Air conditioner operation parameter recommendation method and related device

Abstract

The application discloses an air conditioner operation parameter recommendation method and related equipment, which can select a target space model corresponding to a target indoor environment from a preset space model based on temperature change information of the target indoor environment in which the air conditioner is located; estimate a refrigerating capacity of the air conditioner when the temperature of the target indoor environment meets a preset temperature change condition based on the target space model, indoor and outdoor environment temperatures corresponding to the air conditioner, and a set target temperature; collect object feature information of a target object using the air conditioner; perform human comfort degree analysis on the target object through the object feature information to obtain a target comfortable temperature matched with the target object; correct the refrigerating capacity of the air conditioner according to the target comfortable temperature and historical behavior information of the target object using the air conditioner, and recommend air conditioner operation parameters corresponding to the corrected refrigerating capacity to the target object. The application can improve the accuracy of air conditioner operation parameter recommendation, effectively reduce user operation, and enhance user comfort degree.

Description

Technical Field

[0001] This application relates to the field of air conditioner technology, specifically to a method for recommending air conditioner operating parameters and related equipment. Background Technology

[0002] As people's living standards continue to improve, smart home appliances are becoming increasingly popular, and the market share of smart air conditioners is also rapidly increasing. Currently, smart air conditioners generally determine recommended operating parameters based solely on indoor and outdoor ambient temperature and humidity, as well as the user's set target temperature. Users then control the air conditioner according to these recommended parameters. Under these recommended operating parameters, the air conditioner can achieve faster cooling and heating effects. However, because this approach is relatively simple, the accuracy of recommending air conditioner operating parameters is low, which is not conducive to improving user comfort.

[0003] Therefore, how to further improve the accuracy of air conditioner parameter recommendations to enhance user experience is a technical problem that needs to be solved. Summary of the Invention

[0004] This application provides a method for recommending air conditioning operating parameters and related equipment. The related equipment may include an air conditioning operating parameter recommendation device, electronic equipment, computer-readable storage medium, and computer program product, which can improve the accuracy of air conditioning operating parameter recommendations, effectively reduce user operations, and enhance user comfort.

[0005] This application provides a method for recommending air conditioning operating parameters, including:

[0006] Acquire temperature change information of the target indoor environment where the air conditioner is located, and select the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information;

[0007] Based on the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature, the cooling capacity of the air conditioner is estimated when the temperature of the target indoor environment meets the preset temperature change conditions.

[0008] Collect object feature information of the target object using the air conditioner;

[0009] By using the object feature information, human comfort analysis is performed on the target object to obtain the target comfort temperature that matches the target object;

[0010] Based on the target comfort temperature and the target user's historical air conditioning usage information, the cooling capacity of the air conditioner is corrected, and the air conditioner operating parameters corresponding to the corrected cooling capacity are recommended to the target user.

[0011] Accordingly, this application provides an air conditioning operating parameter recommendation device, comprising:

[0012] The acquisition unit is used to acquire temperature change information of the target indoor environment where the air conditioner is located, and select the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information.

[0013] The estimation unit is used to estimate the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions, based on the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature.

[0014] The acquisition unit is used to acquire object feature information of the target object using the air conditioner;

[0015] The analysis unit is used to perform human comfort analysis on the target object using the object feature information, and obtain a target comfort temperature that matches the target object.

[0016] The recommendation unit is used to correct the cooling capacity of the air conditioner based on the target comfort temperature and the target object's historical behavior information of using the air conditioner, and recommend the air conditioner operating parameters corresponding to the corrected cooling capacity to the target object.

[0017] Optionally, in some embodiments of this application, the acquisition unit may include a model acquisition subunit, a calculation subunit, and a selection subunit, as follows:

[0018] The model acquisition subunit is used to acquire at least one preset space model, and different preset space models correspond to different air conditioning usage scenarios.

[0019] The calculation subunit is used to calculate the similarity between the temperature change information of the target indoor environment and the temperature change information corresponding to each preset space model;

[0020] A sub-unit is selected to select the target space model corresponding to the target indoor environment from the preset space models based on the similarity.

[0021] Optionally, in some embodiments of this application, the recommendation unit may include an estimation subunit and a cooling capacity correction subunit, as follows:

[0022] The estimation subunit is used to estimate the temperature distribution information of the target indoor environment during a preset time period under the operation of the air conditioner, based on the cooling capacity.

[0023] The cooling capacity correction subunit is used to correct the cooling capacity of the air conditioner based on the target comfort temperature, the temperature distribution information, and the target object's historical behavior information regarding air conditioning use.

[0024] Optionally, in some embodiments of this application, the estimation unit may be specifically used to calculate the refrigerant flow rate of the air conditioner based on the compressor suction density, volumetric efficiency, and operating efficiency of the air conditioner; and to estimate the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions based on the refrigerant flow rate of the air conditioner, the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature.

[0025] Optionally, in some embodiments of this application, the analysis unit may include a first acquisition subunit, a comparison subunit, and a first determination subunit, as follows:

[0026] The first acquisition subunit is used to acquire reference feature information of at least one reference object and the corresponding reference comfort temperature;

[0027] The comparison subunit is used to perform comparison processing on at least one human comfort dimension between the reference feature information and the object feature information.

[0028] The first determining subunit is used to determine, based on the comparison results, a target comfort temperature that matches the target object from the reference comfort temperatures.

[0029] Optionally, in some embodiments of this application, the recommendation unit may include a second acquisition subunit, a second determination subunit, and a correction subunit, as follows:

[0030] The second acquisition subunit is used to acquire historical behavior information of the target object using the air conditioner, the historical behavior information including the behavior information of the target object in at least one historical usage scenario;

[0031] The second determining subunit is used to determine the target's behavior information under the target's historical usage scenario from the historical behavior information based on the scenario in which the target object is currently using the air conditioner.

[0032] The correction subunit is used to correct the cooling capacity of the air conditioner based on the target comfort temperature and the behavioral information of the target historical usage scenarios.

[0033] An electronic device provided in this application includes a processor and a memory. The memory stores multiple instructions, and the processor loads the instructions to execute the steps in the air conditioner operating parameter recommendation method provided in this application.

[0034] This application also provides a computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps in the air conditioning operating parameter recommendation method provided in this application.

[0035] Furthermore, this application also provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement the steps in the air conditioning operating parameter recommendation method provided in this application.

[0036] This application provides a method and related equipment for recommending air conditioning operating parameters. It can acquire temperature change information of the target indoor environment where the air conditioner is located, and based on this temperature change information, select a target space model corresponding to the target indoor environment from a preset space model. Based on the target space model, the indoor and outdoor temperatures corresponding to the air conditioner, and a set target temperature, it estimates the cooling capacity of the air conditioner when the temperature of the target indoor environment meets preset temperature change conditions. It collects object feature information of the target object using the air conditioner; through the object feature information, it performs human comfort analysis on the target object to obtain a target comfort temperature matching the target object; based on the target comfort temperature and the target object's historical behavior information regarding air conditioning use, it corrects the cooling capacity of the air conditioner and recommends the corrected cooling capacity corresponding to the air conditioner operating parameters to the target object. This application can combine the target space model of the indoor environment where the air conditioner is located, the user's object feature information, and historical behavior information to determine the recommended operating parameters of the air conditioner, which helps improve the accuracy of the air conditioner operating parameter recommendations, effectively reduces user operations, and enhances user comfort. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1a This is a schematic diagram of a scenario for the air conditioning operating parameter recommendation method provided in an embodiment of this application;

[0039] Figure 1b This is a flowchart of the air conditioning operating parameter recommendation method provided in the embodiments of this application;

[0040] Figure 1c This is another flowchart of the air conditioning operating parameter recommendation method provided in the embodiments of this application;

[0041] Figure 1d This is another flowchart of the air conditioning operating parameter recommendation method provided in the embodiments of this application;

[0042] Figure 2This is another flowchart of the air conditioning operating parameter recommendation method provided in the embodiments of this application;

[0043] Figure 3 This is a schematic diagram of the air conditioning operating parameter recommendation device provided in the embodiments of this application;

[0044] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] This application provides a method for recommending air conditioner operating parameters and related equipment. The related equipment may include an air conditioner operating parameter recommendation device, electronic equipment, a computer-readable storage medium, and a computer program product. Specifically, the air conditioner operating parameter recommendation device may be integrated into an electronic device, which may be a terminal or a server, etc.

[0047] It is understood that the air conditioning operating parameter recommendation method in this embodiment can be executed on the terminal, or it can be executed jointly by the terminal and the server. The above examples should not be construed as limiting this application.

[0048] like Figure 1a As shown, the air conditioning operating parameter recommendation method is implemented jointly by a terminal and a server as an example. The air conditioning operating parameter recommendation system provided in this application includes a terminal 10 and a server 11, etc.; the terminal 10 and the server 11 are connected through a network, such as through a wired or wireless network, etc., wherein the air conditioning operating parameter recommendation device can be integrated into the terminal.

[0049] Terminal 10 can be used to: collect object feature information of the target object using the air conditioner, obtain temperature change information of the target indoor environment where the air conditioner is located, indoor and outdoor ambient temperatures, and the set target temperature; and send the object feature information, temperature change information, indoor and outdoor ambient temperatures, and target temperature to server 11 so that server 11 can determine the recommended operating parameters of the air conditioner based on these data; terminal 10 can also be used to receive the corrected air conditioner operating parameters corresponding to the cooling capacity sent by server 11, and recommend the air conditioner operating parameters to the user. Terminal 10 may include a smart air conditioner, mobile phone, tablet computer, laptop computer, or personal computer, etc.

[0050] Server 11 can be used to: acquire temperature change information of the target indoor environment where the air conditioner is located, and select a target space model corresponding to the target indoor environment from a preset space model based on the temperature change information; estimate the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions based on the target space model, the indoor and outdoor temperatures corresponding to the air conditioner, and the set target temperature; collect object feature information of the target object using the air conditioner; perform human comfort analysis on the target object through the object feature information to obtain a target comfort temperature matching the target object; correct the cooling capacity of the air conditioner according to the target comfort temperature and the target object's historical behavior information of using the air conditioner, and recommend the air conditioner operating parameters corresponding to the corrected cooling capacity to terminal 10. Server 11 can be a single server, a server cluster composed of multiple servers, or a cloud server.

[0051] The air conditioning operating parameter recommendation method provided in this application involves computer vision technology in the field of artificial intelligence.

[0052] Artificial intelligence (AI) is the theory, methods, technology, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a comprehensive technology within computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can react in a way similar to human intelligence. AI studies the design principles and implementation methods of various intelligent machines, enabling them to have perception, reasoning, and decision-making capabilities. AI technology is a comprehensive discipline involving a wide range of fields, encompassing both hardware and software technologies. AI software technology mainly includes computer vision, speech processing, natural language processing, as well as machine learning / deep learning, autonomous driving, and intelligent transportation.

[0053] Computer vision (CV) is a science that studies how to enable machines to "see." More specifically, it refers to machine vision, which uses cameras and computers to replace human eyes for target recognition, tracking, and measurement, and then performs image processing to create images more suitable for human observation or transmission to instruments. As a scientific discipline, computer vision studies related theories and technologies, attempting to build artificial intelligence systems capable of extracting information from images or multidimensional data. Computer vision technologies typically include image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, 3D object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping (SLAM), autonomous driving, intelligent transportation, and common biometric recognition technologies such as facial recognition and fingerprint recognition.

[0054] The air conditioning operating parameter recommendation method provided in this application also relates to big data in the field of cloud technology.

[0055] Cloud technology refers to a hosting technology that unifies hardware, software, and network resources within a wide area network (WAN) or local area network (LAN) to achieve data computation, storage, processing, and sharing. Cloud technology is a general term encompassing network technology, information technology, integration technology, management platform technology, and application technology based on the cloud computing business model. It can form resource pools, providing flexible and convenient on-demand access. Cloud computing technology will become a crucial support. Backend services of technical network systems require substantial computing and storage resources, such as video websites, image websites, and many portal websites. With the rapid development and application of the internet industry, every item may have its own identification mark in the future, requiring transmission to backend systems for logical processing. Data at different levels will be processed separately, and various industry data will require robust system support, which can only be achieved through cloud computing.

[0056] Big data refers to data sets that cannot be captured, managed, and processed within a certain timeframe using conventional software tools. It represents massive, rapidly growing, and diverse information assets that require new processing models to achieve stronger decision-making, insightful discovery, and process optimization capabilities. With the advent of the cloud era, big data has attracted increasing attention. Big data requires specialized technologies to effectively process large amounts of data within a tolerable timeframe. Technologies suitable for big data include massively parallel processing databases, data mining, distributed file systems, distributed databases, cloud computing platforms, the internet, and scalable storage systems.

[0057] The following sections provide detailed descriptions of each example. It should be noted that the order in which the embodiments are described is not intended to limit the preferred order of the embodiments.

[0058] This embodiment will be described from the perspective of an air conditioner operating parameter recommendation device, which can be integrated into an electronic device, such as a server or a terminal.

[0059] like Figure 1b As shown, the specific process of this method for recommending air conditioner operating parameters can be as follows:

[0060] 101. Obtain temperature change information of the target indoor environment where the air conditioner is located, and select the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information.

[0061] Specifically, the temperature change information can include the time it takes for the temperature to rise and the time it takes for the temperature to drop in the target indoor environment where the air conditioner is located. The temperature rise time refers to the time it takes for the temperature to increase, and the temperature drop time refers to the time it takes for the temperature to decrease.

[0062] In this embodiment, the spatial structure of the environment can be determined based on temperature change information. It is understood that, with the same air conditioning operating parameters, user comfort may differ depending on the spatial structure of the air conditioning environment. This embodiment can determine recommended air conditioning operating parameters by combining the target space model corresponding to the air conditioning operating environment, thus improving user comfort.

[0063] Optionally, in this embodiment, the step "selecting the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information" may include:

[0064] Obtain at least one preset space model, with different preset space models corresponding to different air conditioning usage scenarios;

[0065] Calculate the similarity between the temperature change information of the target indoor environment and the temperature change information corresponding to each preset space model;

[0066] Based on the similarity, a target space model corresponding to the target indoor environment is selected from the preset space models.

[0067] The air conditioning usage scenario corresponding to the preset space model here may include inner loop temperature, outer loop temperature, and user-set temperature, etc., and this embodiment does not impose specific limitations on this. Inner loop temperature can refer to indoor ambient temperature, and outer loop temperature can refer to outdoor ambient temperature.

[0068] Before calculating the similarity, models that are the same as or similar to the current air conditioner usage scenario can be selected from the preset spatial models. These models are used as candidate spatial models. Then, the similarity between the temperature change information of the target indoor environment and the temperature change information corresponding to the candidate spatial models is calculated. Based on the similarity, the target spatial model corresponding to the target indoor environment is selected from the candidate spatial models.

[0069] The similarity between the temperature change information of the target indoor environment and the temperature change information corresponding to the preset spatial model can be specifically the difference between the temperature rise time and temperature fall time of the target indoor environment and the temperature rise time and temperature fall time of the preset spatial model. The larger the difference, the lower the similarity; conversely, the smaller the difference, the higher the similarity. In this embodiment, a preset spatial model with the same air conditioning usage scenario and a temperature change information similarity greater than the preset similarity can be used as the target spatial model corresponding to the target indoor environment.

[0070] In some embodiments, a preset spatial model can be selected as the target spatial model corresponding to the target indoor environment if the difference between the temperature rise time and the temperature fall time is less than a preset value and the similarity of the air conditioning usage scenario is greater than a preset similarity. The preset value and preset similarity can be set according to the actual situation, and this embodiment does not limit them. For example, they can be set according to the accuracy requirements of the recommended air conditioning operating parameters. If the accuracy requirement is higher, the preset value can be set smaller and the preset similarity can be set larger.

[0071] In one specific embodiment, a room space model corresponding to the current air conditioner operation (i.e., the target space model in the above embodiment) can be established. Specifically, the air conditioner is set to a temperature (e.g., 27°C) and runs for a period of time, recording the room temperature rise time and temperature fall time. Then, the temperature rise time and temperature fall time of rooms with similar scenarios in the same area can be compared. The closer the times are, the more similar the room space models are, and the comparable building loads are considered. For example, rooms located in the same town as the target indoor environment can be obtained. Rooms with air conditioner usage scenarios similar to the target indoor environment can be used as candidate space models. The temperature rise time and temperature fall time of the candidate space models can be recorded. Then, the temperature rise time and temperature fall time of the candidate space models can be compared with the temperature rise time and temperature fall time of the target indoor environment to determine the target space model corresponding to the target indoor environment.

[0072] 102. Based on the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature, estimate the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions.

[0073] The indoor and outdoor ambient temperatures can include both indoor and outdoor ambient temperatures, and the target temperature is the temperature set by the user (i.e., the target object) for the air conditioner.

[0074] Specifically, the cooling capacity of the air conditioner when the target indoor temperature meets the preset temperature change conditions is the heat exchange capacity when the target indoor temperature rises or falls. This preset temperature change condition can be defined as maintaining the indoor temperature within a certain range during air conditioner operation; this range can be determined based on actual conditions.

[0075] In some embodiments, the heat exchange required for a room to meet temperature rise and fall can be estimated using the flow rate method based on the target space model corresponding to the room, the current inner and outer ring temperatures, and the user-set temperature. The flow rate method is a way to estimate the cooling capacity of an air conditioner. To ensure that user behavior and actual usage conditions remain unchanged, and to avoid interfering with the user or adding extra equipment, this embodiment can indirectly calculate the refrigerant flow rate using the compressor volumetric efficiency method, thereby deriving the air conditioner's cooling capacity.

[0076] Optionally, in this embodiment, the step "based on the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature, estimating the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions" may include:

[0077] Calculate the refrigerant flow rate of the air conditioner based on the compressor suction density, volumetric efficiency, and operating efficiency.

[0078] Based on the refrigerant flow rate of the air conditioner, the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature, the cooling capacity of the air conditioner is estimated when the temperature of the target indoor environment meets the preset temperature change conditions.

[0079] Specifically, the refrigerant flow rate can be defined as the cooling capacity of the air conditioner per second.

[0080] The calculation process for estimating refrigerant flow rate based on the compressor volumetric efficiency method can be shown in formula (1):

[0081] m ref =ρ ref ×(η v V d )×f (1)

[0082] Where, m ref ρ represents the refrigerant flow rate of an air conditioner. ref The compressor's suction density, expressed in kg / m³. 3 η v V represents volumetric efficiency.d Theoretical volumetric gas delivery capacity, the unit can be m³. 3 / s; f is the compressor frequency, and the unit can be 1 / s.

[0083] Here, volumetric efficiency can be the ratio of the actual gas delivery volume to the theoretical gas delivery volume of the air conditioner. In this embodiment, the refrigerant flow rate of the air conditioner can be calculated by combining the compressor's suction density, volumetric efficiency, and operating frequency. Furthermore, in some embodiments, the calculation of refrigerant flow rate may also consider relative clearance volume correction, linear functions of the compressor motor speed, etc.

[0084] 103. Collect object feature information of the target object using the air conditioner.

[0085] The target user of the air conditioner can be one or more. Specifically, this embodiment can combine the user's object characteristic information to recommend air conditioner operating parameters, which helps to improve the accuracy of the recommendations.

[0086] The object feature information may include the age, gender, and scene mode of each target object. The scene mode may include sleep mode, exercise mode, sitting mode, etc.

[0087] In some embodiments, infrared sensors can be used to collect object feature information of the target object.

[0088] 104. Using the object feature information, perform human comfort analysis on the target object to obtain a target comfort temperature that matches the target object.

[0089] Optionally, in this embodiment, the step "using the object feature information to perform human comfort analysis on the target object and obtain a target comfort temperature matching the target object" may include:

[0090] Obtain reference characteristic information of at least one reference object and its corresponding reference comfort temperature;

[0091] The reference feature information and the object feature information are compared in at least one dimension of human comfort.

[0092] Based on the comparison results, a target comfort temperature matching the target object is determined from the reference comfort temperatures.

[0093] The reference characteristics of the reference object can include its age, gender, and the scene mode it is in. This reference characteristics can be obtained through big data analysis. The reference comfort temperature for the reference object is specifically the comfort temperature for that object within its corresponding scene mode.

[0094] Among them, at least one human comfort dimension may include comfort dimensions related to age, comfort dimensions related to gender, comfort dimensions related to scene mode, etc.

[0095] Through comparison processing, reference feature information that matches the object feature information of the target object can be determined, and the reference comfort temperature of the reference object corresponding to the reference feature information is determined as the target comfort temperature.

[0096] In some embodiments, sensor-collected object feature information can be used to collect the target object's characteristics, which may include: user age (e.g., child, adult, elderly), gender (male, female), and usage scenario mode. Then, by comparing with big data, a target comfort temperature matching the target object can be obtained. Based on this target comfort temperature, the cooling capacity can be adjusted for different user groups and usage scenarios, with adjustments including increasing or decreasing heat exchange.

[0097] Specifically, an image sensor can be used to acquire an image containing the target object, and then image recognition can be performed. Based on the image recognition results, object feature information of the target object can be obtained.

[0098] For example, through big data comparison, it was found that the comfortable room temperature for the elderly in sleep mode is 28℃, while for ordinary adults it is 26℃. After estimating the heat exchange of the air conditioner, the output cooling capacity of the air conditioner can be improved based on the collected object characteristic information, and the comfort model of the air conditioner can be corrected.

[0099] 105. Based on the target comfort temperature and the target object's historical behavior information regarding air conditioning use, the cooling capacity of the air conditioner is corrected, and the air conditioner operating parameters corresponding to the corrected cooling capacity are recommended to the target object.

[0100] Optionally, in this embodiment, the step "correcting the cooling capacity of the air conditioner based on the target comfort temperature and the target object's historical behavior information regarding air conditioning use" may include:

[0101] Based on the cooling capacity, the temperature distribution information of the target indoor environment under the operation of the air conditioner is estimated within a preset time period;

[0102] The cooling capacity of the air conditioner is adjusted based on the target comfort temperature, the temperature distribution information, and the target object's historical behavior information regarding air conditioning use.

[0103] The preset time period can be one hour or two hours, etc., and this embodiment does not limit it.

[0104] In this embodiment, the required cooling capacity of the air conditioner can be estimated based on the current target space model, indoor and outdoor ambient temperatures, and the set target temperature. This allows for the estimation of room temperature distribution over cumulative operating times of 1 hour, 2 hours, ..., 6 hours. Based on the temperature distribution information, it can be determined whether the indoor temperature is evenly distributed (e.g., whether there is stratification between upper and lower floors) and whether the temperature is too high or too low. Therefore, the cooling capacity can be adjusted based on the temperature distribution information.

[0105] In some embodiments, temperature distribution information can be collected using an infrared sensor.

[0106] Optionally, in this embodiment, the step "correcting the cooling capacity of the air conditioner based on the target comfort temperature and the target object's historical behavior information regarding air conditioning use" may include:

[0107] Obtain historical behavior information of the target object using air conditioning, the historical behavior information including the behavior information of the target object in at least one historical usage scenario;

[0108] Based on the current usage scenario of the target object using air conditioning, determine the target's historical usage scenario behavior information from the historical behavior information;

[0109] The cooling capacity of the air conditioner is adjusted based on the target comfort temperature and the behavioral information of the target historical usage scenarios.

[0110] The historical usage scenarios here can include the indoor and outdoor ambient temperatures, the set target temperature, and the scene mode of the target user when using the air conditioner during a historical period. The behavioral information within these historical usage scenarios can specifically include actions such as raising or lowering the set temperature, adjusting the fan speed, or turning the air conditioner off.

[0111] Among these methods, historical usage scenarios that match the current air conditioning usage scenario can be used as target historical usage scenarios; then, the cooling capacity of the air conditioner can be adjusted based on behavioral information and target comfort temperature under the target historical usage scenarios.

[0112] Alternatively, in some embodiments, user habits can be learned through big data, so that the air conditioner can run directly based on similar scenarios the next time it is turned on.

[0113] In this embodiment, the cooling capacity output of the air conditioner can be corrected using a comfort model. Specifically, this comfort model is a model built on big data. It can analyze the human comfort of the target object through object feature information to obtain a target comfort temperature that matches the target object. It can also correct the cooling capacity of the air conditioner based on the target comfort temperature and the target object's historical behavior information when using the air conditioner.

[0114] The air conditioning operating parameter recommendation method provided in this application can utilize big data to obtain user habits, user groups, user sensitivity to cold and heat, and room conditions (such as area size). By comparing similar usage scenarios in the region or in the past, it can estimate the cooling capacity required by the air conditioning system, establish a room comfort model, and intelligently recommend comfortable and energy-saving air conditioning operating parameters to users, thereby improving user comfort.

[0115] In one embodiment, such as Figure 1c The flowchart shown is the corresponding method for recommending air conditioning operating parameters provided in this application, and the specific description is as follows:

[0116] 1. During the operation of the air conditioner, the room space model (i.e., the target space model in the above embodiment) is established by comparing the room temperature rise or fall time under similar inner loop, outer loop, and user-set temperature conditions.

[0117] 2. Estimate the required cooling capacity of the air conditioner using the flow rate method;

[0118] 3. Collect object feature information corresponding to the user through sensors, compare it with big data, and then determine the target comfortable temperature that meets the user's needs in the current usage scenario;

[0119] 4. Adjust the air conditioner's cooling capacity based on user behavior and upload the corresponding parameters to the cloud.

[0120] In one specific embodiment, after the air conditioner has been running for a period of time, users may change the air conditioner settings by raising or lowering the set temperature, fan speed, or turning it off. The heat exchange output of the air conditioner and the room temperature distribution before the setting change can be preliminarily calculated to determine whether the heat exchange output (i.e., cooling capacity) of the air conditioner at this time is too low or too high. That is, the current heat exchange output of the air conditioner does not meet the user's needs. At the same time, the current heat exchange output of the air conditioner can be transmitted to the comfort model as a reference for the correction of the comfort model. When the air conditioner is turned on in the future, by comparing historical data, the air conditioner can correct the comfort model according to the user's usage habits when the same scenario occurs. This can reduce the user's repeated operation of the air conditioner.

[0121] For example, such as Figure 1dAs shown, after the air conditioner is turned on, it can record the operating frequency F of the air conditioning system and the current usage scenario. Before the user changes the air conditioner settings, the cooling capacity Q1 output by the air conditioner within the operating time t1 can be calculated using the refrigerant flow method. The specific calculation process is as described in the previous embodiment and will not be repeated here. Then, by comparing the cooling capacity Q output by the air conditioner within the operating time t under the same usage scenario, and considering the case where the temperature distribution between the upper and lower floors of the room is uniform; when Q1 < Q, it indicates that the current output cooling capacity is low and the room temperature is layered; conversely, when Q1 > Q, it indicates that the current output cooling capacity is high.

[0122] If a user changes the set temperature, the difference between the original and new set temperatures can be used to determine whether the user has raised or lowered the air conditioner's set temperature. If the difference is less than zero, it indicates that the temperature has been raised, meaning the current cooling capacity Q1 output by the air conditioner is greater than the user's demand. In the future, when the air conditioner encounters the same scenario, the compressor frequency can be reduced by X Hz based on the operating time t1 and compressor frequency F, where X can be determined according to the actual situation. If the difference is greater than zero, it indicates that the temperature has been lowered, meaning the current cooling capacity Q1 output by the air conditioner is less than the user's demand. In the future, when the air conditioner encounters the same scenario, the compressor frequency can be increased by X Hz based on the operating time t1 and compressor frequency F.

[0123] Optionally, in this embodiment, feature extraction can be performed on all user control data of air conditioners in each area to formulate an overall control strategy for air conditioners in each area during the corresponding time period and weather.

[0124] As can be seen from the above, this embodiment can obtain temperature change information of the target indoor environment where the air conditioner is located, and select the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information; based on the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature, estimate the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions; collect the object feature information of the target object using the air conditioner; perform human comfort analysis on the target object through the object feature information to obtain the target comfort temperature matching the target object; correct the cooling capacity of the air conditioner according to the target comfort temperature and the target object's historical behavior information of using the air conditioner, and recommend the air conditioner operating parameters corresponding to the corrected cooling capacity to the target object. This application can combine the target space model of the indoor environment where the air conditioner is located, the user's object feature information, and historical behavior information to determine the recommended operating parameters of the air conditioner, which helps to improve the accuracy of the air conditioner operating parameter recommendation, effectively reduces user operations, and enhances user comfort.

[0125] Based on the method described in the preceding embodiments, the following will provide a more detailed explanation by taking the specific integration of the air conditioner operating parameter recommendation device into the server as an example.

[0126] This application provides a method for recommending air conditioning operating parameters, such as... Figure 2 As shown, the specific process of this method for recommending air conditioner operating parameters can be as follows:

[0127] 201. The server obtains the temperature change information of the target indoor environment where the air conditioner is located, and selects the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information.

[0128] Specifically, the temperature change information can include the time it takes for the temperature to rise and the time it takes for the temperature to drop in the target indoor environment where the air conditioner is located. The temperature rise time refers to the time it takes for the temperature to increase, and the temperature drop time refers to the time it takes for the temperature to decrease.

[0129] Optionally, in this embodiment, the step "selecting the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information" may include:

[0130] Obtain at least one preset space model, with different preset space models corresponding to different air conditioning usage scenarios;

[0131] Calculate the similarity between the temperature change information of the target indoor environment and the temperature change information corresponding to each preset space model;

[0132] Based on the similarity, a target space model corresponding to the target indoor environment is selected from the preset space models.

[0133] The air conditioning usage scenarios corresponding to the preset space model here may include inner ring temperature, outer ring temperature, and user-set temperature, etc. This embodiment does not impose specific limitations on this.

[0134] Before calculating the similarity, models that are the same as or similar to the current air conditioner usage scenario can be selected from the preset spatial models. These models are used as candidate spatial models. Then, the similarity between the temperature change information of the target indoor environment and the temperature change information corresponding to the candidate spatial models is calculated. Based on the similarity, the target spatial model corresponding to the target indoor environment is selected from the candidate spatial models.

[0135] The similarity between the temperature change information of the target indoor environment and the temperature change information corresponding to the preset spatial model can be specifically the difference between the temperature rise time and temperature fall time of the target indoor environment and the temperature rise time and temperature fall time of the preset spatial model. The larger the difference, the lower the similarity; conversely, the smaller the difference, the higher the similarity. In this embodiment, a preset spatial model with the same air conditioning usage scenario and a temperature change information similarity greater than the preset similarity can be used as the target spatial model corresponding to the target indoor environment.

[0136] 202. Based on the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature, the server estimates the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions.

[0137] Specifically, the cooling capacity of the air conditioner when the target indoor temperature meets the preset temperature change conditions is the heat exchange capacity when the target indoor temperature rises or falls. This preset temperature change condition can be defined as maintaining the indoor temperature within a certain range during air conditioner operation; this range can be determined based on actual conditions.

[0138] Optionally, in this embodiment, the step "based on the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature, estimating the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions" may include:

[0139] Calculate the refrigerant flow rate of the air conditioner based on the compressor suction density, volumetric efficiency, and operating efficiency.

[0140] Based on the refrigerant flow rate of the air conditioner, the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature, the cooling capacity of the air conditioner is estimated when the temperature of the target indoor environment meets the preset temperature change conditions.

[0141] Specifically, the refrigerant flow rate can be defined as the cooling capacity of the air conditioner per second.

[0142] 203. The server collects the object feature information of the target object using the air conditioner.

[0143] The target user of the air conditioner can be one or more. Specifically, this embodiment can combine the user's object characteristic information to recommend air conditioner operating parameters, which helps to improve the accuracy of the recommendations.

[0144] The object feature information may include the age, gender, and scene mode of each target object. The scene mode may include sleep mode, exercise mode, sitting mode, etc.

[0145] 204. The server performs human comfort analysis on the target object using the object feature information to obtain a target comfort temperature that matches the target object.

[0146] Optionally, in this embodiment, the step "using the object feature information to perform human comfort analysis on the target object and obtain a target comfort temperature matching the target object" may include:

[0147] Obtain reference characteristic information of at least one reference object and its corresponding reference comfort temperature;

[0148] The reference feature information and the object feature information are compared in at least one dimension of human comfort.

[0149] Based on the comparison results, a target comfort temperature matching the target object is determined from the reference comfort temperatures.

[0150] The reference characteristics of the reference object can include its age, gender, and the scene mode it is in. This reference characteristics can be obtained through big data analysis. The reference comfort temperature for the reference object is specifically the comfort temperature for that object within its corresponding scene mode.

[0151] Among them, at least one human comfort dimension may include comfort dimensions related to age, comfort dimensions related to gender, comfort dimensions related to scene mode, etc.

[0152] Through comparison processing, reference feature information that matches the object feature information of the target object can be determined, and the reference comfort temperature of the reference object corresponding to the reference feature information is determined as the target comfort temperature.

[0153] 205. The server adjusts the cooling capacity of the air conditioner based on the target comfort temperature and the target object's historical air conditioning usage information, and recommends the air conditioner operating parameters corresponding to the adjusted cooling capacity to the target object.

[0154] Optionally, in this embodiment, the step "correcting the cooling capacity of the air conditioner based on the target comfort temperature and the target object's historical behavior information regarding air conditioning use" may include:

[0155] Based on the cooling capacity, the temperature distribution information of the target indoor environment under the operation of the air conditioner is estimated within a preset time period;

[0156] The cooling capacity of the air conditioner is adjusted based on the target comfort temperature, the temperature distribution information, and the target object's historical behavior information regarding air conditioning use.

[0157] In this embodiment, the required cooling capacity of the air conditioner can be estimated based on the current target space model, indoor and outdoor ambient temperatures, and the set target temperature. This allows for the estimation of room temperature distribution over cumulative operating times of 1 hour, 2 hours, ..., 6 hours. Based on the temperature distribution information, it can be determined whether the indoor temperature is evenly distributed (e.g., whether there is stratification between upper and lower floors) and whether the temperature is too high or too low. Therefore, the cooling capacity can be adjusted based on the temperature distribution information.

[0158] Optionally, in this embodiment, the step "correcting the cooling capacity of the air conditioner based on the target comfort temperature and the target object's historical behavior information regarding air conditioning use" may include:

[0159] Obtain historical behavior information of the target object using air conditioning, the historical behavior information including the behavior information of the target object in at least one historical usage scenario;

[0160] Based on the current usage scenario of the target object using air conditioning, determine the target's historical usage scenario behavior information from the historical behavior information;

[0161] The cooling capacity of the air conditioner is adjusted based on the target comfort temperature and the behavioral information of the target historical usage scenarios.

[0162] The historical usage scenarios here can include the indoor and outdoor ambient temperatures, the set target temperature, and the scene mode of the target user when using the air conditioner during a historical period. The behavioral information within these historical usage scenarios can specifically include actions such as raising or lowering the set temperature, adjusting the fan speed, or turning the air conditioner off.

[0163] Among these methods, historical usage scenarios that match the current air conditioning usage scenario can be used as target historical usage scenarios; then, the cooling capacity of the air conditioner can be adjusted based on behavioral information and target comfort temperature under the target historical usage scenarios.

[0164] As can be seen from the above, this embodiment can obtain temperature change information of the target indoor environment where the air conditioner is located through a server, and select the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information; based on the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature, estimate the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions; collect the object feature information of the target object using the air conditioner; perform human comfort analysis on the target object through the object feature information to obtain the target comfort temperature matching the target object; correct the cooling capacity of the air conditioner according to the target comfort temperature and the target object's historical behavior information of using the air conditioner, and recommend the air conditioner operating parameters corresponding to the corrected cooling capacity to the target object. This application can combine the target space model of the indoor environment where the air conditioner is located, the user's object feature information, and historical behavior information to determine the recommended operating parameters of the air conditioner, which helps to improve the accuracy of the air conditioner operating parameter recommendation, effectively reduces user operations, and enhances user comfort.

[0165] To better implement the above methods, this application also provides an air conditioning operating parameter recommendation device, such as... Figure 3 As shown, the air conditioner operating parameter recommendation device may include an acquisition unit 301, a prediction unit 302, a data collection unit 303, an analysis unit 304, and a recommendation unit 305, as follows:

[0166] (1) Obtain unit 301;

[0167] The acquisition unit is used to acquire temperature change information of the target indoor environment where the air conditioner is located, and select the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information.

[0168] Optionally, in some embodiments of this application, the acquisition unit may include a model acquisition subunit, a calculation subunit, and a selection subunit, as follows:

[0169] The model acquisition subunit is used to acquire at least one preset space model, and different preset space models correspond to different air conditioning usage scenarios.

[0170] The calculation subunit is used to calculate the similarity between the temperature change information of the target indoor environment and the temperature change information corresponding to each preset space model;

[0171] A sub-unit is selected to select the target space model corresponding to the target indoor environment from the preset space models based on the similarity.

[0172] (2) Prediction Unit 302;

[0173] The estimation unit is used to estimate the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions, based on the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature.

[0174] Optionally, in some embodiments of this application, the estimation unit may be specifically used to calculate the refrigerant flow rate of the air conditioner based on the compressor suction density, volumetric efficiency, and operating efficiency of the air conditioner; and to estimate the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions based on the refrigerant flow rate of the air conditioner, the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature.

[0175] (3) Acquisition unit 303;

[0176] The acquisition unit is used to acquire object feature information of the target object using the air conditioner.

[0177] (4) Analysis Unit 304;

[0178] The analysis unit is used to perform human comfort analysis on the target object using the object feature information, and obtain a target comfort temperature that matches the target object.

[0179] Optionally, in some embodiments of this application, the analysis unit may include a first acquisition subunit, a comparison subunit, and a first determination subunit, as follows:

[0180] The first acquisition subunit is used to acquire reference feature information of at least one reference object and the corresponding reference comfort temperature;

[0181] The comparison subunit is used to perform comparison processing on at least one human comfort dimension between the reference feature information and the object feature information.

[0182] The first determining subunit is used to determine, based on the comparison results, a target comfort temperature that matches the target object from the reference comfort temperatures.

[0183] (5) Recommended Unit 305;

[0184] The recommendation unit is used to correct the cooling capacity of the air conditioner based on the target comfort temperature and the target object's historical behavior information of using the air conditioner, and recommend the air conditioner operating parameters corresponding to the corrected cooling capacity to the target object.

[0185] Optionally, in some embodiments of this application, the recommendation unit may include an estimation subunit and a cooling capacity correction subunit, as follows:

[0186] The estimation subunit is used to estimate the temperature distribution information of the target indoor environment during a preset time period under the operation of the air conditioner, based on the cooling capacity.

[0187] The cooling capacity correction subunit is used to correct the cooling capacity of the air conditioner based on the target comfort temperature, the temperature distribution information, and the target object's historical behavior information regarding air conditioning use.

[0188] Optionally, in some embodiments of this application, the recommendation unit may include a second acquisition subunit, a second determination subunit, and a correction subunit, as follows:

[0189] The second acquisition subunit is used to acquire historical behavior information of the target object using the air conditioner, the historical behavior information including the behavior information of the target object in at least one historical usage scenario;

[0190] The second determining subunit is used to determine the target's behavior information under the target's historical usage scenario from the historical behavior information based on the scenario in which the target object is currently using the air conditioner.

[0191] The correction subunit is used to correct the cooling capacity of the air conditioner based on the target comfort temperature and the behavioral information of the target historical usage scenarios.

[0192] As can be seen from the above, this embodiment can improve the acquisition unit 301's ability to acquire temperature change information of the target indoor environment where the air conditioner is located, and select the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information; the estimation unit 302 estimates the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions based on the target space model, the indoor and outdoor temperatures corresponding to the air conditioner, and the set target temperature; the acquisition unit 303 acquires the object feature information of the target object using the air conditioner; the analysis unit 304 performs human comfort analysis on the target object based on the object feature information to obtain the target comfort temperature matching the target object; and the recommendation unit 305 corrects the cooling capacity of the air conditioner based on the target comfort temperature and the target object's historical behavior information when using the air conditioner, and recommends the air conditioner operating parameters corresponding to the corrected cooling capacity to the target object. This application can combine the target space model of the indoor environment where the air conditioner is located, the user's object feature information, and historical behavior information to determine the recommended operating parameters of the air conditioner, which helps to improve the accuracy of the air conditioner operating parameter recommendation, effectively reduces user operations, and enhances user comfort.

[0193] This application also provides an electronic device, such as... Figure 4The diagram shows a structural schematic of an electronic device involved in an embodiment of this application. This electronic device can be a terminal or a server, specifically:

[0194] The electronic device may include components such as a processor 401 with one or more processing cores, a memory 402 with one or more computer-readable storage media, a power supply 403, and an input unit 404. Those skilled in the art will understand that... Figure 4 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0195] The processor 401 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 402, and by calling data stored in the memory 402, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 401 may include one or more processing cores; preferably, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 401.

[0196] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.

[0197] The electronic device also includes a power supply 403 that supplies power to the various components. Preferably, the power supply 403 can be logically connected to the processor 401 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 403 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0198] The electronic device may also include an input unit 404, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0199] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 401 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 402 according to the following instructions, and the processor 401 runs the applications stored in the memory 402 to realize various functions, as follows:

[0200] The system acquires temperature change information of the target indoor environment where the air conditioner is located, and selects a target space model corresponding to the target indoor environment from a preset space model based on the temperature change information. Based on the target space model, the indoor and outdoor temperatures corresponding to the air conditioner, and the set target temperature, it estimates the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions. It collects object feature information of the target object using the air conditioner. Through the object feature information, it performs human comfort analysis on the target object to obtain a target comfort temperature that matches the target object. Based on the target comfort temperature and the target object's historical behavior information when using the air conditioner, it corrects the cooling capacity of the air conditioner and recommends the air conditioner operating parameters corresponding to the corrected cooling capacity to the target object.

[0201] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0202] As can be seen from the above, this embodiment can obtain temperature change information of the target indoor environment where the air conditioner is located, and select the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information; based on the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature, estimate the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions; collect the object feature information of the target object using the air conditioner; perform human comfort analysis on the target object through the object feature information to obtain the target comfort temperature matching the target object; correct the cooling capacity of the air conditioner according to the target comfort temperature and the target object's historical behavior information of using the air conditioner, and recommend the air conditioner operating parameters corresponding to the corrected cooling capacity to the target object. This application can combine the target space model of the indoor environment where the air conditioner is located, the user's object feature information, and historical behavior information to determine the recommended operating parameters of the air conditioner, which helps to improve the accuracy of the air conditioner operating parameter recommendation, effectively reduces user operations, and enhances user comfort.

[0203] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0204] Therefore, embodiments of this application provide a computer-readable storage medium storing a plurality of instructions that can be loaded by a processor to execute steps in any of the air conditioning operating parameter recommendation methods provided in embodiments of this application. For example, the instructions can execute the following steps:

[0205] The system acquires temperature change information of the target indoor environment where the air conditioner is located, and selects a target space model corresponding to the target indoor environment from a preset space model based on the temperature change information. Based on the target space model, the indoor and outdoor temperatures corresponding to the air conditioner, and the set target temperature, it estimates the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions. It collects object feature information of the target object using the air conditioner. Through the object feature information, it performs human comfort analysis on the target object to obtain a target comfort temperature that matches the target object. Based on the target comfort temperature and the target object's historical behavior information when using the air conditioner, it corrects the cooling capacity of the air conditioner and recommends the air conditioner operating parameters corresponding to the corrected cooling capacity to the target object.

[0206] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0207] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0208] Since the instructions stored in the computer-readable storage medium can execute the steps in any of the air conditioner operating parameter recommendation methods provided in the embodiments of this application, the beneficial effects that any of the air conditioner operating parameter recommendation methods provided in the embodiments of this application can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0209] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in various alternative implementations of the above-described air conditioning operating parameter recommendation aspect.

[0210] The above provides a detailed description of an air conditioning operating parameter recommendation method and related equipment provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for recommending air conditioning operating parameters, characterized in that, include: Acquire temperature change information of the target indoor environment where the air conditioner is located, and select the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information; Based on the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature, the cooling capacity of the air conditioner is estimated when the temperature of the target indoor environment meets the preset temperature change conditions. Collect object feature information of the target object using the air conditioner; By using the object feature information, human comfort analysis is performed on the target object to obtain the target comfort temperature that matches the target object; Based on the target comfort temperature and the target user's historical air conditioning usage information, the cooling capacity of the air conditioner is corrected, and the air conditioner operating parameters corresponding to the corrected cooling capacity are recommended to the target user. The step of selecting the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information includes: Obtain at least one preset space model, with different preset space models corresponding to different air conditioning usage scenarios; Based on the current usage scenario of the air conditioner and the air conditioner usage scenario of the at least one preset space model, a candidate space model is selected from the at least one preset space model; Calculate the similarity between the temperature change information of the target indoor environment and the temperature change information corresponding to the candidate spatial model; Based on the similarity, the target spatial model corresponding to the target indoor environment is selected from the candidate spatial models.

2. The method according to claim 1, characterized in that, The step of adjusting the cooling capacity of the air conditioner based on the target comfort temperature and the target user's historical air conditioning usage information includes: Based on the cooling capacity, the temperature distribution information of the target indoor environment under the operation of the air conditioner is estimated within a preset time period; The cooling capacity of the air conditioner is adjusted based on the target comfort temperature, the temperature distribution information, and the target object's historical behavior information regarding air conditioning use.

3. The method according to claim 1, characterized in that, The step of estimating the cooling capacity of the air conditioner when the target indoor temperature meets preset temperature change conditions, based on the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature, includes: Calculate the refrigerant flow rate of the air conditioner based on the compressor suction density, volumetric efficiency, and operating efficiency. Based on the refrigerant flow rate of the air conditioner, the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature, the cooling capacity of the air conditioner is estimated when the temperature of the target indoor environment meets the preset temperature change conditions.

4. The method according to claim 1, characterized in that, The step of performing human comfort analysis on the target object using the object feature information to obtain a target comfort temperature matching the target object includes: Obtain reference characteristic information of at least one reference object and its corresponding reference comfort temperature; The reference feature information and the object feature information are compared in at least one dimension of human comfort. Based on the comparison results, a target comfort temperature matching the target object is determined from the reference comfort temperatures.

5. The method according to claim 1, characterized in that, The step of adjusting the cooling capacity of the air conditioner based on the target comfort temperature and the target user's historical air conditioning usage information includes: Obtain historical behavior information of the target object using air conditioning, the historical behavior information including the behavior information of the target object in at least one historical usage scenario; Based on the current usage scenario of the target object using air conditioning, determine the target's historical usage scenario behavior information from the historical behavior information; The cooling capacity of the air conditioner is adjusted based on the target comfort temperature and the behavioral information of the target historical usage scenarios.

6. An air conditioner operating parameter recommendation device, characterized in that, include: The acquisition unit is used to acquire temperature change information of the target indoor environment where the air conditioner is located, and select the target space model corresponding to the target indoor environment from the preset space model based on the temperature change information. The estimation unit is used to estimate the cooling capacity of the air conditioner when the temperature of the target indoor environment meets the preset temperature change conditions, based on the target space model, the indoor and outdoor ambient temperatures corresponding to the air conditioner, and the set target temperature. The acquisition unit is used to acquire object feature information of the target object using the air conditioner; The analysis unit is used to perform human comfort analysis on the target object using the object feature information, and obtain a target comfort temperature that matches the target object. The recommendation unit is used to correct the cooling capacity of the air conditioner based on the target comfort temperature and the target object's historical behavior information of using the air conditioner, and recommend the air conditioner operating parameters corresponding to the corrected cooling capacity to the target object; The acquisition unit includes a model acquisition subunit, a calculation subunit, and a selection subunit; The model acquisition subunit is used to acquire at least one preset space model, and different preset space models correspond to different air conditioning usage scenarios. Based on the current usage scenario of the air conditioner and the air conditioner usage scenario of the at least one preset space model, a candidate space model is selected from the at least one preset space model; The calculation subunit is used to calculate the similarity between the temperature change information of the target indoor environment and the temperature change information corresponding to the candidate spatial model; The selection sub-unit is used to select the target spatial model corresponding to the target indoor environment from the candidate spatial models based on the similarity.

7. An electronic device, characterized in that, It includes a memory and a processor; the memory stores an application program, and the processor runs the application program in the memory to perform the operations in the air conditioning operating parameter recommendation method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted for loading by a processor to perform the steps in the method for recommending air conditioning operating parameters according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps in the method for recommending air conditioning operating parameters as described in any one of claims 1 to 5.

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