Control method and device for environmental control device, intelligent environmental control system

Abstract

The present invention provides a method and apparatus for controlling an environment adjusting apparatus, and an intelligent environment adjusting system. The present invention makes it possible to accurately reflect an environment condition of an entire space on the basis of environment parameters gathered at limited positions, and to easily acquire accurate environment parameters corresponding to the positions of a plurality of users, thus making it possible to acquire the environment parameter distribution of the entire space at low cost, and to apply the parameter distribution to a number of large spatial regions. In addition, by acquiring, from a condition parameter overall value reflecting the overall condition of a plurality of users, a target parameter distribution for an environment adjusting apparatus using a first predicting model, it becomes possible to accurately adapt the target parameter distribution to the plurality of users as a whole. Thus, each of the plurality of users can be placed in a comfortable condition and, at the same time, the deviation between the actual setting and an ideal setting can be reduced, thereby improving the apparatus performance and user experience.

Description

[Technical field]

[0001] The present invention relates to the field of environmental conditioning devices, and more particularly to a control method and device for an environmental conditioning device, and an intelligent environmental conditioning system. [Background technology]

[0002] With the improvement of economic level, the intelligence of "environmental control device" has become the research direction in the industry. People's demands for indoor living environment are increasing. For example, the demand for air conditioning in large areas and office areas is not limited to simple heating and cooling functions, but also to pursue a more comfortable, healthy, and less fatigued indoor environment. In particular, for office environments, there is a demand for something that can improve work efficiency while maintaining the quality and comfort of the indoor environment with energy saving.

[0003] Desensitization control is a hot technology that has been researched in recent years, which aims to reduce human involvement as much as possible during the operation of an intelligent environmental adjustment system, and allow the system to automatically capture the user's usage habits and preferences, ultimately achieving the goal of integrating people and home systems into one.

[0004] Patent Document 1 discloses an online learning-based non-inductive control method for air conditioning temperature, which constructs a human thermal comfort prediction model for an individual user, obtains an optimal indoor air temperature prediction model by solving the prediction model, uses an online learning method to collect recent historical data recorded by an intelligent environmental control system, continuously fits the prediction model, adjusts model parameters, predicts optimal indoor air temperatures that satisfy the preferences of the individual user at different time periods, and sets the optimal indoor air temperature as a set value for the air conditioning temperature to realize non-inductive control of the air conditioning temperature, thereby intelligently controlling the indoor air temperature and meeting the user's needs for optimal comfort. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] CN111486554A

[0006] It should be noted that the above technical background introduction is only provided to facilitate a clear and complete description of the technical solutions of the present invention, and to be easily understood by those skilled in the art, and it is not considered that the above technical solutions are known to those skilled in the art just because they are mentioned in the background of the present invention. Summary of the Invention

[0007] However, the inventors discovered that the conventional technique disclosed in Patent Document 1 has the following problems.

[0008] This method can only satisfy the preferences of individual users within a small spatial area, but cannot ensure the comfort of multiple users in a large area with many users and heavy foot traffic.

[0009] The optimal indoor temperature value output by this method is the final target value and belongs to the idealized indoor temperature value. However, in actual adjustment, due to factors such as the characteristics of the air conditioning and architecture, there will be a difference between the actual indoor temperature and the idealized temperature. Therefore, if the idealized indoor temperature value is used as the control value, it will result in a decrease in comfort.

[0010] When the indoor space is large, air conditioning control is performed based only on data collected by a single temperature and humidity sensor or sensors with uneven distribution, which makes it difficult to accurately reflect the temperature and humidity distribution conditions throughout the space, making it difficult for users throughout the room to be comfortable.

[0011] The actual temperature distribution of the indoor temperature is non-uniform, and without a comprehensive analysis of the accurate indoor temperature distribution, the comfort level regulated by the corresponding air conditioning will be inaccurate.

[0012] The distribution of the human surrounding temperature and the indoor temperature both change at different times each day, and the user cannot be in an optimal state in all weather conditions.

[0013] In order to solve at least one of the above problems, the embodiment of the present invention provides a control method and device for an environmental adjustment device, an intelligent environmental adjustment system, and determines an environmental parameter distribution model based on multiple environmental parameters at different positions. The embodiment of the present invention determines environmental parameters corresponding to the positions of multiple users based on the environmental parameter distribution model, accurately reflects the environmental state of the entire space based on the environmental parameters collected at limited positions, and can easily obtain accurate environmental parameters corresponding to the positions of multiple users, thereby obtaining the environmental parameter distribution of the entire space at a low cost. The embodiment of the present invention can be applied to many large spatial areas such as schools, office places, department stores, schools, exhibition areas or multi-function halls, and can also be applied to home use. The embodiment of the present invention calculates state parameters of multiple users respectively based on accurate environmental parameters corresponding to the positions of multiple users, and determines a user's overall state parameter value based on the state parameters of the multiple users, so that the overall state parameter value reflects the overall state of the multiple users. The embodiment of the present invention obtains a target parameter distribution of the environmental adjustment device by a first prediction model from the overall state parameter value reflecting the overall state of the multiple users, thereby enabling the target parameter distribution to be accurately adapted to the entirety of the multiple users. An embodiment of the present invention controls a device based on the target parameter distribution to provide a comfortable experience for multiple users, thereby improving device performance and user experience.

[0014] In a first aspect of an embodiment of the present invention, a method for controlling an environmental conditioning device is provided, the method including: acquiring a plurality of environmental parameters at different positions in a room; determining an environmental parameter distribution model based on the plurality of environmental parameters at the different positions; determining a plurality of environmental parameters corresponding to locations of a plurality of users based on the environmental parameter distribution model; calculating state parameters of the plurality of users based on the plurality of environmental parameters corresponding to the locations of the plurality of users, respectively, and determining a total state parameter value of the users based on the state parameters of the plurality of users; inputting the total state parameter value into a first prediction model; and outputting a target parameter distribution of the environmental conditioning device.

[0015] In a second aspect of the embodiment of the present invention, there is provided a control device for an environmental conditioning device, including an acquisition unit for acquiring multiple environmental parameters at different positions in a room, a first determination unit for determining an environmental parameter distribution model based on the multiple environmental parameters at different positions, a second determination unit for determining multiple environmental parameters corresponding to multiple users' locations based on the environmental parameter distribution model, a third determination unit for calculating state parameters of the multiple users based on the multiple environmental parameters corresponding to the multiple users' locations and determining a total state parameter value of the users based on the state parameters of the multiple users, and a fourth determination unit for inputting the total state parameter value into a first prediction model and outputting a target parameter distribution of the environmental conditioning device.

[0016] In a third aspect of the present invention, an intelligent environment adjustment system is provided, which includes an environment adjustment device for adjusting an indoor environment, and an environment adjustment device control device described in the second aspect of the present invention for controlling the environment adjustment device.

[0017] One of the beneficial effects of the embodiment of the present invention is as follows. An environmental parameter distribution model is determined according to multiple environmental parameters at different positions, and environmental parameters corresponding to the positions of multiple users are determined according to the environmental parameter distribution model, so that the environmental state of the entire space can be accurately reflected based on the environmental parameters collected at limited positions, and accurate environmental parameters corresponding to the positions of multiple users can be easily obtained, thereby obtaining the environmental parameter distribution of the entire space at a low cost, which can be applied to many large spatial areas such as schools, office places, department stores, schools, exhibition areas or multi-function halls, and can also be applied to home use. Then, state parameters of the multiple users are calculated based on accurate environmental parameters corresponding to the positions of the multiple users, and a total state parameter value of the users is determined based on the state parameters of the multiple users, so that the total state parameter value reflects the overall state of the multiple users. Furthermore, by obtaining a target parameter distribution of the environmental conditioning device from the overall state parameter values ​​reflecting the overall state of the multiple users by the first prediction model, the target parameter distribution can be accurately adapted to the multiple users as a whole. By controlling the device based on the target parameter distribution, the multiple users can all be in a comfortable state, while reducing the deviation between the actual setting and the ideal setting, thereby improving the performance of the device and the user experience.

[0018] With reference to the following description and drawings, specific embodiments of the present invention are disclosed in detail, and the manner in which the principles of the present invention may be employed is clearly shown. It should be understood that the embodiments of the present invention are not limited in scope. Numerous changes, modifications and equivalents are encompassed within the spirit and terms of the appended claims.

[0019] Characteristic information described or shown in one embodiment may be used in the same or similar manner in one or more other embodiments, or may be combined with characteristic information in the other embodiments, or may replace characteristic information in the other embodiments.

[0020] It should be emphasized that the term "comprising" as used in this specification refers to the presence of a feature, element, step or assembly, but does not exclude the presence or addition of one or more other features, elements, steps or assemblies. [Brief description of the drawings]

[0021] Many aspects of the present invention can be better understood with reference to the following drawings. The elements in the drawings are not drawn to scale, but are merely for illustrating the principles of the invention. Corresponding parts in the drawings may be enlarged or reduced in size to facilitate illustrating and explaining the various portions of the invention. Elements and feature information shown in one drawing or one embodiment of the present invention may be combined with elements and feature information shown in one or more other drawings or embodiments. Also, in the drawings, like reference numerals may be used to show corresponding parts in the several drawings and to indicate corresponding parts used in one or more embodiments. In the drawings: [Figure 1] 4 is a flowchart of a control method for the environment conditioning device according to the first embodiment of the present invention. [Diagram 2] 2 is a flowchart of a method for implementing step 101 according to the first embodiment of the present invention; [Diagram 3] 4 is another flowchart of the method for implementing step 101 according to the first embodiment of the present invention. [Figure 4] 4 is a flowchart of a method for implementing step 102 according to the first embodiment of the present invention. [Diagram 5] 4 is a flowchart of a method for implementing step 103 according to the first embodiment of the present invention. [Figure 6] 4 is a flowchart of a method for implementing step 104 according to the first embodiment of the present invention. [Figure 7] 10 is a flowchart of another method for implementing step 106 according to the first embodiment of the present invention. [Figure 8]3 is a schematic diagram of a data processing process of the control method for the environment conditioning device according to the first embodiment of the present invention. FIG. [Figure 9] 3 is a flowchart of an air conditioning control method according to the first embodiment of the present invention. [Figure 10] 5 is another flowchart of the control method of the environment conditioning device according to the first embodiment of the present invention. [Figure 11] 4 is a schematic diagram of a correspondence relationship between the total state parameter value and a target parameter distribution according to the first embodiment of the present invention. FIG. [Figure 12] FIG. 11 is a schematic diagram of a control device of an environment conditioning device according to a second embodiment of the present invention. [Figure 13] FIG. 4 is a structural diagram of an intelligent environment adjusting system according to a third embodiment of the present invention; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings.

[0023] Example 1 A first embodiment of the present invention provides a method for controlling an environment conditioning device. FIG. 1 is a flowchart of a method for controlling an environment conditioning device according to a first embodiment of the present invention. As shown in FIG. 1, the method includes: Step 101 of acquiring a plurality of environmental parameters at different positions in a room; determining 102 an environmental parameter distribution model based on the plurality of environmental parameters at different locations; determining 103 a plurality of environmental parameters corresponding to the locations of a plurality of users based on the environmental parameter distribution model; Calculating state parameters of a plurality of users based on a plurality of environmental parameters corresponding to the locations of the plurality of users, respectively, and determining a total value of the state parameters of the users based on the plurality of state parameters of the users; Step 105: inputting the total state parameter value into a first prediction model and outputting a target parameter distribution of the environmental conditioning device; Includes.

[0024] In step 105, after obtaining the target parameter distribution of the environmental conditioning device, the environmental conditioning device can be controlled based on the target parameter distribution of the environmental conditioning device.

[0025] In this way, an environmental parameter distribution model is determined based on multiple environmental parameters at different positions, and environmental parameters corresponding to the positions of multiple users are determined based on the environmental parameter distribution model, and the environmental state of the entire space is accurately reflected based on the environmental parameters collected at limited positions, and accurate environmental parameters corresponding to the positions of multiple users can be easily obtained. This allows the environmental parameter distribution of the entire space to be obtained at a low cost, and can be applied to many large spatial areas such as schools, office places, department stores, schools, exhibition areas, or multi-function halls, and can also be applied to home use. The state parameters of multiple users are calculated based on the accurate environmental parameters corresponding to the positions of multiple users, and the overall state parameter value of the user is determined based on the state parameters of the multiple users, so that the overall state parameter value reflects the overall state of the multiple users. The target parameter distribution of the environmental adjustment device is obtained from the overall state parameter value reflecting the overall state of the multiple users through the first prediction model, making it possible to accurately adapt the target parameter distribution to the entire multiple users. By controlling the device based on the target parameter distribution, all of the multiple users can be in a comfortable state, thereby improving the performance of the device and the user experience.

[0026] In the embodiments of the present invention, the environmental conditioning device may be various types of environmental conditioning devices, such as at least one of an air conditioner, an air purifier, a fresh air device, a humidifier, a disinfection device, a lighting device, and an audio device.

[0027] In an embodiment of the present invention, the environmental conditioning device may be used for home, commercial or public purposes.

[0028] For example, the environmental conditioning device may be used in a home environment, a commercial environment such as an office, an office building, a department store, or a public environment such as a school.

[0029] In the embodiment of the present invention, an air conditioner is taken as an example for illustrative purposes, which may be a separate type or multi-type air conditioner, or may be a central air conditioner system.

[0030] In an embodiment of the present invention, the environmental parameters may be various parameters that characterize the indoor environment, for example, the environmental parameters include at least one of temperature, humidity, wind direction, wind volume, sound volume, sound frequency, brightness, color temperature, and air quality, where the air quality includes, for example, oxygen content, PM2.5 content, PM10 content, and toxic gas (e.g., VOC) content.

[0031] The environmental parameters may further include physiological parameters of the user, such as perceived temperature, heart rate, etc. For example, if the perceived temperature of the user is higher than the normal value, a warning or early warning is issued.

[0032] In step 101, acquiring a plurality of environmental parameters at different positions in a room may be performed by a plurality of sensors, or may be performed by a single movable sensor.

[0033] For example, the multiple environmental parameters at different locations are obtained from multiple sensors distributed at different locations in a room, which may be randomly distributed in the room, or the multiple environmental parameters at different locations are obtained from a sensor that is movable in the room.

[0034] In an embodiment of the present invention, one kind of environmental parameter can be acquired, or multiple kinds of environmental parameters can be acquired, for example, multiple temperatures and humidity at different positions in a room for an air conditioner.

[0035] If multiple kinds of environmental parameters need to be obtained, sensors for collecting corresponding parameters can be provided, for example, multiple temperature sensors and multiple humidity sensors are placed at different positions in the room.

[0036] In an embodiment of the present invention, the environmental parameters that need to be obtained relate to parameters that need to be set or adjusted for an environmental conditioning device, for example, if the temperature and humidity of an air conditioning device needs to be set, then the temperature and humidity can be collected.

[0037] The determination of which parameters the environmental conditioning device needs to set or adjust can be determined according to the object of adjustment. For example, if the main focus is on human comfort, the air conditioning device will mainly set the temperature, and if the main focus is on human fatigue, the air conditioning device will mainly set the humidity.

[0038] For example, in the case of a central air conditioning system, the refrigerant system may be a system in which one outdoor unit is connected to one indoor unit, or a system in which one outdoor unit is connected to multiple indoor units, or a system in which multiple outdoor units are connected to multiple indoor units, in which case the temperature, humidity, air volume and air speed can be adjusted separately or preferentially adjusted; the water system may be a system in which one outdoor unit is connected to multiple indoor unit vents, in which case the total temperature and the air volume of each vent can be adjusted preferentially without adjusting the humidity.

[0039] In the embodiment of the present invention, the data collected by the sensor can be collected in various ways, the following takes temperature and humidity as an example.

[0040] FIG. 2 is a flowchart of a method for implementing step 101 according to the first embodiment of the present invention. As shown in FIG. 2, the method includes: A sensor acquires its own position data, temperature data and humidity data in a spatial domain, step 201; Step 202, in which the sensor transmits the acquired position data, temperature data, and humidity data to a data management center; Step 203: acquiring location data, temperature data, and humidity data from a data management center; Includes.

[0041] FIG. 3 is another flowchart of the method for implementing step 101 according to the first embodiment of the present invention. As shown in FIG. 3, the method includes: Step 301, in which a sensor is connected to a location base station network in an area in the form of Bluetooth communication (Bluetooth is a registered trademark); Step 302, in which the sensor periodically transmits temperature, humidity, and location information to the gateway; Step 303, in which the gateway transmits the collected data to the IOT cloud via HTTP protocol or MQTT protocol; Step 304: acquiring the data from the IOT cloud via HTTP or MQTT; Includes.

[0042] As shown in FIG. 1, after obtaining a plurality of environmental parameters at different locations, in step 102, an environmental parameter distribution model is determined based on the plurality of environmental parameters at different locations.

[0043] FIG. 4 is a flowchart of a method for implementing step 102 according to the first embodiment of the present invention. As shown in FIG. 4, the method includes: Establishing a machine learning model, step 401; inputting the plurality of environmental parameters and location data corresponding to the plurality of environmental parameters into the machine learning model to train it, step 402; Once the training is completed, the environmental parameter distribution model is obtained, step 403; Includes.

[0044] In embodiments of the present invention, the machine learning model may be various types of machine learning models, for example, a support vector machine (SVM) regression model, or a model based on a neural network.

[0045] The process of performing training using the data collected in step 402 is also a fitting process.

[0046] In an embodiment of the present invention, different environmental parameter distribution models can be obtained according to different types of environmental parameters, and corresponding to the obtained environmental parameters, the environmental parameter distribution models can include at least one of a temperature distribution model, a humidity distribution model, a wind direction distribution model, a wind volume distribution model, a sound volume distribution model, a sound frequency distribution model, a luminance distribution model, a color temperature distribution model and an air quality distribution model.

[0047] For example, if the environmental parameters include temperature and humidity, a temperature distribution model and a humidity distribution model are obtained.

[0048] As shown in Fig. 1, after determining the environmental parameter distribution model, in step 103, a plurality of environmental parameters corresponding to the locations of a plurality of users are determined based on the environmental parameter distribution model. Fig. 5 is a flowchart of a method for implementing step 103 according to the first embodiment of the present invention. As shown in Fig. 5, the method includes: Identifying the locations of multiple users in a room, step 501; Step 502: inputting the locations of the users into the environmental parameter distribution model, and outputting a plurality of environmental parameters corresponding to the locations of the users; Includes.

[0049] In embodiments of the present invention, multiple users within a room can be located in a variety of ways.

[0050] For example, the locations of multiple users in a room can be identified by detecting images captured by an indoor camera, or the locations of multiple users in a room can be identified by transmitting ultrasonic signals, radar signals or infrared signals into the room, or the locations of multiple users in a room can be identified by detecting wireless signals transmitted from terminal devices carried by multiple users in the room.

[0051] The process of identifying the user's location using the above method may be referred to the related prior art, and will not be described in detail here.

[0052] In an embodiment of the present invention, the positions of the users may be two-dimensional positions not including height or three-dimensional positions including height. For example, the positions of the users are expressed by two-dimensional coordinates f(x, y) or three-dimensional coordinates f(x, y, z).

[0053] When the positions of multiple users are three-dimensional positions including height, different heights due to different heights or sitting postures of users are taken into consideration. For example, it is possible to not only ensure the comfort of the user's body parts, but also ensure that the user's head is in a comfortable state, maximizing the user's comfort, improving the user's overall comfort in the area, and reducing fatigue. The height may be set to a fixed value by a user command. For example, the height is 1.5 meters, that is, it is only necessary to obtain temperature data and humidity data of the user's location on a horizontal plane at a height of 1.5 meters.

[0054] In an embodiment of the present invention, the locations of the users can include the locations of the users themselves and the locations around the users, so that the data of the environmental parameters obtained based on the location data includes the locations around the users, and the comfort of the user after the control can be further improved.

[0055] In step 502, the positions of the multiple users are input into the environmental parameter distribution model, and multiple environmental parameters corresponding to the positions of the multiple users are output; that is, since there is a specific correspondence between the positions and the environmental parameters in the environmental parameter distribution model, multiple environmental parameters corresponding to the positions of the multiple users can be obtained based on the positions of the multiple users.

[0056] As shown in FIG. 1, after a plurality of environmental parameters corresponding to the locations of a plurality of users, in step 104, state parameters of the plurality of users are calculated respectively based on the plurality of environmental parameters corresponding to the locations of the plurality of users, and a total value of the state parameters of the users is determined based on the plurality of state parameters of the plurality of users.

[0057] In an embodiment of the present invention, the user's state parameter may be various parameters for expressing the user's state, for example, a comfort level and a fatigue level.

[0058] Hereinafter, calculation of the comfort level and fatigue level of the user based on the temperature and humidity will be described as an example.

[0059] FIG. 6 is a flowchart of a method for implementing step 104 according to the first embodiment of the present invention. As shown in FIG. 6, the method includes: Step 601: calculating comfort levels and / or fatigue levels of a plurality of users based on temperatures and humidity corresponding to the locations of the users; Calculating a user overall comfort level and / or overall fatigue level based on the comfort levels and fatigue levels of the plurality of users (step 602); Includes.

[0060] In an embodiment of the present invention, calculating the comfort level and / or fatigue level of a user based on temperature and humidity can be obtained by a model.

[0061] In step 601, for example, a multi-layer perceptron regression model is trained based on the training data of temperature and humidity, and the output is the comfort level and / or fatigue level of the user. In addition, the model can be further trained based on the gender of the user, and the model can be applied to obtain the comfort level and / or fatigue level of users of different genders.

[0062] In step 602, for example, the sum, average, variance or standard deviation of the comfort levels of the multiple users is calculated, and / or the sum, average, variance or standard deviation of the fatigue levels of the multiple users is calculated to obtain an overall comfort level and / or an overall fatigue level of the users.

[0063] In some embodiments of the present invention, the calculated overall comfort value and / or overall fatigue value may be selected based on a desired outcome.

[0064] For example, if what is desired is the overall comfort and fatigue levels in the entire region, the calculation method for the overall value is to add up the comfort and fatigue levels of all users, respectively. A small overall value indicates that the comfort and fatigue levels of all users in the entire region are generally small, indicating high comfort and low fatigue, and a large overall value indicates that the comfort and fatigue levels of all users in the entire region are generally large, indicating low comfort and high fatigue.

[0065] For example, the comfort levels and fatigue levels of all users can be added together based on the following formulas (1) and (2) to obtain an overall comfort level value and an overall fatigue level value. TIFF0007648943000001.tif30142(1) TIFF0007648943000002.tif28136(2)

[0066] Here, TIFF0007648943000003.tif1322 indicates that the total comfort level is the sum of the comfort levels of each user, TIFF0007648943000004.tif1233 shows the comfort level of each user, TIFF0007648943000005.tif1319 indicates that the total fatigue level is the sum of the fatigue levels of each user. TIFF0007648943000006.tif1242 indicates the fatigue level of each user, and N is the number of users.

[0067] For example, if what is desired is the balance of comfort and fatigue levels in the entire region, the method of calculating the overall value is to calculate the average value of the comfort and fatigue levels of all users. If the overall value is small, that is, the comfort and fatigue levels of all users in the entire region are all in a small balance, and the influence of particularly small data is not eliminated. If the overall value is large, that is, the comfort and fatigue levels of all users in the entire region are all in a large balance, and the influence of particularly large data is not eliminated.

[0068] For example, the average values ​​of the comfort levels and fatigue levels of all users can be calculated based on the following equations (3) and (4) to obtain the overall comfort level and overall fatigue level. TIFF0007648943000007.tif25109(3) TIFF0007648943000008.tif27116(4)

[0069] Here, TIFF0007648943000009.tif1229 indicates that the overall comfort level is the average of the comfort levels of each user. TIFF0007648943000010.tif1134 shows the comfort level of each user, TIFF0007648943000011.tif1229 indicates that the overall fatigue level is the average fatigue level of each user. TIFF0007648943000012.tif1141 indicates the fatigue level of each user, and N is the number of users.

[0070] For example, if what is desired is the variance of the comfort and fatigue levels of each user in the region, i.e., the difference between the comfort and fatigue levels of each user, the method for calculating the overall value is to find the variance or standard deviation for the comfort and fatigue levels of all users, respectively. If the overall value is small, that is, the difference between the comfort and fatigue levels of each user in the region is not large. If the overall value is large, that is, the comfort and fatigue levels of each user in the region are significantly different.

[0071] For example, the variances of the comfort levels and fatigue levels of all users can be calculated based on the following equations (5) and (6) to obtain the overall comfort level value and the overall fatigue level value. TIFF0007648943000013.tif29152(5) TIFF0007648943000014.tif26153(6)

[0072] Here, TIFF0007648943000015.tif1123 shows that the overall comfort level is the variance of each user's comfort level. TIFF0007648943000016.tif932 shows the comfort level of each user. TIFF0007648943000017.tif1120 shows that the total fatigue level is the variance of the fatigue levels of each user. TIFF0007648943000018.tif941 shows the fatigue level of each user. TIFF0007648943000019.tif1130 shows the average comfort level for each user. TIFF0007648943000020.tif1028 shows the average fatigue level of each user. N is the number of users.

[0073] In an embodiment of the present invention, as shown in FIG. 6, the method includes: Step 603: comparing the calculated overall comfort level and / or overall fatigue level of the user with a preset optimal comfort level and / or optimal fatigue level to determine whether the calculated overall comfort level and / or overall fatigue level of the user is equal to or lower than the preset optimal comfort level and / or optimal fatigue level, in other words, determining whether the calculated overall value is equal to or lower than the preset optimal value; if the determination result is "yes", proceed to step 604; if the determination result is "no", return to step 601 and perform processing based on the temperature and humidity corresponding to the locations of the multiple users at the next time; Step 604: setting the calculated overall comfort level and / or overall fatigue level of the user as an overall state parameter value of the user; It may further include:

[0074] That is, steps 101 to 104 can be repeated at a constant cycle to obtain the optimal total state parameter value.

[0075] In this way, since environmental parameters (e.g., temperature and humidity) are constantly changing, periodic detection by the sensor allows the user to set a detection cycle to collect data and perform processing based on the collected data, which not only saves energy but also ensures that the user is in an optimal user condition in all weather conditions.

[0076] As shown in FIG. 1, after determining the overall state parameter value of the user, in step 105, the overall state parameter value is input into a first prediction model, and a target parameter distribution of the environmental conditioning device is output.

[0077] In an embodiment of the present invention, the target parameter distribution is a distribution of target values ​​of the setting parameters of the environmental conditioning devices in the room space, the parameters corresponding to the collected environmental parameters, and the target values ​​are idealized setting values, i.e., the target parameters obtained by the embodiment of the present invention are not a single target parameter, but target parameters related to different positions in the room space, i.e., target parameters representing the entire room.

[0078] For example, if the collected environmental parameters include temperature and humidity, the output target parameter distribution includes a target temperature distribution, which is the distribution of target values ​​of temperature in the indoor space, and a target humidity distribution, which is the distribution of target values ​​of humidity in the indoor space.

[0079] For example, if the collected environmental parameters include luminance, the output target parameter distribution includes a target luminance distribution, which is a distribution of target values ​​of luminance in an indoor space.

[0080] In an embodiment of the present invention, when the target parameter distribution includes multiple types of target parameter distributions, the multiple types of target parameter distributions can be obtained by one first prediction model trained with multiple types of target parameters. Or, multiple first prediction models can be trained respectively based on the types of target parameters. Each first prediction model corresponds to one type of target parameter and outputs one type of target parameter distribution.

[0081] In an embodiment of the present invention, the first prediction model may be a trained multi-layer perceptron model or a deep neural network. The training data includes, for example, historical data of the user's overall state parameter values.

[0082] In some embodiments of the present invention, the environmental conditioning device can be controlled based on the target parameter distribution, for example by directly configuring the environmental conditioning device based on the target parameter distribution.

[0083] Alternatively, the target parameter distribution can be further processed to further improve the accuracy of the set points.

[0084] As shown in FIG. 1, the method includes: inputting the target parameter distribution into a second predictive model and outputting parameter settings for the environmental conditioning device, step 106; Step 107: controlling the environmental conditioning device based on the parameter settings of the environmental conditioning device; It may further include:

[0085] In this way, the target parameter distribution is further input into a second predictive model to obtain parameter settings to control the device, taking into account the characteristics of the device itself and other factors, and the parameter settings are more suitable for the actual environment, thereby further improving device performance and user experience.

[0086] As shown in FIG. 1, after outputting the target parameter distribution for the environmental conditioning device, in step 106, the target parameter distribution is input into a second predictive model to output parameter settings for the environmental conditioning device.

[0087] In the embodiment of the present invention, the target parameter distribution is the ideal setting value of the environmental conditioning device, but due to the actual factors of the application scene of some environmental conditioning devices, such as the building, room type and the design of the environmental conditioning device, the environmental conditioning device cannot quickly or accurately reach the state of the target parameter distribution. On the other hand, in the embodiment of the present invention, the actual parameter setting value of the environmental conditioning device, which is the parameter setting value of the environmental conditioning device, can be determined based on the target parameter distribution and these actual factors. In this way, by taking into account the characteristics of the device itself and other factors, the parameter setting value is more suitable for the actual environment, allowing the environment to quickly reach the parameter setting value, thereby improving the device performance and user experience.

[0088] In an embodiment of the present invention, the parameter set value corresponds to a target parameter distribution, for example, when the target parameter distribution includes a target temperature distribution and a target humidity distribution, the parameter set value output by the second predictive model includes a temperature set value and a humidity set value.

[0089] In an embodiment of the present invention, when the target parameter distribution includes multiple types of target parameter distributions, multiple types of parameter setting values ​​can be obtained by one second prediction model trained with the multiple types of target parameter distributions. Alternatively, multiple second prediction models can be trained respectively based on the types of target parameter distributions. Each second prediction model corresponds to one type of target parameter distribution and outputs one type of parameter setting value.

[0090] In an embodiment of the present invention, the second predictive model may be a trained multi-layer perceptron model or a deep neural network, and the training data includes, for example, historical data of target parameter distribution and actual factors of the application scene of the environmental conditioning device.

[0091] In another method for implementing step 106, in addition to inputting the target parameter distribution into the second predictive model, a time-related environmental parameter can also be input into the second predictive model as input data.

[0092] 7 is a flowchart of another method for implementing step 106 according to the first embodiment of the present invention. As shown in FIG. 7, the method includes: Step 701: obtaining a sequence of time-related environmental parameter distributions based on an output of an environmental parameter distribution model within a certain period of time prior to a current first time; inputting the sequence of time-related environmental parameter distributions into a third predictive model to obtain an environmental parameter distribution for a second time after the certain period of time, step 702; inputting the environmental parameter distribution at the second time and the target parameter distribution into a second prediction model, and outputting a parameter setting value of the environmental conditioning device; and Includes.

[0093] When encountering a certain sudden situation, the actual indoor environmental parameter distribution is changed, which affects the actual adjustment by the environmental adjustment device and degrades the user experience. For example, sudden situations such as wind starting to blow outside or air conditioning breaking down change the actual indoor temperature distribution, affecting the actual adjustment by the air conditioning, further affecting the accuracy of the comfort level and degrading the user experience. By obtaining spatial data of the indoor distribution at a later time using the third prediction model, in this way, when the actual temperature setting value is further calculated based on the temperature distribution at the second time when the prediction is completed and the target temperature distribution, a setting value closest to the actual indoor temperature distribution can be calculated. By adjusting the air conditioning with such a setting value, the adjustment result by the air conditioning can be closer to the actual one, further improving the adjustment accuracy and improving the user experience.

[0094] In an embodiment of the present invention, the output of the environmental parameter distribution model is not only used to determine a number of environmental parameters corresponding to the locations of a number of users in step 103, but is also used to obtain a sequence of time-related environmental parameter distributions in step 701.

[0095] When the environmental parameters include different types of environmental parameters, a sequence of the distributions of the different types of environmental parameters is obtained respectively.

[0096] For example, if the environmental parameters include temperature and humidity, in step 701, a sequence of temperature distribution with respect to time and a sequence of humidity distribution with respect to time are obtained, respectively.

[0097] In step 702, the sequence of time-related environmental parameter distributions is input into a third prediction model to obtain the environmental parameter distribution at a second time after the certain period of time, i.e., to predict the future environmental parameter distribution.

[0098] In an embodiment of the present invention, the third prediction model may be a trained deep neural network including a long short-term memory (LSTM) structure or a gated recurrent unit (GRU) structure. The training data includes, for example, historical data of environmental parameters acquired at the same interval time point.

[0099] As shown in FIG. 1, after the parameter settings of the environmental conditioning device are obtained, in step 107, the environmental conditioning device is controlled based on the parameter settings of the environmental conditioning device.

[0100] For example, after obtaining the temperature setting value of an air conditioner, the temperature setting value of the air conditioner is adjusted to the obtained temperature setting value.

[0101] FIG. 8 is a schematic diagram of the data processing process of the control method of the environmental conditioning device according to the first embodiment of the present invention. As shown in FIG. 8, at least one sensor 801 collects a plurality of environmental parameters at different positions in a room and provides them to a machine learning model 802. After fitting, an environmental parameter distribution model 803 is obtained. Based on the positions of the plurality of users, the environmental parameter distribution model 803 outputs a plurality of environmental parameters corresponding to the positions of the plurality of users to a state parameter calculation module 804. The state parameter calculation module 804 outputs a state parameter total value of the user. The state parameter total value is input to a first prediction model 805, and a target parameter distribution of the environmental conditioning device is output. In addition, the environmental parameter distribution model 803 further outputs a sequence of environmental parameter distributions related to times within a certain period of time to a third prediction model 806. The third prediction model 806 outputs the environmental parameter distribution of a second time after the certain period of time. The target parameter distribution output from the first predictive model 805 and the environmental parameter distribution for the second time output from the third predictive model 806 are both input to a second predictive model 807 to obtain the parameter setting values ​​of the environmental conditioning device.

[0102] Below, an illustrative explanation will be given using air conditioning as an example.

[0103] FIG. 9 is a flowchart of an air conditioning control method according to the first embodiment of the present invention. As shown in FIG. 9, the method includes the following steps: Step 901: acquiring multiple temperatures and humidity values ​​at different positions in a room; determining a temperature distribution model and a humidity distribution model based on a plurality of temperatures and humidity at different locations, step 902; Step 903: obtaining positions f(x,y,z) of multiple users; determining a plurality of temperatures and humidity corresponding to a plurality of user locations f(x,y,z) based on the temperature distribution model and the humidity distribution model, step 904; Calculating comfort levels and fatigue levels of a plurality of users based on a plurality of temperatures and humidities corresponding to the locations f(x, y, z) of the plurality of users, and calculating a total comfort level value and a total fatigue level value of the users based on the comfort levels and fatigue levels of the plurality of users in step 905; Step 906 is to determine whether the calculated overall comfort level and overall fatigue level are equal to or lower than a preset optimum comfort level and optimum fatigue level, in other words, whether the calculated overall values ​​are equal to or lower than a preset optimum value; if the result of the determination is "yes", the process proceeds to step 907; if the result of the determination is "no", the process returns to step 901 and processes based on the temperature and humidity at the next time; Step 907: setting the calculated overall comfort level and overall fatigue level of the user as the overall comfort level and overall fatigue level of the user; Step 908: inputting the overall comfort level and the overall fatigue level into a first prediction model, and outputting a target temperature distribution and a target humidity distribution for air conditioning; After step 902, a step 909 is performed to obtain a sequence of temperature distribution and a sequence of humidity distribution related to time based on the output of the temperature distribution model and the humidity distribution model within a certain period before a current first time. inputting the sequence of temperature distribution and the sequence of humidity distribution related to time into a third prediction model to obtain a temperature distribution and a humidity distribution for a second time after the certain period of time, step 910; Step 911: inputting the target temperature distribution, the target humidity distribution, and the temperature distribution and humidity distribution for a second time into a second prediction model, and outputting the temperature setting value and the humidity setting value of the air conditioning; Controlling the air conditioning based on the temperature and humidity setpoints of the air conditioning, step 912; Includes.

[0104] In the embodiment of the present invention, there is no restriction on the order of execution between steps 909, 910 and steps 903 to 908, and steps 909, 910 and steps 903 to 908 may be executed in parallel independently of each other, or may be executed sequentially.

[0105] In the above embodiment, the target parameter distribution of the environmental conditioning device is obtained by inputting the entire state parameter value into the first prediction model, but the present invention is not limited to the method of obtaining the target parameter distribution of the environmental conditioning device using a prediction model.

[0106] For example, the target parameter distribution of the environmental conditioning device can be determined further by a look-up table method.

[0107] FIG. 10 is another flowchart of the control method of the environment conditioning device according to the first embodiment of the present invention. As shown in FIG. 10, the method includes the following steps: Acquiring a plurality of environmental parameters at different positions in a room, step 1001; determining an environmental parameter distribution model based on the plurality of environmental parameters at different locations, step 1002; determining a plurality of environmental parameters corresponding to the locations of a plurality of users based on the environmental parameter distribution model, step 1003; Calculating state parameters of a plurality of users based on a plurality of environmental parameters corresponding to the locations of the plurality of users, respectively, and determining a total value of the state parameters of the users based on the plurality of state parameters of the users in step 1004; determining a target parameter distribution for the environmental conditioning device corresponding to the overall state parameter value using a look-up table method, step 1005; Includes.

[0108] The contents of steps 1001 to 1004 are the same as steps 101 to 104, and therefore will not be described repeatedly here.

[0109] In step 1005, by replacing the method of using the first prediction model in step 105 with a look-up table method, the target parameter distribution of the environmental conditioning device can be determined in a simple manner.

[0110] In an embodiment of the present invention, a correspondence table between the overall state parameter value and the target parameter distribution can be established in advance. For example, the overall state parameter value can include at least one of the overall comfort value and the overall fatigue value. A target parameter distribution table corresponding to the overall comfort value can be established, a target parameter distribution table corresponding to the overall fatigue value can be established, or a target parameter distribution table corresponding to both the overall comfort value and the overall fatigue value can be established. The target parameter distribution can include a temperature distribution and / or a humidity distribution.

[0111] Fig. 11 is a schematic diagram of the correspondence relationship between the total state parameter value and the target parameter distribution according to the first embodiment of the present invention. As shown in Fig. 11, for example, for the total comfort value, within a room temperature range of 26 to 30°C, discomfort can be reduced by lowering the humidity, and for the total fatigue value, fatigue is clearly reduced at 28°C and a humidity of 40%, and fatigue also tends to be reduced at a humidity of 55% or less. That is, within a certain temperature range, a high total comfort value corresponds to a low humidity, and a low total fatigue value corresponds to a high humidity.

[0112] Moreover, what is shown in FIG. 11 is merely an example, and does not restrict the correspondence relationship between the total state parameter values ​​and the target parameter distribution.

[0113] Also, as shown in FIG. 10, the method includes: determining parameter settings of the environmental conditioning devices corresponding to the target parameter distribution using a look-up table method, step 1006; controlling the environmental conditioning device based on the parameter settings of the environmental conditioning device, step 1007; Further includes:

[0114] In step 1006, by replacing the use of the second predictive model in step 106 with a look-up table method, parameter settings of the environmental conditioning devices can be determined in a simplified manner.

[0115] That is, a correspondence table between target parameter distributions and parameter setting values ​​can be established in advance. The target parameter distributions can include temperature distributions and / or humidity distributions. The parameter setting values ​​can include temperature setting values ​​and / or humidity setting values.

[0116] Steps 1006 and 1007 are optional steps. Instead of steps 1006 and 1007, steps 105 and 106 may be used.

[0117] As can be seen from the above embodiment, an environmental parameter distribution model is determined based on multiple environmental parameters at different positions, and environmental parameters corresponding to the positions of multiple users are determined based on the environmental parameter distribution model, so that the environmental state of the entire space can be accurately reflected based on the environmental parameters collected at limited positions, and accurate environmental parameters corresponding to the positions of multiple users can be easily obtained. This allows the environmental parameter distribution of the entire space to be obtained at a low cost, and can be applied to many large spatial areas such as schools, office places, department stores, schools, exhibition areas or multi-function halls, and can also be applied to home use. The state parameters of multiple users are calculated based on the accurate environmental parameters corresponding to the positions of multiple users, and the overall state parameter value of the user is determined based on the state parameters of the multiple users, so that the overall state parameter value can reflect the overall state of the multiple users. Furthermore, the target parameter distribution of the environmental adjustment device is obtained from the overall state parameter value reflecting the overall state of the multiple users through the first prediction model, making it possible to accurately adapt the target parameter distribution to the entire multiple users. By controlling the device based on the target parameter distribution, all of the multiple users can be in a comfortable state, thereby improving the performance of the device and the user experience.

[0118] Example 2 Example 2 of the present invention provides a control device for an environmental conditioning device corresponding to the control method for an environmental conditioning device described in Example 1, and its specific implementation can refer to the implementation of the method described in Example 1, and the same or related contents will not be described repeatedly.

[0119] FIG. 12 is a schematic diagram of a control device of an environmental conditioning device according to a second embodiment of the present invention. As shown in FIG. 12, the control device 1200 of the environmental conditioning device has the following functions: An acquisition unit 1201 for acquiring multiple environmental parameters at different positions in a room; A first determining unit 1202 for determining an environmental parameter distribution model based on the plurality of environmental parameters at different positions; A second determining unit 1203 for determining a plurality of environmental parameters corresponding to the locations of a plurality of users according to the environmental parameter distribution model; A third determining unit 1204, which calculates state parameters of a plurality of users according to a plurality of environment parameters corresponding to the locations of the plurality of users respectively, and determines a total value of the state parameters of the users according to the plurality of users' state parameters; a fourth determining unit 1205 for inputting the state parameter total value into a first prediction model and outputting a target parameter distribution of the environmental conditioning device; Includes.

[0120] In an embodiment of the present invention, as shown in FIG. 12, the apparatus 1200 includes: a fifth determining unit 1206 for inputting the target parameter distribution into a second predictive model and outputting a parameter setting value for the environmental conditioning device; a control unit 1207 for controlling the environmental conditioning device based on parameter settings of the environmental conditioning device; It may further include.

[0121] In the embodiment of the present invention, the implementation of the functions of each unit above can refer to the contents of the relevant steps in the first embodiment, and will not be described again here.

[0122] As can be seen from the above embodiment, an environmental parameter distribution model is determined based on multiple environmental parameters at different positions, and environmental parameters corresponding to the positions of multiple users are determined based on the environmental parameter distribution model, so that the environmental state of the entire space can be accurately reflected based on the environmental parameters collected at limited positions, and accurate environmental parameters corresponding to the positions of multiple users can be easily obtained. This allows the environmental parameter distribution of the entire space to be obtained at a low cost, and can be applied to many large spatial areas such as schools, office places, department stores, schools, exhibition areas or multi-function halls, and can also be applied to home use. The state parameters of multiple users are calculated based on the accurate environmental parameters corresponding to the positions of multiple users, and the overall state parameter value of the user is determined based on the state parameters of the multiple users, so that the overall state parameter value can reflect the overall state of the multiple users. Furthermore, the target parameter distribution of the environmental adjustment device is obtained from the overall state parameter value reflecting the overall state of the multiple users through the first prediction model, so that the target parameter distribution can be accurately adapted to the entirety of the multiple users. By controlling the device based on the target parameter distribution, all of the multiple users can be in a comfortable state, thereby improving the performance of the device and the user experience.

[0123] Example 3 Example 3 of the present invention provides an intelligent environment adjusting system, including a control device of the environment adjusting device described in Example 2, and the specific implementation thereof may refer to the implementation of the device described in Example 2 and the method described in Example 1, and the same or related contents will not be described repeatedly.

[0124] FIG. 13 is a structural diagram of an intelligent environment adjusting system according to a third embodiment of the present invention. As shown in FIG. 13, the intelligent environment adjusting system 1300 includes: An environmental adjustment device 2000; An environmental adjustment device control device 1200 that controls the environmental adjustment device 2000; Includes.

[0125] In an embodiment of the present invention, the control device 1000 of the environmental conditioning device may be a separate device or may be integrated into the environmental conditioning device 2000 .

[0126] In an embodiment of the present invention, the environmental conditioning device may be various types of furniture devices, for example, the environmental conditioning device is at least one of an air conditioner, an air purifier, a fresh air device, a humidifier, a disinfection device, a lighting device, and an audio device.

[0127] In an embodiment of the present invention, the intelligent environment control system 1300 may be used for home use, commercial use, or public use.

[0128] For example, the intelligent environment adjustment system 1300 may be used in a home environment, a commercial environment such as an office, an office building, a department store, or a public environment such as a school.

[0129] In this embodiment of the present invention, the specific structure and function of the control device 1200 of the environment conditioning device can refer to the device described in the second embodiment and the method described in the first embodiment, and will not be described again here.

[0130] Furthermore, the control device 1200 of the environmental conditioning device may further include other control functions, such as switch control, timing control, etc., in addition to the control functions described in the embodiment of the present invention.

[0131] As can be seen from the above embodiment, an environmental parameter distribution model is determined based on multiple environmental parameters at different positions, and environmental parameters corresponding to the positions of multiple users are determined based on the environmental parameter distribution model, so that the environmental state of the entire space can be accurately reflected based on the environmental parameters collected at limited positions, and accurate environmental parameters corresponding to the positions of multiple users can be easily obtained. This allows the environmental parameter distribution of the entire space to be obtained at a low cost, and can be applied to many large spatial areas such as schools, office places, department stores, schools, exhibition areas or multi-function halls, and can also be applied to home use. The state parameters of multiple users are calculated based on the accurate environmental parameters corresponding to the positions of multiple users, and the overall state parameter value of the user is determined based on the state parameters of the multiple users, so that the overall state parameter value can reflect the overall state of the multiple users. Furthermore, the target parameter distribution of the environmental adjustment device is obtained from the overall state parameter value reflecting the overall state of the multiple users through the first prediction model, so that the target parameter distribution can be accurately adapted to the entirety of the multiple users. By controlling the device based on the target parameter distribution, all of the multiple users can be in a comfortable state, thereby improving the performance of the device and the user experience.

[0132] The above apparatus and methods according to the embodiments of the present invention may be realized in hardware or a combination of hardware and software. The present invention relates to a computer readable program that, when executed by a logic component, causes the logic component to realize the above apparatus or component or to implement the various methods or steps described above.

[0133] The embodiment of the present invention further relates to a storage medium for storing the above programs, such as a hard disk, a magnetic disk, an optical disk, a DVD, or a flash memory.

[0134] In addition, the limitations on each step of the present solution do not limit the order of the steps, provided that they do not affect the implementation of the specific solution. A step written in front may be executed first, later, or even simultaneously. As long as the present solution can be implemented, any of these steps should be considered to fall within the scope of protection of the present application.

[0135] Although the present invention has been described above with reference to specific embodiments, those skilled in the art will understand that these descriptions are merely illustrative and do not limit the scope of protection of the present invention. Those skilled in the art may make various modifications and amendments to the present invention based on the spirit and principles of the present invention, and these modifications and amendments are also within the scope of the present invention.

Claims

1. acquiring a plurality of environmental parameters at different positions in a room; determining an environmental parameter distribution model based on the plurality of environmental parameters at different locations; determining a plurality of environmental parameters corresponding to a plurality of user locations based on the environmental parameter distribution model; Calculating state parameters of the plurality of users based on a plurality of environmental parameters corresponding to the locations of the plurality of users, respectively, and determining a total value of the state parameters of the users based on the state parameters of the plurality of users; inputting the overall state parameter value into a first prediction model and outputting a target parameter distribution of the environmental conditioning device; Including, The user's condition parameters include a fatigue level and a comfort level, The environmental parameters include temperature and humidity.

13. A method for controlling an environmental conditioning device comprising:

2. The step of acquiring a plurality of environmental parameters at different positions in a room includes: acquiring said plurality of environmental parameters at different positions from a plurality of sensors distributed at different positions in a room; or acquiring said plurality of environmental parameters at different positions from a sensor movable within a room; Includes 2. The method for controlling an environment conditioning device according to claim 1.

3. The step of determining an environmental parameter distribution model based on the plurality of environmental parameters comprises: establishing a machine learning model; inputting the plurality of environmental parameters and position data corresponding to the plurality of environmental parameters into the machine learning model to train it; Once training is complete, obtaining the environmental parameter distribution model; Includes 2. The method for controlling an environment conditioning device according to claim 1.

4. The step of determining a plurality of environmental parameters corresponding to the locations of a plurality of users based on the environmental parameter distribution model includes: Locating a number of users within a room; inputting the locations of the plurality of users into the environmental parameter distribution model, and outputting a plurality of environmental parameters corresponding to the locations of the plurality of users; Includes 2. The method for controlling an environment conditioning device according to claim 1.

5. The step of identifying the locations of a plurality of users in a room comprises: Identifying the locations of a plurality of users in a room by detecting images captured by an indoor camera; or Locating a plurality of users within a room by transmitting ultrasonic, radar or infrared signals within the room; or Identifying the locations of a plurality of users in a room by detecting wireless signals transmitted from terminal devices carried by the plurality of users in the room; Includes 5. The method for controlling an environment conditioning device according to claim 4.

6. The positions of the multiple users are two-dimensional positions not including height or three-dimensional positions including height.

5. The method for controlling an environment conditioning device according to claim 4.

7. The locations of the users include the locations of the users themselves and locations around the users.

5. The method for controlling an environment conditioning device according to claim 4.

8. The step of calculating state parameters of the plurality of users based on a plurality of environmental parameters corresponding to the locations of the plurality of users, and determining a total value of the state parameters of the users based on the state parameters of the plurality of users, calculating comfort levels and fatigue levels of the plurality of users based on temperatures and humidity corresponding to the locations of the plurality of users; calculating a total comfort level value and a total fatigue level value of a user based on the comfort levels and fatigue levels of the plurality of users; Includes 2. The method for controlling an environment conditioning device according to claim 1.

9. The step of calculating a total comfort level value and a total fatigue level value of a user based on the comfort levels and fatigue levels of the plurality of users includes: calculating a sum, an average value, a variance or a standard deviation of the comfort levels of the plurality of users, and calculating a sum, an average value, a variance or a standard deviation of the fatigue levels of the plurality of users to obtain a total comfort level value and a total fatigue level value of the users; Includes 9. The method for controlling an environment conditioning device according to claim 8.

10. The step of calculating state parameters of the plurality of users based on a plurality of environmental parameters corresponding to the locations of the plurality of users, and determining a total value of the state parameters of the users based on the state parameters of the plurality of users, comparing the calculated overall comfort level and overall fatigue level of the user with a preset optimal comfort level and an optimal fatigue level; When the calculated overall comfort level and overall fatigue level of the user are equal to or lower than a preset optimum comfort level and optimum fatigue level, the calculated overall comfort level and overall fatigue level of the user are set as an overall state parameter value of the user; Also includes 9. The method for controlling an environment conditioning device according to claim 8.

11. inputting the target parameter distribution into a second predictive model and outputting parameter settings for the environmental conditioning device; controlling the environmental conditioning device based on parameter settings of the environmental conditioning device; Also includes 2. The method for controlling an environment conditioning device according to claim 1.

12. The first prediction model and the second prediction model are trained multi-layer perceptron models or deep neural networks. The method for controlling an environmental conditioning device according to claim 11.

13. inputting the target parameter distribution into a second predictive model and outputting parameter settings of an environmental conditioning device, obtaining a sequence of time-related environmental parameter distributions based on outputs of the environmental parameter distribution model within a certain time period prior to a current first time; inputting the sequence of time-related environmental parameter distributions into a third predictive model to obtain an environmental parameter distribution for a second time after the certain period of time; inputting the environmental parameter distribution at the second time and the target parameter distribution into a second predictive model and outputting parameter setting values ​​for the environmental conditioning device; Includes The method for controlling an environmental conditioning device according to claim 11.

14. The third prediction model is a trained deep neural network including a long short-term memory (LSTM) structure or a gated recurrent unit (GRU) structure. The method for controlling an environment conditioning device according to claim 13 .

15. The environmental parameters include at least one of temperature, humidity, wind direction, wind volume, sound volume, sound frequency, brightness, color temperature, and air quality; The environmental parameter distribution model includes at least one of a temperature distribution model, a humidity distribution model, a wind direction distribution model, an air volume distribution model, a sound volume distribution model, a sound frequency distribution model, a luminance distribution model, a color temperature distribution model, and an air quality distribution model.

15. The method for controlling an environment conditioning device according to claim 1.

16. The environmental conditioning device is at least one of an air conditioner, an air purifier, an air freshener, a humidifier, a disinfection device, a lighting device, and an audio device.

15. The method for controlling an environment conditioning device according to claim 1.

17. acquiring a plurality of environmental parameters at different positions in a room; determining an environmental parameter distribution model based on the plurality of environmental parameters at different locations; determining a plurality of environmental parameters corresponding to a plurality of user locations based on the environmental parameter distribution model; Calculating state parameters of the plurality of users based on a plurality of environmental parameters corresponding to the locations of the plurality of users, respectively, and determining a total value of the state parameters of the users based on the state parameters of the plurality of users; determining a target parameter distribution for the environmental conditioning device corresponding to the overall state parameter value using a look-up table method; Including, The user's condition parameters include a fatigue level and a comfort level, The environmental parameters include temperature and humidity.

13. A method for controlling an environmental conditioning device comprising:

18. determining parameter settings of the environmental conditioning device corresponding to the target parameter distribution using a look-up table technique; controlling the environmental conditioning device based on parameter settings of the environmental conditioning device; Also includes 20. The method of claim 17, wherein the control method is a method for controlling an environment conditioning device.

19. an acquisition unit for acquiring a plurality of environmental parameters at different positions in a room; a first determining unit for determining an environmental parameter distribution model based on the plurality of environmental parameters at different positions; a second determining unit for determining a plurality of environmental parameters corresponding to the locations of a plurality of users based on the environmental parameter distribution model; a third determination unit for calculating state parameters of the plurality of users according to a plurality of environmental parameters corresponding to the locations of the plurality of users, respectively, and determining a total value of the state parameters of the plurality of users according to the state parameters of the plurality of users; a fourth determination unit for inputting the state parameter total value into a first prediction model and outputting a target parameter distribution of the environmental conditioning device; Including, The user's condition parameters include a fatigue level and a comfort level, The environmental parameters include temperature and humidity. A control device for an environmental conditioning device.

20. a fifth determination unit for inputting the target parameter distribution into a second predictive model and outputting a parameter setting value for the environmental conditioning device; a control unit for controlling the environmental conditioning device based on parameter setting values ​​of the environmental conditioning device; Also includes The control device for an environmental conditioning device according to claim 19 .

21. An environmental adjustment device; A control device for an environmental conditioning device according to claim 19 or 20, which controls the environmental conditioning device; Includes An intelligent environment adjustment system.

22. The method further includes a plurality of sensors distributed at different positions in the room or a sensor movable within the room, which collects the plurality of environmental parameters at different positions. The intelligent environment adjustment system according to claim 21 .

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