized in that, The intelligent control system includes: The data acquisition module is used to monitor the operating status of the intelligent building computer room and collect environmental status information of the intelligent building computer room; The processing module is used to decompose the environmental state information to obtain the steady-state confidence vector of the intelligent building equipment room during operation, and to determine the temperature correction coefficient for intelligent control of the operating temperature of the intelligent building equipment room based on the steady-state confidence vector. The adjustment module is used to acquire the temperature control values ​​of the dedicated airflow channel and the airflow channel of the intelligent building computer room, construct a temperature characteristic identification model for the operation of the intelligent building computer room based on the temperature control values, and adjust the thermal response deviation in the temperature characteristic identification model for the operation of the intelligent building computer room through the temperature correction coefficient. Specifically, the decomposition of the environmental state information to obtain the steady-state confidence vector of the intelligent building computer room during operation includes: The environmental state information is weighted to obtain the environmental weight vector during the operation of the intelligent building computer room. The environmental weight vector is mapped onto the steady-state space of the intelligent building computer room during operation to obtain a steady-state operation sample set; The steady-state confidence vector of the intelligent building computer room during operation is determined based on the steady-state operation sample set. Among them, the steady-state confidence vector refers to the degree of operational reliability of the data center under a stable state; The calibration module is used to determine the system load characteristics during the operation of the intelligent building computer room, determine the energy consumption fluctuation during the operation of the intelligent building computer room based on the system load characteristics, and correct the adaptation compensation value in the energy consumption adjustment of the cooling side energy consumption model of the intelligent building computer room through the energy consumption fluctuation. Among them, the adaptation compensation value refers to the value used to correct the difference between model prediction and actual energy consumption during the energy consumption adjustment process; the energy consumption fluctuation refers to the difference between actual energy consumption and predicted energy consumption. The feedback control module is used to perform feedback control on the working status of the intelligent building's computer room based on the thermal response deviation in the temperature characteristic identification model after adjustment and the adaptation compensation value during energy consumption adjustment in the energy consumption model of the cooling side of the intelligent building's computer room after correction.

2. The high-efficiency intelligent control system for computer rooms suitable for intelligent buildings as described in claim 1, characterized in that, The environmental status information of the intelligent building computer room is obtained by reading the database.

3. The high-efficiency intelligent control system for computer rooms suitable for intelligent buildings as described in claim 1, characterized in that, A dedicated airflow channel for a smart building computer room refers to an airflow channel specifically designed to provide cooling air for the equipment within the computer room.

4. The high-efficiency intelligent control system for computer rooms suitable for intelligent buildings as described in claim 1, characterized in that, Airflow channels in a computer room refer to airflow channels that are naturally or artificially formed within the computer room, primarily used for air circulation and ventilation.

5. The high-efficiency intelligent control system for computer rooms suitable for intelligent buildings as described in claim 1, characterized in that, Determining the system load characteristics of a smart building computer room during operation specifically includes: Determine the controlled parameters of the load regulation system during the operation of the intelligent building computer room; Obtain the predicted target value of energy consumption during the operation of the intelligent building computer room; The system load characteristics of the building computer room during operation are determined based on the adjusted parameters and the predicted target values.

6. The high-efficiency intelligent control system for computer rooms suitable for intelligent buildings as described in claim 1, characterized in that, The energy consumption fluctuation of the intelligent building computer room during operation, determined based on the system load characteristics, specifically includes: The output heat balance parameters corresponding to the energy consumption changes during the operation of the intelligent building computer room are extracted from the system load characteristics. Determine the energy consumption control strategy for the operation of the intelligent building computer room; The energy consumption fluctuation during the operation of the intelligent building computer room is determined based on the output heat balance parameters and the energy consumption control strategy.

7. The high-efficiency intelligent control system for computer rooms suitable for intelligent buildings as described in claim 1, characterized in that, The specific methods for correcting the energy consumption fluctuation in the energy consumption model of the cooling side of the intelligent building computer room to adjust the energy consumption compensation value include: The energy consumption model of the cooling side of the intelligent building computer room outputs the adaptation compensation value when adjusting the energy consumption. Determine the adjustment target information when adjusting the energy consumption of intelligent building computer rooms; The adaptation compensation value is coordinated and corrected using the adjusted target information.

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