An air conditioning apparatus

Abstract

The application provides an air conditioning equipment, which comprises a shell, a compressor arranged in the shell, and a controller arranged in the shell. The controller is configured to: acquire operation data of the compressor, the operation data comprising a total number of start-ups of the compressor, a total number of stoppages, a total running time and a total stoppage time; input the operation data into a temperature prediction model to obtain an indoor average temperature output by the temperature prediction model; if the air conditioning equipment is in a heating mode, the indoor average temperature is greater than or equal to a first temperature threshold, and the return air temperature is greater than or equal to a second temperature threshold, the compressor is controlled to stop running; and if the air conditioning equipment is in a cooling mode, the indoor average temperature is less than or equal to the first temperature threshold, and the return air temperature is less than or equal to the second temperature threshold, the compressor is controlled to stop running. The application can realize precise control of the air conditioning compressor at low cost, thereby guaranteeing user comfort and improving the energy-saving control effect of the air conditioning equipment.

Description

Technical Field

[0001] This application relates to the field of air conditioning technology, and more particularly to an air conditioning device. Background Technology

[0002] Air conditioning equipment is an indispensable environmental control device in modern buildings, providing users with a comfortable living and working environment. During the operation of air conditioning equipment, temperature is usually regulated by controlling the start and stop of the compressor to ensure efficient operation and optimize energy management.

[0003] Most air conditioning systems rely on sensors to collect the temperature of the return air vents and use this as a feedback signal to regulate the compressor's operation. However, this only provides the temperature of the return air vents, and because the sensors are fixed in location, the collected return air temperature is difficult to reflect the overall indoor temperature. This results in poor accuracy in controlling the compressor to stop, affecting the precision of indoor temperature regulation. Some air conditioning systems use temperature sensors placed in multiple locations indoors to obtain more comprehensive temperature information, but this significantly increases hardware costs and complicates wiring and installation. Summary of the Invention

[0004] This application provides an air conditioning device that can achieve precise control of the air conditioning compressor at low cost, thereby ensuring user comfort and improving the energy-saving control effect of the air conditioning device.

[0005] In a first aspect, some embodiments of this application provide an air conditioning device, including: a housing; a compressor disposed within the housing; and a controller disposed within the housing;

[0006] The controller is configured to:

[0007] The operating data of the compressor is obtained, including the total number of starts, the total number of stops, the total running time, and the total downtime of the compressor;

[0008] The operating data is input into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model. The temperature prediction model is used to extract features from the total number of starts, the total number of stops, the total operating time, and the total downtime in the operating data. It also performs weighted calculations based on the extracted feature vectors and their corresponding weights. The weighted feature vectors are then processed based on a set activation function to obtain the indoor average temperature.

[0009] When the air conditioning unit is in heating mode, and the average indoor temperature is greater than or equal to a first temperature threshold and the return air temperature is greater than or equal to a second temperature threshold, the compressor is controlled to stop running. The return air temperature is used to reflect the temperature at the return air of the air conditioning unit. The first temperature threshold and the second temperature threshold are determined according to the expected temperature of the ambient space where the air conditioning unit is located.

[0010] When the air conditioning unit is in cooling mode, the average indoor temperature is less than or equal to the first temperature threshold, and the return air temperature is less than or equal to the second temperature threshold, the compressor is controlled to stop operating.

[0011] In this embodiment, the compressor's start-up, shutdown, and corresponding operating time directly affect the overall indoor temperature, while the indoor temperature, in turn, affects the compressor's start-up and shutdown. This means there is a complex correlation between the compressor's operating data and the overall indoor temperature. The temperature prediction model can effectively learn this complex relationship. Therefore, when the temperature prediction model performs weighted calculations based on the extracted feature vectors of each data point and their corresponding weights, it can accurately quantify the contribution of each feature vector to the prediction of the indoor average temperature. Simultaneously, it can fully consider the impact of different features on the indoor average temperature, improving the accuracy of the weighted feature vectors. Then, the weighted feature vectors are processed based on an activation function. By introducing nonlinear factors through the activation function, the temperature prediction model can accurately capture the complex relationships in the data, thereby accurately predicting the average temperature of the environment where the air conditioning equipment is located, and obtaining the required indoor average temperature. Furthermore, by combining the functional modes of the air conditioning equipment, it can determine whether the predicted average indoor temperature and the detected return air temperature meet the corresponding temperature thresholds, thereby determining whether to control the compressor to stop running. Compared to relying solely on the return air temperature, this method can effectively improve the accuracy of compressor shutdown control, thereby improving the precision of temperature regulation, ensuring user comfort, and enhancing the energy-saving control effect of the air conditioning equipment. Moreover, it eliminates the need to install multiple additional sensors, effectively reducing hardware costs and the complexity of wiring and installation for the air conditioning equipment.

[0012] In one possible implementation of the first aspect, the controller, when performing the control to stop the compressor from running, is configured to:

[0013] If any data in the operating data is less than or equal to a predetermined threshold corresponding to that data, the compressor is controlled to stop operating.

[0014] In the above technical solution, the controller determines that the relationship between the indoor average temperature and the return air temperature and their corresponding temperature thresholds meets the requirements of the functional mode, and that each data in the operating data is less than or equal to the predetermined threshold corresponding to that data, before controlling the compressor to stop running. This avoids frequent start-stop of the compressor and improves the energy-saving control effect of the air conditioning equipment.

[0015] In one possible implementation of the first aspect, before the controller stops operating the compressor when each piece of data in the operating data is less than or equal to a predetermined threshold corresponding to that data, it is further configured to:

[0016] The predetermined threshold corresponding to each data point is determined based on the first and second operating data of the compressor. The first operating data includes the operating data of the compressor during the current operation of the air conditioning equipment, and the second operating data includes the operating data of the compressor during the historical operation of the air conditioning equipment.

[0017] In the above technical solution, based on the real-time operating data of the compressor during the current operation of the air conditioning equipment and the operating data of the historical operation process, the predetermined threshold corresponding to each operating data is dynamically calculated. This allows for flexible adjustment of each predetermined threshold in combination with the actual operating conditions of the air conditioning equipment. Compared with using fixed thresholds, this can accurately avoid situations such as premature or late compressor shutdown leading to energy waste or decreased user comfort, thereby improving the control effect of the air conditioning equipment.

[0018] In one possible implementation of the first aspect, when the controller performs the task of determining the predetermined threshold corresponding to each data based on first and second operating data of the compressor, it is configured to:

[0019] Based on the first running data and the second running data, calculate the average value of each data point in the running data;

[0020] The offset value of each data point is determined based on the temperature deviation and the standard deviation of each data point. The temperature deviation is used to reflect the deviation between the historical indoor average temperature and the corresponding first temperature threshold.

[0021] The predetermined threshold for each data point is determined based on the average value and the offset value, respectively.

[0022] In the above technical solution, since the temperature deviation can reflect the residual between the temperature of the ambient space where the air conditioning equipment is located and the expected temperature threshold, and the standard deviation of each data in the operating data can accurately reflect the dispersion of each data, the offset value of each data is calculated based on the temperature deviation and the standard deviation of the data, and then the predetermined threshold of each data is calculated according to the ratio of the average value and the offset value of the data. This allows for accurate adjustment of the predetermined threshold in combination with the actual distribution of the data, overall changes, and actual temperature deviation. Compared with a fixed threshold, this can improve the adaptability and accuracy of the predetermined threshold, thereby improving the accuracy and reliability of compressor shutdown control.

[0023] In one possible implementation of the first aspect, the operating data includes first start-stop data and second start-stop data, and the controller, when acquiring the compressor's operating data, is configured to:

[0024] The first start / stop data is obtained based on the compressor's switch control circuit;

[0025] The second start / stop data is obtained based on the compressor's operating current and a set current threshold.

[0026] In the above technical solution, the first start-stop data is obtained through the start-stop control circuit that intuitively reflects the start-stop control status of the compressor. At the same time, the second start-stop data is obtained through the working current that accurately reflects the actual working status of the compressor and the set current threshold, thus balancing the efficiency and accuracy of obtaining start-stop data.

[0027] In one possible implementation of the first aspect, when the controller performs the operation of inputting the operating data into a temperature prediction model to obtain the indoor average temperature output by the temperature prediction model, it is configured to:

[0028] If the difference between the first start-stop data and the second start-stop data is less than or equal to a set difference threshold, the first start-stop data and / or the second start-stop data are input into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model.

[0029] In the above technical solution, due to differences in measurement accuracy or measurement methods, there may be differences between the first start-stop data and the second start-stop data obtained through different methods in the operation data. Therefore, judging whether the difference between the first start-stop data and the second start-stop data is reasonable based on whether the difference is less than or equal to the set difference threshold can accurately determine whether there are any abnormalities in the first start-stop data and the second start-stop data. This avoids directly predicting the indoor average temperature based on the first start-stop data and the second start-stop data that may have abnormalities, and improves the accuracy and reliability of subsequent indoor average temperature predictions.

[0030] In one possible implementation of the first aspect, when the controller performs the operation of inputting the operating data into a temperature prediction model to obtain the indoor average temperature output by the temperature prediction model, it is configured to:

[0031] If the difference between the first start-stop data and the second start-stop data is greater than the difference threshold, abnormal start-stop data is determined based on the first start-stop data and the second start-stop data. The abnormal start-stop data is start-stop data in the first start-stop data and the second start-stop data that has an abnormal jump.

[0032] The abnormal start / stop data is corrected according to the set correction method to obtain the corrected start / stop data;

[0033] The corrected start-stop data and abnormal start-stop data are input into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model.

[0034] In the above technical solution, if the difference between the first start-stop data and the second start-stop data obtained through different methods exceeds a reasonable range, anomaly detection can be performed on the first start-stop data and the second start-stop data to identify abnormal start-stop data. After the abnormal start-stop data is corrected using a set correction method, the corrected start-stop data and non-abnormal start-stop data are input into the temperature prediction model to predict the indoor average temperature, thereby improving the reliability of the input data of the temperature prediction model and avoiding prediction errors caused by data anomalies.

[0035] In one possible implementation of the first aspect, the controller, when performing the control to stop the compressor from running, is configured to:

[0036] When the energy efficiency is greater than or equal to a set efficiency threshold, the compressor is controlled to stop operating. The energy efficiency is used to reflect the benefits that can be generated by the energy saved when the compressor is currently controlled to stop operating.

[0037] In the above technical solution, the ability to control the compressor to stop running in advance is determined by considering different dimensions such as indoor average temperature, return air temperature and energy efficiency. By taking into account factors such as overall indoor temperature and energy efficiency, the rationality and reliability of compressor shutdown control are improved, while ensuring that energy-saving control of air conditioning equipment is achieved while meeting user needs.

[0038] In one possible implementation of the first aspect, the controller is further configured to: input the operating data into a temperature prediction model to obtain the indoor average temperature output by the temperature prediction model before performing the following steps:

[0039] Acquire return air temperature data, which includes multiple return air temperatures collected at different times during the operation of the air conditioning equipment.

[0040] The return air temperature data is preprocessed to obtain processed return air temperature data. The preprocessing includes filtering and / or interpolation.

[0041] Correspondingly, when the controller executes the operation of inputting the running data into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model, it is configured as follows:

[0042] The processed return air temperature data and the operating data are input into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model. The temperature prediction model is used to perform feature extraction processing on the total number of starts, the total number of shutdowns, the total operating time, the total shutdown time, and the processed return air temperature data. It performs weighted calculation based on each extracted feature vector and the weight corresponding to the feature vector. The weighted feature vector is then processed based on a set activation function to obtain the indoor average temperature.

[0043] In the above technical solution, multiple data such as the total number of starts, start-stop data, and running time of the air conditioning equipment are input into the temperature prediction model to predict the indoor average temperature. This fully considers multiple factors affecting the overall temperature of the environment where the air conditioning equipment is located, as well as the degree of influence of different factors on the temperature. This allows for a more comprehensive consideration of the causes of changes in the indoor average temperature, thereby improving the reliability and accuracy of the indoor average temperature obtained by the temperature prediction model.

[0044] In one possible implementation of the first aspect, after the controller executes the control to stop the compressor, it is further configured to:

[0045] If the detected temperature change rate is greater than or equal to a set change threshold, the compressor is controlled to start. The temperature change rate reflects the rate of temperature change in the ambient space where the air conditioning equipment is located.

[0046] In the above technical solution, since the temperature change rate can better reflect the dynamic changes in temperature within the environment where the air conditioning equipment is located, determining whether to start the compressor based on whether the temperature change rate is greater than or equal to the change threshold can more accurately control the compressor compared to methods based on a fixed temperature threshold. This allows for more precise maintenance of indoor temperature stability, improving user comfort. Furthermore, determining whether to start the compressor based on the temperature change rate and change threshold can adapt to differences in indoor temperature caused by different environmental conditions, improving the flexibility and reliability of the air conditioning equipment.

[0047] Secondly, some embodiments of this application also provide an air conditioning equipment control method, the method comprising:

[0048] The compressor's operating data is obtained through the controller, including the total number of compressor starts, the total number of compressor stops, the total operating time, and the total downtime.

[0049] The operating data is input into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model. The temperature prediction model is used to extract features from the total number of starts, total number of shutdowns, total operating time, and total shutdown time in the operating data. The extracted feature vectors and their corresponding weights are weighted and calculated. The weighted feature vectors are then processed based on a set activation function to obtain the indoor average temperature.

[0050] When the air conditioning unit is in heating mode, and the average indoor temperature is greater than or equal to a first temperature threshold and the return air temperature is greater than or equal to a second temperature threshold, the compressor is controlled to stop running. The return air temperature is used to reflect the temperature at the return air of the air conditioning unit. The first temperature threshold and the second temperature threshold are determined according to the expected temperature of the ambient space where the air conditioning unit is located.

[0051] When the air conditioning unit is in cooling mode, the average indoor temperature is less than or equal to the first temperature threshold, and the return air temperature is less than or equal to the second temperature threshold, the compressor is controlled to stop operating.

[0052] Thirdly, embodiments of this application provide a control device, including a module for performing the air conditioning equipment control method of the second aspect.

[0053] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the air conditioning equipment control method described in the second aspect above.

[0054] Fifthly, embodiments of this application provide a computer program product that, when run on an air conditioning device, causes the air conditioning device to execute the air conditioning device control method described in the second aspect above.

[0055] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram illustrating the operational scenarios between air conditioning equipment and control equipment provided in some embodiments of this application;

[0058] Figure 2 This is a schematic diagram of the hardware configuration of the control device provided in some embodiments of this application;

[0059] Figure 3 This application provides a schematic diagram of the structure of an air conditioning device according to some embodiments;

[0060] Figure 4 A timing interaction diagram of an air conditioning equipment control method provided in some embodiments of this application;

[0061] Figure 5 A flowchart illustrating an air conditioning equipment control method provided in some embodiments of this application;

[0062] Figure 6 A flowchart illustrating an air conditioning equipment control method provided in some embodiments of this application;

[0063] Figure 7 A flowchart illustrating an air conditioning equipment control method provided in some embodiments of this application;

[0064] Figure 8 A timing interaction diagram of an air conditioning equipment control method provided in some embodiments of this application;

[0065] Figure 9 A timing interaction diagram of an air conditioning equipment control method provided in some embodiments of this application;

[0066] Figure 10 A flowchart illustrating an air conditioning equipment control method provided in some embodiments of this application;

[0067] Figure 11 A flowchart illustrating an air conditioning equipment control method provided in some embodiments of this application;

[0068] Figure 12 A flowchart illustrating an air conditioning equipment control method provided in some embodiments of this application;

[0069] Figure 13A flowchart illustrating an air conditioning equipment control method provided in some embodiments of this application;

[0070] Figure 14 This is a schematic diagram of the structure of an air conditioning equipment control device provided in some embodiments of this application. Detailed Implementation

[0071] The embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described below do not represent all embodiments consistent with this application. They are merely examples of systems and methods consistent with some aspects of this application as detailed in the claims.

[0072] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0073] The terms "first," "second," "third," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar or related objects or entities, and do not necessarily imply a specific order or sequence, unless otherwise specified. It should be understood that such terms are interchangeable where appropriate.

[0074] The terms “comprising” and “having”, and any variations thereof, are intended to cover but not exclude inclusion, for example, a product or device that includes a range of components is not necessarily limited to all of the components that are clearly listed, but may include other components that are not clearly listed or that are inherent to such product or device.

[0075] The term "module" refers to any known or subsequently developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code that is capable of performing the functions associated with that element.

[0076] Figure 1 This is a schematic diagram illustrating an operational scenario between an air conditioning unit and a control unit, provided in some embodiments of this application. For example... Figure 1 As shown, users can operate the air conditioning unit 200 via touch operation, mobile terminal 300, and control device 100. For example, control device 100 can be a remote control, stylus, or handle.

[0077] In some embodiments, the control device 100 can be a remote control or a smart home controller, etc. For example, the control device is a remote control, and the communication methods between the remote control and the air conditioner 200 include, but are not limited to, infrared protocol communication, Bluetooth protocol communication, or other short-range communication methods, to control the air conditioner 200 wirelessly or via wired means. Users can input user commands through buttons on the remote control, voice input, or control panel input to control the air conditioner 200.

[0078] In some embodiments, a mobile terminal 300 (such as a tablet computer, computer, or mobile phone) can also be used to control the air conditioning unit 200. For example, an application running on the mobile terminal 300 can be used to control the air conditioning unit 200.

[0079] In some embodiments, the air conditioning device may receive instructions not through the aforementioned mobile terminal 300 or control device 100, but through buttons or other means on the air conditioning device.

[0080] Figure 2 Provided for some embodiments of this application Figure 1 Hardware configuration block diagram of the central control device. (Example) Figure 2 As shown, the control device 100 may include: a controller 110, a communication interface 130, a user input / output interface, a memory, and a power supply.

[0081] The control device 100 is configured to control the air conditioning device 200, and to receive user input operation commands and convert the operation commands into commands that the air conditioning device 200 can recognize and respond to, thus acting as an intermediary for interaction between the user and the air conditioning device 200.

[0082] In some embodiments, the control device 100 may be an intelligent device. For example, the control device 100 may be equipped with various applications for controlling the air conditioning device 200 according to user needs.

[0083] In some embodiments, such as Figure 1 As shown, a mobile terminal 300 or other smart electronic device can perform similar functions to control device 100 after installing an application for controlling air conditioning device 200.

[0084] The controller 110 includes a processor 112, RAM 113, ROM 114, a communication interface 130, and a communication bus. The controller 110 is used to control the operation of the control device 100, as well as the communication and cooperation between internal components and the external and internal data processing functions.

[0085] Under the control of the controller 110, the communication interface 130 enables communication of control signals and data signals with the air conditioning equipment 200. The communication interface 130 may include at least one of other near-field communication modules such as WiFi chip 131, Bluetooth module 132, and NFC module 133.

[0086] User input / output interface 140, wherein the input interface includes at least one of other input interfaces such as microphone 141, touchpad 142, sensor 143, and button 144.

[0087] In some embodiments, the control device 100 includes at least one of a communication interface 130 and an input / output interface 140. The control device 100 is configured with the communication interface 130, such as a WiFi, Bluetooth, or NFC module, which can encode user input commands via WiFi, Bluetooth, or NFC protocols and send them to the air conditioning device 200.

[0088] The memory 190 is used to store various operating programs, data, and applications for driving and controlling the control device 100 under the control of the controller. The memory 190 can also store various control signal instructions input by the user.

[0089] The power supply 180 is used to provide operating power support for the various components of the control device 100 under the control of the controller.

[0090] Figure 3 The diagram shows a structural schematic of an air conditioning device provided in some embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0091] Reference Figure 3 The air conditioning unit includes a housing 310, a compressor 320, and a controller 330. Among them,

[0092] The compressor 320 is disposed within the housing 310.

[0093] The aforementioned controller 330, disposed within the aforementioned housing 310, is configured as follows:

[0094] The operating data of the compressor is obtained, including the total number of starts, the total number of stops, the total running time, and the total downtime of the compressor;

[0095] The operating data is input into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model. The temperature prediction model is used to extract features from the total number of starts, total number of shutdowns, total operating time, and total shutdown time in the operating data. The extracted feature vectors and their corresponding weights are weighted and calculated. The weighted feature vectors are then processed based on a set activation function to obtain the indoor average temperature.

[0096] When the air conditioning unit is in heating mode, and the average indoor temperature is greater than or equal to a first temperature threshold and the return air temperature is greater than or equal to a second temperature threshold, the compressor is controlled to stop running. The return air temperature is used to reflect the temperature at the return air of the air conditioning unit. The first temperature threshold and the second temperature threshold are determined according to the expected temperature of the ambient space where the air conditioning unit is located.

[0097] When the air conditioning unit is in cooling mode, the average indoor temperature is less than or equal to the first temperature threshold, and the return air temperature is less than or equal to the second temperature threshold, the compressor is controlled to stop operating.

[0098] Figure 4 The diagram illustrates the timing interaction of an air conditioning equipment control method according to some embodiments of this application. See also... Figure 4 The controller is configured to perform the following steps:

[0099] S401. Obtain the operating data of the compressor, including the total number of starts, total number of stops, total operating time, and total shutdown time of the compressor.

[0100] It should be understood that the operating status of a compressor typically includes a start-up state and a stop state. After the controller starts the compressor, the compressor usually transitions from the stop state to the start-up state; after the controller stops the compressor, the compressor usually transitions from the start-up state to the stop state.

[0101] In this embodiment, the operating data may include the total number of compressor starts, the total number of compressor stops, the total operating time, and the total shutdown time. In other embodiments, the operating data may include, but is not limited to, the compressor start time, shutdown time, the duration of operation after start-up (i.e., the duration of the start-up running state), and the duration of shutdown after shutdown (i.e., the duration of the shutdown state).

[0102] The total operating time of the compressor usually refers to the total time the compressor is in operation, that is, the sum of the operating duration after each start-up of the compressor; the shutdown time of the compressor usually refers to the total time the compressor is in shutdown, that is, the sum of the shutdown duration after each shutdown of the compressor.

[0103] Optionally, the compressor's operating data can be determined based on one or more of the compressor's current data, pressure data, temperature data, vibration data, and status data. This application embodiment does not impose specific restrictions on the method of obtaining the compressor's operating data.

[0104] S402. Input the above operating data into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model. The temperature prediction model is used to perform feature extraction processing on the above operating data, including the total number of starts, the total number of shutdowns, the total operating time, and the total shutdown time. It also performs weighted calculation based on the extracted feature vectors and the weights corresponding to the feature vectors. The weighted feature vectors are then processed based on the set activation function to obtain the indoor average temperature.

[0105] It should be understood that the feature vector is the vector form of data obtained by the temperature prediction model from the input data through feature extraction. When the temperature prediction model performs feature extraction on the input data, it can extract the features that can reflect the characteristics of the data, and at the same time convert the original data into a simpler and easier-to-process vector form to obtain the feature vector corresponding to the data.

[0106] It should be understood that the weights corresponding to each feature vector are also the weights corresponding to each data point. These weights are typically learned during the training of the temperature prediction model and can reflect the contribution of the data to the indoor average temperature prediction task (i.e., the importance of the data to the indoor average temperature prediction task). In other embodiments, the weights corresponding to each feature vector may also be obtained by the controller adjusting the learned weights based on user input or other intelligent algorithms.

[0107] In some embodiments, the average indoor temperature can be expressed in the following form:

[0108]

[0109] in, w represents the indoor average temperature predicted by the temperature prediction model. i Let x represent the feature vector of the i-th data point in the data input to the temperature prediction model. i σ represents the weight corresponding to the feature vector of the i-th data point, b represents the set bias, and σ represents the set activation function.

[0110] In some embodiments, the data input to the temperature prediction model may include the following seven data: total number of starts, total number of shutdowns, total running time, total shutdown time, return air temperature data, start / stop data, and air conditioning equipment running time.

[0111] The start-stop data (such as first start-stop data and / or second start-stop data) can reflect the start-up time, stop-stop time, operating duration after each start-up, and stop-stop duration after each stop; the air conditioning equipment operating time refers to the duration of the air conditioning equipment's operation; the return air temperature data can include the return air temperature detected at a set detection frequency (such as once per minute) during the air conditioning equipment's operation (reflecting the temperature at the air return point of the air conditioning equipment), or it can include the average return air temperature. Optionally, the average return air temperature can include the average return air temperature during the air conditioning equipment's operation, the average return air temperature during each start-up and operation of the compressor, or the average return air temperature during each stop of the compressor.

[0112] Alternatively, the temperature prediction model can be built based on network structures such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), or Transformer.

[0113] Optionally, when training the temperature prediction model, the input data for the training samples can be determined based on the compressor's historical operating data (i.e., the compressor's historical operating data). The output label for the training samples is determined based on the actual average temperature corresponding to the historical operating data to construct the training samples. After constructing the training samples, the input data of the training samples is input into the constructed temperature prediction model. The temperature prediction model extracts features from the input data and predicts the average temperature of the ambient space where the air conditioning equipment is located based on the extracted features, thus obtaining the predicted average temperature output by the temperature prediction model. The model parameters of the temperature prediction model are adjusted based on the difference between the predicted average temperature and the output label to obtain the trained temperature prediction model. It should be understood that the actual average temperature corresponding to the historical operating data can be calculated based on the temperature measured by multiple temperature sensors installed indoors, or it can be obtained based on user input.

[0114] In some embodiments, when constructing training samples based on the compressor's historical operating data of the air conditioning equipment, the time interval can be calculated by combining the operating characteristics of the air conditioning equipment and the periodicity of temperature changes. Based on the time interval, the historical operating data of the compressor is divided into multiple operating sub-data corresponding to different time periods, and then training samples are constructed separately for each operating sub-data. In the above process, dividing the operation process of the air conditioning equipment into multiple representative stages by time period division and then constructing training samples separately can provide more structured samples for the training of the temperature prediction model. At the same time, it enables the temperature prediction model to accurately learn the relationship between the total number of compressor starts, total number of shutdowns, total shutdown duration, total operating duration, etc., in different operating stages of the air conditioning equipment and the changes in the indoor average temperature, thereby improving the accuracy of the temperature prediction model.

[0115] Optionally, during the training of the temperature prediction model, after calculating the difference between the predicted average temperature and the output label based on the set loss function to obtain the loss value, an improved stochastic gradient descent algorithm can be used to optimize the model parameters of the temperature prediction model based on the loss value, resulting in a trained temperature prediction model. Through the above processing, the model parameters can be dynamically adjusted during the training process, enabling the obtained temperature prediction model to better adapt to different operating environments and conditions, thus improving the accuracy and reliability of the temperature prediction model.

[0116] As an example, the adjusted model parameters can be represented in the following form:

[0117]

[0118] Among them, w i Represents the adjusted model parameters i, w i,prev η represents the model parameters i before adjustment; η represents the learning rate, the value of which can be determined based on the scale of the training data and the complexity of the temperature prediction model to better control the step size of the model parameter adjustment; L represents the set loss function; λ represents the set momentum factor, which is usually used to accelerate the convergence of the temperature prediction model. λ can be obtained from user settings or input, or it can be calculated by the controller.

[0119] In this embodiment, considering that the number of compressor starts and stops, as well as the duration of operation and shutdown, directly affect the overall indoor temperature, which in turn affects the temperature of the air conditioner's return air vent, thus influencing the compressor's start and stop, there is a complex correlation between the compressor's operating data and the overall indoor temperature. The temperature prediction model can accurately learn these linear or nonlinear relationships during training. Therefore, when the controller uses the trained temperature prediction model to perform weighted calculations based on the extracted features and their corresponding weights, it can accurately quantify the contribution of each feature vector to the prediction of the indoor average temperature. Simultaneously, it fully considers the impact of different features on the indoor average temperature, improving the accuracy of the weighted feature vectors. Then, processing the weighted feature vectors based on an activation function introduces nonlinear factors, enabling the temperature prediction model to accurately capture the complex relationships in the data and improve the accuracy of the predicted indoor average temperature.

[0120] S403. When the air conditioning equipment is in heating mode, and the average indoor temperature is greater than or equal to a first temperature threshold and the return air temperature is greater than or equal to a second temperature threshold, the compressor is controlled to stop running. The return air temperature is used to reflect the temperature at the return air of the air conditioning equipment. The first temperature threshold and the second temperature threshold are determined based on the expected temperature of the ambient space where the air conditioning equipment is located.

[0121] S404. When the air conditioning equipment is in cooling mode, the average indoor temperature is less than or equal to the first temperature threshold, and the return air temperature is less than or equal to the second temperature threshold, the compressor is controlled to stop operating.

[0122] It should be understood that since the conditions for controlling the compressor to stop running usually differ when the air conditioning equipment is in different functional modes (i.e., cooling mode or heating mode), in order to accurately control the compressor to stop running, after the controller obtains the indoor average temperature output by the temperature prediction model, it can determine how to control the compressor to stop running based on the current functional model mode of the air conditioning equipment, that is, to select and execute step S403 or step S404 based on the functional mode of the air conditioning equipment.

[0123] It should be understood that the expected temperature of the environment in which the air conditioning equipment is located usually refers to the target temperature of the air conditioning equipment. This target temperature can be obtained based on user input or settings, or it can be calculated by intelligent algorithms such as large models based on historical target temperatures or climate data. This application does not impose specific limitations on this.

[0124] It should be understood that the first temperature threshold and the second temperature threshold can be the same temperature threshold or they can be different temperature thresholds.

[0125] See Figure 4 When the controller stops the compressor, it can do so by sending a stop control signal to the compressor. Upon receiving the stop control signal, the compressor can stop operating in an appropriate manner, depending on the requirements. For example, the compressor can stop by directly cutting off its power supply, or by gradually reducing the motor frequency until the motor stops running.

[0126] As an example, the controller can send a pre-set shutdown control signal (such as 1, indicating stop operation) to the compressor's relay control circuit. After receiving the shutdown control signal, the compressor's relay control circuit can cut off the power supply to the compressor, thereby quickly controlling the compressor to stop operating.

[0127] In some embodiments, since hot air with lower density usually rises naturally during the heating process, the uneven heat distribution leads to a relatively large difference between the return air temperature and the actual average indoor temperature. In contrast, cold air with higher density usually sinks naturally during the cooling process, resulting in a relatively small difference between the return air temperature and the actual average indoor temperature. Therefore, to improve the accuracy of the return air temperature determination, a second temperature threshold corresponding to the return air temperature can be determined by combining the current functional mode of the air conditioning equipment and the target temperature.

[0128] In some embodiments, when the current functional mode of the air conditioning device is heating mode, a second temperature threshold can be calculated based on a set heating weighting coefficient (greater than 1, such as 1.1) and the target temperature; when the current functional mode of the air conditioning device is cooling mode, a second temperature threshold can be calculated based on a set cooling weighting coefficient (less than 1, such as 0.95) and the target temperature. The absolute value of the difference between the heating weighting coefficient and 1 is greater than the absolute value of the difference between the cooling weighting coefficient and 1.

[0129] For example, suppose the air conditioner is currently in heating mode with a target temperature of 28°C; suppose the first temperature threshold is determined to be equal to the target temperature based on user settings, i.e., the first temperature threshold is 28°C; suppose the heating weighting coefficient calculated by the intelligent algorithm is 1.08, then the second temperature threshold is 30.24°C (calculated based on 28 * 1.08). Correspondingly, when the air conditioner is in heating mode, the average indoor temperature is greater than or equal to the first temperature threshold (28°C), and the return air temperature is greater than or equal to the second temperature threshold (30.24°C), the compressor can be controlled to stop running.

[0130] For example, suppose the air conditioner is currently in cooling mode with a target temperature of 20°C. Assume that, based on user settings, the first temperature threshold is determined to be 0.975 times the target temperature, i.e., the first temperature threshold is 19.5°C (calculated using 20 * 0.975). Assume that the cooling weighting coefficient calculated by the intelligent algorithm is 0.9, then the second temperature threshold is 18°C ​​(calculated using 20 * 0.9). Correspondingly, when the air conditioner is in cooling mode, the average indoor temperature is less than or equal to the first temperature threshold (19.5°C), and the return air temperature is less than or equal to the second temperature threshold (18°C), the compressor can be controlled to stop running.

[0131] In this embodiment, the start-up and shutdown of the compressor and its corresponding duration directly affect the overall indoor temperature, while the indoor temperature, in turn, affects the start-up and shutdown of the compressor. This means there is a complex correlation between the compressor's operating data and the overall indoor temperature. The temperature prediction model can accurately learn this complex relationship. Therefore, when the temperature prediction model performs weighted calculations based on the feature vectors of each extracted data point and their corresponding weights, it can accurately quantify the contribution of each feature vector to the prediction of the indoor average temperature. Simultaneously, it fully considers the impact of different features on the indoor average temperature, improving the accuracy of the weighted feature vectors. Then, the weighted feature vectors are processed based on an activation function. By introducing nonlinear factors through the activation function, the temperature prediction model can accurately capture the complex relationships in the data, thereby accurately predicting the average temperature of the environment where the air conditioning equipment is located, and obtaining the required indoor average temperature. Furthermore, based on the functional mode of the air conditioning equipment, and considering the relationship between the indoor average temperature and the detected return air temperature and their corresponding temperature thresholds, a comprehensive judgment is made on whether to control the compressor to stop running. Compared to relying solely on the return air temperature, this improves the accuracy of compressor shutdown control, thereby enhancing the precision of temperature regulation. Moreover, it eliminates the need for multiple additional sensors, effectively reducing hardware costs and the complexity of wiring and installation for the air conditioning equipment.

[0132] Figure 5 A flowchart illustrating an air conditioning equipment control method according to some embodiments is shown. See also Figure 5 When the aforementioned controller is controlling the compressor to stop running, it is configured to perform the following steps:

[0133] If any of the above operating data is less than or equal to the predetermined threshold corresponding to the above data, the compressor is controlled to stop operating.

[0134] It should be understood that, in the embodiments of this application, the operating data includes at least the total number of compressor starts, the total number of compressor stops, the total operating time, and the total shutdown time. Before the controller stops the compressor, it also performs the following judgments:

[0135] Whether the total number of compressor starts is less than or equal to a predetermined threshold for the total number of starts (e.g., 3 times); whether the total number of compressor stops is less than or equal to a predetermined threshold for the total number of stops (e.g., 4 times); whether the total operating time of the compressor is less than or equal to a predetermined threshold for the total operating time (e.g., 120 minutes); whether the total shutdown time of the compressor is less than or equal to a predetermined threshold for the total shutdown time (e.g., 100 minutes).

[0136] It should be understood that the predetermined thresholds corresponding to each data point can be determined by user input or settings, or they can be calculated by intelligent algorithms such as large models based on information such as the actual application requirements of air conditioning equipment.

[0137] In this embodiment, the controller stops the compressor only when it determines that the relationship between the indoor average temperature and the return air temperature and their corresponding temperature thresholds meets the requirements of the functional mode, and that each data in the operating data is less than or equal to the predetermined threshold corresponding to that data. This avoids frequent start-stop of the compressor and improves the energy-saving control effect of the air conditioning equipment.

[0138] Figure 6 A flowchart illustrating an air conditioning equipment control method according to some embodiments is shown. For example... Figure 6 As shown, before the controller stops the compressor, it can also determine whether the compressor control time interval is greater than or equal to a set interval threshold (e.g., 20 minutes). This control time interval can refer to the time interval since the controller last controlled the compressor (e.g., start-up or shutdown control). The interval threshold can be determined by user input or setting, or it can be calculated by intelligent algorithms such as large models based on information such as the actual application requirements of the air conditioning equipment.

[0139] If the control time interval is greater than or equal to the interval threshold, then the compressor is stopped. If the control time interval is less than the interval threshold, the process can wait until the control time interval is greater than or equal to the interval threshold before stopping the compressor. In some embodiments, if the control time interval is less than the interval threshold, the compressor may not be stopped; instead, the process returns to the step of obtaining the compressor's operating data.

[0140] By setting the above parameters, continuous compressor control is avoided in a short period of time, thereby preventing large temperature fluctuations and compressor damage, ensuring the reliability of the air conditioning equipment and user comfort.

[0141] Figure 7 A flowchart illustrating an air conditioning equipment control method according to some embodiments is shown. See also Figure 7Before the controller stops the compressor when any of the aforementioned operating data is less than or equal to a predetermined threshold corresponding to that data, it is also configured to perform the following steps:

[0142] The predetermined thresholds corresponding to each data point are determined based on the first and second operating data of the compressor. The first operating data includes the operating data of the compressor during the current operation of the air conditioning equipment, and the second operating data includes the operating data of the compressor during the historical operation of the air conditioning equipment.

[0143] In some embodiments, the second operating data may include the operating data of the compressor during the previous N runs (N is greater than or equal to 1) of the air conditioning equipment, where N can be obtained based on user input or settings, or calculated by a smart algorithm.

[0144] Optionally, when determining the predetermined threshold corresponding to each data based on the first and second operating data of the compressor, the required predetermined threshold can be determined by means of intelligent algorithms, fuzzy logic, or statistical analysis based on the first and second operating data.

[0145] For example, the first and second operating data of the compressor can be input into a trained threshold analysis model. The threshold analysis model extracts features from the input first and second operating data and obtains the predetermined thresholds corresponding to each data point based on the extracted features. It should be understood that the threshold analysis model can be trained based on input data including the first and second operating data and output labels including the predetermined thresholds corresponding to each data point. This allows the threshold analysis model to accurately learn the complex relationships between the first and second operating data and the predetermined thresholds corresponding to each data point during training. Therefore, in practical applications, based on the trained threshold analysis model, the dynamic calculation of each predetermined threshold can be performed quickly and accurately, improving the calculation efficiency and accuracy of the predetermined thresholds.

[0146] In this embodiment, the predetermined threshold corresponding to each operating data is dynamically calculated based on the operating data of the compressor during the current operation of the air conditioning equipment and the operating data of the historical operation process. This allows for flexible adjustment of each predetermined threshold in combination with the actual operating conditions of the air conditioning equipment. Compared with using fixed thresholds, this can accurately avoid situations such as energy waste or decreased user comfort caused by controlling the compressor to stop too early or too late, thereby improving the control effect of the air conditioning equipment.

[0147] In some embodiments, when the controller determines the predetermined thresholds corresponding to each data point based on the first and second operating data of the compressor, it is configured to perform the following steps:

[0148] Based on the first and second running data mentioned above, the average value of each data point in the running data is calculated.

[0149] The offset value of each data point is determined based on the temperature deviation and the standard deviation of each data point. The temperature deviation is used to reflect the deviation between the historical indoor average temperature and the corresponding first temperature threshold.

[0150] The predetermined thresholds for each data point are determined based on the average value and the offset value mentioned above.

[0151] It should be understood that the temperature deviation can be calculated based on the difference between the previously predicted indoor average temperature and its corresponding first temperature threshold, or it can be calculated based on the difference between each indoor average temperature predicted in the previous M times (M is an integer greater than 1) and its corresponding first temperature threshold (such as the average of the differences between each indoor average temperature and its corresponding first temperature threshold). This application embodiment does not impose specific limitations on this.

[0152] In this embodiment, since the temperature deviation can reflect the residual between the temperature of the ambient space where the air conditioning equipment is located and the expected temperature threshold, and the standard deviation of each data in the operating data can accurately reflect the dispersion of each data, the offset value of each data is calculated based on the temperature deviation and the standard deviation of the data, and then the predetermined threshold of each data is calculated based on the ratio of the average value and the offset value of the data. This allows for accurate adjustment of the predetermined threshold in combination with the actual distribution of the data, overall changes, and actual temperature deviation. Compared with a fixed threshold, this can improve the adaptability and accuracy of the predetermined threshold, thereby improving the accuracy and reliability of the compressor shutdown control.

[0153] In some embodiments, the predetermined thresholds can be expressed in the following form:

[0154]

[0155] Where, N on N represents a predetermined threshold for the total number of compressor starts. off ΔT represents a predetermined threshold for the total number of compressor shutdowns. on ΔT represents a predetermined threshold for the total operating time of the compressor. off This represents a predetermined threshold for the total downtime of the compressor.

[0156] N represents the average number of times the compressor starts. off ΔT represents the average number of times the compressor stops. on ΔT represents the average total operating time of the compressor. offThis represents the average total compressor downtime. It should be understood that the above averages are typically calculated based on first and second operating data. In some embodiments, the averages of the data may also be calculated based on the second operating data.

[0157] γ represents the set adjustment coefficient, and γ1, γ2, γ3, and γ4 correspond to the adjustment coefficients for the total number of compressor starts, the total number of compressor stops, the total compressor running time, and the total compressor stopping time, respectively. Optionally, the adjustment coefficients corresponding to each data point can be dynamically adjusted according to the operating status of the air conditioning equipment during this operation, so as to dynamically control the change range of each predetermined threshold by adjusting the adjustment coefficients.

[0158] σ represents the standard deviation. and These correspond to the standard deviations of the total number of compressor starts, the total number of compressor stops, the total compressor running time, and the total compressor stopping time, respectively.

[0159] T represents the average indoor temperature obtained from the previous prediction. s This represents the first temperature threshold corresponding to the average indoor temperature. In other embodiments, T s It can also be the target temperature corresponding to the average indoor temperature (i.e., the temperature that the air conditioning equipment is expected to reach in the environment).

[0160] Figure 8 A timing interaction diagram of an air conditioning equipment control method provided in some embodiments is shown. See also Figure 8 The operating data includes first start-stop data and second start-stop data. When the controller acquires the operating data of the compressor, it is configured to perform the following steps:

[0161] S1001. Obtain the first start-stop data based on the compressor's switching control circuit.

[0162] S1002. Obtain the second start-stop data based on the operating current of the compressor and the set current threshold.

[0163] The compressor's switching control circuit typically refers to the circuit used to control the power supply to the compressor motor. When the controller starts the compressor, it usually sends a start control signal to the compressor. After detecting the start control signal, the compressor's switching control circuit can connect the power supply to the compressor motor, thereby controlling the compressor to start running. When the controller stops the compressor, it usually sends a stop control signal to the compressor. After detecting the stop control signal, the compressor's switching control circuit can disconnect the power supply to the compressor motor, thereby controlling the compressor to stop running.

[0164] The operating current of a compressor typically refers to the current flowing through the compressor motor. Since current usually flows through the compressor motor after the power supply is turned on, monitoring the compressor's operating current provides a relatively intuitive way to determine the compressor's operating status.

[0165] It should be understood that the first start-stop data obtained by the controller based on the switch control circuit and the second start-stop data obtained based on the operating current can both include the total number of compressor starts, the total number of shutdowns, the total running time, and the total shutdown time.

[0166] Alternatively, the compressor's operating current can be obtained through sensors, vector control algorithms, or calculations based on motor parameters. For example, the compressor's operating current can be obtained directly by monitoring the compressor motor current using a high-precision current sensor, eliminating the need for various calculations and thus reducing the computational complexity and power consumption of the air conditioning equipment.

[0167] In order to improve the accuracy of the data obtained by the controller, when obtaining the second start-stop data based on the compressor's operating current, the operating status of the compressor can be comprehensively judged by combining the set current threshold, so as to avoid interference from fault current or abnormal current.

[0168] For example, assuming the current time is T10, the controller obtains the compressor's operating current at 10 times from T0 to T10, with current values ​​sequentially as (0A, 3.3A, 3.5A, 3.4A, 0A, 0A, 0A, 0A, 3.4A, 3.4A, 3.4A). Assuming the set current threshold is 3A, the judgment logic is: if the operating current is greater than or equal to the current threshold, the compressor is determined to be in a running state; if the operating current is less than the current threshold, the compressor is determined to be in a stopped state. Based on the above operating current and current threshold, the following judgment results can be obtained: the compressor is in a stopped state at time T0, in a running state from time T1 to T3, in a stopped state from time T4 to T7, and in a running state from time T8 to T10. Based on the above judgment results, the following second start-stop data can be obtained: shutdown at time T0; startup at time T1, and continued to run for 3 time periods; shutdown at time T4, and continued to be shut down for 4 time periods; startup at time T8, and has been running continuously until now.

[0169] In some embodiments, when determining the compressor's operating status based on the compressor's operating current and a set current threshold to obtain second start-stop data, the compressor can be determined to be started if the operating current is greater than or equal to the set current threshold and the duration is greater than or equal to a set start-up duration threshold (e.g., 3 seconds); otherwise, the compressor is determined to be stopped. This process fully considers the different characteristics of each stage during the compressor's start-up and operation, avoiding misjudgments caused by current fluctuations.

[0170] In this embodiment, the first start-stop data is obtained through the start-stop control circuit that intuitively reflects the start-stop control status of the compressor. At the same time, the second start-stop data is obtained through the working current that accurately reflects the actual working status of the compressor and the set current threshold. This approach can better balance the efficiency and accuracy of obtaining start-stop data.

[0171] Figure 9 A timing interaction diagram of an air conditioning equipment control method provided in some embodiments is shown. See also Figure 9 When the controller inputs the above operating data into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model, it is configured to perform the following steps:

[0172] If the difference between the first start-stop data and the second start-stop data is less than or equal to a set difference threshold, the first start-stop data and / or the second start-stop data are input into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model.

[0173] Optionally, when determining the difference between the first start-stop data and the second start-stop data, the difference can be determined by statistical distribution, error analysis or correlation analysis, etc. Correspondingly, the value of the difference threshold can be adjusted according to the analysis method adopted.

[0174] In some embodiments, since the first start-stop data and the second start-stop data obtained through different methods can reflect the start-up time and stop-down time of the same compressor, determining the difference between the first start-stop data and the second start-stop data can be done by comparing the start-up time of the compressor in the first start-stop data with the start-up time of the compressor in the second start-stop data, and calculating the start-up time difference value (such as the average of the differences of the differences of the start-up times) based on the difference of the start-up times; comparing the stop-down time of the compressor in the first start-stop data with the stop-down time of the compressor in the second start-stop data, and calculating the stop-down time difference value (such as the average of the differences of the differences of the start-up times) based on the difference of the stop-down times; determining whether the start-up time difference value and the stop-down time difference value are both less than or equal to a set difference threshold (such as 3 seconds), or determining whether any one of the start-up time difference value and the stop-down time difference value is less than or equal to a set difference threshold.

[0175] Due to differences in measurement accuracy or measurement methods, there may be discrepancies between the first start-stop data and the second start-stop data obtained through different methods in the operation data. Therefore, to ensure the accuracy of the operation data, the reasonableness of the difference can be determined by whether the difference between the first start-stop data and the second start-stop data is less than or equal to a set difference threshold.

[0176] If the difference between the first start-stop data and the second start-stop data is determined to be less than or equal to the difference threshold (if the difference is less than or equal to the difference threshold), then the difference can be considered to be a reasonable deviation caused by differences in measurement accuracy, etc. The first start-stop data and the second start-stop data in the operation data are relatively accurate data, and the first start-stop data and the second start-stop data can be directly input into the temperature prediction model to predict the indoor average temperature.

[0177] Alternatively, to minimize the computational load of the prediction model, either the first start-stop data or the second start-stop data can be used as input to the temperature prediction model. Both the first and second start-stop data can accurately reflect the compressor's start-stop status and the duration of each operating state. Therefore, through the above processing, the computational efficiency and accuracy of the prediction model can be balanced, improving the practical application effect.

[0178] See Figure 9 If it is determined that the difference between the first start-stop data and the second start-stop data is greater than the difference threshold, the process can return to the step of obtaining the compressor's operating data (i.e., the first start data and the second start-stop data) and determining whether the difference between the first start-stop data and the second start-stop data is less than or equal to the difference threshold, until the difference between the obtained first start-stop data and the second start-stop data is less than or equal to the difference threshold.

[0179] It should be understood that Figure 9 The following example illustrates the steps from returning the compressor's operating data (i.e., the first start data and the second start-stop data) to determining whether the difference between the first start-stop data and the second start-stop data is less than or equal to the difference threshold. In other embodiments, the controller may also perform anomaly detection on the first start-stop data and the second start-stop data, or obtain abnormal start-stop data through other means, etc. The specific settings can be configured according to actual application requirements.

[0180] In this embodiment, due to differences in measurement accuracy or measurement methods, there may be differences between the first start-stop data and the second start-stop data obtained through different methods in the operation data. Therefore, the reasonableness of the difference between the first start-stop data and the second start-stop data is judged by whether the difference between them is less than or equal to a set difference threshold. This can better determine whether there are any abnormalities in the first start-stop data and / or the second start-stop data, avoiding the direct prediction of indoor average temperature based on the potentially abnormal first start-stop data and the second start-stop data, and effectively ensuring the accuracy and reliability of subsequent indoor average temperature prediction.

[0181] Figure 10 A flowchart illustrating an air conditioning equipment control method according to other embodiments is shown. See also Figure 10When the controller inputs the above operating data into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model, it is configured to perform the following steps:

[0182] If the difference between the first start-stop data and the second start-stop data exceeds the difference threshold, abnormal start-stop data is determined based on the first start-stop data and the second start-stop data. The abnormal start-stop data is the start-stop data in the first start-stop data and the second start-stop data that has an abnormal jump.

[0183] The abnormal start / stop data is corrected according to the set correction method to obtain the corrected start / stop data.

[0184] The corrected start-stop data and abnormal start-stop data are input into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model.

[0185] It should be understood that when there is an abnormal jump in the first start / stop data, the abnormal start / stop data may include the first start / stop data; when there is an abnormal jump in the second start / stop data, the abnormal start / stop data may include the second start / stop data.

[0186] Abnormal jumps in start-stop data typically refer to abnormal changes in the compressor's operating status (i.e., start-up or shutdown). For example, the compressor's operating status usually alternates between start-up and shutdown. If, in continuous records of start-stop data, there are two consecutive start-up states without a shutdown state, or two consecutive shutdown states without a start-up state, it can be considered that the abnormal jumps in the acquired start-stop data are due to sensor malfunction or interference.

[0187] It should be understood that the correction method used can be determined based on actual application requirements. For example, if a user-defined correction method for start / stop data is detected, the user-defined correction method can be used as the set correction method to correct abnormal start / stop data.

[0188] In some embodiments, the correction method may be: processing the abnormal data points to be removed or corrected based on one or more data points adjacent to the abnormal data points in the abnormal start-stop data.

[0189] For example, suppose the abnormal start-stop data includes the first start-stop data, in which the compressor's operating state at time t has an abnormal jump and the operating state at time t needs to be corrected; then when correcting the operating state at time t, the operating state at time t can be corrected based on the operating states of the time before and the time after time t (i.e., time t-1 and time t+1) and the set correction logic, so as to obtain the corrected operating state at time t.

[0190] In other embodiments, the correction method may be: when the abnormal start-stop data includes one of the first start-stop data and the second start-stop data, the abnormal start-stop data is corrected based on the other start-stop data (for example, when the abnormal start-stop data includes the first start-stop data, the abnormal first start-stop data is corrected based on the second start-stop data); when the abnormal start-stop data includes the first start-stop data and the second start-stop data, a third start-stop data reflecting the total number of compressor starts, total number of shutdowns, total running time, and total shutdown time is obtained through a set acquisition method (such as based on return air temperature change data), and the abnormal start-stop data is corrected based on the third start-stop data.

[0191] It should be understood that non-abnormal start / stop data generally refers to start / stop data that does not contain abnormal jumps (excluding corrected start / stop data). For example, if the first start / stop data contains an abnormal jump, but the second start / stop data does not, the non-abnormal start / stop data includes the second start / stop data; if both the first and second start / stop data contain abnormal jumps, the non-abnormal start / stop data may include the third start / stop data used to correct it.

[0192] In this embodiment of the application, if the difference between the first start-stop data and the second start-stop data obtained through different methods exceeds a reasonable range, anomaly detection can be performed on the first start-stop data and the second start-stop data to identify abnormal start-stop data. After the abnormal start-stop data is corrected using a set correction method, the corrected start-stop data and non-abnormal start-stop data are input into the temperature prediction model to predict the indoor average temperature, thereby ensuring the reliability of the input data of the temperature prediction model and avoiding prediction errors caused by data anomalies.

[0193] Figure 11 A flowchart illustrating an air conditioning equipment control method according to some embodiments is shown. See also Figure 11 When the controller stops the compressor from running, it is configured to perform the following steps:

[0194] When the energy efficiency is greater than or equal to a set efficiency threshold, the compressor is controlled to stop operating. The energy efficiency is used to reflect the benefits that can be generated by the energy saved when the compressor is controlled to stop operating.

[0195] It should be understood that the benefit threshold can be obtained based on user input or settings, or it can be calculated by intelligent algorithms based on the second operating data and corresponding user feedback data, or it can be determined through experimental testing and corresponding user feedback data. No specific restrictions are imposed here.

[0196] It should be understood that the benefits of energy savings include, but are not limited to, economic benefits (such as electricity costs or air conditioning equipment maintenance costs), environmental benefits (such as carbon emissions or other pollutant emissions), and health benefits (such as indoor air quality or noise pollution).

[0197] In some embodiments, energy efficiency includes at least an energy-to-comfort ratio, which may be the ratio of energy saved when the compressor is currently stopped to the comfort level of the target user. The target user comfort level may refer to the comfort level of people in the environment where the air-conditioning equipment is located during the early shutdown period (i.e., the time period between the current time and the set shutdown time).

[0198] Since the controller usually waits until the set shutdown time for the air conditioning equipment or compressor before turning off the compressor when the user sets the shutdown time, the temperature in the room where the air conditioning equipment is located may have already reached the set temperature. Continuing to run the compressor may lead to excessively high or low indoor temperatures or wasted energy. Therefore, in order to reduce unnecessary energy consumption as much as possible while meeting user needs, if the relationship between the indoor average temperature and return air temperature and their corresponding temperature thresholds meets the requirements of the functional mode, it is possible to analyze whether the current energy efficiency is greater than or equal to the set efficiency threshold.

[0199] If the current energy efficiency is greater than or equal to the set efficiency threshold (e.g., 2), the compressor can be directly controlled to stop running without waiting for the downtime.

[0200] If the current energy efficiency is less than the set efficiency threshold, the system can return to the step of obtaining the compressor's operating data to determine whether the energy efficiency is greater than or equal to the efficiency threshold, until it is determined that the current energy efficiency is greater than or equal to the efficiency threshold, or the set shutdown time has been reached, and then control the compressor to stop running.

[0201] In this embodiment, the ability to control the compressor to stop running in advance is determined by considering different dimensions such as indoor average temperature, return air temperature, and energy efficiency. This allows for a more comprehensive consideration of factors such as overall indoor temperature and energy efficiency, improving the rationality and reliability of compressor shutdown control, while ensuring energy-saving control of air conditioning equipment while meeting user needs.

[0202] Figure 12 A flowchart illustrating an air conditioning equipment control method according to some embodiments is shown. See also Figure 12 Before the controller inputs the above operating data into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model, it is also configured to perform the following steps:

[0203] Obtain return air temperature data, which includes multiple return air temperatures collected at different times during the operation of the air conditioning equipment.

[0204] The above return air temperature data is preprocessed to obtain processed return air temperature data. The preprocessing includes filtering and / or interpolation.

[0205] Correspondingly, when the controller inputs the above-mentioned operating data into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model, it is configured to perform the following steps:

[0206] The processed return air temperature data and the operating data are input into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model. The temperature prediction model is used to perform feature extraction processing on the total number of starts, the total number of shutdowns, the total operating time, the total shutdown time, and the processed return air temperature data. The model performs weighted calculations based on the extracted feature vectors and the weights corresponding to the feature vectors. The weighted feature vectors are then processed based on the set activation function to obtain the indoor average temperature.

[0207] It should be understood that the return air temperature can be obtained by a temperature sensor installed at the return air of the air conditioning unit, or it can be predicted by a prediction model based on input data (such as air conditioning unit operating data, compressor operating data, or hot and cold aisle temperatures). This application embodiment does not impose specific restrictions on the method of collecting the return air temperature.

[0208] In some embodiments, during the operation of the air conditioning equipment, the return air temperature can be collected at a set collection frequency (e.g., once every 10 minutes) to obtain multiple return air temperatures collected at different times.

[0209] To improve the accuracy of return air temperature data, after the controller acquires the return air temperature data, it can first preprocess the return air temperature data. Through preprocessing, any abnormal data in the return air temperature data can be corrected, resulting in a more reliable processed return air temperature.

[0210] To reduce the impact of electromagnetic interference and other factors on return air temperature data, the return air temperature data can be filtered. Filtering can remove random noise from the return air temperature data and make the data smoother, thus more clearly reflecting the true trend of return air temperature changes and improving the reliability of the return air temperature data.

[0211] It should be understood that filtering processes include, but are not limited to, low-pass filtering, high-pass filtering, or sliding window filtering.

[0212] In some embodiments, the return air temperature data can be filtered based on a set sliding window. The length of the window can be obtained by user setting or input, or by experimental testing, in order to comprehensively balance the stability and real-time performance of the data.

[0213] As an example, the filtered return air temperature data can be represented in the following form:

[0214]

[0215] Among them, T filtered This represents the return air temperature data for a window after filtering, where n represents the length of the sliding window (i.e., the number of data points contained within the window), i represents the number of data points in the return air temperature data, and T... j This represents the j-th data point within the window, i.e., the j-th return air temperature. In the sliding window filtering process described above, the return air temperature within the window is averaged to smooth it out, remove outliers, and provide more stable and accurate return air temperature data for subsequent indoor average temperature prediction.

[0216] Alternatively, to improve the resolution of return air temperature data, interpolation can be performed on the data. Since interpolation can add new data points or fill in missing data points, interpolating the return air temperature data can yield more complete and continuous data, thus improving the accuracy of subsequent average temperature predictions.

[0217] It should be understood that interpolation processing includes, but is not limited to, linear interpolation, polynomial interpolation, or nearest neighbor interpolation.

[0218] In some embodiments, anomaly detection can be performed on the return air temperature data first. If missing data is detected in the return air temperature data, interpolation can be performed on the return air temperature data. Optionally, interpolation can be performed based on the return air temperatures corresponding to the two adjacent data points before and after the missing data point to obtain the return air temperature corresponding to the missing time point.

[0219] As an example, the return air temperature corresponding to a missing data point can be represented in the following form:

[0220]

[0221] Among them, T filled This represents the missing data points obtained through interpolation (i.e., t). m The return air temperature corresponding to (time); T m-1 Indicates the relationship with t m Adjacent time t m-1 The corresponding return air temperature at that moment; m+1 Indicates the relationship with tm Adjacent time t m+1 The return air temperature at that moment. In the above processing, interpolation is used to reasonably fill in missing data, avoiding analytical errors caused by missing data, and ensuring the accuracy and reliability of indoor average temperature prediction.

[0222] In some embodiments, in order to further improve the reliability and smoothness of return air temperature data, the return air temperature data can be filtered and interpolated. Filtering removes noise interference and smooths the return air temperature data, while interpolation further smooths the return air temperature data and fills in missing data.

[0223] In this embodiment, the return air temperature data is filtered and / or interpolated to smooth the return air temperature data, remove noise and correct abnormal data, and improve the reliability and accuracy of the processed return air temperature data. Then, the processed return air temperature data and operating data are input into the temperature prediction model to predict the indoor average temperature, thereby improving the accuracy of the indoor average temperature prediction.

[0224] In this embodiment, multiple data such as the total number of starts, start-stop data, and air conditioning equipment running time are input into the temperature prediction model to predict the indoor average temperature. This fully considers multiple factors affecting the overall temperature of the environment where the air conditioning equipment is located, as well as the degree of influence of different factors on the temperature, thereby more comprehensively considering the reasons for changes in the indoor average temperature and improving the reliability and accuracy of the indoor average temperature obtained by the temperature prediction model.

[0225] Figure 13 A flowchart illustrating an air conditioning equipment control method according to some embodiments is shown. See also Figure 13 After the controller stops the compressor, it is also configured to perform the following steps:

[0226] If the detected temperature change rate is greater than or equal to the set change threshold, the compressor is controlled to start. The temperature change rate reflects the rate of temperature change in the ambient space where the air conditioning equipment is located.

[0227] It should be understood that the rate of temperature change is usually calculated based on the temperature data (such as return air temperature) of the ambient space where the air conditioning equipment is located per unit time. It is used to reflect the rate of temperature change (such as ℃ / min, i.e., degrees Celsius per minute) within the ambient space where the air conditioning equipment is located.

[0228] For example, the rate of temperature change in the environment where the air conditioning unit is located can be calculated based on the temperature data of the environment within a set past time period (such as the time period from the last time the compressor was stopped to the current time), thus obtaining the required rate of temperature change.

[0229] Optionally, the aforementioned temperature change rate can be the return air temperature change rate, the indoor average temperature change rate, or a change rate obtained by weighting the return air temperature change rate, the indoor average temperature change rate, and their corresponding weighting coefficients. The specific settings can be configured according to actual application requirements.

[0230] It should be understood that the change threshold can be obtained through user input or setting, or through intelligent algorithms such as large models, fixed empirical thresholds, or physical models of the room. In some embodiments, the change threshold can be determined based on the thermal insulation performance of the environment in which the air conditioning equipment is located and the user's sensitivity to temperature changes.

[0231] In some embodiments, the controller can detect the temperature change rate at a set detection frequency (e.g., once per minute) after a preset time (e.g., 10 minutes) following the shutdown of the compressor, in order to avoid restarting the compressor in a short period of time. At the same time, by detecting the temperature change rate at a certain frequency, the controller can ensure that the compressor can be started in time when the temperature change rate is greater than or equal to the change threshold, while minimizing the number of calculations by the controller and reducing the burden on the air conditioning equipment.

[0232] In this embodiment, since the temperature change rate can better reflect the dynamic changes in temperature within the environment where the air conditioning equipment is located, determining whether to start the compressor based on whether the temperature change rate is greater than or equal to a change threshold allows for more precise compressor control compared to methods based on a fixed temperature threshold. This helps maintain the stability of the indoor temperature, improves user comfort, and allows for better adaptation to differences in indoor temperature caused by different environmental conditions, effectively improving the flexibility and reliability of the air conditioning equipment.

[0233] The above text combined Figures 4 to 13 The method of the embodiments of this application is described in detail below, and will be combined with Figure 14 This application describes an embodiment of the apparatus. It should be understood that the apparatus in the embodiments of this application can perform the various methods described in the foregoing embodiments of this application. That is, the specific working processes of the various products described below can be referred to the corresponding processes in the foregoing method embodiments.

[0234] Figure 14 A schematic diagram of the structure of an air conditioning equipment control device provided in some embodiments of this application is shown.

[0235] See Figure 14 The air conditioning equipment control device includes: an operating data acquisition module 1410, an average temperature prediction module 1420, a heating shutdown control module 1430, and a cooling shutdown control module 1440.

[0236] The operation data acquisition module 1410 is used to acquire the operation data of the compressor, including the total number of starts, the total number of stops, the total running time, and the total downtime of the compressor.

[0237] The average temperature prediction module 1420 is used to input the above-mentioned operating data into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model. The temperature prediction model is used to perform feature extraction processing on the above-mentioned total number of starts, total number of shutdowns, total operating time and total shutdown time in the above-mentioned operating data, and to perform weighted calculation based on the extracted feature vectors and the weights corresponding to the feature vectors. Based on the set activation function, the weighted feature vectors are processed to obtain the above-mentioned indoor average temperature.

[0238] The heating shutdown control module 1430 is used to control the compressor to stop running when the air conditioning equipment is in heating mode, the average indoor temperature is greater than or equal to a first temperature threshold, and the return air temperature is greater than or equal to a second temperature threshold. The return air temperature is used to reflect the temperature at the return air of the air conditioning equipment. The first temperature threshold and the second temperature threshold are determined according to the expected temperature of the ambient space where the air conditioning equipment is located.

[0239] The refrigeration shutdown control module 1440 is used to control the compressor to stop operating when the air conditioning equipment is in cooling mode, the average indoor temperature is less than or equal to the first temperature threshold, and the return air temperature is less than or equal to the second temperature threshold.

[0240] Each unit module of the device 1400 can execute the corresponding steps in the above method embodiments, so the details of each unit module will not be elaborated here. Please refer to the description of the corresponding steps above for details.

[0241] This application also provides a computer-readable storage medium storing a computer program that, when executed by a controller, can implement the steps in the above-described method embodiments.

[0242] This application provides a computer program product that, when run on a controller, enables the controller to implement the steps of the control methods described in the above embodiments.

[0243] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographic device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0244] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0245] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0246] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0247] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0248] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An air conditioning device, characterized in that, include: case; A compressor is installed inside the housing; The controller, located within the housing, is configured as follows: The operating data of the compressor is obtained, including the total number of starts, the total number of stops, the total running time, and the total downtime of the compressor; The operating data is input into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model. The temperature prediction model is used to extract features from the total number of starts, the total number of stops, the total operating time, and the total downtime in the operating data. It also performs weighted calculations based on the extracted feature vectors and their corresponding weights. The weighted feature vectors are then processed based on a set activation function to obtain the indoor average temperature. When the air conditioning unit is in heating mode, and the average indoor temperature is greater than or equal to a first temperature threshold and the return air temperature is greater than or equal to a second temperature threshold, the compressor is controlled to stop running. The return air temperature is used to reflect the temperature at the return air of the air conditioning unit. The first temperature threshold and the second temperature threshold are determined according to the expected temperature of the ambient space where the air conditioning unit is located. When the air conditioning unit is in cooling mode, the average indoor temperature is less than or equal to the first temperature threshold, and the return air temperature is less than or equal to the second temperature threshold, the compressor is controlled to stop operating.

2. The air conditioning equipment as described in claim 1, characterized in that, The controller, when controlling the compressor to stop running, is configured to: If any data in the operating data is less than or equal to a predetermined threshold corresponding to that data, the compressor is controlled to stop operating.

3. The air conditioning equipment as described in claim 2, characterized in that, Before the controller stops the compressor when any data in the operating data is less than or equal to a predetermined threshold corresponding to that data, it is further configured to: The predetermined threshold corresponding to each data point is determined based on the first and second operating data of the compressor. The first operating data includes the operating data of the compressor during the current operation of the air conditioning equipment, and the second operating data includes the operating data of the compressor during the historical operation of the air conditioning equipment.

4. The air conditioning equipment as described in claim 3, characterized in that, When the controller executes the determination of the predetermined threshold corresponding to each data based on the first and second operating data of the compressor, it is configured to: Based on the first running data and the second running data, calculate the average value of each data point in the running data; The offset value of each data point is determined based on the temperature deviation and the standard deviation of each data point. The temperature deviation is used to reflect the deviation between the historical indoor average temperature and the corresponding first temperature threshold. The predetermined threshold for each data point is determined based on the average value and the offset value, respectively.

5. The air conditioning equipment as described in claim 1, characterized in that, The operating data includes first start / stop data and second start / stop data. When the controller acquires the compressor's operating data, it is configured to: The first start / stop data is obtained based on the compressor's switch control circuit; The second start / stop data is obtained based on the compressor's operating current and a set current threshold.

6. The air conditioning equipment as described in claim 5, characterized in that, When the controller executes the operation of inputting the running data into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model, it is configured as follows: If the difference between the first start-stop data and the second start-stop data is less than or equal to a set difference threshold, the first start-stop data and / or the second start-stop data are input into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model.

7. The air conditioning equipment as described in claim 6, characterized in that, When the controller executes the operation of inputting the running data into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model, it is configured as follows: If the difference between the first start-stop data and the second start-stop data is greater than the difference threshold, abnormal start-stop data is determined based on the first start-stop data and the second start-stop data. The abnormal start-stop data is start-stop data in the first start-stop data and the second start-stop data that has an abnormal jump. The abnormal start / stop data is corrected according to the set correction method to obtain the corrected start / stop data; The corrected start-stop data and abnormal start-stop data are input into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model.

8. The air conditioning equipment as described in claim 1, characterized in that, The controller, when controlling the compressor to stop running, is configured to: When the energy efficiency is greater than or equal to a set efficiency threshold, the compressor is controlled to stop operating. The energy efficiency is used to reflect the benefits that can be generated by the energy saved when the compressor is currently controlled to stop operating.

9. The air conditioning equipment as described in claim 1, characterized in that, Before the controller executes the process of inputting the operating data into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model, it is further configured to: Acquire return air temperature data, which includes multiple return air temperatures collected at different times during the operation of the air conditioning equipment. The return air temperature data is preprocessed to obtain processed return air temperature data. The preprocessing includes filtering and / or interpolation. Correspondingly, when the controller executes the operation of inputting the running data into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model, it is configured as follows: The processed return air temperature data and the operating data are input into the temperature prediction model to obtain the indoor average temperature output by the temperature prediction model. The temperature prediction model is used to perform feature extraction processing on the input total number of starts, total number of shutdowns, total operating time, total shutdown time, and processed return air temperature data, and to perform weighted calculation based on each extracted feature vector and the weight corresponding to the feature vector. The weighted feature vector is then processed based on a set activation function to obtain the indoor average temperature.

10. The air conditioning equipment according to any one of claims 1 to 9, characterized in that, After the controller executes the command to stop the compressor, it is further configured to: If the detected temperature change rate is greater than or equal to a set change threshold, the compressor is controlled to start. The temperature change rate reflects the rate of temperature change in the ambient space where the air conditioning equipment is located.

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