Control device and method for air conditioner

Abstract

The invention discloses a control device and method for an air conditioner. The control method for the air conditioner comprises the steps that current human body parameters and current environment parameters of a user in a target area are obtained; the current environment parameters and the current human body parameters are input into an intelligent model, and adjusting parameters of the air conditioner are obtained through calculation; and the air conditioner adjusts the running state according to the adjusting parameters, and obtains a sample set to optimize the intelligent model. The operation state of the air conditioner is adjusted in a personalized and intelligent mode for the user through the current human body parameters and the current environment parameters, the air conditioner is adjusted in a targeted, personalized and refined mode according to individual differences, the requirements of the user are better met, the use experience of the user is improved, the intelligent model is optimized through the sample set, and the user experience is improved. The requirements of the user are accurately obtained, then the personalized and healthy use environment is created, when the requirements of the user do not accord with the air conditioner operation parameters, adjustment is conducted in time, unnecessary energy consumption of the air conditioner can be avoided, and energy conservation and emission reduction are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the air conditioning technical field, specifically to a control device and method of air conditioner. BACKGROUND

[0002] The traditional air conditioner has the functions of refrigeration, heating, dehumidification, self-cleaning, sleep mode and air purification, and is remotely controlled and monitored through a remote controller or a mobile phone APP. Some intelligent air conditioners support voice cooperation, and save energy by adjusting the speed of the compressor. However, most air conditioners are not intelligent enough to accurately adjust the running state of the air conditioner according to human parameters and environmental parameters. The running state of the air conditioner cannot be adjusted or is manually adjusted, such as the fixed or manual adjustment of the new air intake amount, which cannot be automatically adjusted according to the indoor air quality. Due to the lack of intelligent control of the air conditioner, the user still needs to rely on the remote controller or the mobile phone APP to manually set the temperature and mode, which is inconvenient to use, cannot actively recognize the user's needs, and cannot provide personalized comfortable experience, resulting in unnecessary energy waste.

[0003] When the user is standing or sitting, in a tense or relaxed state, the human parameters such as body temperature, metabolic rate, heart rate, blood pressure, etc. are also different. These human parameters cannot be captured and then responded, for example, adjusting the temperature, humidity, fresh air volume, angle, etc. Moreover, most users generally do not know the air conditioner setting parameters they need, so it is impossible to meet the user's needs. Some air conditioners are equipped with temperature, humidity, PM2.5 and other sensors to monitor the indoor environment in real time. However, these sensors can only monitor environmental parameters and lack monitoring of human parameters, and do not have targeted adjustments for individual differences. SUMMARY

[0004] To solve the problem that the air conditioner cannot be adjusted according to the user's differences in the prior art, the present application provides a control device and method of air conditioner.

[0005] The present application adopts the following technical solutions.

[0006] The first aspect of the present application discloses a control method of air conditioner, the method comprises:

[0007] Obtaining the current human parameters and the current environmental parameters of the user in the target area;

[0008] Inputting the current environmental parameters and the current human parameters into an intelligent model, and calculating to obtain the adjustment parameters of the air conditioner;

[0009] The air conditioner adjusts the running state according to the adjustment parameters, and obtains a sample set to optimize the intelligent model.

[0010] Preferably, the current human parameters of the user in the target area are obtained by:

[0011] obtaining monitoring data of the user in the target area, obtaining the current human body parameter based on the monitoring data, or obtaining input information of the user in the target area, and obtaining the current human body parameter based on the input information.

[0012] Preferably, the inputting the current environment parameter and the current human body parameter into the intelligent model and obtaining the adjustment parameter of the air conditioner by calculation comprises:

[0013] comparing the current environment parameter and the current human body parameter with corresponding threshold values to obtain a comparison result, and determining a weight coefficient and a risk coefficient in an abnormal state;

[0014] obtaining the adjustment parameter of the air conditioner based on the weight coefficient and the risk coefficient.

[0015] Preferably, the comparing the current environment parameter and the current human body parameter with corresponding threshold values to obtain a comparison result in an abnormal state comprises:

[0016] obtaining a required environment threshold value, and identifying an environment abnormal state if the current environment parameter exceeds the required environment threshold value;

[0017] obtaining a required human body threshold value, and identifying a human body abnormal state if the current human body parameter exceeds the required human body threshold value.

[0018] Preferably, the adjustment parameter is equal to a product of a preset basic quantity, the weight coefficient and the risk coefficient.

[0019] The weight coefficient comprises a health weight, an environment weight and a comfort weight, initial values of the health weight, the environment weight and the comfort weight are determined based on a human body abnormal state and an environment abnormal state, a health risk level is judged, the health risk level is introduced as a dynamic adjustment factor to correct each initial value, and the maximum initial value after correction is selected as the weight coefficient.

[0020] The risk coefficient is equal to a preset proportion of a risk level plus a preset reference value, and the risk level is judged based on the human body abnormal state.

[0021] Preferably, the obtaining a sample set to optimize the intelligent model comprises:

[0022] storing historical environment parameters, historical human body parameters and historical adjustment parameters of a user in a target area as the sample set, and pre-processing the sample set;

[0023] inputting the pre-processed sample set into the intelligent model for training and learning;

[0024] predict the corresponding threshold value based on the intelligent model after training.

[0025] The second aspect of the present application discloses a control device of an air conditioner, a control method of the air conditioner, comprising:

[0026] The acquisition module is used to obtain the current environment parameters and the current human body parameters of the user in the target area.

[0027] The communication module is used to receive the data of the current human body parameters and the current environment parameters, and perform data transmission.

[0028] The edge device module is used to preprocess the current environment parameters and the current human body parameters.

[0029] The cloud module has an intelligent model built-in, and the adjustment parameters of the air conditioner are obtained through the intelligent model calculation, and the sample set is obtained to optimize the intelligent model.

[0030] The decision module is used to construct an execution logic based on the adjustment parameters, and control the air conditioner to adjust the operation state according to the execution logic.

[0031] Preferably, the cloud module is connected with the air conditioners in multiple target areas, and shares the data of the user.

[0032] The third aspect of the present application discloses an air conditioner comprising the control device of the air conditioner.

[0033] The fourth aspect of the present application discloses an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is loaded into the processor to realize the control method of the air conditioner.

[0034] The fifth aspect of the present application discloses a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the control method of the air conditioner.

[0035] The present application has the beneficial effects that compared with the prior art.

[0036] The present application realizes the individualized, intelligent and refined adjustment of the operation state of the air conditioner according to the individual differences of the user by monitoring the current human body parameters and the current environment parameters, which is more in line with the needs of the user, improves the user experience, and optimizes the intelligent model through the sample set to accurately obtain the needs of the user, thereby creating a personalized and healthy use environment.

[0037] The current human body parameter of the user in the target area is obtained, can be obtained through monitoring data, or can be obtained through the user actively uploading input health information, and then the human body parameter of the user can be more comprehensively obtained, and the air conditioner can be more intelligently operated according to individual differences.

[0038] The current environment parameter and the current human body parameter are compared with corresponding threshold values to judge abnormal states, and the adjustment parameters of the air conditioner are calculated, so that dynamic and personalized threshold standards can be formulated for different users, the user experience is improved, and energy consumption is reduced, and the intelligent green is realized.

[0039] The environment abnormal state and the human body abnormal state are identified respectively, on the one hand, the user's comfort is improved through the environment abnormal state, and on the other hand, the risk is prevented through the human body abnormal state, so that the health and safety risks of the user are avoided, and the safety and reliability are improved.

[0040] The weight coefficient of the application confirms the initial value of the health, environment and comfort weight based on the human body abnormal state and the environment abnormal state, and dynamically corrects based on the health risk level, realizes the maximization of improving comfort and energy saving while ensuring health, and the risk coefficient is dynamically adjusted through the risk level, further ensures the health and safety of the user, improves the safety of the air conditioner operation, and builds a safe, comfortable and efficient environment.

[0041] The application obtains historical parameters as a sample set, and trains and learns the sample set through an intelligent model, continuously optimizes itself, and then predicts corresponding threshold values for different users, so as to accurately analyze the user demand, and then individually, healthily and energy-efficiently operate the air conditioner.

[0042] The cloud module of the application is connected with the air conditioners in a plurality of target areas, shares user data, realizes global optimization, and enables the user to individually, healthily and energy-efficiently operate the air conditioner in any target area. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is the principle diagram of the control method of the air conditioner of the application;

[0044] Figure 2 is the implementation flowchart of the control method of the air conditioner of the application;

[0045] Figure 3 is the flowchart of obtaining the current human body parameter of the control method of the air conditioner of the application;

[0046] Figure 4 is the structural diagram of the control device of the air conditioner of the application;

[0047] Figure 5 is the implementation flowchart of the intelligent model of the application. DETAILED DESCRIPTION

[0048] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the present application. The embodiments described in the present application are only a part of the embodiments of the present application, but not all the embodiments. Based on the spirit of the present application, other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0049] As shown in Figure 1 , Figure 2 , Figure 3 and Figure 5 , the embodiment 1 of the present application discloses a control method of an air conditioner, comprising:

[0050] Step 1: obtaining the current human body parameters and the current environment parameters of the user in the target area;

[0051] Specifically, the current human body parameters of the user in the target area can be obtained by obtaining the monitoring data of the user in the target area, monitoring the body temperature, heart rate, blood pressure, blood oxygen saturation and the like by the human body sensor worn by the user, and then adjusting the operating temperature of the air conditioner when the body temperature is too high or too low, adjusting the fresh air introduction amount of the air conditioner when the blood oxygen saturation is too low or too high, and other conditions are better for adjusting the operating state of the air conditioner according to the needs of the user. At the same time, the number, position, entering time of the target area and the like of the user can be monitored by infrared cameras, radars and the like, and then the on-off, blowing direction, wind speed and the like of the air conditioner are adjusted according to the number, position, posture and the like of the user. In addition, the sound decibel of the user in the target area can be monitored by the sound receiving device to monitor whether the user has abnormal physical conditions such as coughing.

[0052] The current human body parameters can also be obtained by obtaining the input information uploaded by the user in the target area, such as obtaining the case or medical examination report uploaded by the user, for example, the user has asthma, cold and the like, and then the air conditioner is intelligently operated according to the pathological state of the user.

[0053] The present application obtains the current human body parameters of the user in the target area, which can be obtained by monitoring data or by the user uploading input health information, and then the human body parameters of the user can be more comprehensively obtained, and the air conditioner can be more intelligently operated.

[0054] As shown in Figure 3As shown, the user's monitoring data can be divided into continuous state parameters and discontinuous state parameters. Continuous state parameters are parameters that need to be monitored and fed back in real time, such as body temperature, heart rate, blood pressure, and blood oxygen saturation. Discontinuous state parameters are parameters that remain unchanged after monitoring and determination, such as the number of users, location, cough, and wheezing.

[0055] The acquisition of current environmental parameters of users within the target area can be achieved by using environmental sensors to monitor temperature, humidity, carbon dioxide concentration, and harmful gas concentration within the target area.

[0056] When there is missing data in the current human body parameters and the current environment parameters, the missing items will be replaced by preset default values.

[0057] Preferably, but not limitingly, the human body sensor can be a contact sensor to ensure accuracy and effectiveness, or a proximity sensor to reduce physical obstacles and improve detection sensitivity. The environmental sensor can be installed inside the air conditioner.

[0058] Step 2: Input the current environmental parameters and the current human body parameters into the intelligent model, and calculate the adjustment parameters of the air conditioner;

[0059] Specifically, step 2 includes:

[0060] Step 2.1: Compare the current environmental parameters and the current human body parameters with the corresponding thresholds. If the comparison result indicates an abnormal state, determine the weighting coefficient and risk coefficient.

[0061] Specifically, the required environmental threshold is obtained. If the current environmental parameter exceeds the required environmental threshold, it is identified as an abnormal environmental state. For example, when the temperature exceeds the required temperature threshold, it will be identified as a temperature abnormality, and when the humidity exceeds the required humidity threshold, it will be identified as a humidity abnormality.

[0062] The system obtains the required human body thresholds. If the current human body parameters exceed the required human body thresholds, it is identified as an abnormal human body state. For example, if the user's heart rate exceeds the required heart rate threshold and blood oxygen saturation exceeds the required blood oxygen saturation threshold, it will be identified as the user panting. If the user's voice decibel exceeds the required sound threshold, it will be identified as coughing.

[0063] If the current environmental parameters do not exceed the required environmental threshold and the current human body parameters do not exceed the required human body threshold, it is identified as a normal state, and the air conditioner operates according to the default operating state. The default operating state can be a preset operating state or the operating state of the previous executed command.

[0064] The required environmental threshold and the required human body threshold are obtained by using the historical environmental parameters, historical human body parameters and historical adjustment parameters of users in the target area as the sample set, which is preprocessed and then input into the intelligent model for training. For example, the model can memorize the user's comfort needs, high-frequency environmental parameters and main human body parameters, or capture the actual long-term and short-term dependencies in the time series, thereby predicting the required environmental threshold and the required human body threshold of users in the target area.

[0065] It is understood that the intelligent model can be a reinforcement learning model.

[0066] Preferably, but not limitingly, the required environmental threshold and the required human body threshold can be numerical values, numerical ranges, or spatial ranges, etc.

[0067] This invention compares current environmental parameters and current human body parameters with corresponding thresholds to determine abnormal states, calculates air conditioning adjustment parameters, and realizes dynamic and personalized standards for different users, thereby improving user experience, reducing energy consumption, and promoting green and intelligent operation.

[0068] This invention identifies abnormal environmental conditions and abnormal human conditions separately. On the one hand, it improves user comfort by addressing abnormal environmental conditions, and on the other hand, it prevents risks by addressing abnormal human conditions, thus avoiding health and safety risks for users and improving safety and reliability.

[0069] Step 2.2: Calculate the adjustment parameters of the air conditioner based on the weighting coefficient and the risk coefficient.

[0070] Specifically, the adjustment parameter is equal to the product of the preset base quantity, the weighting coefficient, and the risk coefficient;

[0071] The weighting coefficients include: health weight, environment weight, and comfort weight. The initial values ​​of the health weight, environment weight, and comfort weight are determined based on the abnormal human body state and the abnormal environment state. At the same time, the health risk level is determined, and the health risk level is introduced as a dynamic adjustment factor to correct the initial values ​​of each item. The initial value with the largest correction is selected as the weighting coefficient.

[0072] For example, an initial value range can be assigned to each weight, which can be from zero to one. When a user's heart rate exceeds the required heart rate threshold and the carbon dioxide concentration also exceeds the required carbon dioxide concentration, it will be judged as an abnormal state of both human heart rate and environmental carbon dioxide concentration. Adjustment parameters will be calculated separately. Based on the abnormal human heart rate state, an initial value of 0.8 will be automatically assigned to the health weight, an initial value of 0.3 to the environmental weight, and an initial value of 0.2 to the comfort weight. At the same time, based on the abnormal human heart rate state, the health risk level will be judged as high risk, and it will be corrected through multiple dynamic adjustment factors, thereby increasing the health weight and decreasing the environmental and comfort weights. The corrected health weight is 0.9, the environmental weight is 0.2, and the comfort weight is... The initial value is 0.1. A health weight of 0.9 is selected as the weighting coefficient to calculate the adjustment parameters. Based on the above adjustment parameters, the temperature and humidity need to be reduced and the fresh air volume increased. At the same time, based on the abnormal state of the ambient carbon dioxide concentration, an initial value of 0.5 is automatically assigned to the health weight, an initial value of 0.8 to the environment weight, and an initial value of 0.4 to the comfort weight. Based on the abnormal state of the ambient carbon dioxide concentration, the health risk level is judged to be low risk. After being corrected by multiple dynamic adjustment factors, the initial values ​​of each weight remain basically unchanged. An environmental weight of 0.8 is selected as the weighting coefficient to calculate the adjustment parameters. Based on the above adjustment parameters, the fresh air volume needs to be increased. Since both adjustment parameters require an increase in fresh air volume, the adjustment parameter with the largest value is selected to increase the fresh air volume.

[0073] Meanwhile, the health risk level can be determined through multi-layered risk assessment, taking into account the number and severity of abnormal human conditions, individual differences, and other factors. For example, if a user is allergic to pollen, the pollen concentration in the target area can be detected. If the pollen concentration exceeds the required pollen threshold, it will be judged as medium risk. Then, the health weight will be increased predictively to calculate adjustment parameters, thereby increasing the amount of fresh air to improve air quality and reduce pollen concentration.

[0074] Preferably, but not limitingly, the comfort weight can be adjusted through air conditioning energy-saving strategies, human comfort, etc.

[0075] By combining health risk levels, individual differences and the environment can be fully analyzed, avoiding situations where some users have pathological characteristics such as allergies or asthma, making it impossible to conduct targeted analysis and adjustments based on the environment. This deep integration of individual differences enables dynamic and forward-looking adjustment of the air conditioning operation status.

[0076] The higher the health risk level, the higher the value of the health weight increased through dynamic adjustment, and the lower the value of the environmental weight and comfort weight decreased.

[0077] The risk coefficient is equal to the risk level of the preset proportion plus the preset benchmark value. The risk level is determined based on the abnormal state of the human body. The risk level can also be determined through multi-level risk assessment, based on the number and severity of the abnormal state of the human body.

[0078] Preferably, but not limitingly, the risk coefficient can be equal to 0.1 times the risk level plus one. The risk level can be one to ten levels. When a change in the user's posture causes the heart rate to exceed the required heart rate threshold and then fall back to within the required heart rate threshold, it will be judged as a level two risk. When the user's body temperature continuously exceeds the required body temperature threshold, it will be judged as a level five risk. When the user's blood oxygen saturation continuously exceeds the required blood oxygen saturation threshold and the heart rate continuously exceeds the required heart rate threshold, it will be judged as a level eight risk. Thus, the air conditioning operation status can be adjusted while fully ensuring the user's health and safety.

[0079] The weighting coefficients of this invention confirm the initial values ​​of health, environment, and comfort weights based on abnormal human and environmental conditions, and are dynamically corrected based on health risk levels. This maximizes comfort and energy efficiency while ensuring health. At the same time, the risk coefficients are dynamically adjusted through risk levels to further ensure user health and safety, improve the safety of air conditioning operation, and create a safe, comfortable, and efficient environment.

[0080] Step 3: The air conditioner adjusts its operating state according to the adjustment parameters and obtains a sample set to optimize the intelligent model.

[0081] Specifically, the adjustment of the operating status includes, but is not limited to, adjusting the temperature, temperature adjustment rate, humidity, wind speed, wind direction, fresh air volume, negative ion concentration, etc. of the air conditioner, thereby enabling multiple personalized adjustments for the user. The above-mentioned adjustment of the operating status can be further updated with research, user's human body parameters, environmental parameters, etc.

[0082] like Figure 5As shown, the process of acquiring a sample set to optimize the intelligent model includes: initializing information, then storing historical environmental parameters, historical human body parameters, and historical adjustment parameters of users within the target area as the sample set, preprocessing the sample set, inputting the preprocessed sample set into the intelligent model for training, predicting the required environmental threshold and required human body threshold for users within the target area based on the trained intelligent model, and uploading the user data to the cloud for sharing to achieve global optimization. This allows users to immediately operate the air conditioner in a personalized manner upon arriving at any target area, optimizing the air conditioner energy-saving strategy. Simultaneously, the cloud module can learn and train user needs and air conditioner energy consumption, analyze the relationship between user needs and air conditioner energy consumption, and select an air conditioner operating state that meets user needs and has low energy consumption by adjusting comfort weights, further optimizing the air conditioner energy-saving strategy.

[0083] This invention obtains historical parameters as a sample set and trains the sample set through an intelligent model to continuously optimize itself. It then predicts corresponding thresholds for different users, thereby achieving accurate analysis of user needs and enabling personalized, healthy, and energy-efficient operation of the air conditioner.

[0084] This invention monitors current human body parameters and environmental parameters to achieve personalized, intelligent, and precise adjustment of the air conditioner's operating status based on individual user differences. This better meets user needs, improves the user experience, and optimizes the intelligent model through a sample set to accurately obtain user needs, thereby creating a personalized and healthy user environment. At the same time, when user needs do not match the air conditioner's operating parameters, timely adjustments can avoid unnecessary energy consumption, thus saving energy and reducing emissions.

[0085] like Figure 4 As shown, Embodiment 2 of the present invention discloses an air conditioner control device and a control method for operating the air conditioner, including:

[0086] The data acquisition module is used to obtain the current environmental parameters and current human body parameters of users within the target area;

[0087] The communication module is used to receive the current environmental parameters and the data of the current environmental parameters, and transmit the data to the edge device module;

[0088] The edge device module is used to preprocess the current environmental parameters and current human parameters, and then transmit the preprocessed parameters to the cloud module.

[0089] The cloud module has a built-in intelligent model that calculates the air conditioner's adjustment parameters and obtains a sample set to optimize the intelligent model.

[0090] The decision module is used to construct execution logic based on the adjustment parameters and control the air conditioner to adjust its operating state according to the execution logic.

[0091] The cloud module can communicate with the edge device modules of multiple air conditioners to share the user's sample set and achieve global optimization. For example, the air conditioners in different rooms can be coordinated and adjusted according to the sample sets of different users to adapt to the user's needs more quickly and improve flexibility and response speed.

[0092] The cloud module of this invention connects to air conditioners in multiple target areas, shares user data, and achieves global optimization, enabling users to operate the air conditioner in a personalized, healthy, and energy-efficient manner whenever they arrive at any target area.

[0093] The cloud module can also predict the user's comfort needs based on the acquired sample set and generate a comfort curve, providing a basis for air conditioning control. Based on the comfort curve and global optimization strategy, it adjusts the air conditioning operation in real time and collects online feedback data to further optimize the control strategy. This closed-loop control mechanism ensures that the air conditioning system meets user comfort needs while minimizing energy consumption.

[0094] In addition, when there are multiple users in the target area, users with abnormal physical conditions will be processed first to meet their needs.

[0095] Preferably, but not limitingly, when a user enters a target area, the air conditioner first broadcasts a one-time identifier and completes a zero-trust handshake with the user's monitoring device. The user information and multimodal interaction module information network are initialized, and a trust weight handshake is completed. The modules reach a consensus by exchanging trust weight information. When the user leaves the room, the handshake is released, and the user will automatically pair when entering other target areas.

[0096] Embodiment 3 of the present invention discloses an air conditioner, including a control device for the air conditioner.

[0097] Embodiment 4 of the present invention discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the control method of the air conditioner.

[0098] Embodiment 5 of the present invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the control method for the air conditioner.

[0099] The beneficial effects of the present invention are compared with those of the prior art.

[0100] This invention monitors current human body parameters and environmental parameters to achieve personalized, intelligent, and precise adjustment of the air conditioner's operating status based on individual user differences. This better meets user needs, improves the user experience, and optimizes the intelligent model through a sample set to accurately obtain user needs, thereby creating a personalized and healthy user environment. At the same time, when user needs do not match the air conditioner's operating parameters, timely adjustments can avoid unnecessary energy consumption, thus saving energy and reducing emissions.

[0101] This invention obtains the current human body parameters of users within a target area. This can be obtained through monitoring data or through users actively uploading and inputting health information, thereby enabling a more comprehensive acquisition of users' human body parameters and more intelligent operation of air conditioning based on individual differences.

[0102] This invention compares current environmental parameters and current human body parameters with corresponding thresholds to determine abnormal states, calculates air conditioning adjustment parameters, and realizes dynamic and personalized threshold standards for different users, thereby improving user experience, reducing energy consumption, and promoting green and intelligent operation.

[0103] This invention identifies abnormal environmental conditions and abnormal human conditions separately. On the one hand, it improves user comfort by addressing abnormal environmental conditions, and on the other hand, it prevents risks by addressing abnormal human conditions, thus avoiding health and safety risks for users and improving safety and reliability.

[0104] The weighting coefficients of this invention confirm the initial values ​​of health, environment, and comfort weights based on abnormal human and environmental conditions, and are dynamically corrected based on health risk levels. This maximizes comfort and energy efficiency while ensuring health. At the same time, the risk coefficients are dynamically adjusted through risk levels to further ensure user health and safety, improve the safety of air conditioning operation, and create a safe, comfortable, and efficient environment.

[0105] This invention obtains historical parameters as a sample set, analyzes the sample set to adjust the corresponding thresholds, and then makes refined and personalized adjustments to the required adjustment parameters, continuously optimizing itself, thereby achieving accurate analysis of user needs and realizing personalized, healthy, and energy-saving air conditioning operation.

[0106] The cloud module of this invention connects to air conditioners in multiple target areas, shares user data, and achieves global optimization, enabling users to operate the air conditioner in a personalized, healthy, and energy-efficient manner whenever they arrive at any target area.

[0107] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0108] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0109] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0110] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute 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 a remote computer, the remote computer may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions to implement various aspects of this disclosure.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A control method of an air conditioner, characterized by: The method comprises: obtaining the current human body parameters and the current environment parameters of the user in the target area; inputting the current environment parameters and the current human body parameters into an intelligent model, and calculating the adjustment parameters of the air conditioner; the air conditioner adjusts the operating state according to the adjustment parameters, and obtains a sample set to optimize the intelligent model.

2. The air conditioner control method of claim 1, wherein: obtaining the current human body parameters of the user in the target area comprises: obtaining monitoring data of the user in the target area, and obtaining the current human body parameters based on the monitoring data, or obtaining input information of the user in the target area, and obtaining the current human body parameters based on the input information.

3. The air conditioner control method of claim 1, wherein: inputting the current environment parameters and the current human body parameters into an intelligent model, and calculating the adjustment parameters of the air conditioner comprises: comparing the current environment parameters and the current human body parameters with corresponding threshold values to obtain comparison results in an abnormal state, determining a weight coefficient and a risk coefficient; based on the weight coefficient and the risk coefficient, the adjustment parameters of the air conditioner are calculated.

4. The air conditioner control method of claim 3, wherein: if the comparison results are in an abnormal state, comparing the current environment parameters and the current human body parameters with corresponding threshold values comprises: obtaining a required environment threshold value, and if the current environment parameters exceed the required environment threshold value, identifying an environment abnormal state; obtaining a required human body threshold value, and if the current human body parameters exceed the required human body threshold value, identifying a human body abnormal state.

5. The air conditioner control method of claim 3, wherein: the adjustment parameters are equal to the product of a preset basic quantity, the weight coefficient and the risk coefficient; the weight coefficient comprises a health weight, an environment weight and a comfort weight, the initial values of the health weight, the environment weight and the comfort weight are determined based on the human body abnormal state and the environment abnormal state, the health risk level is judged, the health risk level is introduced as a dynamic adjustment factor, each initial value is corrected, and the maximum initial value after correction is selected as the weight coefficient; the risk coefficient is equal to the risk level of a preset proportion plus a preset reference value, and the risk level is determined based on the human body abnormal state.

6. The air conditioner control method of claim 1, wherein: obtaining a sample set to optimize the intelligent model comprises: storing the historical environment parameters, the historical human body parameters and the historical adjustment parameters of the user in the target area as the sample set, and preprocessing the sample set; inputting the preprocessed sample set into the intelligent model for training and learning; based on the trained and learned intelligent model, the corresponding threshold values are predicted.

7. A control device of an air conditioner, which operates the control method of the air conditioner according to any one of claims 1 to 6, characterized by comprises: a collection module for obtaining the current environment parameters and the current human body parameters of the user in the target area; a communication module for receiving data of the current human body parameters and the current environment parameters, and performing data transmission; An edge device module is configured to preprocess the current environment parameter and the current human body parameter; An intelligent model is built in the cloud module, and the adjustment parameter of the air conditioner is obtained by calculation of the intelligent model, and a sample set is obtained to optimize the intelligent model; A decision module is configured to construct an execution logic based on the adjustment parameter, and control the air conditioner to adjust the operation state according to the execution logic.

8. The control device of an air conditioner according to claim 7, characterized in that: The cloud module is connected with the air conditioners in a plurality of target areas, and shares the data of the user.

9. An air conditioner characterized by comprising: The control device of an air conditioner according to claim 7 or 8.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program is loaded into the processor to realize the control method of the air conditioner according to any one of claims 1 to 6.

11. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1-10. The computer program is executed by the processor to realize the control method of the air conditioner according to any one of claims 1 to 6.

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