A control method and device for dehumidification heat compensation of a constant temperature and humidity air conditioning unit

Abstract

The present invention provides a control method and device for dehumidification heat compensation of a constant temperature and humidity air-conditioning unit. The method first monitors the temperature and humidity data sets within the current time period T indoors and outdoors to obtain a historical temperature and humidity data set; then, the data are input into a trained convolutional neural network model. After the convolutional neural network model outputs the indoor temperature prediction value and humidity prediction value, the data are compared with the preset temperature range and humidity range; when the temperature prediction value or the humidity prediction value exceeds the upper or lower limit of the corresponding preset range, the humidity adjustment mode or the temperature adjustment mode is activated. When the temperature prediction value or the humidity prediction value is within the preset range, the system enters the high-precision fine-tuning mode; finally, the display shows the current temperature and humidity. The present application adopts an improved convolutional neural network model to calculate the temperature and humidity prediction values ​​in real time and compare them with the preset range, and select to activate the humidity adjustment mode, the temperature adjustment mode, or the high-precision fine-tuning mode; thus, the temperature and humidity adjustment accuracy is greatly improved, and the user experience is greatly enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning, and in particular to a control method and device for dehumidification heat compensation of a constant temperature and humidity air conditioning unit. Background Art

[0002] Constant temperature and humidity air conditioning units are common environmental control devices in modern buildings, widely used in locations requiring strict temperature and humidity control, such as laboratories, museums, data centers, and cleanrooms. Their basic function is to maintain a constant indoor environment by regulating air temperature and humidity. However, with advances in building technology and increasing user demands for environmental comfort, traditional constant temperature and humidity air conditioning units often face numerous technical challenges in coping with complex and changing environmental factors.

[0003] First, temperature and humidity control are interrelated. Dehumidification often results in a drop in temperature, necessitating appropriate thermal compensation to maintain temperature stability. Traditional air conditioning units often employ simple temperature or humidity control systems, regulating dehumidification and heating functions separately. However, with increasing environmental complexity, such as fluctuations in the number of people indoors and the influence of outdoor weather conditions, a single control approach can no longer meet the precise requirements for constant temperature and humidity. In particular, when temperature and humidity fluctuate significantly, traditional air conditioning systems may struggle to respond quickly, leading to environmental instability and impacting the functional requirements of the space. Second, existing air conditioning systems often rely on static control methods and fail to fully leverage modern intelligent technologies for predictive regulation. For example, traditional air conditioning units typically rely on real-time monitoring of current temperature and humidity data for simple feedback control, lacking foresight for future temperature and humidity changes. Consequently, when environmental conditions change suddenly, the system's response is slow, easily leading to fluctuations in temperature and humidity, making it difficult to maintain constant temperature and humidity over time. Furthermore, with the development of intelligent buildings, dynamic factors such as the number of people indoors are increasingly impacting environmental control. Using intelligent algorithms to proactively adjust air conditioning systems to meet future demands has become a pressing industry challenge.

[0004] In recent years, intelligent control technology has gradually been applied to constant temperature and humidity air conditioning units. By introducing artificial intelligence and deep learning technologies, air conditioning systems can predict future changes in temperature and humidity based on historical data and current status, thereby adjusting the system's operating parameters in advance. However, existing intelligent air conditioning control systems still have many limitations. For example, most current intelligent control systems are based only on simple neural networks or traditional PID control, and fail to fully consider complex environmental factors such as the dynamic changes in the number of people indoors and fluctuations in external climate conditions. In addition, existing systems usually rely only on static environmental data and cannot dynamically adjust control strategies, resulting in the inability to achieve refined control in practical applications. In addition, existing sound quality enhancement systems generally lack a comprehensive analysis of the user's hearing characteristics and fail to fully consider the impact of the ear canal structure on audio transmission, resulting in less than ideal sound quality enhancement effects. In addition, the existing constant temperature and humidity air conditioners take fewer factors into consideration in terms of adjustment, and do not make comprehensive considerations based on historical indoor and outdoor temperature and humidity data. When the outdoor temperature and humidity are high, the power required for indoor adjustment is higher, and no future prediction values ​​are determined based on the number of people indoors. The temperature and humidity of the air conditioner are adjusted in advance by determining the future prediction values, so that the indoor temperature and humidity are kept in a smaller fluctuation range. In addition, the existing technology does not involve a fine adjustment mode for comprehensive adjustment, resulting in a significant reduction in the efficiency of temperature and humidity adjustment. In view of the limitations of existing air conditioner temperature and humidity adjustment, a new solution is urgently needed for the refined automated real-time temperature and humidity adjustment processing method to improve the efficiency and accuracy of the processing, so as to improve user satisfaction. Summary of the Invention

[0005] In view of the above problems mentioned in the prior art, the present invention provides a control method and device for dehumidification heat compensation of a constant temperature and humidity air-conditioning unit. The method first records the temperature and humidity data set X in the current period T of the indoor and outdoor c and historical temperature and humidity dataset X h ; Secondly, the temperature and humidity dataset X c , indoor population and historical temperature and humidity dataset X h Input to the trained convolutional neural network model, the convolutional neural network model outputs the indoor temperature prediction value T f and humidity prediction value H f Then, compare it with the preset temperature range and humidity range; when the temperature prediction value T f Or humidity prediction value H fWhen the temperature exceeds the upper or lower limit of the corresponding preset range, humidity adjustment mode or temperature adjustment mode is activated. When it is within the preset range, the system enters high-precision fine-tuning mode. Finally, the display shows the current temperature and humidity. This application uses an improved convolutional neural network model to calculate the temperature and humidity prediction values ​​in real time and compare them with the preset range. The system then selects the humidity adjustment mode, temperature adjustment mode, or high-precision fine-tuning mode. This greatly improves the accuracy and efficiency of the air conditioner's temperature and humidity adjustment, greatly enhancing the user experience.

[0006] The present application provides a method for controlling dehumidification heat compensation of a constant temperature and humidity air conditioning unit, comprising the steps of:

[0007] S1: Use humidity sensors and temperature sensors to monitor the relative humidity and air temperature of the indoor environment in real time, and record the temperature and humidity dataset X for the current period T indoors and outdoors. c and historical temperature and humidity dataset X h , X c ={T c , H c}, X h ={T h , H h}, where T c is the indoor and outdoor temperature data vector in the current period T, H c is the indoor and outdoor humidity data vector in the current period T, T h is the historical indoor and outdoor temperature data vector, H h Historical indoor and outdoor humidity data vectors;

[0008] S2: The temperature and humidity dataset X for the current period T of indoor and outdoor c , indoor population and historical temperature and humidity dataset X h Input to the trained convolutional neural network model, the convolutional neural network model outputs the predicted indoor temperature value T at the future time t0 f and humidity prediction value H f ;

[0009] S3: predicted indoor temperature value T at future time t0 f and humidity prediction value H f Compare with the preset temperature range and humidity range respectively. When the temperature prediction value T f Or humidity prediction value H f When the upper or lower limit of the corresponding preset range is exceeded, the humidity adjustment mode or temperature adjustment mode is activated;

[0010] S4: When the predicted temperature and humidity values ​​at the future time t0 are within the preset range, the system enters the high-precision fine-tuning mode and automatically adjusts the humidity and temperature so that the ambient temperature and humidity fluctuation ranges do not exceed the set thresholds respectively;

[0011] S5: The display shows the current temperature and humidity, and the humidity sensor and temperature sensor continue to monitor in real time.

[0012] Preferably, the system enters a high-precision fine-tuning mode, and automatically adjusts the humidity and temperature, including: S41: calculating the temperature error ΔT=T f -T m and humidity error ΔH=H f -H m , where T m is the current indoor temperature, H m is the current indoor humidity; S42: calculate the current heating power P according to the temperature error ΔT and humidity error ΔH h and dehumidification power P d , in, is the temperature proportional gain, is the humidity proportional gain; S43: repeat step S41 until the temperature error ΔT and the humidity error ΔH are both 0.

[0013] Preferably, the temperature proportional gain at time t Among them, α temperature is the adaptive coefficient, K p0 is the initial proportional gain of temperature, is the rate of change of temperature error; the humidity proportional gain at time t Among them, β is the humidity adaptive coefficient, K p1 is the initial proportional gain of humidity, is the rate of change of humidity error at time t.

[0014] Preferably, the trained convolutional neural network model adopts an improved Swish activation function f(x,N):

[0015]

[0016] Among them, x is the input of the convolutional layer, N is the number of people in the room, σ is the adjustment parameter, and e is the base of the natural logarithm.

[0017] Preferably, the number of people in the room is obtained by capturing the current indoor video image data in real time using a camera installed indoors, and using a YOLO target detection model to identify the number of people in the indoor video image to obtain the number of people N in the room.

[0018] The present application also provides a control device for dehumidification heat compensation of a constant temperature and humidity air conditioning unit, comprising:

[0019] Acquisition module: Humidity sensor and temperature sensor monitor the relative humidity and air temperature of the indoor environment in real time, and record the temperature and humidity data set X within the current period T indoors and outdoorsc and historical temperature and humidity dataset X h , X c ={T c , H c}, X h ={T h , H h}, where T c is the indoor and outdoor temperature data vector in the current period T, H c is the indoor and outdoor humidity data vector in the current period T, T h is the historical indoor and outdoor temperature data vector, H h Historical indoor and outdoor humidity data vectors;

[0020] Processing module: The temperature and humidity data set X for the current period T of indoor and outdoor c , indoor population and historical temperature and humidity dataset X h Input to the trained convolutional neural network model, the convolutional neural network model outputs the predicted indoor temperature value T at the future time t0 f and humidity prediction value H f ;

[0021] Adjustment module: indoor temperature prediction value T at future time t0 f and humidity prediction value H f Compare with the preset temperature range and humidity range respectively. When the temperature prediction value T f Or humidity prediction value H f When the upper or lower limit of the corresponding preset range is exceeded, the humidity adjustment mode or temperature adjustment mode is activated;

[0022] Fine-tuning module: When the predicted temperature and humidity values ​​at the future time t0 are within the preset range, the system enters high-precision fine-tuning mode and automatically adjusts the humidity and temperature so that the ambient temperature and humidity fluctuation ranges do not exceed the set thresholds respectively;

[0023] Display module: The display shows the current temperature and humidity, and the humidity sensor and temperature sensor continue to monitor in real time.

[0024] Preferably, the system enters a high-precision fine-tuning mode, and automatically adjusts the humidity and temperature, including: S41: calculating the temperature error ΔT=T f -T m and humidity error ΔH=H f -H m , where T m is the current indoor temperature, H m is the current indoor humidity; S42: calculate the current heating power P according to the temperature error ΔT and humidity error ΔH h and dehumidification power P d, in, is the temperature proportional gain, is the humidity proportional gain; S43: repeat step S41 until the temperature error ΔT and the humidity error ΔH are both 0.

[0025] Preferably, the temperature proportional gain at time t Among them, α temperature is the adaptive coefficient, K p0 is the initial proportional gain of temperature, is the rate of change of temperature error; the humidity proportional gain at time t Among them, β is the humidity adaptive coefficient, K p1 is the initial proportional gain of humidity, is the rate of change of humidity error at time t.

[0026] Preferably, the trained convolutional neural network model adopts an improved Swish activation function f(x,N):

[0027]

[0028] Among them, x is the input of the convolutional layer, N is the number of people in the room, σ is the adjustment parameter, and e is the base of the natural logarithm.

[0029] Preferably, the number of people in the room is obtained by capturing the current indoor video image data in real time using a camera installed indoors, and using a YOLO target detection model to identify the number of people in the indoor video image to obtain the number of people N in the room.

[0030] The present invention provides a control method and device for dehumidification heat compensation of a constant temperature and humidity air conditioning unit, which can achieve the following beneficial technical effects:

[0031] 1. The present invention first records the temperature and humidity data set X within the current period T of indoor and outdoor c and historical temperature and humidity dataset X h ; Secondly, the temperature and humidity dataset X c , indoor population and historical temperature and humidity dataset X h Input to the trained convolutional neural network model, the convolutional neural network model outputs the indoor temperature prediction value T f and humidity prediction value H f Then, compare it with the preset temperature range and humidity range; when the temperature prediction value T f Or humidity prediction value H fWhen the temperature exceeds the upper or lower limit of the corresponding preset range, humidity adjustment mode or temperature adjustment mode is activated. When it is within the preset range, the system enters high-precision fine-tuning mode. Finally, the display shows the current temperature and humidity. This application uses an improved convolutional neural network model to calculate the temperature and humidity prediction values ​​in real time and compare them with the preset range. The system then selects the humidity adjustment mode, temperature adjustment mode, or high-precision fine-tuning mode. This greatly improves the accuracy and efficiency of the air conditioner's temperature and humidity adjustment, greatly enhancing the user experience.

[0032] 2. The trained convolutional neural network model of the present invention adopts an improved Swish activation function. By introducing the number of people N in the room into the activation function, the system can dynamically perceive changes in the indoor environment, especially the impact of the number of people on temperature and humidity. As the number of people increases, the indoor temperature and humidity usually change significantly. Traditional control methods usually only adjust according to the real-time data of the temperature and humidity sensors, and cannot respond in advance to the temperature and humidity fluctuations caused by changes in the number of people. By taking the number of people as an input parameter, the system can dynamically adjust the heating power and dehumidification power according to the current number of people in the room, improving the adaptability and response speed to complex environments. The system can quickly adapt to and predict changes in temperature and humidity in advance, avoiding the lag adjustment problem of traditional systems. The accuracy of the system's temperature and humidity control is significantly improved. Even in the face of smaller environmental fluctuations, it can accurately adjust the temperature and humidity through changes in the number of people to maintain a constant indoor environment.

[0033] 3. The high-precision fine-tuning mode of the present invention automatically adjusts humidity and temperature. By calculating the temperature error and humidity error, the heating power and dehumidification power at the current moment are calculated according to the temperature error and humidity error. The temperature proportional gain and humidity proportional gain are considered in the calculation process, which greatly improves the fine adjustment. Through the setting of high-precision mode and temperature adjustment mode and humidity adjustment mode, the problem of over-adjustment or under-adjustment is avoided, and the overall operation stability is improved.

[0034] 4. The present invention records the temperature and humidity data set X within the current period T of indoor and outdoor c By combining current and historical temperature and humidity data sets, the convolutional neural network model can analyze temperature and humidity change patterns based on current and historical data and predict future trends. This combination not only relies on current environmental conditions but also considers past patterns of change, thereby improving the accuracy of temperature and humidity predictions. Traditional air conditioning systems are typically controlled based on immediate temperature and humidity feedback, which can lead to frequent equipment starts and stops, increasing wear and tear, and shortening equipment lifespan. By using current and historical temperature and humidity data, the system can predict future temperature and humidity changes in advance and take appropriate measures to avoid frequent starts and stops. For example, when historical data indicates that the ambient temperature and humidity fluctuate periodically over a short period of time, the system can intervene in advance to make smoother adjustments and reduce the frequency of overreaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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.

[0036] Figure 1 This is a schematic diagram of the steps of a control method for dehumidification heat compensation of a constant temperature and humidity air conditioning unit of the present invention;

[0037] Figure 2 It is a schematic diagram of a control device for dehumidification heat compensation of a constant temperature and humidity air-conditioning unit of the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0039] Example 1:

[0040] In order to solve the above-mentioned technical problems mentioned in the prior art, the following Figure 1 As shown: This application provides a control method for dehumidification heat compensation of a constant temperature and humidity air-conditioning unit, comprising the steps of:

[0041] S1: Use humidity sensors and temperature sensors to monitor the relative humidity and air temperature of the indoor environment in real time, and record the temperature and humidity dataset X for the current period T indoors and outdoors. c and historical temperature and humidity dataset X h , X c ={T c , H c}, X h ={T h , H h}, where T c is the indoor and outdoor temperature data vector in the current period T, H c is the indoor and outdoor humidity data vector in the current period T, T h is the historical indoor and outdoor temperature data vector, H hHistorical indoor and outdoor humidity data vectors; the constant temperature and humidity air conditioning unit used in the present invention includes the following modules: a humidity sensor is used to monitor the relative humidity indoors and outdoors in real time, and the data unit is %RH (relative humidity percentage); a temperature sensor is used to monitor the air temperature indoors and outdoors in real time, and the data unit is ℃; the data acquisition unit is responsible for recording the temperature and humidity data in the current time period T, and storing these data as data vectors; the unit is also responsible for recording the temperature and humidity data in the historical time period; the convolutional neural network processing unit predicts the temperature and humidity at future times by using the current and historical temperature and humidity data. The system collects temperature and humidity data in a time period of T every 10 minutes. The humidity sensor and temperature sensor in the system will monitor the temperature and humidity conditions indoors and outdoors respectively in each time period, and record these data as data vectors. The system also needs to record and save the historical temperature and humidity data within a certain period of time in the past, so that the subsequent convolutional neural network model can predict the temperature and humidity at future times.

[0042] S2: The temperature and humidity dataset X for the current period T of indoor and outdoor c , indoor population and historical temperature and humidity dataset X h Input to the trained convolutional neural network model, the convolutional neural network model outputs the predicted indoor temperature value T at the future time t0 f and humidity prediction value H f ; Input the above dataset and the number of people N indoors into the trained convolutional neural network model. The convolutional neural network extracts features from these input data through the convolution layer, such as the changing trends of temperature and humidity, the mutual influence between indoor and outdoor, etc. The convolution layer performs convolution operations on the temperature and humidity data to extract features, such as temperature fluctuations and humidity change trends; the fully connected layer combines the features extracted by the convolution layer with the indoor number data to generate a comprehensive feature representation. Output the temperature and humidity prediction values ​​at a certain moment in the future. After processing by the convolutional neural network, the system can output the temperature and humidity prediction values ​​at a certain moment in the future:

[0043] The convolutional neural network (CNN) in the present invention is mainly composed of convolutional layers, pooling layers, fully connected layers and output layers. Its core function is to extract valuable features from input temperature and humidity data and environmental factors such as the number of people indoors, and then predict the temperature and humidity at future times.

[0044] Convolutional layers are the foundation of the network, extracting local features from the input data. Each convolutional layer processes the input data using multiple convolution kernels, which slide across the input data to extract localized features. The primary function of convolutional layers is to capture short-term or localized patterns in the input data. For example, convolution operations can identify features such as short-term fluctuations in temperature and humidity, or the impact of the number of people in a room on the environment. The initial layers of convolutional layers, typically shallower in depth, extract basic features, such as underlying trends in temperature and humidity. As the number of convolutional layers increases, the extracted features become increasingly abstract, capturing higher-level relationships, such as long-term patterns of temperature and humidity fluctuations and the interactions between different input data. Pooling layers reduce the dimensionality of the features extracted by the convolutional layers, reducing computational complexity while preserving key features. Common pooling operations include max pooling and average pooling. Max pooling is typically used to select the maximum value within a region, thereby preserving key features and reducing the impact of noise on predictions. Pooling layers allow the system to refine core feature information, reduce redundant data, and reduce the computational burden of the model. Pooling layers enhance network robustness, ensuring that small input changes don't cause drastic changes in output predictions, thereby improving the model's generalization capabilities. After passing through several convolutional and pooling layers, the convolutional neural network inputs the extracted features into the fully connected layer for comprehensive processing. The fully connected layer flattens all feature vectors and then performs high-level nonlinear combinations through multiple layers of neurons. Through the fully connected layer, the network integrates different input features to form a comprehensive understanding of future temperature and humidity changes. The fully connected layer is responsible for generating the final prediction results—the temperature and humidity at the future moment. The fully connected layer acts like a predictor, comprehensively predicting the temperature and humidity at the future moment based on all extracted features. The output layer, the final layer of the CNN, generates predicted values ​​for temperature and humidity at the future moment. The output layer typically uses a linear activation function to ensure that the output predictions are continuous values ​​that fall within the actual range of temperature and humidity.

[0045] The CNN training process is as follows: Before network training, the system requires a large amount of historical temperature and humidity data, indoor occupancy data, and environmental condition data. This data is divided into a training set and a validation set for training and optimizing the convolutional neural network. The training set is used to learn the model, while the validation set is used to evaluate the model's generalization ability. During training, the input data first passes through the convolutional layer, the pooling layer, and the fully connected layer before being passed to the output layer to generate predicted temperature and humidity values. This process is called forward propagation. The convolutional neural network generates predictions based on the current input data. The network's output is compared with the actual historical temperature and humidity data, and the prediction error is calculated using a loss function. Common loss functions include mean squared error (MSE), which measures the difference between the predicted and actual values. Based on the loss function's output, the system performs backpropagation to adjust the network's weight parameters. The optimizer (such as the Adam optimizer) calculates the gradient of the loss function with respect to each layer's weights and updates the weights of the convolution kernel and fully connected layers layer by layer until the loss function is minimized. This repeated process allows the model to gradually learn the patterns of temperature and humidity fluctuations. Throughout the training process, the model continuously learns through multiple iterations, constantly adjusting weights to reduce prediction errors. The training process is usually divided into multiple epochs, that is, the model learns multiple times on the entire training dataset to ensure that the model can learn stable features from different samples.

[0046] Once the convolutional neural network is trained, it can be used to predict future temperature and humidity in real time. The system inputs the trained convolutional neural network with temperature and humidity data for the current time period, the number of people in the room, and other data. The convolutional layer processes the input data using convolution kernels to extract local features. The pooling layer reduces the data dimensionality while retaining key features. The fully connected layer combines the temperature and humidity data based on the features extracted by the convolutional layer. The model can identify future temperature and humidity trends under current environmental conditions from these extracted features. Finally, the model generates predicted temperature and humidity values ​​for the future at the output layer. The system uses these predictions to determine whether temperature and humidity adjustments are necessary. This prediction process allows the convolutional neural network to predict future temperature and humidity changes, enabling the air conditioning system to make preemptive adjustments, avoiding drastic fluctuations in temperature and humidity and ensuring a stable and comfortable environment.

[0047] S3: predicted indoor temperature value T at future time t0 f and humidity prediction value H f Compare with the preset temperature range and humidity range respectively. When the temperature prediction value T f Or humidity prediction value H f When the upper or lower limit of the corresponding preset range is exceeded, the humidity adjustment mode or temperature adjustment mode is activated;

[0048] In some embodiments, when a constant temperature and humidity air conditioning unit is operating, the system sets a target temperature and humidity range based on environmental requirements. For example, a temperature range of 22°C to 26°C and a humidity range of 40% RH to 60% RH are set based on the specific needs of the location. For example, in places with strict environmental requirements, such as laboratories or museums, temperature and humidity control requires extreme precision.

[0049] After processing current time period temperature and humidity data, historical temperature and humidity data, and factors such as the number of people indoors, the convolutional neural network generates predicted temperature and humidity values ​​for the future. For example, the predicted temperature value for the next 10 minutes is 27°C; the predicted humidity value for the next 10 minutes is 65% RH. These predicted values ​​represent the system's prediction of future temperature and humidity by learning from changing trends in the environment. If the system does not take any adjustment measures, future temperature and humidity may deviate from the preset target range. The system compares the future temperature and humidity predicted values ​​output by the convolutional neural network with the preset temperature and humidity range: Temperature comparison: The future temperature prediction value is 27°C, which exceeds the upper limit of the target temperature range (26°C), so the system needs to adjust the temperature. Humidity comparison: The future humidity prediction value is 65% RH, which exceeds the upper limit of the target humidity range (60% RH), so the system needs to adjust the humidity.

[0050] If the predicted temperature and humidity values ​​exceed the target range, the system initiates the corresponding adjustment mode. Because the future temperature forecast (27°C) exceeds the upper limit of the preset range (26°C), the system determines that the environment is about to overheat. Therefore, the air conditioning unit activates cooling mode. The specific steps are as follows: Based on the predicted temperature value, the system activates the cooling function in advance to lower the indoor temperature. By adjusting the refrigerant flow rate and air speed, the system gradually lowers the temperature to within the target range. The system monitors indoor temperature changes and, if it falls within the target range (e.g., 25°C), gradually reduces the cooling power to maintain a stable temperature.

[0051] The predicted future humidity is 65% RH, exceeding the upper limit of the target humidity range (60% RH), indicating that the environment is about to become too humid. The system will initiate dehumidification mode, which involves the following steps: The air conditioning unit activates its dehumidification function to reduce the humidity in the air. The system may increase the efficiency of the cooling coils, promoting condensation in the air and thus reducing humidity. Once the humidity falls within the target range (e.g., 58% RH), the system will gradually reduce the dehumidification function to maintain humidity stability.

[0052] By leveraging the predictive capabilities of convolutional neural networks, the system can proactively sense future environmental changes and initiate temperature and humidity adjustments before they exceed preset ranges, eliminating the lag inherent in traditional air conditioning systems' passive adjustments. The advantages of this pre-conditioning approach include: Because the system initiates adjustments in advance, temperature and humidity fluctuations are kept within a narrow range, ensuring environmental stability. Pre-conditioning prevents over-adjustment or frequent system starts and stops, optimizing energy use and improving air conditioning system efficiency. Pre-emptive adjustments to temperature and humidity prevent discomfort caused by sudden environmental changes, particularly in locations with strict temperature and humidity requirements, such as laboratories and archives, protecting equipment, artifacts, or samples from these fluctuations. By using convolutional neural networks to predict future temperature and humidity, combined with preset temperature and humidity ranges, the system can proactively implement adjustments based on the predicted results, preventing excessively high or low temperatures. This approach significantly enhances the intelligence of constant temperature and humidity air conditioning systems.

[0053] S4: When the predicted temperature and humidity values ​​at the future time t0 are within the preset range, the system enters the high-precision fine-tuning mode and automatically adjusts the humidity and temperature so that the ambient temperature and humidity fluctuation ranges do not exceed ±0.1°C and ±1%, respectively.

[0054] S5: The display shows the current temperature and humidity, and the humidity sensor and temperature sensor continue to monitor in real time.

[0055] In some embodiments, the system enters a high-precision fine-tuning mode, and automatically adjusts humidity and temperature, including: S41: calculating the temperature error ΔT=T f -T m and humidity error ΔH=H f -H m , where T m is the current indoor temperature, H m is the current indoor humidity; S42: calculate the current heating power P according to the temperature error ΔT and humidity error ΔH h and dehumidification power P d , in, is the temperature proportional gain, is the humidity proportional gain; S43: repeat step S41 until the temperature error ΔT and the humidity error ΔH are both 0.

[0056] In some embodiments, the temperature proportional gain at time t Among them, α temperature is the adaptive coefficient, K p0 is the initial proportional gain of temperature, is the rate of change of temperature error; the humidity proportional gain at time t Among them, β is the humidity adaptive coefficient, Kp1 is the initial proportional gain of humidity, is the rate of change of humidity error at time t.

[0057] In some embodiments, the trained convolutional neural network model uses an improved Swish activation function f(x, N):

[0058]

[0059] Among them, x is the input of the convolutional layer, N is the number of people in the room, σ is the adjustment parameter, and e is the base of the natural logarithm.

[0060] In some embodiments, the number of people indoors is determined by capturing the current indoor video image data in real time using a camera installed indoors, and using the YOLO target detection model to identify the number of people in the indoor video image to obtain the number of people indoors N. This embodiment details how to capture video image data in real time using a camera installed indoors, and use the YOLO target detection model to identify the number of people in the video, thereby determining the current number of people indoors N, and inputting this number information into the air-conditioning system for intelligent control. In this embodiment, the system installs a high-definition camera at a key location indoors (such as a ceiling or a corner) to ensure that the camera can cover most of the activity area. The camera captures dynamic video images indoors in real time at a high frame rate (such as 15 to 30 frames per second). The images captured by the camera contain the activities of the people indoors, and the system extracts static image data from these video frames for subsequent processing. For example: the camera captures an image containing multiple people, and the image shows that several people are active or working indoors. The system uses this image as input for the subsequent YOLO detection model.

[0061] The YOLO object detection model is a deep learning-based object detection technology that can quickly and accurately identify multiple target objects (such as people, animals, and objects) in an image and simultaneously locate their positions. In this embodiment, the YOLO model is trained to detect the "people" category, meaning that the model can identify the specific location of each person in the captured image and calculate the total number of people. The specific operation steps are as follows: The camera passes the captured static image to the YOLO model for processing at regular intervals (such as every second or every few seconds). The YOLO model analyzes the image, quickly identifies the location of each person, and annotates the bounding box of each target person on the image. Based on the identified bounding boxes, the system can determine the total number of people in the image. The number of people in the room returned by the system is the number of people in the current image. The YOLO model processes a static image and successfully detects five bounding boxes in the image, indicating that there are five people in the image. Therefore, the system sets the current number of people in the room, N, to 5.

[0062] The system updates the number of people in a room in real time based on the images captured by the camera and the detection results of the YOLO model. Because people may move around or enter and exit a room, the system needs to continuously monitor video data and update the number of people in the room. For example, every second, the camera captures a frame, and the YOLO model analyzes each frame to determine the latest number of people. If the system captures six bounding boxes in an image within one second, the system updates the number of people in the room to six. If the next captured image shows fewer people, with only four bounding boxes, the system updates the number of people in the room to four. This allows the system to dynamically monitor the number of people in the room and ensures real-time access to the latest occupancy information. After obtaining the number of people in the room, N, the system inputs this data into the air conditioning control system, which serves as a key parameter for regulating temperature and humidity. Changes in the number of people in the room directly affect the room's temperature and humidity. For example, if the number of people in the room increases, the system predicts that the increased number of people will lead to a temperature rise and therefore increases cooling power in advance to prevent overheating. When the number of people in the room decreases, the system reduces cooling power to prevent overcooling. By acquiring and processing indoor occupancy data in real time, the system can dynamically adjust temperature and humidity based on changes in the number of people, ensuring a constant temperature and humidity. Using the YOLO object detection model, the system can accurately identify and count the number of people in a room in real time and adjust the air conditioning system's operating parameters accordingly. Compared to traditional static settings, the YOLO-based dynamic detection method enhances the system's intelligence, ensuring the air conditioning system's timely response to environmental changes, thereby improving the accuracy and efficiency of temperature and humidity regulation.

[0063] The present application also provides a control device for dehumidification heat compensation of a constant temperature and humidity air conditioning unit, such as Figure 2As shown, the control device comprises the following key components, each of which works together to achieve precise control of indoor temperature and humidity: a humidity sensor, a temperature sensor, a camera, a data acquisition module, a processing module (controller), a dehumidifier, a heater, a display module, and an execution module. The humidity and temperature sensors monitor indoor and outdoor temperature and humidity data in real time. These sensors are installed indoors and outdoors and are connected to the data acquisition module via wired or wireless connections, transmitting the monitored temperature and humidity data to the data acquisition module. The camera captures indoor image data in real time and uses the YOLO object detection model to identify the number of people inside. The camera is installed on the ceiling or in a corner of the room and transmits the captured image data to the processing module via a network or wired connection. The processing module analyzes the number of people based on the image data and adjusts the temperature and humidity accordingly. The data acquisition module receives and aggregates data transmitted by the humidity and temperature sensors. The data acquisition module connects to the humidity and temperature sensors and transmits the temperature and humidity data to the processing module via wired or wireless connections. The processing module (controller), serving as the control center of the entire device, is responsible for receiving and analyzing temperature, humidity, and occupancy data, predicting future temperature and humidity changes, and deciding whether to activate dehumidification or temperature adjustment mode. The processing module receives the temperature and humidity data from the data acquisition module and the image data from the camera, processes the data through algorithms, and controls the operation of the dehumidification device and heater.

[0064] Dehumidifiers and Heaters: The dehumidifier reduces moisture in the air, while the heater regulates temperature. The dehumidifier and heater are each connected to a processing module, which controls their on / off status, or adjusts their power based on predicted temperature and humidity fluctuations. The display module displays the current temperature and humidity, as well as the system's operating status. The display module is connected to the processing module and displays the data calculated by the processing module and the system's operating status. The execution module receives control signals from the processing module and actually controls the starting, stopping, and regulating of the heater and dehumidifier. The execution module is connected to the processing module to execute its control commands and is also connected to the dehumidifier and heater.

[0065] Humidity sensors are installed indoors and outdoors to monitor relative humidity in real time. The sensors transmit the collected humidity data to the data acquisition module, which then transmits it to the processing module. Changes in indoor humidity are the primary basis for activating dehumidification mode. Temperature sensors are installed indoors and outdoors to monitor temperature changes in real time. The temperature sensor output signals are transmitted to the processing module via the data acquisition module. Indoor and outdoor temperature data help the processing module decide whether to activate heating or cooling to ensure the temperature remains within the preset range.

[0066] The camera, mounted high in the room, uses the YOLO object detection model to identify and count the number of people indoors in real time. The image data captured by the camera is analyzed by the processing module, which combines the identified number of people indoors with temperature and humidity control. As the number of people indoors increases, the processing module proactively adjusts the temperature and humidity accordingly to prevent drastic fluctuations. The data acquisition module, serving as the interface between the sensors and the processing module, aggregates all data from the humidity and temperature sensors and transmits this data to the processing module. It is a key component in the entire system responsible for data transmission and collection.

[0067] The processing module is the core of the system, responsible for analyzing all data. It receives temperature and humidity data, as well as data on the number of people present. Using a built-in convolutional neural network model, it predicts future temperature and humidity changes. The processing module compares the predicted temperature and humidity with pre-set ranges. If the range is exceeded, it issues commands to control the dehumidifier or heater. The dehumidifier, controlled by the processing module, reduces humidity in the air. When the processing module predicts that the humidity may exceed the set range in the future, it activates the dehumidifier to keep the humidity within the appropriate range. The dehumidifier is directly connected to the execution module and is controlled by commands from the processing module. The heater heats the indoor air to prevent excessively low temperatures. When the processing module predicts that the temperature will fall below the set range, it activates the heater to maintain a stable temperature. The heater is also directly controlled by the execution module, adjusting the temperature based on the processing module's predictions. The display module displays current ambient temperature and humidity data, system operating status, and other relevant information. Users can view current environmental conditions and the operating status of the air conditioning units through the display module. The execution module serves as the interface between the processing module and the heater and dehumidifier. The control signal sent by the processing module is first transmitted to the execution module, and then the execution module controls the start and stop of the dehumidification device and the heater or adjusts the working power.

[0068] Humidity sensors, temperature sensors, and cameras monitor environmental data in real time. This data is transmitted to the processing module via the data acquisition module. The processing module uses a convolutional neural network model to analyze current data and predict future temperature and humidity changes. If the predicted value exceeds the preset range, the processing module determines whether to initiate dehumidification or temperature adjustment. The processing module issues instructions through the execution module to control the dehumidification device or heater to ensure that the indoor temperature and humidity remain within the set range. The system continuously monitors temperature and humidity changes, adjusts the operating status of the heating and dehumidification devices in real time, and displays current environmental information on the display module.

[0069] Acquisition module: Humidity sensor and temperature sensor monitor the relative humidity and air temperature of the indoor environment in real time, and record the temperature and humidity data set X within the current period T indoors and outdoors c and historical temperature and humidity dataset X h , Xc ={T c , H c}, X h ={T h , H h}, where T c is the indoor and outdoor temperature data vector in the current period T, H c is the indoor and outdoor humidity data vector in the current period T, T h is the historical indoor and outdoor temperature data vector, H h Historical indoor and outdoor humidity data vectors;

[0070] Processing module: The temperature and humidity data set X for the current period T of indoor and outdoor c , indoor population and historical temperature and humidity dataset X h Input to the trained convolutional neural network model, the convolutional neural network model outputs the predicted indoor temperature value T at the future time t0 f and humidity prediction value H f ;

[0071] Adjustment module: indoor temperature prediction value T at future time t0 f and humidity prediction value H f Compare with the preset temperature range and humidity range respectively. When the temperature prediction value T f Or humidity prediction value H f When the upper or lower limit of the corresponding preset range is exceeded, the humidity adjustment mode or temperature adjustment mode is activated;

[0072] Fine-tuning module: When the predicted temperature and humidity values ​​at the future time t0 are within the preset range, the system enters high-precision fine-tuning mode and automatically adjusts the humidity and temperature so that the ambient temperature and humidity fluctuation ranges do not exceed ±0.1°C and ±1%, respectively;

[0073] Display module: The display shows the current temperature and humidity, and the humidity sensor and temperature sensor continue to monitor in real time.

[0074] In some embodiments, the system enters a high-precision fine-tuning mode, and automatically adjusts humidity and temperature, including: S41: calculating the temperature error ΔT=T f -T m and humidity error ΔH=H f -H m , where T m is the current indoor temperature, H m is the current indoor humidity; S42: calculate the current heating power P according to the temperature error ΔT and humidity error ΔH h and dehumidification power P d , in, is the temperature proportional gain, is the humidity proportional gain; S43: repeat step S41 until the temperature error ΔT and the humidity error ΔH are both 0.

[0075] In some embodiments, the temperature proportional gain at time t Among them, α temperature is the adaptive coefficient, K p0 is the initial proportional gain of temperature, is the rate of change of temperature error; the humidity proportional gain at time t Among them, β is the humidity adaptive coefficient, K p1 is the initial proportional gain of humidity, is the rate of change of humidity error at time t.

[0076] In some embodiments, the trained convolutional neural network model uses an improved Swish activation function f(x, N):

[0077]

[0078] Among them, x is the input of the convolutional layer, N is the number of people in the room, σ is the adjustment parameter, and e is the base of the natural logarithm.

[0079] In some embodiments, the number of people indoors is obtained by capturing the current indoor video image data in real time using a camera installed indoors, and using a YOLO target detection model to identify the number of people in the indoor video image to obtain the number of people N indoors.

[0080] The present invention provides a control method and device for dehumidification heat compensation of a constant temperature and humidity air conditioning unit, which can achieve the following beneficial technical effects:

[0081] 1. The present invention first records the temperature and humidity data set X within the current period T of indoor and outdoor c and historical temperature and humidity dataset X h ; Secondly, the temperature and humidity dataset X c , indoor population and historical temperature and humidity dataset X h Input to the trained convolutional neural network model, the convolutional neural network model outputs the indoor temperature prediction value T f and humidity prediction value H f Then, compare it with the preset temperature range and humidity range; when the temperature prediction value T f Or humidity prediction value H fWhen the temperature exceeds the upper or lower limit of the corresponding preset range, humidity adjustment mode or temperature adjustment mode is activated. When it is within the preset range, the system enters high-precision fine-tuning mode. Finally, the display shows the current temperature and humidity. This application uses an improved convolutional neural network model to calculate the temperature and humidity prediction values ​​in real time and compare them with the preset range. The system then selects the humidity adjustment mode, temperature adjustment mode, or high-precision fine-tuning mode. This greatly improves the accuracy and efficiency of the air conditioner's temperature and humidity adjustment, greatly enhancing the user experience.

[0082] 2. The trained convolutional neural network model of the present invention adopts an improved Swish activation function. By introducing the number of people N in the room into the activation function, the system can dynamically perceive changes in the indoor environment, especially the impact of the number of people on temperature and humidity. As the number of people increases, the indoor temperature and humidity usually change significantly. Traditional control methods usually only adjust according to the real-time data of the temperature and humidity sensors, and cannot respond in advance to the temperature and humidity fluctuations caused by changes in the number of people. By taking the number of people as an input parameter, the system can dynamically adjust the heating power and dehumidification power according to the current number of people in the room, improving the adaptability and response speed to complex environments. The system can quickly adapt to and predict changes in temperature and humidity in advance, avoiding the lag adjustment problem of traditional systems. The accuracy of the system's temperature and humidity control is significantly improved. Even in the face of smaller environmental fluctuations, it can accurately adjust the temperature and humidity through changes in the number of people to maintain a constant indoor environment.

[0083] 3. The high-precision fine-tuning mode of the present invention automatically adjusts humidity and temperature. By calculating the temperature error and humidity error, the heating power and dehumidification power at the current moment are calculated according to the temperature error and humidity error. The temperature proportional gain and humidity proportional gain are considered in the calculation process, which greatly improves the fine adjustment. Through the setting of high-precision mode and temperature adjustment mode and humidity adjustment mode, the problem of over-adjustment or under-adjustment is avoided, and the overall operation stability is improved.

[0084] 4. The present invention records the temperature and humidity data set X within the current period T of indoor and outdoor c By combining current and historical temperature and humidity data sets, the convolutional neural network model can analyze temperature and humidity change patterns based on current and historical data and predict future trends. This combination not only relies on current environmental conditions but also considers past patterns of change, thereby improving the accuracy of temperature and humidity predictions. Traditional air conditioning systems are typically controlled based on immediate temperature and humidity feedback, which can lead to frequent equipment starts and stops, increasing wear and tear, and shortening equipment lifespan. By using current and historical temperature and humidity data, the system can predict future temperature and humidity changes in advance and take appropriate measures to avoid frequent starts and stops. For example, when historical data indicates that the ambient temperature and humidity fluctuate periodically over a short period of time, the system can intervene in advance to make smoother adjustments and reduce the frequency of overreaction.

[0085] The above is a detailed introduction to a control method and device for dehumidification heat compensation of a constant temperature and humidity air-conditioning unit. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the core idea of ​​the present invention; at the same time, for general technical personnel in this field, based on the ideas and methods of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A control method for dehumidification heat compensation of a constant temperature and humidity air conditioning unit, characterized in that: Including steps: S1: Use humidity sensors and temperature sensors to monitor the relative humidity and air temperature of the indoor environment in real time, and record the temperature and humidity data sets within the current period T indoors and outdoors and historical temperature and humidity datasets , , ,in, is the indoor and outdoor temperature data vector in the current period T, is the indoor and outdoor humidity data vector in the current period T, are historical indoor and outdoor temperature data vectors, Historical indoor and outdoor humidity data vectors; S2: The temperature and humidity data sets for the current period T for indoor and outdoor , indoor population and historical temperature and humidity datasets Input to the trained convolutional neural network model, the convolutional neural network model outputs the future time Predicted indoor temperature and humidity forecast values ; S3: Future Moments Predicted indoor temperature and humidity forecast values Compare with the preset temperature range and humidity range respectively. or humidity forecast When the upper or lower limit of the corresponding preset range is exceeded, the humidity adjustment mode or temperature adjustment mode is activated; S4: Future Moments When the predicted temperature and humidity values ​​are within the preset range, the system enters high-precision fine-tuning mode and automatically adjusts the humidity and temperature so that the ambient temperature and humidity fluctuation ranges do not exceed the set thresholds. S5: The display shows the current temperature and humidity, and the humidity sensor and temperature sensor continue to monitor in real time; The system enters a high-precision fine-tuning mode to automatically adjust humidity and temperature, including: S41: calculating the temperature error respectively and humidity error ,in, is the current indoor temperature, is the current indoor humidity; S42: according to the temperature error and humidity error Calculate the heating power at the current moment and dehumidification power , , ,in, is the temperature proportional gain, is the humidity proportional gain; S43: repeat step S41 until the temperature error and humidity error All are 0; Temperature proportional gain at time t ,in, Temperature is the adaptive coefficient, is the initial proportional gain of temperature, is the rate of change of temperature error; the humidity proportional gain at time t ,in, is the humidity adaptation coefficient, is the initial proportional gain of humidity, is the rate of change of humidity error at time t.

2. A control method for dehumidification heat compensation of a constant temperature and humidity air conditioning unit according to claim 1, characterized in that: The trained convolutional neural network model uses the improved Swish activation function : ； in, is the input of the convolutional layer, N is the number of people in the room, is the adjustment parameter, and e is the base of the natural logarithm.

3. A control method for dehumidification heat compensation of a constant temperature and humidity air conditioning unit according to claim 1, characterized in that: The number of people in the room is obtained by capturing the current indoor video image data in real time using a camera installed indoors, and using a YOLO target detection model to identify the number of people in the indoor video image.

4. A control device for dehumidification heat compensation of a constant temperature and humidity air conditioning unit, characterized in that: include: Acquisition module: Humidity sensor and temperature sensor monitor the relative humidity and air temperature of the indoor environment in real time, and record the temperature and humidity data sets within the current period T indoors and outdoors and historical temperature and humidity datasets , , ,in, is the indoor and outdoor temperature data vector in the current period T, is the indoor and outdoor humidity data vector in the current period T, are historical indoor and outdoor temperature data vectors, Historical indoor and outdoor humidity data vectors; Processing module: The temperature and humidity data sets of indoor and outdoor current period T are processed , indoor population and historical temperature and humidity datasets Input to the trained convolutional neural network model, the convolutional neural network model outputs the future time Predicted indoor temperature and humidity forecast values ; Adjustment Module: Future Moments Predicted indoor temperature and humidity forecast values Compare with the preset temperature range and humidity range respectively. or humidity forecast When the upper or lower limit of the corresponding preset range is exceeded, the humidity adjustment mode or temperature adjustment mode is activated; Fine-tuning the module: Future Moments When the predicted temperature and humidity values ​​are within the preset range, the system enters high-precision fine-tuning mode and automatically adjusts the humidity and temperature so that the ambient temperature and humidity fluctuation ranges do not exceed the set thresholds. Display module: The display shows the current temperature and humidity, and the humidity sensor and temperature sensor continue to monitor in real time; The system enters a high-precision fine-tuning mode to automatically adjust humidity and temperature, including: S41: calculating the temperature error respectively and humidity error ,in, is the current indoor temperature, is the current indoor humidity; S42: according to the temperature error and humidity error Calculate the heating power at the current moment and dehumidification power , , ,in, is the temperature proportional gain, is the humidity proportional gain; S43: repeat step S41 until the temperature error and humidity error All are 0; Temperature proportional gain at time t ,in, Temperature is the adaptive coefficient, is the initial proportional gain of temperature, is the rate of change of temperature error; the humidity proportional gain at time t ,in, is the humidity adaptation coefficient, is the initial proportional gain of humidity, is the rate of change of humidity error at time t.

5. A control device for dehumidification heat compensation of a constant temperature and humidity air conditioning unit as claimed in claim 4, characterized in that: The trained convolutional neural network model uses the improved Swish activation function : ； in, is the input of the convolutional layer, N is the number of people in the room, is the adjustment parameter, and e is the base of the natural logarithm.

6. A control device for dehumidification heat compensation of a constant temperature and humidity air conditioning unit according to claim 4, characterized in that: The number of people in the room is obtained by capturing the current indoor video image data in real time using a camera installed indoors, and using a YOLO target detection model to identify the number of people in the indoor video image.

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