Air conditioner control method and device, air conditioner equipment and readable storage medium

Abstract

The invention relates to an air conditioner control method and device, air conditioner equipment, a computer readable storage medium and a computer program product, power grid demand data is obtained, under the condition that the power grid demand data indicates that load regulation and control need to be carried out, air conditioner operation power is adjusted according to the power grid demand data, and target operation power is obtained; parameter prediction is carried out based on the target operation power and control related data, target control parameters are obtained to control operation of air conditioner equipment, real-time data interaction between an air conditioner and a power load management system is achieved through the Internet of Things technology, and the requirement of a power grid is obtained; on the basis of machine learning and an optimization algorithm, air conditioner operation parameters such as temperature setting and wind speed are adjusted according to the requirements of a power grid, so that air conditioner operation power is reduced, and power supply pressure of the power grid is relieved; through the embedded design, the controller is arranged in the air conditioning unit, and the air conditioning unit is used for supplying power, so that the deployment cost is saved while the space resource consumption is reduced.

Description

Technical Field

[0001] The present application relates to the field of air conditioning technology, and in particular to an air conditioning control method, device, air conditioning equipment, computer-readable storage medium and computer program product. Background Art

[0002] With the rapid development of power systems and the improvement of their intelligence level, traditional power load management methods have been unable to meet the needs of modern power systems. Especially in the case of imbalance between power supply and demand, how to effectively manage and regulate power loads has become an important issue.

[0003] As one of the devices that consume the most energy in homes and commercial buildings, air conditioners have a particularly significant impact on the power grid. Even the operation of some large air conditioning equipment may cause an imbalance in the power load. Summary of the invention

[0004] Based on this, it is necessary to provide an air conditioning control method, device, air conditioning equipment, computer-readable storage medium and computer program product to address the technical problem of unbalanced power load caused by the operation of the above-mentioned air conditioning equipment.

[0005] In a first aspect, the present application provides an air conditioning control method, the method comprising:

[0006] Obtain air conditioning operating power, control-related data, and grid demand data;

[0007] When the grid demand data indicates that load regulation is required, adjusting the air conditioner operating power according to the grid demand data to obtain a target operating power;

[0008] Parameter prediction is performed based on the target operating power and the control-related data to obtain target control parameters, and the operation of the air-conditioning equipment is controlled based on the target control parameters.

[0009] In one embodiment, the performing parameter prediction based on the target operating power and the control-related data to obtain the target control parameter includes:

[0010] The target operating power and the control-related data are input into a parameter prediction model to obtain target control parameters.

[0011] In one embodiment, the obtaining of air conditioner operating power, control-related data, and grid demand data includes:

[0012] According to the preset collection time, continuously obtain air conditioning operating power, control-related data and power grid demand data;

[0013] The method further comprises:

[0014] According to the preset acquisition time, actual control parameters are obtained;

[0015] The parameter prediction model is updated by taking the air conditioner operating power and the control-related data as model inputs and the actual control parameters as model outputs.

[0016] In one of the embodiments, the control-related data at least includes indoor environment temperature and humidity data, outdoor environment temperature and humidity data, and air conditioning operation mode.

[0017] In one of the embodiments, during the updating process of the parameter prediction model, the loss function weight of the air-conditioning operation mode is set to 100%.

[0018] In one of the embodiments, the power grid demand data includes regulation identification data and regulation ratio data;

[0019] When the grid demand data indicates that load regulation is required, adjusting the air conditioner operating power according to the grid demand data to obtain the target operating power includes:

[0020] When the control identification data indicates that control is required, a calculation is performed based on the control ratio data and the air conditioner operating power to obtain a target operating power.

[0021] In one embodiment, the method further comprises:

[0022] When the grid demand data indicates that no load regulation is required, parameter prediction is performed based on the air-conditioning operating power and the control-related data to obtain target control parameters.

[0023] In a second aspect, the present application further provides an air conditioning control device, the device comprising:

[0024] An acquisition module is used to obtain air conditioner operating power, control-related data, and grid demand data;

[0025] A control module, configured to adjust the air conditioner operating power according to the grid demand data to obtain a target operating power when the grid demand data indicates that load control is required;

[0026] The prediction module is used to perform parameter prediction based on the target operating power and the control-related data to obtain target control parameters, and the target control parameters are used to control the operation of the air-conditioning equipment.

[0027] In the third aspect, the present application also provides an air-conditioning device, including a controller, and a data acquisition device and a communication device connected to the controller, the data acquisition device is used to obtain air-conditioning operating power and control related data, the controller also obtains power grid demand data through the communication device, and the controller is used to realize operation control of the air-conditioning equipment according to the above-mentioned air-conditioning control method.

[0028] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0029] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0030] The above-mentioned air-conditioning control method, device, air-conditioning equipment, computer-readable storage medium and computer program product obtain power grid demand data, and when the power grid demand data indicates that load regulation is required, adjust the air-conditioning operating power according to the power grid demand data to obtain the target operating power, and then predict parameters based on the target operating power and control-related data to obtain target control parameters to control the operation of the air-conditioning equipment, thereby achieving the purpose of reducing the air-conditioning load according to power grid demand regulation and further alleviating the power supply pressure of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 A schematic diagram of a system block diagram of an air conditioning device in one embodiment;

[0033] Figure 2 A schematic diagram of a flow chart of an air conditioning control method in one embodiment;

[0034] Figure 3 A schematic flow chart of an air conditioning control method in another embodiment;

[0035] Figure 4 A schematic diagram of a flow chart of an air conditioning control method in another embodiment;

[0036] Figure 5 is a structural block diagram of an air conditioning control device in an embodiment;

[0037] Figure 6 A schematic diagram of a system block diagram of an air conditioning device in another embodiment;

[0038] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0040] The air conditioning control method provided in the embodiment of the present application can be applied to Figure 1 The air conditioning device shown. The air conditioning device includes a controller 102, and a data acquisition device 104 and a communication device 106 connected to the controller 102. The data acquisition device 104 is used to obtain the air conditioning operation power and control related data. The controller 102 also obtains the grid demand data through the communication device 106. The controller 102 is used to realize the operation control of the air conditioning device according to the air conditioning operation power, control related data and grid demand data. The data storage system can store the data that the controller 102 needs to process. The data storage system can be integrated on the controller 102, or it can be placed on the cloud or other network servers.

[0041] Specifically, the controller 102 obtains the air-conditioning operating power and control-related data collected by the data acquisition device 104, and also obtains the power grid demand data through the communication device 106. When the power grid demand data indicates that load regulation is required, the air-conditioning operating power is adjusted according to the power grid demand data to obtain the target operating power, and then parameter prediction is performed based on the target operating power and control-related data to obtain the target control parameters, and the operation of the air-conditioning equipment is controlled based on the target control parameters.

[0042] It can be understood that the controller 102 can be a control chip or a control circuit board disposed in the air-conditioning device, or an external control system based on wireless communication. The external control system can be implemented through devices such as terminals or servers. The terminals can be, but are not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server can be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0043] In an exemplary embodiment, Figure 2 As shown, an air conditioning control method is provided, which is applied to Figure 1 The controller 102 in FIG. 1 is taken as an example to illustrate the method, which includes the following steps 202 to 206. Among them:

[0044] Step 202, obtaining air conditioner operating power, control-related data, and grid demand data.

[0045] Among them, the operating power of the air conditioner refers to the amount of electrical energy consumed by the air conditioner during operation, which generally depends on the cooling / heating capacity and energy efficiency ratio of the air conditioner. In addition, the use environment of the air conditioner will also affect its operating power. For example, when the indoor temperature is high or the outdoor ambient temperature is high, the air conditioner needs to consume more electricity to reach the set temperature, and the operating power will also be greater. The air conditioner operating power can be obtained through the performance parameters of the air conditioner. For example, the air conditioner generally has parameters such as rated power and maximum and minimum power when it leaves the factory. It can also be obtained by adding power sensors or power meters and other collection devices to the air conditioner to obtain the air conditioner operating power of the air conditioner during real-time operation.

[0046] Control-related data refers to data that will affect the setting of control parameters during the process of controlling the operation of the air-conditioning equipment, and may specifically include but is not limited to air-conditioning operation parameters and environmental parameters. Air-conditioning operation parameters may include air-conditioning operation mode, air outlet temperature, wind speed, and power supply parameters, etc. Environmental parameters may include temperature and humidity data of the environment where the indoor unit is located, environmental area and thermal insulation performance, etc., and may also include temperature and humidity data of the environment where the outdoor unit is located. It can be understood that for the above-mentioned different data, the corresponding acquisition devices can be used to collect them for acquisition by the controller. For example, temperature and humidity data can be obtained through acquisition devices such as temperature sensors and humidity sensors, environmental area and thermal insulation performance can be obtained through input from the interactive terminal on the user side, air-conditioning operation mode and power supply parameters can be obtained by the controller monitoring the internal components of the air-conditioning equipment, and air outlet temperature and wind speed can be obtained by adding an acquisition device to the air outlet of the air-conditioning equipment.

[0047] Secondly, the grid demand data characterizes the load situation on the grid side to which the air-conditioning equipment is connected, and is used to indicate whether load regulation is required on the air-conditioning equipment side to alleviate the power supply pressure on the grid. The grid demand data can be obtained by the controller communicating with the power supply master station in real time through a communication device. It can be understood that the power supply master station can make a judgment based on the real-time power supply parameters of the power grid, and then send the power grid demand data to the controller on the air-conditioning equipment side. The real-time power supply parameters may include load rate, bus voltage, and fault conditions. Taking the case where the grid demand data indicates that load regulation is required on the air-conditioning equipment side as an example, the power supply master station may judge that the real-time load rate exceeds the load rate limit, resulting in tight power consumption on the grid, and then send the power grid demand data to the controller on the air-conditioning equipment side to indicate that load regulation is required on the air-conditioning equipment side.

[0048] Step 205: When the grid demand data indicates that load regulation is required, the air conditioner operating power is adjusted according to the grid demand data to obtain a target operating power.

[0049] The target operating power is the operating power adjusted according to the grid demand data.

[0050] Specifically, when the grid demand data indicates that load regulation is required, the air conditioner operating power needs to be adjusted to reduce the amount of power consumed by the air conditioner during operation. It can be understood that the adjusted target operating power is an operating power value that consumes less power than the air conditioner operating power, and then after the next step of air conditioner operation control is performed according to the target operating power, the air conditioner load can be effectively reduced to meet the grid regulation requirements.

[0051] Furthermore, the method of adjusting the air conditioner operating power according to the power grid demand data can be determined according to the specific form of the transmitted power grid demand data. For example, if the transmitted power grid demand data includes a power value that can directly set the target operating power, the power data can be directly used to replace the current air conditioner operating power to obtain the target operating power. In other examples, if the transmitted power grid demand data includes an adjustment range for the current air conditioner operating power, the air conditioner operating power can be adjusted based on the adjustment range to obtain the target operating power.

[0052] In an exemplary embodiment, the grid demand data includes regulation identification data and regulation ratio data.

[0053] Specifically, the control identification data is identification information of whether load control is required, which may include whether control is required or not. It can be understood that when the control identification data sent down indicates that control is required, the grid demand data indicates that load control is required; and when the control identification data sent down indicates that control is not required, the grid demand data indicates that load control is not required.

[0054] Furthermore, the control ratio data is a specific parameter used when adjusting the air conditioner operating power, which represents the adjustment range of the current air conditioner operating power. The specific value of the control ratio data is not fixed, and can be set by the power supply master station based on the real-time power supply parameters of the current power grid. It can be understood that after adjusting the air conditioner operating power according to the control ratio data, the air conditioner load can be effectively reduced to meet the power grid control requirements.

[0055] Exemplarily, step 205 includes: when the control identification data indicates that control is required, calculating according to the control ratio data and the air conditioner operating power to obtain the target operating power.

[0056] Specifically, the control ratio data may indicate the extent to which the air conditioner operating power needs to be increased or decreased. The power adjustment amplitude may be obtained based on the product of the control ratio data and the air conditioner operating power, and then the target operating power may be obtained based on the superposition of the power adjustment amplitude and the air conditioner operating power.

[0057] For example, the target operating power can be calculated according to the following formula: target operating power = air conditioner operating power * (1-regulation ratio data). In the embodiment of the present application, in order to ensure that the regulation of the air conditioner operating power of the air conditioner does not affect the actual user experience, a value that takes into account both the power grid regulation requirements and the user experience can be obtained based on a preliminary test, that is, it can be understood that the regulation amplitude can just meet the power grid regulation requirements, but the regulation amplitude will not make the user feel uncomfortable. For example, in this embodiment, the regulation ratio data can be set to 25%, that is, the target operating power = air conditioner operating power * (1-25%).

[0058] Step 206: perform parameter prediction based on the target operating power and control-related data to obtain target control parameters, and control the operation of the air-conditioning equipment based on the target control parameters.

[0059] Among them, the target control parameters are parameters for controlling the operation of the air-conditioning equipment, which may include control parameters for various components in the air-conditioning equipment, such as the air-conditioning operation mode, the target temperature value for controlling the cooling / heating module, and the target wind speed value for controlling the fan module. In addition, when the air-conditioning equipment includes other auxiliary components such as a fresh air module, a dehumidification module, and a sterilization module, the target control parameters may also include parameter values ​​for controlling the operation of the above modules.

[0060] Specifically, after the controller acquires control-related data and the target operating power after regulation based on grid demand, the target control parameters can be predicted through the above parameters.

[0061] In this embodiment, a parameter prediction model can be established according to a variety of methods to achieve the prediction of target control parameters. For example, in the time dimension, a parameter prediction model can be constructed by algorithms such as time series analysis, support vector machines or neural networks, and then the model can be trained based on historical data to obtain a model to achieve the prediction of target control parameters. In addition, it can also be based on the physical model dimension, and a physical model of the operation of the air-conditioning equipment can be constructed by energy balance method or simulation method, and then the physical model can be used as a parameter prediction model to achieve the prediction of target control parameters. Of course, it can also be based on the empirical formula method, according to the basic principles and operating experience of the air-conditioning equipment, to establish an empirical formula as a parameter prediction model to achieve the prediction of target control parameters.

[0062] The above-mentioned air-conditioning control method, device, air-conditioning equipment, computer-readable storage medium and computer program product obtain power grid demand data, and when the power grid demand data indicates that load regulation is required, adjust the air-conditioning operating power according to the power grid demand data to obtain the target operating power, and then predict parameters based on the target operating power and control-related data to obtain target control parameters to control the operation of the air-conditioning equipment, thereby achieving the purpose of reducing the air-conditioning load according to power grid demand regulation and further alleviating the power supply pressure of the power grid.

[0063] In an exemplary embodiment, Figure 3 As shown, in step 206, parameter prediction is performed based on the target operating power and control-related data to obtain target control parameters, including: inputting the target operating power and control-related data into a parameter prediction model to obtain the target control parameters.

[0064] Specifically, this embodiment takes the artificial neural network algorithm to construct a parameter prediction model as an example, and then inputs the target operating power and control-related data into the parameter prediction model to obtain the target control parameters.

[0065] Among them, the input features targeted by this application are air-conditioning operating power and control-related data, and the output features are multiple actual control parameters, so the solution is a typical multi-input and multi-output regression problem. As for the above-mentioned complex nonlinear relationship, this embodiment considers using a multi-layer perceptron (MLP) algorithm to construct a parameter prediction model. It consists of an input layer, at least one hidden layer, and an output layer. Each layer consists of multiple neurons (or nodes), and the neurons are connected by weights and biases. For the activation function, Sigmoid, ReLU (Rectified Linear Unit), Tanh, etc. can be used to convert the input value into a nonlinear output, so that the model can handle more complex nonlinear relationships.

[0066] Furthermore, by inputting the target operating power and control-related data into the parameter prediction model constructed using the multi-layer perceptron algorithm, the input data is forward propagated through each layer of the model, and finally the output result is obtained to obtain the target control parameters.

[0067] It can be understood that the above parameter prediction model is a model pre-stored in the controller of the air-conditioning equipment when leaving the factory.

[0068] Exemplarily, the parameter prediction model may be a model that only has a model framework but no trained network model. After the air-conditioning equipment is started on the user side, the model is trained based on the data during the real-time operation of the air-conditioning equipment to make it more in line with the real-time air-conditioning operating conditions.

[0069] In another embodiment, the parameter prediction model can also be a parameter prediction model that has been pre-trained based on a historical database, and in subsequent use, the model is updated based on data from the real-time operation process. This not only makes the model more consistent with the real-time air-conditioning operation conditions, but also greatly reduces the computational pressure on model training during air-conditioning operation.

[0070] In an exemplary embodiment, referring to Figure 3 , step 202 includes: continuously acquiring air-conditioning operating power, control-related data, and power grid demand data according to a preset collection time.

[0071] Specifically, in the process of the controller monitoring the grid demand data, it can be acquired according to a fixed preset acquisition time. For example, the power supply master station can transmit the grid demand data to the storage space of the controller in real time through a communication device, and the controller can obtain the grid demand data cached in its storage space according to the preset acquisition time. Alternatively, the controller can transmit a grid demand acquisition request to the power supply master station through a communication device according to the preset acquisition time, so as to obtain the grid demand data issued by the power supply master station in response to the grid demand acquisition request.

[0072] Furthermore, the controller can also continuously obtain the air-conditioning operating power and control-related data according to the preset collection time, so as to update the parameter prediction model during the real-time operation of the air-conditioning, so as to make it more in line with the real-time air-conditioning operating conditions.

[0073] Exemplarily, the preset collection time for acquiring the grid demand data and the preset collection time for acquiring the air conditioner operating power and control-related data may be the same time or different time. That is, the frequency of updating the parameter prediction model with the collected data and the frequency of load regulation based on the grid demand data may be the same frequency or different frequencies. In the embodiment of the present application, the above data are collected using the same preset collection time. The specific value of the time is not limited and can be set according to actual technical requirements. For example, the embodiment of the present application may be set to 1 minute.

[0074] In an exemplary embodiment, referring to Figure 3 After step 202, the air conditioning control method further includes the following steps 203 to 204, wherein:

[0075] Step 203, obtaining actual control parameters according to the preset collection time.

[0076] Step 204 , using the air conditioner operating power and control related data as model input and the actual control parameters as model output, the parameter prediction model is updated.

[0077] Specifically, in order to update the parameter prediction model during the real-time operation of the air conditioner, it is also necessary to obtain the actual control parameters during the operation according to the same preset collection time.

[0078] Furthermore, after obtaining the air conditioner operating power and control-related data, as well as the actual control parameters, the air conditioner operating power and control-related data can be used as model input features, and the actual control parameters can be used as model output features to achieve real-time update of the parameter prediction model. It can be understood that during the actual operation of the air conditioner, the controller will collect the air conditioner operating power, control-related data and actual control parameters according to the preset collection time, and continuously update the parameter prediction model, so that the target control parameters predicted by the parameter prediction model can be more in line with the actual operating conditions, and more accurate control can be achieved.

[0079] Exemplarily, the control-related data includes at least indoor environment temperature and humidity data, outdoor environment temperature and humidity data, and air-conditioning operation mode, and the actual control parameters include at least air-conditioning operation mode, target temperature value, and target wind speed value.

[0080] In an exemplary embodiment, during the updating process of the parameter prediction model, the loss function weight of the air conditioning operation mode is set to 100%.

[0081] It can be understood that by setting the weight of the loss function corresponding to the air-conditioning operation mode to 100% during the above model training and updating process, the air-conditioning operation mode in the output feature is the same as the air-conditioning operation mode in the input feature. This effectively ensures that the predicted air-conditioning operation mode is the same as the currently running air-conditioning operation mode, that is, the operation mode before and after regulation cannot change (heating becomes cooling, cooling becomes heating), ensuring effective regulation under the same air-conditioning operation mode.

[0082] In an exemplary embodiment, Figure 4 As shown, the air conditioning control method further includes step 207: when the grid demand data indicates that load regulation is not required, parameter prediction is performed based on the air conditioning operating power and control-related data to obtain target control parameters.

[0083] Specifically, after the parameter prediction model has been trained and iterated to a model with good prediction effect on the control parameters, even if no grid demand data is received to indicate load regulation, the controller can input the air-conditioning operation power and control-related parameters that have not been regulated by the grid demand into the parameter prediction model to predict the target control parameters. The target control parameters are used to control the operation of the air-conditioning equipment to achieve more accurate control of the air-conditioning equipment.

[0084] For example, after obtaining the target control parameters and controlling the operation of the air-conditioning equipment based on the target control parameters, whether it is obtained according to the indication of the power grid demand data or not, it is necessary to wait for the preset control time, and then enter the step of obtaining the target control parameters based on the parameter prediction model. Among them, the preset control time can be greater than the preset acquisition time, and can even be set to a longer time, for example, it can be set to 15 minutes, or 30 minutes. Such a design can avoid frequent adjustment of the air-conditioning control parameters and ensure the smooth operation of the air-conditioning equipment.

[0085] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0086] Based on the same inventive concept, the embodiment of the present application also provides an air conditioning control device for implementing the air conditioning control method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more air conditioning control device embodiments provided below can refer to the limitations of the air conditioning control method above, and will not be repeated here.

[0087] In an exemplary embodiment, Figure 5 As shown, an air conditioning control device is provided, including: an acquisition module 510, a control module 520 and a prediction module 530, wherein:

[0088] An acquisition module 510 is used to acquire air conditioner operating power, control-related data, and grid demand data;

[0089] The control module 520 is used to adjust the air conditioner operating power according to the grid demand data to obtain the target operating power when the grid demand data indicates that load control is required;

[0090] The prediction module 530 is used to perform parameter prediction based on the target operating power and control-related data to obtain target control parameters, which are used to control the operation of the air-conditioning equipment.

[0091] In an exemplary embodiment, the prediction module 530 is further used to input the target operating power and control-related data into the parameter prediction model to obtain the target control parameters.

[0092] In an exemplary embodiment, the acquisition module 510 is further used to continuously acquire the air conditioner operating power, control-related data, and grid demand data according to a preset acquisition time; and is further used to acquire actual control parameters according to a preset acquisition time;

[0093] The air conditioning control device further comprises:

[0094] The updating module is used to update the parameter prediction model by taking the air conditioner operating power and control related data as the model input and the actual control parameters as the model output.

[0095] In an exemplary embodiment, the control-related data acquired by the acquisition module 510 at least includes indoor environment temperature and humidity data, outdoor environment temperature and humidity data, and air-conditioning operation mode.

[0096] In an exemplary embodiment, the updating module is further used to set the loss function weight of the air-conditioning operation mode to 100% during the updating process of the parameter prediction model.

[0097] In an exemplary embodiment, the grid demand data includes regulation identification data and regulation ratio data;

[0098] The control module 520 is further used to obtain the target operating power by performing calculations based on the control ratio data and the air conditioner operating power when the control identification data indicates that control is required.

[0099] In an exemplary embodiment, the prediction module 530 is further used to perform parameter prediction based on the air conditioner operating power and control-related data to obtain target control parameters when the grid demand data indicates that load regulation is not required.

[0100] Each module in the above-mentioned air conditioning control device can be implemented in whole or in part by software, hardware or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0101] In an exemplary embodiment, Figure 1As shown, an air-conditioning device is provided, including a controller 102, and a data acquisition device 104 and a communication device 106 connected to the controller 102. The data acquisition device 104 is used to obtain air-conditioning operating power and control-related data. The controller 102 also obtains power grid demand data through the communication device 106. The controller 102 is used to realize operation control of the air-conditioning device according to the air-conditioning operating power, control-related data and power grid demand data.

[0102] Specifically, the controller 102 obtains the air-conditioning operating power and control-related data collected by the data acquisition device 104, and also obtains the power grid demand data through the communication device 106. When the power grid demand data indicates that load regulation is required, the air-conditioning operating power is adjusted according to the power grid demand data to obtain the target operating power, and then parameter prediction is performed based on the target operating power and control-related data to obtain the target control parameters, and the operation of the air-conditioning equipment is controlled based on the target control parameters.

[0103] In a specific embodiment, Figure 6 As shown, an air-conditioning device is provided, which includes an embedded air-conditioning intelligent controller.

[0104] Specifically, the embedded air conditioner smart controller can be implemented with a high-performance microprocessor (≥800MHz), which is responsible for data processing and control logic. The embedded air conditioner smart controller can be integrated with a 4G / 5G communication module to meet the real-time data interaction needs with the power grid, such as obtaining power grid demand data. It can also be integrated with an RS-485 module to meet the interaction needs with other components inside the air conditioning equipment, such as obtaining air conditioning operating power and control-related data, and controlling the operation of other components based on target control parameters.

[0105] Furthermore, the air-conditioning equipment is also integrated with devices such as power, temperature and humidity sensors to monitor the air-conditioning operating power and control related data in real time.

[0106] Among them, the specific working process of the embedded air conditioner intelligent controller is as follows:

[0107] Step 1: Collect air conditioning control parameters (air conditioning operation mode, target temperature value and target wind speed value), air conditioning operation power, indoor and outdoor ambient temperature, and indoor and outdoor ambient humidity at a fixed collection cycle (1 minute);

[0108] Step 2: Use the operating data to train the parameter prediction model. This embodiment corresponds to a typical multiple-input multiple-output (MIMO) regression problem, which can be constructed using a multi-layer perceptron (MLP) algorithm model. The input features of the parameter prediction model are the air-conditioning operation mode, air-conditioning operation power, indoor ambient temperature and humidity, and outdoor ambient temperature and humidity. The output features of the parameter prediction model are the air-conditioning control parameters (air-conditioning operation mode, target temperature value, and target wind speed value). Constraints can be added during the training process, and the loss function weight of the air-conditioning operation mode of the input feature is set to 100%, so that the air-conditioning operation mode of the output feature is the same as the air-conditioning operation mode of the input feature, ensuring effective regulation under the same air-conditioning operation mode.

[0109] Step 3: Keep going with Steps 1 and 2 while moving on to the next step;

[0110] Step 4: Obtain grid demand data. If the grid demand data indicates regulation, proceed to the next step. Otherwise, proceed to step 4.

[0111] Step 5: Get the current air conditioner operating power and calculate the target operating power, target operating power = air conditioner operating power * (1-25%). Get the air conditioner operating mode, indoor ambient temperature and humidity, and outdoor ambient temperature and humidity, input the target operating power, air conditioner operating mode, indoor ambient temperature and humidity, and outdoor ambient temperature and humidity into the trained parameter prediction model, and predict the target control parameters.

[0112] Step 6: Convert the target control parameters into specific control instructions and send them to the corresponding components in the air conditioning equipment;

[0113] Step 7: Wait for 15 minutes and return to step 4.

[0114] In this embodiment, the Internet of Things technology is used to realize real-time data interaction between the air conditioner and the power load management system to obtain the needs of the power grid; based on machine learning and optimization algorithms, the operating parameters of the air conditioner, such as temperature setting, wind speed, etc., are adjusted according to the needs of the power grid, so as to reduce the operating power of the air conditioner and alleviate the power supply pressure of the power grid; through an embedded design, the controller is placed inside the air-conditioning unit and powered by the power supply of the air-conditioning unit, which reduces the consumption of space resources and saves deployment costs.

[0115] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store air-conditioning operating power, control-related data and power grid demand data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an air-conditioning control method is implemented.

[0116] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0117] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0118] Obtain air conditioning operating power, control-related data, and grid demand data;

[0119] When the grid demand data indicates that load regulation is required, the air conditioner operating power is adjusted according to the grid demand data to obtain the target operating power;

[0120] Parameter prediction is performed based on the target operating power and control-related data to obtain the target control parameters, and the operation of the air-conditioning equipment is controlled based on the target control parameters.

[0121] In an exemplary embodiment, when the processor executes the computer program, the following steps are also implemented:

[0122] The target operating power and control-related data are input into the parameter prediction model to obtain the target control parameters.

[0123] In an exemplary embodiment, when the processor executes the computer program, the following steps are also implemented:

[0124] According to the preset collection time, continuously obtain the air conditioner operating power, control-related data, power grid demand data and actual control parameters;

[0125] The parameter prediction model is updated with the air-conditioning operating power and control-related data as model input and the actual control parameters as model output.

[0126] In an exemplary embodiment, when the processor executes the computer program, the following steps are also implemented:

[0127] When the control identification data indicates that control is required, the target operating power is obtained by calculation based on the control ratio data and the air conditioner operating power.

[0128] In an exemplary embodiment, when the processor executes the computer program, the following steps are also implemented:

[0129] When the grid demand data indicates that there is no need for load regulation, parameter prediction is performed based on the air-conditioning operating power and control-related data to obtain the target control parameters.

[0130] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0131] Obtain air conditioning operating power, control-related data, and grid demand data;

[0132] When the grid demand data indicates that load regulation is required, the air conditioner operating power is adjusted according to the grid demand data to obtain the target operating power;

[0133] Parameter prediction is performed based on the target operating power and control-related data to obtain the target control parameters, and the operation of the air-conditioning equipment is controlled based on the target control parameters.

[0134] In an exemplary embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0135] The target operating power and control-related data are input into the parameter prediction model to obtain the target control parameters.

[0136] In an exemplary embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0137] According to the preset collection time, continuously obtain the air conditioner operating power, control-related data, power grid demand data and actual control parameters;

[0138] The parameter prediction model is updated with the air-conditioning operating power and control-related data as model input and the actual control parameters as model output.

[0139] In an exemplary embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0140] When the control identification data indicates that control is required, the target operating power is obtained by calculation based on the control ratio data and the air conditioner operating power.

[0141] In an exemplary embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0142] When the grid demand data indicates that there is no need for load regulation, parameter prediction is performed based on the air-conditioning operating power and control-related data to obtain the target control parameters.

[0143] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0144] Obtain air conditioning operating power, control-related data, and grid demand data;

[0145] When the grid demand data indicates that load regulation is required, the air conditioner operating power is adjusted according to the grid demand data to obtain the target operating power;

[0146] Parameter prediction is performed based on the target operating power and control-related data to obtain the target control parameters, and the operation of the air-conditioning equipment is controlled based on the target control parameters.

[0147] In an exemplary embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0148] The target operating power and control-related data are input into the parameter prediction model to obtain the target control parameters.

[0149] In an exemplary embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0150] According to the preset collection time, continuously obtain the air conditioner operating power, control-related data, power grid demand data and actual control parameters;

[0151] The parameter prediction model is updated with the air-conditioning operating power and control-related data as model input and the actual control parameters as model output.

[0152] In an exemplary embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0153] When the control identification data indicates that control is required, the target operating power is obtained by calculation based on the control ratio data and the air conditioner operating power.

[0154] In an exemplary embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0155] When the grid demand data indicates that there is no need for load regulation, parameter prediction is performed based on the air-conditioning operating power and control-related data to obtain the target control parameters.

[0156] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0157] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0158] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0159] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An air conditioning control method, characterized in that: The method comprises: Obtain air conditioning operating power, control-related data, and grid demand data; When the grid demand data indicates that load regulation is required, adjusting the air conditioner operating power according to the grid demand data to obtain a target operating power; Parameter prediction is performed based on the target operating power and the control-related data to obtain target control parameters, and the operation of the air-conditioning equipment is controlled based on the target control parameters.

2. The method according to claim 1, characterized in that The performing parameter prediction based on the target operating power and the control-related data to obtain target control parameters includes: The target operating power and the control-related data are input into a parameter prediction model to obtain target control parameters.

3. The method according to claim 2, characterized in that The obtaining of air conditioner operating power, control related data and grid demand data includes: According to the preset collection time, continuously obtain air conditioning operating power, control-related data and power grid demand data; The method further comprises: According to the preset acquisition time, actual control parameters are obtained; The parameter prediction model is updated by taking the air conditioner operating power and the control-related data as model inputs and the actual control parameters as model outputs.

4. The method according to claim 3, characterized in that The control-related data at least includes indoor environment temperature and humidity data, outdoor environment temperature and humidity data and air conditioning operation mode.

5. The method according to claim 4, characterized in that During the updating process of the parameter prediction model, the loss function weight of the air-conditioning operation mode is set to 100%.

6. The method according to claim 1, characterized in that The power grid demand data includes regulation identification data and regulation ratio data; When the grid demand data indicates that load regulation is required, adjusting the air conditioner operating power according to the grid demand data to obtain the target operating power includes: When the control identification data indicates that control is required, a calculation is performed based on the control ratio data and the air conditioner operating power to obtain a target operating power.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: When the grid demand data indicates that no load regulation is required, parameter prediction is performed based on the air-conditioning operating power and the control-related data to obtain target control parameters.

8. An air conditioning control device, characterized in that: The device comprises: An acquisition module is used to obtain air conditioner operating power, control-related data, and grid demand data; A control module, configured to adjust the air conditioner operating power according to the grid demand data to obtain a target operating power when the grid demand data indicates that load control is required; The prediction module is used to perform parameter prediction based on the target operating power and the control-related data to obtain target control parameters, and the target control parameters are used to control the operation of the air-conditioning equipment.

9. An air conditioning device, characterized in that: It includes a controller, and a data acquisition device and a communication device connected to the controller, the data acquisition device is used to obtain air-conditioning operating power and control-related data, the controller also obtains power grid demand data through the communication device, and the controller is used to realize operation control of the air-conditioning equipment according to the air-conditioning control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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