Air conditioner control method, air conditioner, storage medium and computer program product

Abstract

The invention discloses an air conditioner control method, an air conditioner, a storage medium and a computer program product, and the method comprises the steps: predicting a target capability demand of a user through at least one of a user portrait of the user in an action space of the air conditioner, a behavior analysis result of the user and a scene analysis result corresponding to the current moment; and the air conditioner is controlled to operate based on the parameters capable of enabling the target capability demand to be optimal, so that the performance of the air conditioner is closer to the actual demand of a user, and the use experience of the air conditioner is effectively improved.

Description

Technical Field

[0001] This application relates to the field of air conditioners, and more particularly to a control method for an air conditioner, an air conditioner, and a computer program product for a storage medium. Background Technology

[0002] Most air conditioners on the market tend to offer standardized functions and performance, comprehensively regulating the air conditioner based on multiple capabilities such as cooling efficiency, energy efficiency, comfort, noise levels, and reliability, without fully considering the specific needs of different users in different scenarios. For example, some users may prioritize cooling efficiency, while others may value noise control or comfort, or the same user may have different needs for the air conditioner at different times. Therefore, the lack of personalized customization options in related technologies makes it difficult for air conditioners to meet specific user needs, resulting in a poor user experience. Summary of the Invention

[0003] The main purpose of this application is to provide a control method for an air conditioner, an air conditioner, and a storage medium, aiming to solve the technical problem that air conditioners are unable to meet the specific needs of users, resulting in a poor user experience.

[0004] To achieve the above objectives, embodiments of this application provide a control method for an air conditioner, the method comprising:

[0005] Based on the user profile of the user in the air conditioner's operating space, at least one of the user's behavior analysis results and the scene analysis results corresponding to the current moment, the target capability requirements are determined.

[0006] Based on the target capacity requirements, determine the operating parameters of the air conditioner;

[0007] The air conditioner is controlled to operate according to the operating parameters so that it meets the target capacity requirements.

[0008] In this embodiment of the application, the step of determining the target capability requirement based on at least one of the user profile of the user within the air conditioner's operating space, the user's behavior analysis results, and the scene analysis results corresponding to the current moment includes:

[0009] Obtain the preset mapping relationship;

[0010] Based on the preset mapping relationship, the user profile is determined, and the target capability requirement corresponding to the behavior analysis result or the scenario analysis result is determined.

[0011] In this embodiment of the application, the method further includes:

[0012] The preset mapping relationship is updated based on the user's associated air conditioner usage records and / or received user settings.

[0013] In this embodiment of the application, the step of determining the target capability requirement based on at least one of the user profile of the user within the air conditioner's operating space, the user's behavior analysis results, and the scene analysis results corresponding to the current moment includes:

[0014] Determine the first set of capability requirements based on the user profile;

[0015] Based on the scenario analysis results, at least one element is selected from the first capability requirement set to form the second capability requirement set;

[0016] Based on the behavioral analysis results, the target capability requirement is selected from the second capability requirement set.

[0017] In this embodiment of the application, the step of determining the target capability requirement based on at least one of the user profile of the user within the air conditioner's operating space, the user's behavior analysis results, and the scene analysis results corresponding to the current moment includes:

[0018] Based on the user profile, the scenario analysis results, and the behavior analysis results, a fusion feature is constructed;

[0019] Determine the target category to which the fused features belong;

[0020] Obtain the target capability requirements associated with the target classification.

[0021] In this embodiment of the application, the step of determining the operating parameters of the air conditioner based on the target capacity requirement includes:

[0022] Obtain user settings parameters and air conditioner operating environment parameters;

[0023] The user settings parameters, air conditioner operating environment parameters, and target capacity requirements are input into a preset air conditioner operating model, and the operating parameters are determined based on the air conditioner operating model.

[0024] In this embodiment of the application, the method further includes:

[0025] Load the basic air conditioner operation model;

[0026] Based on the state parameters during the operation of the air conditioner, the basic air conditioner operation model is optimized.

[0027] In this embodiment of the application, the step of loading the basic air conditioner operation model includes:

[0028] Upon detecting a power-on action, the basic air conditioner operation model is loaded based on memory data; or

[0029] Upon detecting a successful network connection, a loading request is sent to the server, and the basic air conditioner operation model is loaded based on the server's response data.

[0030] This application also provides an air conditioner, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for the air conditioner as described above.

[0031] This application embodiment also provides a storage medium, which is a computer-readable storage medium, and stores a computer program thereon. When the computer program is executed by a processor, it implements the steps of the air conditioner control method described above.

[0032] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the air conditioner control method described above.

[0033] This application discloses an air conditioner control method. By using at least one of the user profile of the user in the air conditioner's operating space, the user's behavior analysis results, and the scene analysis results corresponding to the current moment, the method predicts the user's target capability requirements and controls the air conditioner to operate based on parameters that optimize the target capability requirements. This makes the performance of the air conditioner more closely match the user's actual needs, thus effectively improving the user experience of the air conditioner. Attached Figure Description

[0034] Figure 1 This is a flowchart illustrating an embodiment of the control method for an air conditioner involved in the present application.

[0035] Figure 2 This is a flowchart illustrating another embodiment of the air conditioner control method involved in the embodiments of this application;

[0036] Figure 3 This is a schematic diagram of the structure of the air conditioner involved in this application.

[0037] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0038] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0039] Most air conditioners on the market tend to offer standardized functions and performance, comprehensively regulating the air conditioner based on multiple capabilities such as cooling efficiency, energy efficiency, comfort, noise levels, and reliability, without fully considering the specific needs of different users in different scenarios. For example, some users may prioritize cooling efficiency, while others may value noise control or comfort, or the same user may have different needs for the air conditioner at different times. Therefore, the lack of personalized customization options in related technologies makes it difficult for air conditioners to meet specific user needs, resulting in a poor user experience.

[0040] For example, when a user returns home from get off work on a hot summer day, the temperature inside the house is high. Therefore, the user's primary need at this time is for the air conditioner to cool quickly, while their needs for noise levels and anti-direct-blowing capabilities are less significant. Consequently, in this situation, the overall control scheme will inevitably limit the cooling capacity to some extent.

[0041] Alternatively, if the air conditioner has been running for a while and the outdoor temperature is not too high, and the user is studying or performing other activities that require a relatively quiet environment, forcing the air conditioner to maintain the ambient temperature at the set temperature will significantly reduce the noise control effect.

[0042] Therefore, based on the aforementioned deficiencies in the integrated control schemes of related technologies, this application proposes a control method for an air conditioner. The core of this method is user-centric, controlling the air conditioner based on the user's current demand for its capabilities, while also accommodating other capability requirements. This ensures the air conditioner operates to best meet the user's needs.

[0043] For ease of understanding, the technical solutions in the embodiments of this application will be described below with reference to the accompanying drawings.

[0044] Please refer to Figure 1 In one optional embodiment, the air conditioner control method includes the following steps S10 to S30:

[0045] S10: Determine the target capability requirements based on the user profile of the user in the air conditioner's operating space, at least one of the user's behavior analysis results and the scene analysis results corresponding to the current moment;

[0046] In this embodiment, the user profile represents the user's usage habits and preferences for the air conditioner, while the user behavior analysis results represent the user's current activities, such as watching TV, exercising, sleeping, doing homework, cooking, working, or other daily activities. The scenario analysis results can represent the characteristics of the air conditioner's operating space, including temperature and humidity conditions within that space, the outdoor unit's operating environment, noise levels, the current time of day, and the purpose of the space. Therefore, this embodiment aims to determine the user's most critical air conditioning capacity needs at the current moment, i.e., the target capacity requirement. This is achieved by determining the target capacity requirement based on one or more of the user profile, behavior analysis, and scenario analysis results.

[0047] Understandably, in determining target capability requirements, the more input parameters there are, the closer the predicted target capability requirements will be to the user's actual capability needs, i.e., the more accurate the prediction. Correspondingly, the more input parameters there are, the more complex the data and logic to be processed, and therefore the greater the data processing overhead. Therefore, in implementing the technical solution proposed in this embodiment, one or more of the user profile, behavioral result analysis, and scenario analysis results can be selected as input parameters based on a comprehensive consideration of the air conditioner's computing power or other factors to predict the target capability requirements.

[0048] As an optional implementation, user profiles can be built for different users first. Then, after a user enters the operating space of the air conditioner, the corresponding user profile is obtained based on the user identification results. For example, the air conditioner is equipped with a camera device, or the air conditioner is communicatively connected to other smart furniture with camera devices in the operating space. When a user is detected entering the operating space, facial recognition and / or body posture recognition are performed based on the image information collected by the camera device. The user's identity is determined based on the recognition results. Once the user's identity is determined, their corresponding user profile is read based on that identity.

[0049] To assist those skilled in the art in understanding the above solution, this embodiment also provides an optional user profile construction scheme, which includes four stages: data collection, data processing, preference analysis, and user profile construction.

[0050] For example, during the data collection phase, basic user information such as age, gender, and body type (e.g., thin, normal, overweight) can be obtained first. This basic user information can be obtained through a settings page where users can upload it themselves, or it can be determined based on user information collected from other smart home devices, or it can be determined using user recognition algorithms based on images or other data.

[0051] In addition, the system can collect data on the user's air conditioner usage, including usage time (date, time period), set temperature, fan speed, mode selection (cooling, heating, dehumidifying, fan), energy-saving mode usage, anti-direct-blow mode usage, and remote control / timer function usage. It is understood that some variant implementations may include more or fewer other air conditioner usage records. Furthermore, based on the user's or remote control terminal, the system can also collect user-defined settings such as preferred temperature range, sleep mode preferences (e.g., preference for air purification functions), and humidity preferences.

[0052] After data collection is complete, the collected data undergoes data processing. This processing begins with data cleaning to remove erroneous or outlier data, such as unreasonable time periods or temperature settings. Data types and formats are also standardized. Next, feature extraction is performed to extract frequently used settings from the usage records, such as the most frequently used temperature and fan speed. Usage patterns can also be analyzed, such as nighttime usage frequency and differences between weekends and weekdays. Preliminary classification is then performed based on basic characteristics such as age, gender, and body type. Clustering algorithms (such as K-means) or classification algorithms (such as decision trees and random forests) can be used to further segment users' air conditioning usage behavior and identify different usage pattern groups.

[0053] After data processing, preference analysis can be performed. By analyzing the correlation between user-defined settings and actual usage records, the user's true preferences can be identified. The potential influence of basic user information on preferences can also be considered; for example, older people may prefer higher indoor temperatures. Preference scores are assigned to each user or user group for various capability needs and selection criteria. Preferences are then weighted based on the stability and consistency of user behavior. Finally, in the user profile construction process, the results of basic user information, usage records, custom settings, and preference analysis are integrated to generate a user profile.

[0054] Optionally, in this embodiment, user behavior analysis can be performed based on data collected by sensors installed on the air conditioner, or it can be performed based on data collected by sensors on other smart furniture within the Internet of Things (IoT) where the air conditioner is located. This embodiment uses the example of determining the user behavior analysis result based on data collected from multiple networked smart furniture devices for explanation.

[0055] In one alternative approach, to determine user behavior within the air conditioner's operating space based on smart furniture sensor data, data from multiple sensors can be integrated, and behavior recognition algorithms can be applied to infer the user's ongoing activities.

[0056] As an optional implementation logic, data collection is first conducted based on the deployment of sensors within smart furniture. Infrared human body sensors are deployed on smart furniture such as televisions, refrigerators, robot vacuums, and smart switches, distributed in key areas such as the living room (in front of the television), bedroom (beside the bed), study (next to the desk), and kitchen (cooking area). This allows the infrared human body sensors to detect the presence and activity of people. Similarly, smart furniture equipped with microphones or microphone arrays can capture specific sounds in these areas, such as television sounds, conversations, sports music, and cooking sounds. Ambient light sensors in the smart home can be used to detect indoor light intensity, helping to determine the time of day (e.g., day or night). Optionally, data from smart appliance status sensors, such as the on / off status of televisions, computers, and lights, can also be acquired to reflect potential user activities. After the various sensors collect data, they can be configured to transmit the data wirelessly or via wired connection to a smart home center or cloud server for processing.

[0057] After collecting the initial data from the sensors, data cleaning is performed to remove noise and outliers, such as brief sensor malfunctions. The continuous data stream is then divided into fixed-length time windows (e.g., 5 minutes, 10 minutes) for batch processing. Useful features are then extracted from the data in each time window, such as human activity frequency, sound characteristics (e.g., volume, frequency distribution), light intensity changes, and appliance usage status.

[0058] After extracting data features, machine learning or deep learning models are used for user behavior recognition. For example, random forests, support vector machines, convolutional neural networks (CNNs) combined with long short-term memory networks (LSTMs) can be selected to classify behaviors based on the extracted features, thereby determining the user behavior recognition results.

[0059] For example, the settings for behavior recognition logic may include:

[0060] Watching TV: The system detects that the TV is on, and at the same time, the sound sensor captures the sound characteristics of the TV program, and the human body sensor indicates that someone is stationary or slightly moving in front of the TV.

[0061] Movement: Human body sensors display high-frequency, wide-range activities, while sound sensors may capture exercise music or breathing sounds.

[0062] Sleeping: The bedroom is very dark, the human body sensor shows no significant activity for a long time, and the sound sensor is almost silent.

[0063] Doing homework / working: The study area lights are turned on, the status of the lights on the computer or desk is monitored, and the sound sensor captures the sound of keyboard typing or writing.

[0064] Cooking: The kitchen area is a place where people are frequently active. When the range hood or stove is turned on, the sound sensor captures the cooking sounds.

[0065] Other daily activities: Based on feature combinations and model prediction results, these are classified as unidentified activities.

[0066] It should be noted that this embodiment does not limit the specific implementation algorithm. However, when different algorithms are used to identify user behavior results, the results will vary depending on the characteristics of the algorithm itself and the application scenario of this embodiment. For example, when using Random Forest, the overall accuracy is improved by constructing multiple decision trees and integrating their predictions. When dealing with complex datasets containing various sensor data (such as human activity, sound features, ambient light, etc.), Random Forest can effectively capture the interactions between different features, thus more accurately identifying user behavior. Simultaneously, by randomly selecting features and samples to construct decision trees, Random Forest reduces the risk of overfitting a single decision tree. This is particularly important for processing sensor data containing noise or outliers, as Random Forest can automatically ignore these adverse factors and maintain high generalization ability. Furthermore, although Random Forest is an ensemble learning method, its foundation is decision trees, so its predictions are relatively easy to understand and interpret. This is very useful for debugging and validating the model, especially when it is necessary to explain the model's decisions to non-technical users.

[0067] When using Support Vector Machines (SVM) for implementation, SVM excels at handling high-dimensional feature spaces because it classifies data by finding the maximum margin hyperplane. In smart furniture sensor data, each sensor may contribute multiple features (such as different frequency components of sound, different intensity levels of light, etc.), and SVM can effectively handle these high-dimensional features and find the optimal boundary to distinguish different user behaviors. Furthermore, by introducing kernel tricks (such as RBF, multinomial kernels, etc.), SVM can handle non-linear classification problems. This is particularly important for user behavior recognition, as user behavior is often not a simple linear relationship but is non-linearly influenced by various factors (such as time, environment, personal habits, etc.). Moreover, SVM is robust to noise and outliers because it focuses on maximizing the classification margin rather than minimizing training error. This helps reduce the impact of noise in sensor data on model performance.

[0068] When using a Convolutional Neural Network (CNN) combined with a Long Short-Term Memory (LSTM) network, CNN excels in image processing, automatically extracting useful features from raw data. In smart furniture sensor data, sound signals from sound sensors can be viewed as a time-series image (i.e., a spectrogram). CNN can automatically extract features from these images to distinguish different sounds (such as television sounds, cooking sounds, etc.). LSTM, a special type of Recurrent Neural Network (RNN), is capable of handling dependencies in long-term data series. In user behavior recognition, user behavior is often a continuous process, and LSTM can capture this temporal dependency, thus more accurately identifying the user's ongoing activity. Therefore, combining CNN and LSTM fully leverages the advantages of CNN in feature extraction and LSTM in time-series processing. This combined model can more comprehensively analyze sensor data, improving the accuracy and robustness of user behavior recognition.

[0069] Different algorithms have their own advantages in user behavior recognition solutions driven by sensor data in smart furniture. The choice of which algorithm to use depends on the specific application scenario, data characteristics, and performance requirements of different models; the algorithm best suited to the model should be selected.

[0070] Optionally, in an exemplary implementation of determining the scenario analysis results, the scenario analysis results are determined by classifying the results based on the characteristics of the air conditioner's operating space, such as the space area, the volume of the space requiring cooling, the temperature and humidity conditions within the operating space, the operating environment of the outdoor unit, the noise environment within the operating space, the current time period, and the purpose of the operating space, such as bedroom, living room, study, kitchen, office, etc. The classification can be achieved based on clustering algorithms, such as K-value clustering.

[0071] Understandably, the data collection and preprocessing process can refer to the content of behavior recognition, and will not be elaborated here. In the feature extraction process, spatial features and volume estimation can be performed using infrared human body sensors and camera data, combined with user input or pre-set room layout information, to estimate the area and volume of the space where the air conditioner operates.

[0072] Temperature and humidity characteristics can be determined directly based on sensor data, or, in the case of temperature gradients or uneven humidity, the overall temperature and humidity characteristics can be assessed based on temperature and humidity data from different locations within the space.

[0073] Noise environment characteristics can be constructed by monitoring noise values, or by assessing the noise level in the affected space and whether it affects the comfort of living or working.

[0074] Time period and usage characteristics can be directly coded and described by combining data such as timestamps, light intensity, appliance usage status (such as television, lights, computers, etc.), and human activity patterns, as well as the current time period (such as daytime, nighttime, weekday, weekend) and space usage (bedroom, living room, study, kitchen, office, etc.).

[0075] After extracting features from the collected sensor data, it can be input into a machine learning model (such as a classifier) ​​for scene recognition.

[0076] Furthermore, after determining the user profile, user behavior analysis results, and scenario analysis results, the capability requirements of any one of these mappings can be determined based on a pre-defined mapping relationship, serving as the target capability requirement. Alternatively, the capability requirements can be filtered through multiple levels using the user profile, user behavior analysis results, and scenario analysis results based on the mapping relationship to determine the target capability requirement. Alternatively, a fusion feature can be constructed based on the user profile, user behavior analysis results, and scenario analysis results, and then input into a classifier. The classification result is then used to derive the classification result, and the capability requirement corresponding to the classification result is taken as the target capability requirement.

[0077] Optionally, in a scheme that uses mapping relationships to perform multi-level filtering of capability requirements based on user profiles, user behavior analysis results, and scenario analysis results to determine target capability requirements, developers can construct filtering conditions using these three factors in any existing order, depending on the specific device model and application scenario. For example, a first set of capability requirements can be determined based on a first feature, then at least one element can be selected from the first set of capability requirements based on a second feature to form a second set of capability requirements, and finally, the target capability requirement can be selected from the second set of capability requirements based on a third feature.

[0078] It should be noted that when the first feature can be set to any one of the user profile, user behavior analysis result, and scenario analysis result, the second feature can be set to any one of the two other features in the user profile, user behavior analysis result, and scenario analysis result, excluding the one already set as the first feature, and the third feature is the other two features in the user profile, user behavior analysis result, and scenario analysis result, excluding the one already set as the first feature and the second feature.

[0079] S20: Determine the operating parameters of the air conditioner based on the target capacity requirements;

[0080] S30: Control the air conditioner to operate according to the operating parameters so that the air conditioner meets the target capacity requirements.

[0081] After determining the target capacity requirements, the operating parameters of the air conditioner can be determined based on these requirements. This allows the air conditioner to operate optimally according to the determined parameters, prioritizing the target capacity requirements while ensuring the stability and safety of the air conditioner, and allowing for the fulfillment of other capacity requirements.

[0082] In one alternative implementation, key parameters affecting the target capacity requirement can be determined based on experimental data. For example, for cooling capacity, influencing factors may include compressor operating frequency, fan speed, and electronic expansion valve opening. These three parameters can then be set as key parameters, and adjusted to values ​​that maximize the cooling capacity requirement of the air conditioner under limiting conditions. For instance, when the limiting condition is system safety, the compressor frequency can be adjusted to the maximum operating frequency that meets the safety limit, the fan speed can be adjusted to the maximum speed that meets the safety limit, and the electronic expansion valve opening can be adjusted to the limit value that meets the safety operating conditions.

[0083] It should be noted that the constraints claimed in the above optional implementation schemes can be determined based on user settings or system settings. This embodiment does not limit this. The core of the solution provided in this embodiment is that during the operation of the air conditioner, meeting the target capability requirement is optimal under constraints, while relaxing other capability requirements can reduce the performance of other capability dimensions. For example, when the target capability requirement is cooling capacity, the air conditioner is controlled to achieve optimal cooling capacity under preset constraints, without considering multiple dimensions such as noise, comfort, reliability (relaxed if safety conditions are met), and energy efficiency. For capability requirements that can be relaxed, their corresponding priorities can be determined based on one or more of user profiles, user behavior analysis results, and scenario analysis results. Then, when determining operating parameters based on priorities, the satisfaction of high-priority capability requirements is given priority, while the satisfaction of low-priority capability requirements is ignored. This allows the specific values ​​of the key parameters corresponding to each capability requirement to be determined sequentially based on priority. Where the key parameters involved in higher priority overlap with those involved in lower priority, the specific values ​​of the overlapping key parameters are set as the values ​​that prioritize satisfying the higher-priority capability requirement.

[0084] In another alternative, an air conditioner operation model can be pre-established, so that after the target capacity requirement is input, the model and the operating parameters based on the input parameters can enable the air conditioner to meet the target capacity requirement.

[0085] For example, in one implementation, the air conditioner can load a basic air conditioner operation model based on memory data when a power-on action is detected. This basic air conditioner operation model is a fundamental model of the air conditioner's operating mode, power, speed, temperature, noise, etc., established based on standard laboratory data. For example, the basic air conditioner operation model can be automatically loaded during the power-on test when the air conditioner leaves the factory. Alternatively, it can be loaded when the user powers on the air conditioner for the first time after installation. Optionally, in some variations, due to limitations in the air conditioner's memory space or other reasons, a loading request can be sent to the server after the air conditioner is connected to the network, and the basic air conditioner operation model can be loaded based on the server's response data. For example, the loading request can be triggered when the user connects to the network for the first time after installing the air conditioner. Alternatively, the loading request can be triggered when a model update instruction is received from another terminal. After loading the basic model, the target capability requirements can be directly used as input to the basic model to determine the corresponding operating parameters, and the air conditioner can be controlled to operate according to these parameters.

[0086] In the technical solution provided in this embodiment, the user's target capability requirements are predicted by at least one of the user profile of the user in the air conditioner's operating space, the user's behavior analysis results, and the scene analysis results corresponding to the current moment. The air conditioner is then controlled to operate based on parameters that optimize the target capability requirements, thereby making the performance of the air conditioner closer to the user's actual needs and thus effectively improving the user experience of the air conditioner.

[0087] Please refer to Figure 2 In another optional implementation, steps S40 and S50 are included before step S20:

[0088] S40: Load the basic air conditioner operation model;

[0089] S50: Optimize the basic air conditioner operation model based on the state parameters during the operation of the air conditioner.

[0090] After loading the basic air conditioner operation model, in order to ensure that the operation parameters determined by the model better meet the target requirements, the basic air conditioner operation model can be optimized based on the state parameters during the air conditioner's operation. In the following text, the basic air conditioner model and the model optimized based on the basic air conditioner model are uniformly referred to as "model".

[0091] It should be noted that when the model outputs operating parameters based on a single target capability requirement desired by the user (such as cooling capacity, noise reduction, energy efficiency rating, anti-direct-blow function, or reliability), its logic will focus on optimizing that specific capability while minimizing direct intervention in other non-target capability requirements. Therefore, in an optional model design scheme, the model processing logic is as follows:

[0092] Taking cooling capacity as an example, the user's desired cooling capacity requirement needs to be clearly defined first, corresponding to the expected indoor temperature range, temperature drop rate, or time to reach the set temperature. This allows the model to set initial operating parameters for the cooling capacity based on the air conditioner's hardware characteristics and preset algorithms. These parameters may include, but are not limited to:

[0093] Compressor operating frequency: A key parameter that determines refrigeration power.

[0094] Electronic expansion valve opening: controls refrigerant flow and affects the heat exchange efficiency of the evaporator.

[0095] Fan speed: Although it mainly affects air circulation and air delivery distance, it also indirectly affects the cooling effect.

[0096] Indoor temperature sensor settings: Ensure accurate monitoring of indoor temperature to provide feedback for the control algorithm.

[0097] Then, the model can apply optimization algorithms such as PID control and fuzzy control to iteratively adjust the initial parameters with cooling capacity as the target. During the optimization process, the algorithm monitors indoor temperature changes in real time through an indoor temperature sensor to monitor the actual cooling effect; it calculates the deviation between the actual temperature and the target temperature and compares the deviation with the target demand; based on the magnitude and direction of the deviation, it adjusts parameters such as the compressor operating frequency and the opening of the electronic expansion valve to reduce the deviation. After processing by the optimization algorithm, the model will output a set of operating parameters optimized for cooling capacity. These parameters will ensure that the air conditioner can meet the user's cooling needs to the greatest extent during operation.

[0098] Alternatively, in order to improve the model's performance, a scheme can be designed to optimize the model based on the state parameters during the actual operation of the air conditioner.

[0099] For example, key state parameters of the optimization model can be set first, such as indoor temperature, outdoor temperature, compressor operating frequency, electronic expansion valve opening, fan speed, energy consumption, noise level, wind direction, and wind speed. Then, data for these state parameters is collected. The corresponding state data is then periodically fed back to the server so that the manufacturer can optimize the model based on the data. The optimized content is then published to the server. The air conditioner then downloads the updated content from the server and optimizes the model based on the updated content.

[0100] Optionally, in some implementations, user setting parameters and air conditioner operating environment parameters can be obtained, and then the user setting parameters, air conditioner operating environment parameters, and the target capacity requirement can be input into a preset air conditioner operating model. The operating parameters are then determined based on the air conditioner operating model. This enriches the input parameters, making the model's output more accurate.

[0101] Understandably, compared to solutions that use target capacity requirements as a single input, this solution requires different processing measures for the input parameters. For example, after determining the expected parameters involved in the target capacity requirements, initial parameters can be comprehensively set based on relevant parameters of the air conditioner's operating environment, including current indoor and outdoor temperatures, humidity, and air circulation, as well as user settings, such as specific user settings for the air conditioner, like fan speed preferences, timer on / off settings, and sleep mode. That is, based on the air conditioner's hardware characteristics, initial operating parameters are set for the target capacity requirements, such as compressor operating frequency, electronic expansion valve opening, and fan speed. During the setting of these parameters, corresponding constraints are determined based on the operating environment parameters and user settings.

[0102] This application provides an air conditioner, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the control method of the air conditioner in any of the above embodiments.

[0103] The following is for reference. Figure 3 The diagram illustrates a structural schematic of an air conditioner suitable for implementing embodiments of this application. The air conditioner in the embodiments of this application may include wall-mounted air conditioners, floor-standing air conditioners, and so on. Figure 3 The air conditioner shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0104] like Figure 3As shown, the air conditioner may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.) that can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the air conditioner. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touchscreen, a touchpad, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a disk; and a communication device 1009. The communication device 1009 allows the air conditioner to communicate wirelessly or wiredly with other devices to exchange data. Although the diagram shows air conditioners with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0105] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0106] The air conditioner provided in this application, employing the air conditioner method described in the above embodiments, can solve the technical problem that related air conditioner integrated control cannot adapt to the specific needs of users. Compared with the prior art, the beneficial effects of the air conditioner provided in this application are the same as those of the air conditioner control method provided in the above embodiments, and other technical features of this air conditioner are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0107] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0108] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0109] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the air conditioner control method of the above embodiments.

[0110] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0111] The aforementioned computer-readable storage medium may be included in the air conditioner; or it may exist independently and not be installed in the air conditioner.

[0112] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the air conditioner, the air conditioner: predicts the user's target capability requirements based on at least one of the user profile of the user in the air conditioner's operating space, the user's behavior analysis results, and the scene analysis results corresponding to the current moment, and controls the air conditioner to operate based on parameters that optimize the target capability requirements, thereby making the performance of the air conditioner closer to the user's actual needs, thus effectively improving the user experience of the air conditioner.

[0113] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0114] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0115] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0116] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the control method of the air conditioner described above, which can solve the technical problem of poor user experience. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the control method of the air conditioner provided in the above embodiments, and will not be repeated here.

[0117] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the air conditioner control method described above.

[0118] The computer program product provided in this application can solve the technical problem of how to improve the user experience. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiments of this application are the same as the beneficial effects of the air conditioner control method provided in the above embodiments, and will not be repeated here.

[0119] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural modifications made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

[0120] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0121] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0122] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A control method for an air conditioner, characterized in that, The method includes: Based on the user profile of the user in the air conditioner's operating space, at least one of the user's behavior analysis results and the scene analysis results corresponding to the current moment, the target capability requirements are determined. Based on the target capacity requirements, determine the operating parameters of the air conditioner; The air conditioner is controlled to operate according to the operating parameters so that it meets the target capacity requirements.

2. The method as described in claim 1, characterized in that, The step of determining the target capability requirements based on at least one of the user profiles of users within the air conditioner's operating space, the user behavior analysis results, and the current scene analysis results includes: Obtain the preset mapping relationship; Based on the preset mapping relationship, the user profile is determined, and the target capability requirement corresponding to the behavior analysis result or the scenario analysis result is determined.

3. The method as described in claim 2, characterized in that, The method further includes: The preset mapping relationship is updated based on the user's associated air conditioner usage records and / or received user settings.

4. The method as described in claim 1, characterized in that, The step of determining the target capability requirements based on at least one of the user profiles of users within the air conditioner's operating space, the user behavior analysis results, and the current scene analysis results includes: Determine the first set of capability requirements based on the user profile; Based on the scenario analysis results, at least one element is selected from the first capability requirement set to form the second capability requirement set; Based on the behavioral analysis results, the target capability requirement is selected from the second capability requirement set.

5. The method as described in claim 1, characterized in that, The step of determining the target capability requirements based on at least one of the user profiles of users within the air conditioner's operating space, the user behavior analysis results, and the current scene analysis results includes: Based on the user profile, the scenario analysis results, and the behavior analysis results, a fusion feature is constructed; Determine the target category to which the fused features belong; Obtain the target capability requirements associated with the target classification.

6. The method as described in claim 1, characterized in that, The step of determining the operating parameters of the air conditioner based on the target capacity requirements includes: Obtain user settings parameters and air conditioner operating environment parameters; The user settings parameters, air conditioner operating environment parameters, and target capacity requirements are input into a preset air conditioner operating model, and the operating parameters are determined based on the air conditioner operating model.

7. The method as described in claim 1, characterized in that, The method further includes: Load the basic air conditioner operation model; Based on the state parameters during the operation of the air conditioner, the basic air conditioner operation model is optimized.

8. The method as described in claim 7, characterized in that, The steps for loading the basic air conditioner operation model include: Upon detecting a power-on action, the basic air conditioner operation model is loaded based on memory data; or Upon detecting a successful network connection, a loading request is sent to the server, and the basic air conditioner operation model is loaded based on the server's response data.

9. An air conditioner, characterized in that, The air conditioner includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for the air conditioner as described in any one of claims 1 to 8.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the control method for the air conditioner as described in any one of claims 1 to 8.

11. A computer program product, characterized in that, It includes a computer program, which, when executed by a processor, implements the steps of the control method for an air conditioner as described in any one of claims 1 to 8.

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