Air conditioner control method and device, electronic equipment and medium

Abstract

The embodiment of the invention provides an air conditioner control method and device, electronic equipment and a medium. The air conditioner control method comprises the steps that sleep data and environment data of a user are obtained; multiple sets of air conditioner setting parameters are obtained, and sleep quality information corresponding to the multiple sets of air conditioner setting parameters is predicted according to the multiple sets of air conditioner setting parameters, the sleep data of the user and the environment data; when preset conditions are met, target air conditioner setting parameters are determined according to the sleep quality information corresponding to the multiple sets of air conditioner setting parameters; and controlling the air conditioner to operate according to the target air conditioner setting parameters. The sleep environment most suitable for the current stage is provided according to the state of each sleep stage of the user and the accurate control of the operation parameters of the air conditioner by associating the sleep data of the user with the adjustment of the operation state parameters of the air conditioner.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning, and in particular to an air conditioning control method, an air conditioning control device, an electronic device and a computer-readable storage medium. Background Art

[0002] With the development of social economy and the improvement of people's living standards, air conditioning systems have become an indispensable part of modern life. Air conditioning is not only used to adjust indoor temperature, but also to control humidity, purify air and other functions. In order to improve the energy efficiency, comfort and intelligence level of air conditioning systems, various advanced control methods and technologies continue to emerge.

[0003] Traditional air conditioning adjustment modes mainly rely on simple temperature settings and switch controls. Although these methods are easy to implement, they often fail to meet users' needs for comfort and energy saving in the face of complex and changing indoor environments. When the air conditioner is running at night, it can only run in a set mode and cannot automatically adjust to different sleep stages of the user to provide a more comfortable sleeping environment, resulting in a poor user experience during sleep. Summary of the invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide an air conditioning control method, an air conditioning control device, an electronic device and a corresponding computer-readable storage medium that overcome the above problems or at least partially solve the above problems.

[0005] In order to solve the above problems, an embodiment of the present invention discloses an air conditioning control method, comprising:

[0006] Obtain the user's sleep data and environmental data;

[0007] Get multiple groups of air conditioning setting parameters;

[0008] predicting sleep quality information corresponding to the multiple sets of air conditioning setting parameters according to the multiple sets of air conditioning setting parameters, the user's sleep data and the environmental data;

[0009] When the preset conditions are met, the target air conditioning setting parameters are determined according to the sleep quality information corresponding to the multiple sets of air conditioning setting parameters;

[0010] The air conditioner operation is controlled according to the target air conditioner setting parameters.

[0011] Optionally, the sleep quality information is a sleep quality score, and the predicting the sleep quality information corresponding to the multiple sets of air-conditioning setting parameters according to the multiple sets of air-conditioning setting parameters, the user's sleep data and the environmental data respectively includes:

[0012] Inputting each set of air conditioning setting parameters, the user's sleep data and the environmental data into a preset sleep quality prediction model;

[0013] Obtain a sleep quality score output by the preset sleep quality prediction model; the sleep quality score is positively correlated with the proportion of a deep sleep period and a deep sleep period during sleep.

[0014] Optionally, the acquiring multiple sets of air conditioning setting parameters includes:

[0015] Multiple groups of air conditioning setting parameters are iteratively generated through genetic algorithms.

[0016] Optionally, the preset condition is that the iteration duration of the genetic algorithm is greater than a preset duration threshold, or the sleep quality score reaches a preset score threshold, or the number of iterations of the genetic algorithm is greater than a preset number threshold.

[0017] Optionally, when the preset conditions are met, determining the target air conditioning setting parameters according to the sleep quality information corresponding to the multiple groups of air conditioning setting parameters includes:

[0018] When the preset conditions are met, a set of air-conditioning setting parameters corresponding to the maximum sleep quality score is used as the target air-conditioning setting parameters.

[0019] Optionally, obtaining the user's sleep data and environmental data includes:

[0020] Obtain sleep data and environmental data collected by the user's wearable device.

[0021] Optionally, controlling the operation of the air conditioner according to the target air conditioner setting parameters includes:

[0022] A control message is sent to a preset server, so that the preset server sends the control message to the air conditioner that subscribes to the control message; the control message includes the target air conditioner setting parameters.

[0023] Optionally, the air conditioning setting parameters include: at least one of: target temperature, target humidity, target air supply mode, and target wind speed.

[0024] Optionally, the sleep data includes at least one of heart rate, body temperature and sleep state, and the environmental data includes at least one of indoor temperature and outdoor temperature.

[0025] A second aspect of an embodiment of the present invention discloses an air conditioning control device, comprising:

[0026] A data acquisition module, used to acquire the user's sleep data and environmental data;

[0027] A parameter acquisition module is used to obtain multiple sets of air conditioning setting parameters;

[0028] a prediction module, configured to predict the sleep quality information corresponding to the multiple sets of air conditioning setting parameters according to the multiple sets of air conditioning setting parameters, the user's sleep data and the environmental data;

[0029] A determination module, configured to determine target air conditioning setting parameters according to sleep quality information corresponding to multiple sets of air conditioning setting parameters when a preset condition is met;

[0030] The control module is used to control the operation of the air conditioner according to the target air conditioner setting parameters.

[0031] Optionally, the sleep quality information is a sleep quality score, and the prediction module includes:

[0032] An input submodule, used to input each group of air conditioning setting parameters, the user's sleep data and the environmental data into a preset sleep quality prediction model;

[0033] The score acquisition submodule is used to obtain the sleep quality score output by the preset sleep quality prediction model; the sleep quality score is positively correlated with the proportion of deep sleep and deep sleep during the sleep process.

[0034] Optionally, the parameter acquisition module includes:

[0035] The parameter generation submodule is used to iteratively generate multiple groups of air conditioning setting parameters through a genetic algorithm.

[0036] Optionally, the preset condition is that the iteration duration of the genetic algorithm is greater than a preset duration threshold, or the sleep quality score reaches a preset score threshold, or the number of iterations of the genetic algorithm is greater than a preset number threshold.

[0037] Optionally, the determining module includes:

[0038] The determination submodule is used to determine a group of air-conditioning setting parameters corresponding to the maximum sleep quality score as target air-conditioning setting parameters when a preset condition is met.

[0039] Optionally, the data acquisition module includes:

[0040] The data acquisition submodule is used to obtain sleep data and environmental data collected by the user's wearable device.

[0041] Optionally, the control module includes:

[0042] The message sending submodule is used to send a control message to a preset server, so that the preset server sends the control message to the air conditioner that subscribes to the control message; the control message includes the target air conditioner setting parameters.

[0043] Optionally, the air conditioning setting parameters include: at least one of: target temperature, target humidity, target air supply mode, and target wind speed.

[0044] Optionally, the sleep data includes at least one of heart rate, body temperature and sleep state, and the environmental data includes at least one of indoor temperature and outdoor temperature.

[0045] According to a third aspect of an embodiment of the present invention, an electronic device is disclosed, comprising: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the steps of the air conditioning control method as described above when executed by the processor.

[0046] According to a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is disclosed, on which a computer program is stored. When the computer program is executed by a processor, the steps of the air conditioning control method described above are implemented.

[0047] The embodiments of the present invention include the following advantages:

[0048] The present invention introduces an air conditioning control method, which obtains the user's sleep data and environmental data; obtains multiple sets of air conditioning setting parameters, and predicts the sleep quality information corresponding to the multiple sets of air conditioning setting parameters according to the multiple sets of air conditioning setting parameters, the user's sleep data and environmental data; when the preset conditions are met, the target air conditioning setting parameters are determined according to the sleep quality information corresponding to the multiple sets of air conditioning setting parameters; and the air conditioning operation is controlled according to the target air conditioning setting parameters. By associating the user's sleep data with the adjustment of the air conditioning operation state parameters, it is achieved to provide the most suitable sleep environment for the current stage according to the state of each stage of the user's sleep and the precise control of the air conditioning operation parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flowchart of a method for controlling an air conditioner according to an embodiment of the present invention;

[0050] Figure 2 is a data transmission flow chart of an air conditioning control method provided by an embodiment of the present invention;

[0051] Figure 3 is a genetic algorithm flow chart provided by an embodiment of the present invention;

[0052] Figure 4 It is a structural block diagram of an air conditioning control device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] With the acceleration of the pace of work and life in modern society, the problem of sleep quality has become increasingly prominent and has become an important factor affecting people's health. Existing smart bracelets or other wearable smart devices are usually equipped with the function of monitoring the user's sleep, such as sleep duration, sleep depth, heart rate, etc., but these data are generally only provided to users for viewing, and are not further used to improve the user's sleeping environment. At the same time, although traditional air-conditioning equipment has multiple adjustment modes, it cannot be intelligently adjusted according to the user's actual sleep status.

[0055] One of the core concepts of the embodiments of the present invention is how to link the monitored sleep data with the air conditioner sleep mode, make full use of the user's sleep data, and realize intelligent adjustment of the air conditioner according to the user's sleep state. The patent of this invention provides a method and device to adjust the operation mode of the air conditioner more carefully and reasonably through sleep monitoring data, so as to provide users with a more comfortable sleeping environment.

[0056] Reference Figure 1 , shows a flow chart of the steps of an air conditioning control method provided by an embodiment of the present invention, and the method may specifically include the following steps:

[0057] Step 101, obtaining the user's sleep data and environmental data;

[0058] When the air conditioner is running at night, it can only run in the set mode and cannot automatically adjust according to the user's different sleep stages to provide a more comfortable sleeping environment, resulting in a poor experience for users during sleep. The sleep conditions and environments of users vary greatly from person to person, so adjusting the air conditioner mode based on the user's sleep data and environmental data can provide users with a more comfortable sleeping environment. Using a smartphone or other terminal device that interacts with smart wearable devices and is equipped with a smart home control APP, it can be used to connect to bracelets, air conditioners, and other wearable smart devices to set whether the air conditioner enables sleep data linkage control.

[0059] In one embodiment, step 101 may include the following sub-steps:

[0060] Obtain sleep data and environmental data collected by the user's wearable device.

[0061] At present, common smart wearable devices in daily life include smart watches, smart bracelets, smart glasses, etc., and even smart pillows and smart mattresses. These smart wearable devices are widely used to collect users' health and activity data, including sleep data. At the same time, environmental data can also be obtained through sensors integrated in smart devices or by connecting to other smart environmental monitoring devices, such as connecting to a smartphone to obtain weather conditions through the network.

[0062] In one embodiment, the sleep data includes at least one of heart rate, body temperature and sleep state, and the environmental data includes at least one of indoor temperature and outdoor temperature.

[0063] Regarding sleep data collection, smart wearable devices are usually equipped with a variety of sensors to monitor the user's physiological indicators and collect sleep data. These sensors include but are not limited to: accelerometers, which are used to detect the user's movements and identify sleep states, such as turning over, getting up, etc.; heart rate monitors, which monitor heart rate changes through technologies such as photoplethysmography to distinguish different sleep stages; body temperature sensors, which monitor skin temperature changes to help determine deep sleep and other states; light sensors, which are used to detect the intensity of surrounding light to assist in determining the user's sleeping environment.

[0064] Regarding environmental data collection, smart wearable devices themselves may not have complete environmental monitoring capabilities, but they can obtain environmental data through connections with other smart devices. These environmental data usually include: temperature and humidity, which are directly obtained through temperature and humidity sensors integrated in smart wearable devices, or obtained through environmental monitoring devices connected to smart home systems. Light intensity, obtained through the built-in light sensor of the smart wearable device or the light sensor connected to the smart home system. Noise level, obtained through an integrated microphone or a noise monitoring device connected to the smart home system. Air quality, obtained through an air quality sensor integrated in the smart wearable device or an air quality monitoring device connected to the smart home system.

[0065] Step 102, obtaining multiple sets of air conditioning setting parameters;

[0066] In one embodiment, step 102 may include the following sub-steps:

[0067] Multiple groups of air conditioning setting parameters are iteratively generated through genetic algorithms.

[0068] Genetic Algorithm (GA) is a heuristic search algorithm that mimics the evolutionary process in nature to solve optimization and search problems. In genetic algorithms, the solution to a problem is usually encoded into "individuals", also known as "chromosomes". These individuals form a "population" and evolve through a series of operations such as selection, crossover, mutation, etc. to find the optimal solution or a solution close to the optimal solution.

[0069] Regarding the definition of individuals, in genetic algorithms, each individual represents a potential solution. This solution is usually encoded in the form of a string, which can be composed of binary bits, real numbers, integers, or other types of variables. This encoding method depends on the specific needs of the problem. For example, in the Traveling Salesman Problem (TSP), an individual may be a sequential list of a series of cities; while in a function optimization problem, an individual may be a vector containing multiple variable values.

[0070] The encoding methods of individuals include: binary encoding, which is the most commonly used encoding method, and each individual consists of a string of 0s and 1s; real number encoding, which is used when the solution space consists of real numbers, such as continuous optimization problems; permutation encoding, which is suitable for problems that require a specific order, such as the traveling salesman problem; symbolic encoding, which is used when the solution space consists of symbols or characters, such as automatic program generation in programming languages.

[0071] The implementation steps of the genetic algorithm are divided into: initializing the population, randomly generating a certain number of individuals; calculating fitness, calculating the fitness value of each individual according to the fitness function; selection, selecting individuals for the next step based on the fitness value. Common selection strategies include roulette selection, tournament selection, etc.; crossover, randomly selecting two individuals and exchanging part of their genes to generate new individuals; mutation, changing part of the genes of an individual with a very small probability; repetition, repeating steps "calculating fitness" to "mutation" until the termination condition is met (such as reaching a predetermined number of iterations or finding a good enough solution).

[0072] Genetic algorithms gradually approach the optimal solution by continuously iterating and improving individuals in the population.

[0073] In one embodiment, the air conditioning setting parameters include at least one of a target temperature, a target humidity, a target air supply mode, and a target wind speed.

[0074] In the embodiment of the present invention, the individual definition is a vector x including multiple air conditioning parameters.

[0075] x=(Ts,Ws,Wm,Wg)

[0076] Among them, Ts is temperature, Ws is humidity, Wm is air supply mode, and Wg is wind speed.

[0077] Step 103, predicting sleep quality information corresponding to the multiple sets of air conditioning setting parameters according to the multiple sets of air conditioning setting parameters, the user's sleep data and the environmental data;

[0078] The sleep quality information is a sleep quality score;

[0079] To predict sleep quality, each set of air-conditioning setting parameters, the user's sleep data and environmental data are tested, and the test score of each group is the sleep quality information, which is used to evaluate the quality of predicted sleep quality.

[0080] In one embodiment, step 103 may include the following sub-steps:

[0081] Sub-step S11, inputting each set of air conditioning setting parameters, the user's sleep data and the environmental data into a preset sleep quality prediction model;

[0082] The preset sleep quality prediction model is the M model, which is pre-trained through machine learning. In the field of optimization, a specific optimization model or problem is sometimes referred to as the "M model". M model as a general term: In optimization problems, "M model" may refer to a specific type of mathematical model or problem statement, especially those involving multi-objective optimization or multi-constraint optimization. In this case, "M" may stand for "Multi-objective" or "Model".

[0083] In multi-objective optimization, it is often necessary to optimize multiple conflicting objective functions simultaneously. In this case, the "M model" may refer to a multi-objective optimization problem or model. In some specific fields, such as machine learning, operations research, or engineering optimization, there may be specific models called "M models".

[0084] Genetic algorithm is a heuristic search method for solving optimization problems. In the embodiment of the present invention, "M model" refers to a certain optimization problem or model, and genetic algorithm can be used to solve such problems. Genetic algorithm finds approximate solutions to optimization problems by simulating the process of natural selection (including operations such as selection, crossover and mutation). In this case, genetic algorithm can be used to find a set of solutions (called Pareto optimal solutions) that provide the best trade-off between cost and performance.

[0085] According to the implementation steps of the genetic algorithm, after defining the individuals of the genetic algorithm and generating a certain number of individuals, it is necessary to calculate the fitness value of each individual according to the fitness function. The fitness function is a standard used to evaluate the quality of individuals. It measures the quality of the solution and determines the probability of an individual being selected in the selection process. The design of the fitness function depends on the characteristics of the specific problem, and the function scores the individuals according to the requirements of the problem. For minimization problems, the smaller the output of the fitness function, the better; for maximization problems, the opposite is true.

[0086] Calculate the fitness value: For each individual in the population, calculate its fitness value according to the fitness function. In the embodiment of the present invention, the method for obtaining the best setting parameters of the air conditioner uses a genetic algorithm, and the fitness of multiple sets of air conditioner setting parameters randomly generated by the genetic algorithm is calculated. The larger the calculation result, the higher the score, the better the evaluation of the set of parameters, that is, the highest sleep quality score, and the optimal air conditioner setting parameters can be obtained.

[0087] The fitness function of the embodiment of the present invention is:

[0088] fit(x)=M(Ts,Ws,Wm,Wg,To,Ti,Hr,Tb,Sp)

[0089] Among them, To is the outdoor temperature, Ti is the indoor temperature, Hr is the heart rate, Tb is the body temperature, and Sp is the sleep state. The sleep state uses 0 to 4 to represent the falling asleep period, light sleep period, deep sleep period, deep sleep period, and rapid eye movement period respectively. It is obtained from the smart wearable device mentioned in the previous step.

[0090] Sub-step S12, obtaining a sleep quality score output by the preset sleep quality prediction model; the sleep quality score is positively correlated with the proportion of deep sleep and deep sleep during the sleep process.

[0091] The wristband collects the user's sleep data, Hr heart rate, Tb body temperature, Sp sleep status, etc. The wristband and other wearable devices are bound to the air conditioner. After the smartphone receives the sleep data of the wearable device, it inputs it together with To outdoor temperature, Ti indoor temperature, and randomly generated air conditioning setting parameters of each group Ts temperature, Ws humidity, Wm air supply mode, and Wg wind speed into the M model. According to the iterative calculation using the M model, the predicted sleep quality score is output. The judgment basis of the score is the sleep quality, that is, the proportion of deep sleep and deep sleep in the sleep process. The calculated score of the fitness function value is proportional to the proportion of deep sleep and deep sleep in the sleep process.

[0092] Step 104, when the preset conditions are met, determining the target air conditioning setting parameters according to the sleep quality information corresponding to the multiple sets of air conditioning setting parameters;

[0093] When the preset conditions are met, the algorithm iteration termination condition is reached, and the iteration termination algorithm ends. The iteration termination condition of the genetic algorithm is an important part of the algorithm design, which determines when the algorithm stops the search process. The choice of termination condition is crucial to ensure that the algorithm can find a satisfactory solution while avoiding unnecessary calculations.

[0094] Some common genetic algorithm iteration termination conditions are: reaching the maximum number of iterations, setting a maximum number of iterations (such as 1000 times), and the algorithm automatically stops when it reaches this number, which is simple and easy to implement; reaching the optimal solution or a good enough solution, stopping when an individual in the population meets the pre-set optimal solution standard or reaches a good enough solution, which can ensure that the algorithm finds at least one solution that meets the requirements; the quality of the solution no longer improves, stopping when the individual with the best fitness has not improved significantly for several consecutive generations, which can avoid falling into the local optimum and continuing meaningless iterations, but the standard of "significant improvement" needs to be defined, which may vary depending on the problem ; When the maximum running time is reached, a maximum running time limit is set. When the algorithm running time exceeds this limit, it stops. It is suitable for application scenarios with strict time limits; When the population diversity is lower than the threshold, it stops when the population diversity (such as the degree of difference between individuals in the population) is lower than a certain threshold. It helps to avoid the algorithm from converging to the local optimal solution too early, but the choice of diversity threshold may affect the performance of the algorithm; User-defined termination conditions. According to the characteristics of the specific problem, the user can customize some special termination conditions. It is highly flexible and can be adjusted according to the characteristics of the problem, but the user needs to have certain experience and knowledge to correctly set the termination conditions.

[0095] In one embodiment, step 104 may include the following sub-steps:

[0096] In sub-step S21, the preset condition is that the iteration duration of the genetic algorithm is greater than a preset duration threshold, or the sleep quality score reaches a preset score threshold, or the number of iterations of the genetic algorithm is greater than a preset number threshold.

[0097] In practical applications, multiple termination conditions are usually used in combination to ensure the robustness and effectiveness of the algorithm. For example, in the embodiment of the present invention, the termination condition is a combination of three termination conditions, so that if the algorithm iteration time reaches a preset time threshold, or the sleep quality score reaches a preset score threshold, or the number of iterations of the algorithm is greater than a preset number threshold, the algorithm will stop.

[0098] Algorithm iteration time timeout: When the algorithm running time exceeds the set threshold, the operation is terminated; Fitness reaches the set value: According to scientific research tables, the proportion of deep sleep and nap period in people of different age groups is different. Therefore, different fitness function thresholds are set according to user age, gender and other information, that is, the proportion of deep sleep and deep sleep in the entire sleep process. When it reaches the normal level, the iteration is stopped and the control parameters are output; The number of algorithm iterations exceeds the threshold: Since the genetic algorithm has a certain degree of randomness, it may happen that individuals in the population cannot meet the fitness function, resulting in continuous iteration. Therefore, when the set number of iterations is exceeded, the population will be reinitialized for an attempt, but the running time should not exceed the preset duration threshold.

[0099] In one embodiment, step 104 may further include the following sub-steps:

[0100] Sub-step S22, when the preset conditions are met, a group of air-conditioning setting parameters corresponding to the maximum sleep quality score is used as the target air-conditioning setting parameters.

[0101] At the end of a normal iteration, the genetic algorithm will output the individual x value with the highest fitness function score during the iteration, that is, the corresponding air-conditioning setting parameters: Ts temperature, Ws humidity, Wm air supply mode, and Wg wind speed as the target air-conditioning setting parameters.

[0102] Step 105: Control the operation of the air conditioner according to the target air conditioner setting parameters.

[0103] In one embodiment, step 105 may include the following sub-steps:

[0104] A control message is sent to a preset server, so that the preset server sends the control message to the air conditioner that subscribes to the control message; the control message includes the target air conditioner setting parameters.

[0105] After the algorithm is executed, the current optimal air conditioner operating parameters will be output, that is, a set of x that makes the fitness function reach the maximum value during the iteration process. The mobile phone will encapsulate the control parameters into the message topi (topic) of the specified air conditioner and send the message to the MQTT (Message Queuing Telemetry Transport) server. MQTT is a subscription consumption message transmission protocol designed for low-bandwidth and unreliable network environments. It is very suitable for Internet of Things (IoT) application scenarios such as smart homes, industrial automation, remote monitoring, etc. The main features of the MQTT protocol are low overhead, low latency, easy implementation, and the ability to support large-scale device connections.

[0106] After a device subscribes to a topic, the MQTT message server will forward messages with the topic to the device. In the embodiment of the present invention, a data interaction method for linkage control between a smart bracelet and other wearable smart devices and an air conditioner is provided: a local area network message subscription and consumption communication method based on the MQTT-SN protocol is provided. After the bracelet or other smart wearable device transmits the sleep monitoring data to the mobile phone via Bluetooth, the mobile phone APP obtains the control parameter message of the air conditioner after processing the data through an algorithm, adds the topic and sends it to the MQTT message server, and finally forwards it to the target air conditioner for execution.

[0107] The embodiment of the present invention obtains the user's sleep data and environmental data; obtains multiple sets of air conditioning setting parameters, and predicts the sleep quality information corresponding to the multiple sets of air conditioning setting parameters according to the multiple sets of air conditioning setting parameters, the user's sleep data and environmental data; when the preset conditions are met, the target air conditioning setting parameters are determined according to the sleep quality information corresponding to the multiple sets of air conditioning setting parameters; and the air conditioning operation is controlled according to the target air conditioning setting parameters. By associating the user's sleep data with the adjustment of the air conditioning operation state parameters, it is achieved to accurately control the air conditioning operation parameters according to the state of each stage of the user's sleep, and provide the most suitable sleep environment for the current stage.

[0108] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0109] Reference Figure 2 , shows a data transmission flow chart of an air conditioning control method provided by an embodiment of the present invention, and clearly and completely describes the technical solution in the embodiment of the present invention. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0110] First of all, a smart bracelet or other smart wearable devices with equivalent functions, such as smart pillows and smart mattresses, are needed, which should have heart rate monitoring, motion monitoring and sleep status analysis functions; smart bracelets and other wearable smart devices use built-in algorithms, combined with heart rate, gyroscope and other data, to analyze the current user's sleep status in real time, distinguish between different sleep states of the user, such as awake, light sleep, deep sleep, rapid eye movement, etc., collect the user's sleep data, and transmit the data to the smart home control APP (Application) on the smartphone.

[0111] Secondly, there are smartphones for data interaction with smart wearable devices, which are equipped with smart home control apps, which can be used to connect to wristbands, air conditioners and other wearable smart devices, and can set whether the air conditioner is enabled for sleep data linkage control, customize sleep mode, etc. On the smart home app on the mobile phone, before using the automatic control of the air conditioner's operating status based on sleep, the user binds the wearable smart device and the air conditioner in the app, and when there are multiple devices, specifies the air conditioner to be controlled by sleep data association.

[0112] When the sleep data-linked control of air conditioning is turned on, it is detected that the user is in a sleeping state, and the mobile phone APP receives the user's sleep data collected by the bracelet. The APP calls the genetic algorithm to generate the air conditioning setting parameters, and then uses the built-in model M to calculate the current optimal air conditioning operation state based on weather conditions, sleep data, indoor temperature and other data to provide the most comfortable sleeping environment. The M model is obtained through machine learning pre-training. The model uses the current air conditioning control parameters and surrounding environmental factors as input to predict the sleep quality in the current situation, and outputs the air conditioning setting parameter score that is positively correlated with the proportion of deep sleep and deep sleep in the sleep process.

[0113] Reference Figure 3 , showing a genetic algorithm flow chart provided by an embodiment of the present invention, initializing the population, i.e., randomly generating multiple groups of air conditioning setting parameters, such as Ts temperature, Ws humidity, Wm air supply mode, Wg wind speed, etc.; then using the M model to calculate the fitness, if the preset termination condition is reached, the air conditioning setting parameters Ts, Ws, Wm, Wg corresponding to the maximum fitness score in this process are output and the algorithm is terminated; if the preset termination condition is not reached, the selection, crossover, mutation and other steps are executed to continue to iteratively generate different individuals to expand the population capacity, and then the individual parameters are returned to the step of using the M model to calculate the fitness, and the execution is repeated until the preset termination condition number is reached and the control parameters are output to end the iteration.

[0114] After obtaining the optimal setting parameters for the air conditioner, if no control command is sent, the process ends. If a control command is to be sent, the control command is sent to the target air conditioner. The specific steps are: add the target air conditioner setting parameter message to the bound specified air conditioner consumption topic, and then send it to the MQTT message server; the MQTT message server forwards the message to the subscribed air conditioner device.

[0115] Finally, the air conditioner can receive control messages from the MQTT message server, control parameters according to the message content, and adjust the operation status. The air conditioner consumes the specified message and executes the control message parameters to provide the user with the best sleeping environment, and the process ends.

[0116] Through the above implementation scheme, the present invention can effectively improve the user's sleeping comfort and realize the application innovation of data interconnection between smart home and smart wearable devices. The data monitoring frequency of smart bracelets and other wearable smart devices can be set according to needs to ensure real-time and accuracy. The temperature adjustment range and air supply mode of the air conditioner can be adjusted according to the user's personalized needs and saved as user preference settings, with priority higher than algorithm calculation.

[0117] Reference Figure 4 , shows a structural block diagram of an air conditioning control device provided by an embodiment of the present invention, which may specifically include the following modules:

[0118] The data acquisition module 401 is used to acquire the user's sleep data and environmental data;

[0119] The parameter acquisition module 402 is used to acquire multiple sets of air conditioning setting parameters;

[0120] A prediction module 403, configured to predict the sleep quality information corresponding to the multiple sets of air conditioning setting parameters according to the multiple sets of air conditioning setting parameters, the user's sleep data and the environmental data;

[0121] A determination module 404, configured to determine target air conditioning setting parameters according to sleep quality information corresponding to multiple sets of air conditioning setting parameters when a preset condition is met;

[0122] The control module 405 is used to control the operation of the air conditioner according to the target air conditioner setting parameters.

[0123] In one embodiment, the sleep quality information is a sleep quality score, and the prediction module includes:

[0124] An input submodule, used to input each group of air conditioning setting parameters, the user's sleep data and the environmental data into a preset sleep quality prediction model;

[0125] The score acquisition submodule is used to obtain the sleep quality score output by the preset sleep quality prediction model; the sleep quality score is positively correlated with the proportion of deep sleep and deep sleep during the sleep process.

[0126] In one embodiment, the parameter acquisition module includes:

[0127] The parameter generation submodule is used to iteratively generate multiple groups of air conditioning setting parameters through a genetic algorithm.

[0128] In one embodiment, the preset condition is that the iteration duration of the genetic algorithm is greater than a preset duration threshold, or the sleep quality score reaches a preset score threshold, or the number of iterations of the genetic algorithm is greater than a preset number threshold.

[0129] In one embodiment, the determining module includes:

[0130] The determination submodule is used to determine a group of air-conditioning setting parameters corresponding to the maximum sleep quality score as target air-conditioning setting parameters when a preset condition is met.

[0131] The preset condition is that the iteration duration of the genetic algorithm is greater than a preset duration threshold, or the sleep quality score reaches a preset score threshold, or the number of iterations of the genetic algorithm is greater than a preset number threshold.

[0132] In one embodiment, the data acquisition module includes:

[0133] The data acquisition submodule is used to obtain sleep data and environmental data collected by the user's wearable device.

[0134] In one embodiment, the control module includes:

[0135] The message sending submodule is used to send a control message to a preset server, so that the preset server sends the control message to the air conditioner that subscribes to the control message; the control message includes the target air conditioner setting parameters.

[0136] In one embodiment, the air conditioning setting parameters include at least one of a target temperature, a target humidity, a target air supply mode, and a target wind speed.

[0137] In one embodiment, the sleep data includes at least one of heart rate, body temperature and sleep state, and the environmental data includes at least one of indoor temperature and outdoor temperature.

[0138] The embodiment of the present invention obtains the user's sleep data and environmental data; obtains multiple sets of air conditioning setting parameters, and predicts the sleep quality information corresponding to the multiple sets of air conditioning setting parameters according to the multiple sets of air conditioning setting parameters, the user's sleep data and environmental data; when the preset conditions are met, the target air conditioning setting parameters are determined according to the sleep quality information corresponding to the multiple sets of air conditioning setting parameters; and the air conditioning operation is controlled according to the target air conditioning setting parameters. By associating the user's sleep data with the adjustment of the air conditioning operation state parameters, it is achieved to accurately control the air conditioning operation parameters according to the state of each stage of the user's sleep, and provide the most suitable sleep environment for the current stage.

[0139] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0140] An embodiment of the present invention further provides an electronic device, including:

[0141] It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned air-conditioning control method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0142] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned air-conditioning control method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0143] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0144] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0145] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0148] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0149] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0150] The air conditioning control method and the air conditioning control device provided by the present invention are introduced in detail above. The principles and implementation methods of the present invention are explained in this article by using specific examples. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An air conditioning control method, characterized in that: include: Obtain the user's sleep data and environmental data; Get multiple groups of air conditioning setting parameters; predicting sleep quality information corresponding to the multiple sets of air conditioning setting parameters according to the multiple sets of air conditioning setting parameters, the user's sleep data and the environmental data; When the preset conditions are met, the target air conditioning setting parameters are determined according to the sleep quality information corresponding to the multiple sets of air conditioning setting parameters; The air conditioner operation is controlled according to the target air conditioner setting parameters.

2. The method according to claim 1, characterized in that The sleep quality information is a sleep quality score, and the predicting the sleep quality information corresponding to the multiple sets of air conditioning setting parameters according to the multiple sets of air conditioning setting parameters, the user's sleep data and the environmental data respectively includes: Inputting each set of air conditioning setting parameters, the user's sleep data and the environmental data into a preset sleep quality prediction model; Obtain a sleep quality score output by the preset sleep quality prediction model; the sleep quality score is positively correlated with the proportion of a deep sleep period and a deep sleep period during sleep.

3. The method according to claim 2, characterized in that The obtaining of multiple sets of air conditioning setting parameters includes: Multiple groups of air conditioning setting parameters are iteratively generated through genetic algorithms.

4. The method according to claim 3, characterized in that The preset condition is that the iteration duration of the genetic algorithm is greater than a preset duration threshold, or the sleep quality score reaches a preset score threshold, or the number of iterations of the genetic algorithm is greater than a preset number threshold.

5. The method according to claim 1, characterized in that When the preset conditions are met, determining the target air conditioning setting parameters according to the sleep quality information corresponding to the multiple sets of air conditioning setting parameters includes: When the preset conditions are met, a set of air-conditioning setting parameters corresponding to the maximum sleep quality score is used as the target air-conditioning setting parameters.

6. The method according to claim 1, characterized in that The step of obtaining the user's sleep data and environmental data includes: Obtain sleep data and environmental data collected by the user's wearable device.

7. The method according to claim 1, characterized in that The step of controlling the operation of the air conditioner according to the target air conditioner setting parameters includes: A control message is sent to a preset server, so that the preset server sends the control message to the air conditioner that subscribes to the control message; the control message includes the target air conditioner setting parameters.

8. The method according to claim 1, characterized in that The air conditioning setting parameters include: at least one of a target temperature, a target humidity, a target air supply mode, and a target wind speed.

9. The method according to claim 1, characterized in that: The sleep data includes at least one of heart rate, body temperature and sleep state, and the environmental data includes at least one of indoor temperature and outdoor temperature.

10. An air conditioning control device, characterized in that: include: A data acquisition module, used to acquire the user's sleep data and environmental data; A parameter acquisition module is used to obtain multiple sets of air conditioning setting parameters; a prediction module, configured to predict the sleep quality information corresponding to the multiple sets of air conditioning setting parameters according to the multiple sets of air conditioning setting parameters, the user's sleep data and the environmental data; A determination module, configured to determine target air conditioning setting parameters according to sleep quality information corresponding to multiple sets of air conditioning setting parameters when a preset condition is met; The control module is used to control the operation of the air conditioner according to the target air conditioner setting parameters.

11. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the air conditioning control method as described in any one of claims 1 to 9 are implemented.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the air conditioning control method according to any one of claims 1 to 9 are implemented.

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