An intelligent energy-saving control system and method for air conditioning in an efficient refrigeration machine room

Abstract

The present invention discloses a highly efficient intelligent energy-saving control system and method for refrigeration room air conditioners, which relates to the field of intelligent air conditioner control. The control system includes collecting and preprocessing air conditioner operating parameters, room equipment operating data, and room environment data; using a deep Q-learning reinforcement learning algorithm to build a deep neural network to obtain the room heat load demand; introducing a nonlinear dynamic compensation function to calculate the air conditioner dynamic energy efficiency coefficient and the air conditioner refrigeration power, and calculating the air conditioner compressor speed with reference to the air conditioner operating parameters; using a PID controller to perform preliminary frequency conversion on the room air conditioner, and using a multi-stage adjustment mechanism to perform real-time correction on the preliminary frequency conversion results based on changes in the room equipment operating data and room environment data and the frequency conversion situation. The present invention achieves highly efficient air conditioner energy-saving control by combining a deep Q-learning reinforcement learning algorithm, a nonlinear dynamic compensation function, and a PID controller.

Description

Technical Field

[0001] The present invention relates to the field of intelligent air-conditioning control, and in particular to a high-efficiency energy-saving intelligent control system and method for an air-conditioning in a refrigeration room. Background Art

[0002] With the rapid development of information technology, especially in computer room environments such as data centers and communication base stations, the energy consumption of air conditioning systems has become a significant factor restricting computer room management costs and environmental sustainability. Traditional computer room air conditioning systems rely on preset temperature control strategies and fixed cooling capacities, typically maintaining indoor temperatures through constant cooling power from air conditioning equipment. However, this approach suffers from significant energy efficiency losses when adapting to fluctuating computer room loads, environmental fluctuations, and dynamic equipment demands. In recent years, with the advancement of the Internet of Things, artificial intelligence, and control technologies, increasing research and practice have focused on improving the efficiency and reducing energy consumption of computer room air conditioning systems through intelligent and dynamic adjustment. Currently, air conditioning energy efficiency optimization technologies based on methods such as model predictive control (MPC), fuzzy control, and classical PID control are gradually being applied. However, most of these methods rely on static control rules or assumptions about system models, making them difficult to adapt in real time to changes in computer room heat load demand and fluctuations in air conditioning system status in complex real-world environments.

[0003] However, existing air conditioning control technologies still have many shortcomings in terms of dynamic energy efficiency optimization. Although traditional PID control methods perform well in certain application scenarios, their inherent limitation is that they lack sufficient adaptive capabilities and cannot make accurate real-time adjustments based on changes in environmental data and the dynamic changes in the heat load demand of the computer room. Model-based control methods, such as MPC, although they can provide relatively accurate prediction and adjustment, rely on accurate system models and have high computational complexity, making it difficult to achieve rapid response in practical applications. Therefore, how to accurately adjust the air conditioning cooling power and compressor speed through real-time monitoring data analysis and heat load prediction to achieve intelligent energy-saving optimization of the computer room air conditioning system remains an urgent problem to be solved. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a high-efficiency energy-saving intelligent control method for refrigeration room air conditioners to solve the problem of being unable to make accurate real-time adjustments according to changes in environmental data and dynamic changes in the room's heat load requirements.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, the present invention provides an efficient energy-saving intelligent control method for refrigeration room air conditioners, which includes collecting air conditioning operating parameters, machine room equipment operating data and machine room environmental data, and preprocessing the machine room equipment operating data and machine room environmental data; based on the preprocessed machine room equipment operating data and machine room environmental data, using a deep Q-learning reinforcement learning algorithm to build a deep neural network, constructing a heat load assessment model by fitting the Q value function, and obtaining the machine room heat load demand; according to the air conditioning operating parameters, machine room equipment operating data and machine room environmental data, introducing a nonlinear dynamic compensation function to calculate the dynamic energy efficiency coefficient of the air conditioner, and then calculating the air conditioning refrigeration power according to the dynamic energy efficiency coefficient of the air conditioner and the machine room heat load demand, and finally calculating the air conditioning compressor speed with reference to the air conditioning operating parameters; according to the air conditioning compressor speed, using a PID controller to perform preliminary frequency conversion on the machine room air conditioner, while monitoring the changes in the machine room equipment operating data and machine room environmental data, and using a multi-stage adjustment mechanism to perform real-time correction on the preliminary frequency conversion results according to the changes in the machine room equipment operating data and machine room environmental data and the frequency conversion situation.

[0008] As a preferred solution of the energy-saving intelligent control method for high-efficiency refrigeration room air conditioner of the present invention, wherein: the air conditioning operating parameters, the computer room equipment operating data and the computer room environment data are collected, and the computer room equipment operating data and the computer room environment data are pre-processed, the specific steps are:

[0009] The air conditioner operating parameters include the maximum energy efficiency coefficient of the air conditioner, the current speed of the air conditioner compressor, and the maximum speed of the air conditioner compressor, which are collected through the sensor interface of the air conditioner;

[0010] The equipment room equipment operation data includes equipment power consumption, equipment load rate, equipment recommended operating temperature, equipment recommended operating humidity and equipment maximum load, which are collected through smart meters, power consumption metering equipment and equipment documents;

[0011] The computer room environment data includes the internal temperature of the computer room, the external temperature of the computer room, the internal humidity of the computer room, and the external humidity of the computer room, which are collected by temperature and humidity sensors;

[0012] The collected computer room equipment operation data and computer room environment data are cleaned, normalized and standardized, and timestamp calibrated, and transmitted to the data processing unit and stored in the database.

[0013] As a preferred solution of the energy-saving intelligent control method for high-efficiency refrigeration room air conditioners of the present invention, the method comprises the following steps: based on the pre-processed equipment operation data and environment data of the computer room, a deep neural network is constructed using a deep Q-learning reinforcement learning algorithm, and a heat load evaluation model is constructed by fitting the Q value function to obtain the heat load demand of the computer room.

[0014] Extract pre-processed computer room equipment operation data and computer room environment data from the database and set the state space and action space;

[0015] According to the state space and action space, the Q value function is set. The expression of the Q value function is:

[0016]

[0017] Among them, Q(s,a) is the expected return of taking action a in state s, R(s,a) is the immediate reward obtained after taking action a in state s, γ is the discount factor, is the maximum value after taking all possible actions a' in the next state s', where a is the action taken in the action space and s is the state in the state space. is the weighted average symbol;

[0018] A multi-layer perceptron structure was used to construct a feedforward neural network. The input features of the input layer, the number of layers and neurons in the hidden and output layers were set. The ReLU nonlinear activation function was used to improve the fitting ability of the feedforward neural network and to construct a heat load assessment model.

[0019] The heat load assessment model is trained using the Q-value function. The Q-value is updated based on the error between the actual observed instant reward and the predicted value, and the heat load demand of the computer room is output.

[0020] As a preferred solution of the energy-saving intelligent control method for high-efficiency refrigeration room air conditioners of the present invention, wherein: the nonlinear dynamic compensation function is introduced to calculate the dynamic energy efficiency coefficient of the air conditioner based on the air conditioner operating parameters, the equipment operating data of the room and the environment data of the room, and the specific steps are:

[0021] Extract the internal temperature and humidity of the computer room from the computer room environmental data, extract the recommended operating temperature and humidity of the equipment from the computer room equipment operation data, and extract the maximum energy efficiency coefficient of the air conditioner from the air conditioner operation parameters;

[0022] According to the above extracted parameters, the dynamic energy efficiency coefficient of the air conditioner is calculated, and the nonlinear dynamic compensation function expression is:

[0023]

[0024] Among them, η(t) is the dynamic energy efficiency coefficient of the air conditioner at the current time, η max is the maximum energy efficiency coefficient of the air conditioner, T p (t) is the internal temperature of the computer room at the current time, H p (t) is the humidity inside the computer room at the current time, T o Recommended operating temperature for the equipment, H ois the recommended operating humidity for the device, α1 is the dynamic energy efficiency temperature adjustment coefficient, α2 is the dynamic energy efficiency humidity adjustment coefficient, and t is the current time.

[0025] As a preferred solution of the energy-saving intelligent control method for the high-efficiency refrigeration room air conditioner of the present invention, wherein: the air conditioner refrigeration power is calculated by the air conditioner dynamic energy efficiency coefficient and the heat load demand of the room, and finally the air conditioner compressor speed is calculated with reference to the air conditioner operating parameters. The specific steps are:

[0026] The air conditioning cooling power is calculated based on the dynamic energy efficiency coefficient of the air conditioner and the heat load demand of the computer room. The expression is:

[0027] P = E·η(t);

[0028] Where P is the cooling power of the air conditioner, E is the heat load demand of the computer room, and η(t) is the dynamic energy efficiency coefficient of the air conditioner at the current time;

[0029] The maximum speed of the air conditioner compressor is extracted from the air conditioner operating parameters. Combined with the heat load demand of the computer room, the cooling power of the air conditioner, and the computer room environment data, the speed of the air conditioner compressor is calculated using the speed adjustment function. The expression is:

[0030]

[0031] Among them, N is the air conditioning compressor speed, that is, the target speed, N max is the maximum speed of the air-conditioning compressor, β1 is the influence factor of temperature change on cooling power, β2 is the nonlinear adjustment coefficient caused by temperature fluctuation, and β3 is the influence factor of humidity on cooling power.

[0032] As a preferred solution of the energy-saving intelligent control method for the high-efficiency refrigeration room air conditioner of the present invention, wherein: the PID controller is used to perform preliminary frequency conversion on the room air conditioner according to the speed of the air conditioner compressor, and the specific steps are as follows:

[0033] The current speed of the air-conditioning compressor is extracted from the air-conditioning operating parameters, and the control signal is calculated using the PID controller to adjust the current speed of the air-conditioning compressor. The expression is:

[0034]

[0035] e(t)=NN now (t);

[0036] Among them, u(t) is the control signal, K1 is the proportional coefficient, e(t) is the speed error at the current time t, N now (t) is the current speed of the air-conditioning compressor, K2 is the integral coefficient, is the error accumulation, τ is the time integral variable, K3 is the differential coefficient, is the rate of change of the error;

[0037] Adjust and optimize PID parameters through manual adjustment method;

[0038] The control signal is input to the inverter, which adjusts the speed of the compressor by changing the frequency of the current input to the compressor.

[0039] As a preferred solution of the energy-saving intelligent control method for the high-efficiency refrigeration room air conditioner of the present invention, wherein: the multi-stage adjustment mechanism is used, and the specific steps are:

[0040] The operating status of the computer room is divided into the initial adjustment stage, the stable adjustment stage and the fine adjustment stage;

[0041] During the initial adjustment phase, the speed of the air conditioning compressor is adjusted significantly through the PID controller;

[0042] Reduce the adjustment range of the PID controller during the stable regulation stage;

[0043] In the fine adjustment stage, the speed of the air conditioning compressor is fine-tuned through the PID controller.

[0044] In a second aspect, the present invention provides a high-efficiency energy-saving intelligent control system for refrigeration room air conditioners, comprising a data acquisition module, a heat load assessment module, a compressor speed calculation module, and a PID multi-stage control module;

[0045] The data acquisition module is used to collect air conditioning operating parameters, computer room equipment operating data and computer room environment data, and pre-process the computer room equipment operating data and computer room environment data;

[0046] The heat load assessment module is used to build a deep neural network based on the pre-processed equipment operation data and environment data of the computer room using the deep Q-learning reinforcement learning algorithm, and to construct a heat load assessment model by fitting the Q value function to obtain the heat load demand of the computer room;

[0047] The compressor speed calculation module is used to calculate the dynamic energy efficiency coefficient of the air conditioner based on the air conditioner operating parameters, the computer room equipment operating data and the computer room environment data, introduce a nonlinear dynamic compensation function, and then calculate the air conditioner cooling power based on the dynamic energy efficiency coefficient of the air conditioner and the heat load demand of the computer room. Finally, the air conditioner compressor speed is calculated with reference to the air conditioner operating parameters;

[0048] The PID multi-stage control module is used to perform preliminary frequency conversion on the computer room air conditioner using a PID controller based on the speed of the air conditioner compressor, while monitoring changes in the computer room equipment operating data and computer room environmental data. It uses a multi-stage adjustment mechanism to make real-time corrections to the preliminary frequency conversion results based on changes in the computer room equipment operating data and computer room environmental data and the frequency conversion conditions.

[0049] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the high-efficiency refrigeration room air conditioner energy-saving intelligent control method as described in the first aspect of the present invention is implemented.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the high-efficiency refrigeration room air conditioner energy-saving intelligent control method as described in the first aspect of the present invention is implemented.

[0051] The beneficial effects of the present invention are as follows: by collecting and preprocessing air-conditioning operating parameters, computer room equipment data, and environmental data, combined with a deep Q-learning reinforcement learning algorithm, a nonlinear dynamic compensation function, and a PID controller, an efficient intelligent energy-saving control method for computer room air conditioners is constructed. This method can dynamically optimize the operation of the air-conditioning system according to real-time environmental and load changes, and accurately calculate the air-conditioning cooling power and compressor speed. By optimizing the thermal load assessment through deep learning, combined with a dynamic energy efficiency coefficient and a multi-stage adjustment mechanism, the problems of low energy efficiency and energy waste in traditional air-conditioning systems are avoided, and more efficient energy-saving control is achieved. This not only improves the adaptability and energy efficiency of the air conditioner, extends the life of the equipment, but also reduces the energy consumption of the computer room. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 This is a flow chart of the energy-saving intelligent control method for the high-efficiency refrigeration room air conditioner in Example 1.

[0054] Figure 2 This is a module diagram of the energy-saving intelligent control system for the high-efficiency refrigeration room air conditioner in Example 1. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0058] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a high-efficiency energy-saving intelligent control method for a refrigeration room air conditioner, comprising the following steps:

[0059] S1: Collect air conditioning operating parameters, computer room equipment operating data, and computer room environment data, and pre-process the computer room equipment operating data and computer room environment data.

[0060] Specifically, the following steps are included:

[0061] S1.1: Air conditioner operating parameters include the maximum energy efficiency coefficient of the air conditioner, the current speed of the air conditioner compressor, and the maximum speed of the air conditioner compressor, which are collected through the sensor interface of the air conditioner.

[0062] Specifically, the maximum energy efficiency coefficient is obtained from the air conditioner's operating manual or device firmware parameters and serves as the air conditioner's theoretical maximum energy efficiency standard. The compressor speed (current speed and maximum speed) is directly collected by the air conditioner's built-in sensor and transmitted to the data processing unit via the data transmission interface.

[0063] S1.2: Equipment operating data in the computer room includes equipment power consumption, equipment load rate, recommended equipment operating temperature, recommended equipment operating humidity, and maximum equipment load. This data is collected through smart meters, power consumption metering devices, and equipment documentation.

[0064] Specifically, smart meters or power consumption meters collect real-time current, voltage, and power data from devices to provide accurate device power consumption. The device load factor is calculated using built-in sensors or the ratio of power consumption to maximum rated power. The load factor reflects the device's current workload and affects its energy efficiency and operating status. Recommended operating temperature and humidity are obtained from the device's operating manual or manufacturer's standard values, or collected using the device's built-in temperature and humidity sensors. The recommended operating temperature and humidity represent the optimal balance between device performance and ensure efficient operation.

[0065] S1.3: The computer room environment data includes the internal temperature, external temperature, internal humidity, and external humidity of the computer room, which are collected through temperature and humidity sensors.

[0066] Specifically, temperature and humidity sensors are installed at various locations inside and outside the computer room to collect data. The internal temperature and humidity are used to adjust the air conditioning settings, while the external temperature and humidity are input into a heat load assessment model to evaluate the impact of the external environment on the air conditioning operation inside the computer room and assist in energy optimization.

[0067] S1.4: Perform data cleaning, data normalization and standardization, and time stamp calibration on the collected computer room equipment operation data and computer room environment data, and transmit them to the data processing unit and store them in the database.

[0068] It should be understood that data cleaning refers to removing outliers, missing values, and noisy data to ensure the quality and validity of the data collected. Interpolation is used to fill in missing data and remove unreasonable outliers.

[0069] Data normalization refers to scaling the collected equipment operation data and environment data to a uniform range (such as [0,1]) to eliminate the impact of data of different dimensions.

[0070] Data normalization involves converting the collected data on equipment operation and environment in the computer room into a standard normal distribution, with a mean of 0 and a standard deviation of 1. This is done to prevent poor training results and the generalization capability of the heat load assessment model from being affected by large discrepancies in data dimensions when constructing the model.

[0071] Timestamp alignment is used to ensure time synchronization across different data sources, ensuring data consistency due to potential latency in data collection sources. Time synchronization technologies such as the Network Time Protocol (NTP) can help align timestamps across devices, ensuring data is integrated along the same timeline.

[0072] The collected data are transmitted to the data processing unit (central server or cloud platform) via LAN connection and stored in the MySQL database.

[0073] Ideally, by collecting and preprocessing air conditioner operating parameters, computer room equipment operating data, and computer room environmental data, we can monitor and accurately understand the operating status, load conditions, and environmental changes of air conditioners and equipment in real time. The air conditioner's maximum energy efficiency coefficient and compressor speed data provide the basis for energy efficiency evaluation and optimization. Equipment power consumption, load rate, and recommended operating temperature and humidity data help improve equipment operating efficiency and extend equipment life. Data on temperature and humidity inside and outside the computer room optimizes the air conditioner's operating status. Data preprocessing ensures the quality and consistency of data collection, providing high-quality data support for subsequent heat load assessment and air conditioner energy efficiency optimization, ultimately achieving energy savings, improved energy efficiency, and enhanced stability.

[0074] S2: Based on the pre-processed equipment operation data and environment data of the computer room, a deep neural network is built using the deep Q-learning reinforcement learning algorithm. A heat load assessment model is constructed by fitting the Q-value function to obtain the heat load demand of the computer room.

[0075] Specifically, the following steps are included:

[0076] S2.1: Extract the pre-processed computer room equipment operation data and computer room environment data from the database, and set the state space and action space.

[0077] Specifically, SQL query statements are used to extract pre-processed computer room equipment operation data and computer room environment data from the MySQL database.

[0078] The state space includes all relevant information about the current status of the computer room air conditioner. Each state describes the computer room environment and equipment operation status, including: temperature and humidity inside the computer room, temperature and humidity outside the computer room, equipment load rate, and equipment power consumption.

[0079] The action space includes all possible operations or adjustment measures that can be selected, including: air conditioning compressor speed adjustment, air conditioning temperature setting and equipment operating status adjustment.

[0080] S2.2: According to the state space and action space, set the Q value function. The Q value function expression is:

[0081]

[0082] Among them, Q(s,a) is the expected return of taking action a in state s, R(s,a) is the immediate reward obtained after taking action a in state s, γ is the discount factor, is the maximum value after taking all possible actions a' in the next state s', where a is the action taken in the action space and s is the state in the state space. is the weighted average symbol.

[0083] S2.3: Use a multi-layer perceptron structure to build a feedforward neural network, set the input features of the input layer, the number of layers and neurons in the hidden layer and output layer, use the ReLU nonlinear activation function to improve the fitting ability of the feedforward neural network, and build a heat load assessment model.

[0084] Specifically, the feedforward neural network structure includes:

[0085] Input layer: The input layer receives data about equipment operation and the environment in the computer room and passes it to the next layer of the network. The input layer receives data including the temperature and humidity inside the computer room, the temperature and humidity outside the computer room, equipment load rate, equipment power consumption, the current speed of the air conditioner compressor, and the maximum energy efficiency coefficient of the air conditioner. These input features serve as the network's input vectors. The number of neurons in the input layer is equal to the number of features.

[0086] Hidden Layer: The hidden layer is the core of the multilayer perceptron and contains multiple neurons responsible for extracting higher-level features from the input data. Each hidden layer neuron is connected to all neurons in the previous layer. After a weighted summation, a nonlinear transformation is performed using the ReLU activation function. The number of hidden layers is set to two, and the number of neurons in each layer is determined based on experimental tuning during training.

[0087] Output layer: The number of neurons in the output layer is equal to the number of actions in the action space, and it outputs the heat load demand of the computer room.

[0088] Furthermore, the ReLU function can effectively avoid the gradient vanishing problem and has strong nonlinear fitting capabilities, making it suitable for processing complex environments and device data relationships.

[0089] S2.4: Use the Q-value function to train the heat load assessment model. Update the Q-value based on the error between the actual observed instant reward and the predicted value, and output the heat load demand of the computer room.

[0090] Preferably, the Q-value function is used to train the heat load assessment model, and the Q-value is updated by the error between the actual observed instant reward and the predicted value. The expression is:

[0091]

[0092] Among them, Q(s t ,a t ) is the Q value at the current time, θ is the learning rate, which controls the step size of each update, R(s t ,a t ) is the instantaneous reward at the current time, i.e., the actual heat load demand, is the optimal Q value at the next time;

[0093] The weights and biases of the feedforward neural network are randomly initialized, and the weights of the feedforward neural network are adjusted through the backpropagation algorithm. Through multiple iterations, the Q value of the feedforward neural network becomes more and more accurate, and eventually the heat load demand of the computer room air conditioner can be effectively evaluated.

[0094] Finally, by using the trained heat load assessment model and inputting new computer room environmental data, the network can output a heat load prediction value. This prediction value is the current heat load demand of the computer room and is used to adjust and optimize the air conditioning equipment.

[0095] Preferably, a deep Q-learning-based reinforcement learning algorithm, combined with a multilayer perceptron neural network, accurately assesses and optimizes the heat load requirements of computer room air conditioners. By extracting preprocessed computer room environmental data from a database and setting the state space and action space, it is able to intelligently learn and adjust air conditioner operating parameters, such as compressor speed and temperature settings, to improve energy efficiency and stability. Reinforcement learning trains the model through the Q-value function to achieve automated decision-making, continuously optimize heat load assessment, and dynamically adjust control strategies based on real-time data. This approach not only improves the operating efficiency, energy savings, and equipment life of the air conditioner, but also allows for flexible adaptation to the changing environment and equipment status, reducing computer room operating costs and improving environmental stability.

[0096] S3: Based on the air conditioner operating parameters, computer room equipment operating data, and computer room environmental data, a nonlinear dynamic compensation function is introduced to calculate the air conditioner dynamic energy efficiency coefficient. Then, the air conditioner cooling power is calculated based on the air conditioner dynamic energy efficiency coefficient and the computer room heat load demand. Finally, the air conditioner compressor speed is calculated based on the air conditioner operating parameters.

[0097] Specifically, the following steps are included:

[0098] S3.1: Extract the internal temperature and humidity of the computer room from the computer room environmental data, extract the recommended operating temperature and humidity of the equipment from the equipment operating data in the computer room, and extract the maximum energy efficiency coefficient of the air conditioner from the air conditioner operating parameters.

[0099] S3.2: Based on the above extracted parameters, calculate the dynamic energy efficiency coefficient of the air conditioner. The nonlinear dynamic compensation function expression is:

[0100]

[0101] Among them, η(t) is the dynamic energy efficiency coefficient of the air conditioner at the current time, η max is the maximum energy efficiency coefficient of the air conditioner, T p (t) is the internal temperature of the computer room at the current time, H p (t) is the humidity inside the computer room at the current time, T o Recommended operating temperature for the equipment, H ois the recommended operating humidity for the device, α1 is the dynamic energy efficiency temperature adjustment coefficient, α2 is the dynamic energy efficiency humidity adjustment coefficient, and t is the current time.

[0102] Preferably, the dynamic energy efficiency coefficient of the air conditioner reflects the working efficiency of the air conditioner under specific conditions. Ideally, the air conditioner should adjust its energy efficiency according to the temperature and humidity of the computer room and the recommended operating conditions of the equipment. If the temperature and humidity of the computer room deviate from the recommended values, it will affect the working efficiency of the air conditioner. Therefore, it is necessary to adjust the energy efficiency coefficient of the air conditioner through a nonlinear dynamic compensation function. This function will dynamically adjust the energy efficiency coefficient of the air conditioner based on the current temperature and humidity deviation (that is, the difference between the current temperature and the recommended temperature, and the difference between the humidity and the recommended humidity). When the temperature and humidity deviation of the computer room is too large, the energy efficiency coefficient of the air conditioner will decrease, which means that the air conditioner needs to consume more energy to maintain the same cooling effect, that is, the energy efficiency of the air conditioner deteriorates and the cooling effect decreases. Therefore, the air conditioner will optimize its energy efficiency according to real-time environmental changes to adapt to the heat load requirements of the computer room.

[0103] S3.3: Calculate the cooling power of the air conditioner based on the dynamic energy efficiency coefficient of the air conditioner and the heat load demand of the computer room. The expression is:

[0104] P = E·η(t);

[0105] Where P is the cooling power of the air conditioner, E is the heat load demand of the computer room, and η(t) is the dynamic energy efficiency coefficient of the air conditioner at the current time.

[0106] It should be understood that the heat load demand and air conditioning energy efficiency should be combined to ensure that the air conditioner provides sufficient cooling power at the most appropriate energy efficiency state to meet the actual needs of the computer room.

[0107] S3.4: Extract the maximum speed of the air conditioner compressor from the air conditioner operating parameters. Combined with the room's heat load demand, the air conditioner's cooling power, and the room's environmental data, calculate the air conditioner compressor speed using the speed adjustment function. The expression is:

[0108]

[0109] Among them, N is the air conditioning compressor speed, that is, the target speed, N max is the maximum speed of the air-conditioning compressor, β1 is the influence factor of temperature change on cooling power, β2 is the nonlinear adjustment coefficient caused by temperature fluctuation, and β3 is the influence factor of humidity on cooling power.

[0110] Preferably, by combining the effects of temperature and humidity, the air conditioner can achieve more precise dynamic adjustment, ensuring that the optimal air conditioner compressor speed is calculated under different environmental conditions.

[0111] The optimal approach is to adjust the air conditioner's energy efficiency coefficient (EEC) by introducing a nonlinear dynamic compensation function. This optimizes the air conditioner's cooling power and compressor speed by combining the room's heat load demand, the air conditioner's dynamic EEC, and the room's environmental data. The dynamic EEC adjusts in real time based on the room's temperature and humidity deviations, ensuring efficient operation under varying environmental conditions. By incorporating the effects of temperature and humidity into the speed adjustment function, the air conditioner's compressor speed is accurately calculated, achieving optimal cooling performance and energy efficiency. This optimization mechanism enhances the air conditioner's adaptability, reduces energy waste, ensures room environmental stability, and improves the air conditioner's overall operating efficiency.

[0112] S4: Based on the air conditioning compressor speed, use the PID controller to perform preliminary frequency conversion on the computer room air conditioner. At the same time, monitor the changes in the computer room equipment operation data and computer room environment data. Use a multi-stage adjustment mechanism to make real-time corrections to the preliminary frequency conversion results based on the changes and frequency conversion conditions of the computer room equipment operation data and computer room environment data.

[0113] Specifically, the following steps are included:

[0114] S4.1: Extract the current speed of the air conditioner compressor from the air conditioner operating parameters, use the PID controller to calculate the control signal, and adjust the current speed of the air conditioner compressor. The expression is:

[0115]

[0116] e(t)=NN now (t);

[0117] Among them, u(t) is the control signal, K1 is the proportional coefficient, e(t) is the speed error at the current time t, N now (t) is the current speed of the air-conditioning compressor, K2 is the integral coefficient, is the error accumulation, τ is the time integral variable, K3 is the differential coefficient, is the rate of change of the error.

[0118] Specifically, K1 controls the proportional effect of error on the output. This coefficient adjusts the air conditioner speed response. K2 controls the cumulative effect of historical errors on the output. By integrating and accumulating errors, long-term deviations are reduced. K3 controls the impact of the error change rate. This coefficient helps predict error trends and enable proactive adjustments.

[0119] S4.2: Adjust and optimize PID parameters through manual adjustment method.

[0120] Specifically, the PID parameters are adjusted gradually based on actual operating data and responses. First, the proportional coefficient K1 is adjusted, followed by the integral coefficient K2 to eliminate steady-state error, and finally, the differential coefficient K3 is adjusted to reduce overshoot and improve response speed. Given the potential for large temperature fluctuations in the computer room environment, the differential term K3 of the PID controller may need to be relatively small to avoid over-adjustment.

[0121] S4.3: The control signal is input to the inverter, and the inverter adjusts the speed of the compressor by changing the frequency of the current input to the compressor.

[0122] Specifically, the inverter adjusts the compressor's speed, and thus the air conditioner's cooling capacity, by varying the frequency of the current input to the compressor. Based on the control signal output by the PID controller, the inverter converts the frequency adjustment into a voltage signal and outputs it to the air conditioner's compressor, which in turn affects its speed.

[0123] S4.4: Divide the operating status of the computer room into the preliminary adjustment stage, the stable adjustment stage, and the fine adjustment stage.

[0124] Optimally, by dividing air conditioning operation into different adjustment phases and dynamically adjusting the PID controller parameters based on changes in the computer room environment data, more flexible and precise air conditioning control can be achieved. This multi-stage adjustment mechanism ensures efficient operation of the air conditioner in different operating states, thereby reducing energy waste, improving the stability of the computer room environment, and further enhancing the overall energy efficiency and stability of air conditioning operation.

[0125] S4.4.1: During the initial adjustment phase, the air conditioning compressor speed is adjusted significantly using the PID controller.

[0126] Specifically, when the room's heat load demand (E) exceeds 80% of the equipment's maximum load, the temperature fluctuates by more than 2°C, and the humidity fluctuates by more than 10%, the initial adjustment phase begins. The goal is to quickly respond to fluctuations in the room's temperature and humidity, preventing them from exceeding set thresholds to protect the equipment.

[0127] Furthermore, increasing K1 allows the PID controller to react quickly to temperature or humidity fluctuations. This helps the air conditioner make larger speed adjustments when the load changes. Increasing K2 reduces long-term errors and ensures that temperature and humidity quickly return to a stable range even with large fluctuations. Increasing K3 improves the response to the rate of change of temperature and humidity, preventing overreaction of the control signal and oscillation.

[0128] Increasing the amplitude of the control signal allows the air conditioner compressor speed to adjust to the required level more quickly to cope with changes in the room's heat load. By increasing the adjustment amplitude and controlling the air conditioner compressor speed, the air conditioner maintains appropriate cooling capacity even when the load fluctuates significantly.

[0129] S4.4.2: Reduce the adjustment range of the PID controller during the stable regulation phase.

[0130] Specifically, when the room's heat load demand is 50% ≤ E ≤ 80% of the equipment's maximum load, the temperature change rate is less than 0.1°C per minute, and the humidity change rate is less than 0.5% per minute, the system enters the stable regulation phase. The goal is to maintain stable temperature and humidity in the room, reduce air conditioning energy consumption, and maintain a balance between cooling capacity and heat load demand.

[0131] Furthermore, lowering K1 slows down the control signal response and avoids over-regulation. Lowering K2 reduces over-reliance on historical errors and prevents the controller from entering an "over-compensation" state. Lowering K3 reduces over-response to temperature and humidity change rates and avoids frequent control signal fluctuations.

[0132] The amplitude of the control signal changes gradually decreases, gradually adjusting the compressor speed to a more stable state. By reducing the speed adjustment range, the air conditioner's overreaction to environmental changes is reduced, saving energy and ensuring a stable computer room environment.

[0133] S4.4.3: During the fine adjustment phase, the speed of the air conditioning compressor is fine-tuned through the PID controller.

[0134] Specifically, when the room's heat load demand (E) is less than 50% of the equipment's maximum load, the temperature fluctuation is less than 0.5°C, and the humidity fluctuation is less than 2%, the fine-tuning phase begins. The goal is to meticulously adjust the air conditioning system to maintain stable temperature and humidity in the room under low-load conditions with minimal energy consumption.

[0135] Furthermore, further reducing K1 ensures fine adjustments to small temperature and humidity changes. Maintaining a low K2 prevents over-response to accumulated long-term errors. Increasing K3 allows for quick response to subtle changes in temperature and humidity, ensuring precise maintenance of temperature and humidity stability under light loads.

[0136] Minimize the amplitude of control signal fluctuations, adjusting the air conditioner compressor speed only within a very small range to precisely match the actual heat load requirements of the computer room. During this phase, slight speed adjustments are primarily used to ensure that the air conditioner maintains optimal energy efficiency while the computer room environment stabilizes.

[0137] Optimally, by employing a PID controller and a multi-stage adjustment mechanism, the air conditioner can respond to changes in the computer room environment in real time, achieving precise control of temperature and humidity while optimizing its energy efficiency, reducing energy consumption and lowering operating costs. Dynamically adjusting the air conditioner compressor speed based on computer room environmental data ensures stable operation under varying load conditions, minimizing equipment wear and extending service life. Furthermore, adjusting the PID parameters improves the air conditioner's response speed and adaptability, effectively reducing the impact of environmental fluctuations on the equipment. Fine-tuning ensures that the air conditioner always operates in optimal working condition, enhancing its reliability, maintainability, and overall performance.

[0138] This embodiment also provides a high-efficiency energy-saving intelligent control system for a refrigeration computer room air conditioner, comprising: a data acquisition module for collecting air conditioner operating parameters, computer room equipment operating data, and computer room environmental data, and preprocessing the computer room equipment operating data and computer room environmental data; a heat load assessment module for building a deep neural network using a deep Q-learning reinforcement learning algorithm based on the preprocessed computer room equipment operating data and computer room environmental data, constructing a heat load assessment model by fitting a Q-value function, and obtaining the computer room heat load demand; a compressor speed calculation module for calculating the air conditioner dynamic energy efficiency coefficient based on the air conditioner operating parameters, computer room equipment operating data, and computer room environmental data by introducing a nonlinear dynamic compensation function, then calculating the air conditioner cooling power based on the air conditioner dynamic energy efficiency coefficient and the computer room heat load demand, and finally calculating the air conditioner compressor speed with reference to the air conditioner operating parameters; and a PID multi-stage control module for performing preliminary frequency conversion on the computer room air conditioner using a PID controller based on the air conditioner compressor speed, while simultaneously monitoring changes in the computer room equipment operating data and computer room environmental data, and using a multi-stage adjustment mechanism to make real-time corrections to the preliminary frequency conversion results based on changes in the computer room equipment operating data and computer room environmental data and the frequency conversion status.

[0139] This embodiment also provides a computer device suitable for the case of a high-efficiency refrigeration room air conditioner energy-saving intelligent control method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the high-efficiency refrigeration room air conditioner energy-saving intelligent control method proposed in the above embodiment.

[0140] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0141] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the energy-saving intelligent control method for a high-efficiency refrigeration room air conditioner as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0142] In summary, the present invention designs an efficient energy-saving intelligent control method for computer room air conditioners by collecting and preprocessing air-conditioning operating parameters, computer room equipment data, and environmental data, combined with a deep Q-learning reinforcement learning algorithm, a nonlinear dynamic compensation function, and a PID controller. This method can dynamically optimize the operation of the air-conditioning system according to real-time environmental and load changes, and accurately calculate the air-conditioning refrigeration power and compressor speed. By optimizing the thermal load assessment through deep learning, combined with a dynamic energy efficiency coefficient and a multi-stage adjustment mechanism, the problems of low energy efficiency and energy waste in traditional air-conditioning systems are avoided, and more efficient energy-saving control is achieved. This not only improves the adaptability and energy efficiency of the air conditioner, extends the life of the equipment, but also reduces the energy consumption of the computer room.

[0143] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A high-efficiency energy-saving intelligent control method for a refrigeration room air conditioner, characterized by: include, Collect air-conditioning operating parameters, computer room equipment operating data, and computer room environment data, and pre-process the computer room equipment operating data and computer room environment data; Based on the pre-processed equipment operation data and environment data of the computer room, a deep neural network is built using the deep Q-learning reinforcement learning algorithm. A heat load assessment model is constructed by fitting the Q-value function to obtain the heat load demand of the computer room. Based on the air conditioner operating parameters, equipment operating data, and environmental data, a nonlinear dynamic compensation function is introduced to calculate the dynamic energy efficiency coefficient of the air conditioner. The air conditioner cooling power is then calculated using the dynamic energy efficiency coefficient and the heat load demand of the computer room. Finally, the air conditioner compressor speed is calculated based on the air conditioner operating parameters. Based on the air-conditioning compressor speed, a PID controller is used to perform preliminary frequency conversion on the computer room air conditioner. At the same time, the changes in the computer room equipment operating data and computer room environmental data are monitored. A multi-stage adjustment mechanism is used to make real-time corrections to the preliminary frequency conversion results based on the changes and frequency conversion conditions of the computer room equipment operating data and computer room environmental data.

2. The high-efficiency energy-saving intelligent control method for a refrigeration room air conditioner according to claim 1, characterized in that: The specific steps of collecting air-conditioning operation parameters, computer room equipment operation data and computer room environment data, and pre-processing the computer room equipment operation data and computer room environment data are as follows: The air conditioner operating parameters include the maximum energy efficiency coefficient of the air conditioner, the current speed of the air conditioner compressor, and the maximum speed of the air conditioner compressor, which are collected through the sensor interface of the air conditioner; The equipment room equipment operation data includes equipment power consumption, equipment load rate, equipment recommended operating temperature, equipment recommended operating humidity and equipment maximum load, which are collected through smart meters, power consumption metering equipment and equipment documents; The computer room environment data includes the internal temperature of the computer room, the external temperature of the computer room, the internal humidity of the computer room, and the external humidity of the computer room, which are collected by temperature and humidity sensors; The collected computer room equipment operation data and computer room environment data are cleaned, normalized and standardized, and timestamp calibrated, and transmitted to the data processing unit and stored in the database.

3. The high-efficiency energy-saving intelligent control method for a refrigeration room air conditioner according to claim 2, characterized in that: Based on the pre-processed equipment operation data and environment data of the computer room, a deep neural network is built using a deep Q-learning reinforcement learning algorithm, and a heat load evaluation model is constructed by fitting the Q value function to obtain the heat load demand of the computer room. The specific steps are: Extract pre-processed computer room equipment operation data and computer room environment data from the database and set the state space and action space; According to the state space and action space, the Q value function is set. The expression of the Q value function is: Among them, Q(s, a) is the expected return of taking action a in state s, R(s, a) is the immediate reward obtained after taking action a in state s, γ is the discount factor, is the maximum value after taking all possible actions a' in the next state s', a is the action taken in the action space, s is the state in the state space, is the weighted average symbol; A multi-layer perceptron structure was used to construct a feedforward neural network. The input features of the input layer, the number of layers and neurons in the hidden and output layers were set. The ReLU nonlinear activation function was used to improve the fitting ability of the feedforward neural network and to construct a heat load assessment model. The heat load assessment model is trained using the Q-value function. The Q-value is updated based on the error between the actual observed instant reward and the predicted value, and the heat load demand of the computer room is output.

4. The high-efficiency energy-saving intelligent control method for a refrigeration room air conditioner according to claim 3, characterized in that: The nonlinear dynamic compensation function is introduced to calculate the dynamic energy efficiency coefficient of the air conditioner according to the air conditioner operating parameters, the equipment operating data of the computer room and the computer room environment data. The specific steps are: Extract the internal temperature and humidity of the computer room from the computer room environmental data, extract the recommended operating temperature and humidity of the equipment from the computer room equipment operation data, and extract the maximum energy efficiency coefficient of the air conditioner from the air conditioner operation parameters; According to the above extracted parameters, the dynamic energy efficiency coefficient of the air conditioner is calculated, and the nonlinear dynamic compensation function expression is: Among them, η(t) is the dynamic energy efficiency coefficient of the air conditioner at the current time, η max is the maximum energy efficiency coefficient of the air conditioner, T p (t) is the internal temperature of the computer room at the current time, H p (t) is the humidity inside the computer room at the current time, T o Recommended operating temperature for the equipment, H o is the recommended operating humidity for the device, α1 is the dynamic energy efficiency temperature adjustment coefficient, α2 is the dynamic energy efficiency humidity adjustment coefficient, and t is the current time.

5. The high-efficiency energy-saving intelligent control method for a refrigeration room air conditioner according to claim 4, characterized in that: The air conditioning cooling power is calculated by the air conditioning dynamic energy efficiency coefficient and the heat load demand of the computer room, and finally the air conditioning compressor speed is calculated by referring to the air conditioning operating parameters. The specific steps are: The air conditioning cooling power is calculated based on the dynamic energy efficiency coefficient of the air conditioner and the heat load demand of the computer room. The expression is: P = E·η(t); Where P is the cooling power of the air conditioner, E is the heat load demand of the computer room, and η(t) is the dynamic energy efficiency coefficient of the air conditioner at the current time; The maximum speed of the air conditioner compressor is extracted from the air conditioner operating parameters. Combined with the heat load demand of the computer room, the cooling power of the air conditioner, and the computer room environment data, the speed of the air conditioner compressor is calculated using the speed adjustment function. The expression is: Among them, N is the air conditioning compressor speed, that is, the target speed, N max is the maximum speed of the air-conditioning compressor, β1 is the influence factor of temperature change on cooling power, β2 is the nonlinear adjustment coefficient caused by temperature fluctuation, and β3 is the influence factor of humidity on cooling power.

6. The high-efficiency energy-saving intelligent control method for a refrigeration room air conditioner according to claim 5, characterized in that: The PID controller is used to perform preliminary frequency conversion on the computer room air conditioner according to the air conditioner compressor speed. The specific steps are as follows: The current speed of the air-conditioning compressor is extracted from the air-conditioning operating parameters, and the control signal is calculated using the PID controller to adjust the current speed of the air-conditioning compressor. The expression is: e(t)=N-N now (t); Among them, u(t) is the control signal, K1 is the proportional coefficient, e(t) is the speed error at the current time t, N now (t) is the current speed of the air-conditioning compressor, K2 is the integral coefficient, is the error accumulation, τ is the time integral variable, K3 is the differential coefficient, is the rate of change of the error; Adjust and optimize PID parameters through manual adjustment method; The control signal is input to the inverter, which adjusts the speed of the compressor by changing the frequency of the current input to the compressor.

7. The high-efficiency energy-saving intelligent control method for a refrigeration room air conditioner according to claim 6, characterized in that: The multi-stage adjustment mechanism is used, and the specific steps are: The operating status of the computer room is divided into the initial adjustment stage, the stable adjustment stage and the fine adjustment stage; During the initial adjustment phase, the speed of the air conditioning compressor is adjusted significantly through the PID controller; Reduce the adjustment range of the PID controller during the stable regulation stage; In the fine adjustment stage, the speed of the air conditioning compressor is fine-tuned through the PID controller.

8. A high-efficiency refrigeration room air conditioner energy-saving intelligent control system, based on the high-efficiency refrigeration room air conditioner energy-saving intelligent control method according to any one of claims 1 to 7, characterized in that: Including data acquisition module, heat load assessment module, compressor speed calculation module and PID multi-stage control module; The data acquisition module is used to collect air conditioning operating parameters, computer room equipment operating data and computer room environment data, and pre-process the computer room equipment operating data and computer room environment data; The heat load assessment module is used to build a deep neural network based on the pre-processed equipment operation data and environment data of the computer room using the deep Q-learning reinforcement learning algorithm, and to construct a heat load assessment model by fitting the Q value function to obtain the heat load demand of the computer room; The compressor speed calculation module is used to calculate the dynamic energy efficiency coefficient of the air conditioner based on the air conditioner operating parameters, the computer room equipment operating data and the computer room environment data, introduce a nonlinear dynamic compensation function, and then calculate the air conditioner cooling power based on the dynamic energy efficiency coefficient of the air conditioner and the heat load demand of the computer room. Finally, the air conditioner compressor speed is calculated with reference to the air conditioner operating parameters; The PID multi-stage control module is used to perform preliminary frequency conversion on the computer room air conditioner using a PID controller based on the speed of the air conditioner compressor, while monitoring changes in the computer room equipment operating data and computer room environmental data. It uses a multi-stage adjustment mechanism to make real-time corrections to the preliminary frequency conversion results based on changes in the computer room equipment operating data and computer room environmental data and the frequency conversion conditions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the high-efficiency energy-saving intelligent control method for a refrigeration room air conditioner according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the high-efficiency energy-saving intelligent control method for a refrigeration room air conditioner according to any one of claims 1 to 7 are implemented.

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