Intelligent air conditioner regulation and control method and device based on historical control data

Abstract

The invention discloses an air conditioner intelligent regulation and control method and device based on historical control data, and the method comprises the steps: analyzing regulation and control habit information, corresponding to various historical control scenes, of a user for an intelligent air conditioner according to the obtained historical control data of the user for the intelligent air conditioner; according to the target area corresponding to the user, all the historical control scenes and the control habit information corresponding to all the historical control scenes, the control demand degree corresponding to each air conditioner control parameter in the target area is determined; according to the regulation and control demand degrees corresponding to all the air conditioner regulation and control parameters, target regulation and control parameters corresponding to a target air conditioner in the target area are determined from all the air conditioner regulation and control parameters; and according to the target regulation and control parameters corresponding to the target air conditioner, the target air conditioner is controlled to execute regulation and control operation corresponding to the target regulation and control parameters. Therefore, the air conditioner regulation and control flexibility can be improved, and intelligent air conditioner regulation and control can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment control, and in particular to an intelligent air-conditioning control method and device based on historical control data. Background Art

[0002] With the development of technology, smart home systems have gradually come into people's sight. Users can use smart home systems to control air conditioners, lighting, lamps and other equipment in the scene.

[0003] However, in actual applications, if the air conditioner is controlled through the current smart home system, the air conditioner can only be controlled according to the control program set by the user or according to the simple control instructions input by the user. For example, the user inputs a control instruction to a mobile terminal or a remote control device to control the air conditioner based on the mobile terminal or the remote control device. However, this control method can only rely on the current control instruction to control the air conditioner, and the control means are relatively single. Therefore, it is particularly important to propose a technical solution that can improve the flexibility of air conditioner control to achieve intelligent control of air conditioners. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an intelligent air conditioning control method and device based on historical control data, which can improve the flexibility of air conditioning control and facilitate the realization of intelligent air conditioning control.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses an intelligent air conditioning control method based on historical control data, the method comprising:

[0006] Analyzing the user's control habit information corresponding to various historical control scenarios of the smart air conditioner according to the acquired historical control data of the user on the smart air conditioner;

[0007] Determine the control demand degree corresponding to each air conditioning control parameter in the target area according to the target area corresponding to the user, all the historical control scenes and the control habit information corresponding to all the historical control scenes;

[0008] According to the control demand degrees corresponding to all the air conditioning control parameters, determining a target control parameter corresponding to a target air conditioner in the target area from among all the air conditioning control parameters;

[0009] According to the target control parameters corresponding to the target air conditioner, the target air conditioner is controlled to perform the control operation corresponding to the target control parameters.

[0010] As an optional implementation, in the first aspect of the present invention, analyzing the user's control habit information corresponding to multiple historical control scenarios for the smart air conditioner based on the acquired historical control data of the user for the smart air conditioner includes:

[0011] Analyze the acquired historical control data of the user on the smart air conditioner to obtain control data features corresponding to each historical control scenario in the historical control data;

[0012] Analyze each of the historical control scenarios to obtain scene features corresponding to each of the historical control scenarios;

[0013] Determining a target control data feature corresponding to each of the scene features according to the control data features corresponding to all the historical control scenes and the scene features corresponding to all the historical control scenes;

[0014] According to the target control data features corresponding to all the scene features, the control habit information of the user corresponding to each historical control scene of the smart air conditioner is determined.

[0015] As an optional implementation manner, in the first aspect of the present invention, determining the target manipulation data feature corresponding to each of the scene features according to the manipulation data features corresponding to all the historical manipulation scenes and the scene features corresponding to all the historical manipulation scenes includes:

[0016] According to the control data features corresponding to all the historical control scenarios, analyzing the air conditioning operation effect corresponding to each of the control data features;

[0017] According to the scene features corresponding to all the historical control scenes, analyzing the air conditioning operation requirements corresponding to each of the scene features;

[0018] Analyze the effect correlation between the air conditioning operation effect corresponding to each of the control data features and the air conditioning operation demand corresponding to each of the scene features;

[0019] For each of the scene features, at least one target effect correlation degree that is greater than or equal to a correlation degree threshold is selected from all effect correlation degrees corresponding to the scene feature;

[0020] For each of the scene features, the manipulation data features corresponding to each of the target effect correlation degrees are determined as the target manipulation data features corresponding to the scene feature.

[0021] As an optional implementation, in the first aspect of the present invention, determining the control demand degree corresponding to each air conditioning control parameter in the target area according to the target area corresponding to the user, all the historical control scenes, and the control habit information corresponding to all the historical control scenes includes:

[0022] Collecting regional multimodal data of a target area corresponding to the user;

[0023] Analyzing a historical scenario type for each of the historical manipulation scenarios;

[0024] Determining a current scene type corresponding to the target area from all the historical scene types according to the regional multimodal data of the target area and the historical scene types of all the historical manipulation scenes;

[0025] According to the current scene type corresponding to the target area and the control habit information corresponding to all the historical control scenes, the control demand degree corresponding to each air-conditioning control parameter is analyzed.

[0026] As an optional implementation, in the first aspect of the present invention, the determining, based on the regional multimodal data of the target area and the historical scene types of all the historical manipulation scenes, the current scene type corresponding to the target area from all the historical scene types includes:

[0027] Identifying, according to the regional multimodal data of the target area, regional information of the target area, wherein the regional information of the target area includes current environment information of the target area and current personnel information corresponding to the target area;

[0028] Analyzing the scene matching degree between the target area and each of the historical manipulation scenes according to the area information of the target area and the historical scene types of all the historical manipulation scenes;

[0029] Filtering out a target scene matching degree that is higher than or equal to a preset matching degree from all the scene matching degrees;

[0030] Determining a current scene type corresponding to the target area according to the historical control scene corresponding to the target scene matching degree and the area information of the target area;

[0031] Among them, the historical scene type includes a combination of one or more of scene function type, personnel behavior status type, personnel configuration type and time period type; the current environmental information of the target area includes a combination of one or more of the current air information of the target area, the spatial dimension information of the target area and the layout information of the target area; the current personnel information corresponding to the target area includes a combination of one or more of the personnel behavior status of the current personnel in the target area, the personnel position of the current personnel, the number of the current personnel, the personnel type of the current personnel and the corresponding number ratio of each personnel type.

[0032] As an optional implementation, in the first aspect of the present invention, analyzing the control demand degree corresponding to each air conditioning control parameter according to the current scene type corresponding to the target area and the control habit information corresponding to all the historical control scenes includes:

[0033] Analyzing the user's air conditioning control needs for the target area according to the current scene type corresponding to the target area and the area information of the target area;

[0034] Determining the user's control tendency information corresponding to the target area according to the user's corresponding air conditioning control demand, the control habit information corresponding to all the historical control scenes, and the scene matching degree between the target area and each of the historical control scenes;

[0035] According to the control tendency information corresponding to the target area, the control demand degree corresponding to each air-conditioning control parameter is determined.

[0036] As an optional implementation, in the first aspect of the present invention, determining the target control parameter corresponding to the target air conditioner in the target area from all the air conditioning control parameters according to the control demand degrees corresponding to all the air conditioning control parameters includes:

[0037] Acquire historical control information corresponding to each of the air conditioning control parameters, wherein the historical control information includes a historical control demand degree and a historical control weight;

[0038] Analyzing the corresponding relationship between the control demand and the control weight of each air conditioning control parameter according to all the historical control information;

[0039] Determine the current control weight corresponding to each of the air conditioning control parameters according to the control demand degrees corresponding to all the air conditioning control parameters and the corresponding relationship between the control demand degrees and the control weights of all the air conditioning control parameters;

[0040] Filtering out at least one target control weight that is higher than or equal to a preset control weight from all the current control weights;

[0041] At least one target control parameter corresponding to the target air conditioning in the target area is determined according to the air conditioning control parameters corresponding to all the target control weights.

[0042] The second aspect of the present invention discloses an intelligent air conditioning control device based on historical control data, the device comprising:

[0043] an analysis module, configured to analyze, based on the acquired historical control data of the user on the smart air conditioner, the control habit information of the user corresponding to the smart air conditioner with respect to a plurality of historical control scenarios, wherein the smart air conditioner includes at least one smart air conditioner;

[0044] A determination module, configured to determine the control demand degree corresponding to each air conditioning control parameter in the target area according to the target area corresponding to the user, all the historical control scenes and the control habit information corresponding to all the historical control scenes;

[0045] The determination module is further configured to determine, from among all the air conditioning control parameters, a target control parameter corresponding to a target air conditioner in the target area according to the control demand degrees corresponding to all the air conditioning control parameters;

[0046] The control module is used to control the target air conditioner to perform a control operation corresponding to the target control parameter according to the target control parameter corresponding to the target air conditioner.

[0047] As an optional implementation, in the second aspect of the present invention, the specific manner in which the analysis module analyzes the user's control habit information corresponding to multiple historical control scenarios of the smart air conditioner based on the acquired historical control data of the user for the smart air conditioner includes:

[0048] Analyze the acquired historical control data of the user on the smart air conditioner to obtain control data features corresponding to each historical control scenario in the historical control data;

[0049] Analyze each of the historical control scenarios to obtain scene features corresponding to each of the historical control scenarios;

[0050] Determining a target control data feature corresponding to each of the scene features according to the control data features corresponding to all the historical control scenes and the scene features corresponding to all the historical control scenes;

[0051] According to the target control data features corresponding to all the scene features, the control habit information of the user corresponding to each historical control scene of the smart air conditioner is determined.

[0052] As an optional implementation, in the second aspect of the present invention, the specific manner in which the analysis module determines the target manipulation data feature corresponding to each of the scene features according to the manipulation data features corresponding to all the historical manipulation scenes and the scene features corresponding to all the historical manipulation scenes includes:

[0053] According to the control data features corresponding to all the historical control scenarios, analyzing the air conditioning operation effect corresponding to each of the control data features;

[0054] According to the scene features corresponding to all the historical control scenes, analyzing the air conditioning operation requirements corresponding to each of the scene features;

[0055] Analyze the effect correlation between the air conditioning operation effect corresponding to each of the control data features and the air conditioning operation demand corresponding to each of the scene features;

[0056] For each of the scene features, at least one target effect correlation degree that is greater than or equal to a correlation degree threshold is selected from all effect correlation degrees corresponding to the scene feature;

[0057] For each of the scene features, the manipulation data features corresponding to each of the target effect correlation degrees are determined as the target manipulation data features corresponding to the scene feature.

[0058] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the control demand degree corresponding to each air conditioning control parameter in the target area according to the target area corresponding to the user, all the historical control scenes, and the control habit information corresponding to all the historical control scenes includes:

[0059] Collecting regional multimodal data of a target area corresponding to the user;

[0060] Analyzing a historical scenario type for each of the historical manipulation scenarios;

[0061] Determining a current scene type corresponding to the target area from all the historical scene types according to the regional multimodal data of the target area and the historical scene types of all the historical manipulation scenes;

[0062] According to the current scene type corresponding to the target area and the control habit information corresponding to all the historical control scenes, the control demand degree corresponding to each air-conditioning control parameter is analyzed.

[0063] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the current scene type corresponding to the target area from all the historical scene types according to the regional multimodal data of the target area and the historical scene types of all the historical manipulation scenes includes:

[0064] Identifying, according to the regional multimodal data of the target area, regional information of the target area, wherein the regional information of the target area includes current environment information of the target area and current personnel information corresponding to the target area;

[0065] Analyzing the scene matching degree between the target area and each of the historical manipulation scenes according to the area information of the target area and the historical scene types of all the historical manipulation scenes;

[0066] Filtering out a target scene matching degree that is higher than or equal to a preset matching degree from all the scene matching degrees;

[0067] Determining a current scene type corresponding to the target area according to the historical control scene corresponding to the target scene matching degree and the area information of the target area;

[0068] Among them, the historical scene type includes a combination of one or more of scene function type, personnel behavior status type, personnel configuration type and time period type; the current environmental information of the target area includes a combination of one or more of the current air information of the target area, the spatial dimension information of the target area and the layout information of the target area; the current personnel information corresponding to the target area includes a combination of one or more of the personnel behavior status of the current personnel in the target area, the personnel position of the current personnel, the number of the current personnel, the personnel type of the current personnel and the corresponding number ratio of each personnel type.

[0069] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module analyzes the control demand corresponding to each air conditioning control parameter according to the current scene type corresponding to the target area and the control habit information corresponding to all the historical control scenes includes:

[0070] Analyzing the user's air conditioning control needs for the target area according to the current scene type corresponding to the target area and the area information of the target area;

[0071] Determining the user's control tendency information corresponding to the target area according to the user's corresponding air conditioning control demand, the control habit information corresponding to all the historical control scenes, and the scene matching degree between the target area and each of the historical control scenes;

[0072] According to the control tendency information corresponding to the target area, the control demand degree corresponding to each air-conditioning control parameter is determined.

[0073] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the target control parameter corresponding to the target air conditioner in the target area from all the air conditioning control parameters according to the control demand degrees corresponding to all the air conditioning control parameters includes:

[0074] Acquire historical control information corresponding to each of the air conditioning control parameters, wherein the historical control information includes a historical control demand degree and a historical control weight;

[0075] Analyzing the corresponding relationship between the control demand and the control weight of each air conditioning control parameter according to all the historical control information;

[0076] Determine the current control weight corresponding to each of the air conditioning control parameters according to the control demand degrees corresponding to all the air conditioning control parameters and the corresponding relationship between the control demand degrees and the control weights of all the air conditioning control parameters;

[0077] Filtering out at least one target control weight that is higher than or equal to a preset control weight from all the current control weights;

[0078] At least one target control parameter corresponding to the target air conditioning in the target area is determined according to the air conditioning control parameters corresponding to all the target control weights.

[0079] The third aspect of the present invention discloses another intelligent air conditioning control device based on historical control data, the device comprising:

[0080] A memory storing executable program code;

[0081] a processor coupled to the memory;

[0082] The processor calls the executable program code stored in the memory to execute the intelligent air conditioning control method based on historical control data disclosed in the first aspect of the present invention.

[0083] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the intelligent air conditioning control method based on historical control data disclosed in the first aspect of the present invention.

[0084] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0085] In an embodiment of the present invention, based on the acquired historical control data of the user on the smart air conditioner, the user's control habit information corresponding to the smart air conditioner for various historical control scenarios is analyzed; based on the target area corresponding to the user, all historical control scenarios and the control habit information corresponding to all historical control scenarios, the control demand degree corresponding to each air-conditioning control parameter in the target area is determined; based on the control demand degrees corresponding to all air-conditioning control parameters, the target control parameters corresponding to the target air-conditioner in the target area are determined from all air-conditioning control parameters; based on the target control parameters corresponding to the target air-conditioner, the target air-conditioner is controlled to perform the control operation corresponding to the target control parameters. It can be seen that the implementation of the present invention can obtain the control habit information corresponding to various historical control scenarios based on the acquired historical control data of the user for the smart air conditioner, and then determine the control demand corresponding to each air-conditioning control parameter according to the target area, all historical control scenarios and all control habit information, and then determine the target control parameters corresponding to the target air-conditioner from all air-conditioning control parameters according to the control demand, so as to control the target air-conditioner to perform the control operation corresponding to the target control parameters, thereby realizing intelligent control of the air-conditioning operation based on the user's historical control data, which can improve the user's analysis flexibility for the air-conditioning control habits, thereby improving the flexibility of the demand analysis of the air-conditioning control parameters, so as to improve the flexibility of determining the target control parameters, and further improve the air-conditioning control flexibility, which is beneficial to improving the accuracy of air-conditioning control, and further beneficial to improving the adaptability of air-conditioning operation and user needs, so as to improve the user's satisfaction with the use of the smart air conditioner. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0087] Figure 1 It is a schematic diagram of a scenario to which an intelligent air conditioning control method based on historical control data disclosed in an embodiment of the present invention is applicable;

[0088] Figure 2 It is a flow chart of an intelligent air conditioning control method based on historical control data disclosed in an embodiment of the present invention;

[0089] Figure 3 It is a flow chart of another intelligent air conditioning control method based on historical control data disclosed in an embodiment of the present invention;

[0090] Figure 4 It is a structural schematic diagram of an intelligent air-conditioning control device based on historical control data disclosed in an embodiment of the present invention;

[0091] Figure 5 It is a structural schematic diagram of another intelligent air-conditioning control device based on historical control data disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0092] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0093] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0094] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0095] The present invention discloses an intelligent air conditioning control method and device based on historical control data, which can obtain control habit information corresponding to a variety of historical control scenarios according to the historical control data of users obtained for intelligent air conditioners, and then determine the control demand corresponding to each air conditioning control parameter according to the target area, all historical control scenarios and all control habit information, and then determine the target control parameter corresponding to the target air conditioner from all air conditioning control parameters according to the control demand, so as to control the target air conditioner to perform the control operation corresponding to the target control parameter, and realize intelligent control of air conditioning operation based on the historical control data of users, which can improve the analysis flexibility of users for air conditioning control habits, thereby improving the analysis flexibility of the demand degree of air conditioning control parameters, so as to improve the determination flexibility of target control parameters, and further improve the flexibility of air conditioning control, which is conducive to improving the accuracy of air conditioning control, and further conducive to improving the adaptability of air conditioning operation and user needs, so as to improve the user's satisfaction with the use of intelligent air conditioners. The following are detailed descriptions.

[0096] In order to better understand the method and device for intelligently controlling an air conditioner based on historical control data described in the present invention, the scenario to which the method is applicable is first described. The scenario to which the method is applicable is specifically a scenario in which the air conditioner is operated, such as a scenario in which the air conditioner needs to be controlled. Specifically, taking a scenario in which the air conditioner needs to be controlled as an example, the scenario can be as follows: Figure 1 As shown, Figure 1 Schematic diagram of a scenario to which an intelligent air conditioning control method based on historical control data disclosed in an embodiment of the present invention is applicable. Figure 1 As shown, the scene diagram takes the bedroom area in a scene where the air conditioner needs to be controlled as an example. The bedroom area may include an air conditioner, wherein there is historical control data for the air conditioner or other air conditioners in other areas; further, the bedroom area may also include furniture (for example: a bed) and personnel (for example: person A and / or person B), wherein the behavior status of the personnel in the bedroom area may include sleeping, exercising, eating, entertainment, etc., for example: person A is sleeping on the bed, person B is exercising, and the embodiment of the present invention does not limit this; further, the bedroom area may also include doors and windows (for example: windows).

[0097] Furthermore, when the bedroom area is determined as the target area, and the air conditioner in the bedroom area is determined as the target air conditioner, the target air conditioner can be intelligently controlled by performing the following operations: based on the historical control data of the user on the smart air conditioner obtained, analyze the user's control habit information corresponding to the smart air conditioner for various historical control scenarios; determine the control demand corresponding to each air conditioner control parameter in the target area according to the target area corresponding to the user, all historical control scenarios and the control habit information corresponding to all historical control scenarios; based on the control demand corresponding to all air conditioner control parameters, determine the target control parameters corresponding to the target air conditioner in the target area from all air conditioner control parameters; based on the target control parameters corresponding to the target air conditioner, control the target air conditioner to perform the control operation corresponding to the target control parameter.

[0098] It should be noted that Figure 1 The scene diagram shown is only for showing one scene applicable to the air conditioner intelligent control method based on historical control data, and is not used to limit other scenes applicable to the air conditioner intelligent control method and device based on historical control data, and Figure 1 The scene diagram shown does not limit the behavior status of personnel, personnel position, air conditioner model, air conditioner installation location and layout of the target area.

[0099] The above is an example description of one of the scenarios to which the intelligent air conditioning control method and device based on historical control data are applicable. The following is a detailed description of the intelligent air conditioning control method and device based on historical control data.

[0100] Embodiment 1

[0101] See also Figure 2 , Figure 2 1 is a flow chart of an intelligent air conditioning control method based on historical control data disclosed in an embodiment of the present invention. Figure 2 The described air conditioning intelligent control method based on historical control data can be applied to an air conditioning intelligent control device based on historical control data, which may include a control device, a control terminal, a control system and a server, wherein the server includes a cloud server or a local server, and the device can be applied to a smart air conditioner or a control device corresponding to the smart air conditioner; the method can also be applied to a smart home platform or a smart home system, and can also be applied to an air conditioning control application program, which is not limited in the embodiments of the present invention. Figure 2 As shown, the air conditioning intelligent control method based on historical control data may include the following operations:

[0102] 101. Analyze the user's control habit information corresponding to various historical control scenarios of the smart air conditioner based on the acquired historical control data of the user for the smart air conditioner.

[0103] In an embodiment of the present invention, the user's historical control data for a smart air conditioner can be used to represent the data set by the user for controlling the air conditioner, or can be used to represent the data set by the user for the smart air conditioner and actually used to control the air conditioner based on the data, which is not limited in the embodiment of the present invention; wherein, the historical control data can be obtained through a smart home platform corresponding to the smart air conditioner, or can be obtained through an air conditioner control application, which is not limited in the embodiment of the present invention; the historical control data may include historical control data for a variety of historical control scenarios; wherein, there may be one or more smart air conditioners, and each historical control scenario may correspond to one or more smart air conditioners, or each smart air conditioner may correspond to one or more historical control scenarios, which is not limited in the embodiment of the present invention; wherein, the multiple historical control scenarios can be a variety of scenarios defined for the same physical space based on different scenario division criteria, or can be scenes corresponding to multiple physical spaces respectively, which is not limited in the embodiment of the present invention.

[0104] 102. Determine the control demand degree corresponding to each air conditioning control parameter in the target area according to the target area corresponding to the user, all historical control scenes, and the control habit information corresponding to all historical control scenes.

[0105] In an embodiment of the present invention, the parameter type of the air-conditioning control parameter may be a parameter type pre-set by each intelligent air-conditioner and used to control the operation of the air-conditioner, and the specific value of the air-conditioning control parameter is determined according to the actual scenario; wherein the air-conditioning control parameter may include a combination of one or more of the air-conditioning function mode, air-conditioning outlet mode, air-conditioning outlet temperature, air-conditioning outlet direction, air-conditioning outlet speed, air-conditioning outlet volume and air-conditioning outlet range; wherein, the higher the control requirement corresponding to the air-conditioning control parameter, the more attention is paid to the control of the operation control parameter in the actual operation of the air-conditioner; exemplarily, if the control requirement of the air-conditioning outlet temperature is higher than the control requirement of other air-conditioning control parameters, it means that in the actual operation of the air-conditioner, more attention is paid to the control of the air-conditioning outlet temperature.

[0106] 103. According to the control demand degrees corresponding to all the air conditioning control parameters, determine the target control parameters corresponding to the target air conditioner in the target area from all the air conditioning control parameters.

[0107] In the embodiment of the present invention, the target air conditioner may be one of the smart air conditioners, and the target control parameters corresponding to the target air conditioner may be one or more air conditioner control parameters.

[0108] 104. According to the target control parameters corresponding to the target air conditioner, control the target air conditioner to perform the control operation corresponding to the target control parameters.

[0109] It can be seen that the method described in the embodiment of the present invention can obtain the control habit information corresponding to various historical control scenarios based on the acquired historical control data of the user for the smart air conditioner, and then determine the control demand corresponding to each air-conditioning control parameter according to the target area, all historical control scenarios and all control habit information, and then determine the target control parameters corresponding to the target air-conditioner from all air-conditioning control parameters according to the control demand, so as to control the target air-conditioner to perform the control operation corresponding to the target control parameters, thereby realizing intelligent control of the air-conditioning operation based on the user's historical control data, which can improve the user's analysis flexibility for the air-conditioning control habits, thereby improving the analysis flexibility for the air-conditioning control needs, so as to improve the flexibility in determining the target control parameters, and further improve the air-conditioning control flexibility, which is beneficial to improving the accuracy of air-conditioning control, and further beneficial to improving the adaptability of air-conditioning operation and user needs, so as to improve the user's satisfaction with the use of the smart air conditioner.

[0110] In an optional embodiment, analyzing the user's control habit information corresponding to multiple historical control scenarios of the smart air conditioner according to the acquired historical control data of the user on the smart air conditioner may include the following operations:

[0111] Analyze the acquired historical control data of the user on the smart air conditioner to obtain the control data features corresponding to each historical control scenario in the historical control data;

[0112] Analyze each historical control scenario to obtain the scenario characteristics corresponding to each historical control scenario;

[0113] Determine the target control data feature corresponding to each scene feature according to the control data features corresponding to all historical control scenes and the scene features corresponding to all historical control scenes;

[0114] According to the target control data characteristics corresponding to all scene characteristics, the user's control habit information corresponding to each historical control scene of the smart air conditioner is determined.

[0115] Among them, the control data features may include a combination of one or more of the control type features, the control frequency features corresponding to each control type feature, and the control numerical features corresponding to each control type feature; the scene features may include a combination of one or more of the scene function parameter features, the environment parameter features, the personnel behavior features, the scene layout features, and the time period features; among them, the environment parameter features may include a combination of one or more of the environment temperature, the environment humidity, the environment brightness, and the environment ventilation conditions; among them, each scene feature may correspond to multiple target control data features.

[0116] Exemplarily, assuming that the scene characteristics of one of the historical control scenes are the living room function and people in motion, and the control data characteristics corresponding to the historical control scene include mode control characteristics (such as: the control mode is set to dehumidification mode), temperature control characteristics (such as: the temperature control data is set to 26°C) and air outlet control characteristics (such as: the air outlet range is set to the living room area, and the air outlet direction is set to avoid people with the wind), then the target control data characteristics corresponding to the living room function and the people in motion are determined, that is, the target control data characteristics corresponding to the living room function may include the air outlet range set to the living room area, and the target control data characteristics corresponding to the people in motion may include the temperature control data set to 26°C and the air outlet direction set to avoid people with the wind. According to the above-determined target control data characteristics, it can be determined that the user's control habit information corresponding to the historical control scene is that the air outlet range is set to the living room area and the air outlet direction is set to avoid people with the wind.

[0117] It can be seen that this optional embodiment can analyze historical control data to obtain control data characteristics corresponding to each historical control scenario, and analyze historical control scenarios to obtain scene characteristics corresponding to each historical control scenario, determine the target control data characteristics corresponding to each scene characteristic based on the control data characteristics and the scene characteristics, and determine the control habit information corresponding to each historical control scenario based on the target control data characteristics corresponding to all scene characteristics, thereby realizing intelligent analysis of the control habits corresponding to the scenes based on the corresponding connection between the control data and the scenes, which can improve the analysis accuracy of the control data characteristics and the scene characteristics, thereby improving the accuracy of determining the control habits, which in turn is beneficial to improving the analysis accuracy of the air-conditioning control needs, and is beneficial to improving the air-conditioning control accuracy.

[0118] In this optional embodiment, optionally, determining the target manipulation data feature corresponding to each scene feature according to the manipulation data features corresponding to all historical manipulation scenes and the scene features corresponding to all historical manipulation scenes may include the following operations:

[0119] According to the control data characteristics corresponding to all historical control scenarios, analyze the air conditioning operation effect corresponding to each control data characteristic;

[0120] According to the scene characteristics corresponding to all historical control scenes, analyze the air conditioning operation requirements corresponding to each scene characteristic;

[0121] Analyze the effect correlation between the air conditioning operation effect corresponding to each control data feature and the air conditioning operation demand corresponding to each scene feature;

[0122] For each scene feature, at least one target effect correlation degree that is greater than or equal to a correlation degree threshold is selected from all effect correlation degrees corresponding to the scene feature;

[0123] For each scene feature, the manipulation data feature corresponding to each target effect correlation degree is determined as the target manipulation data feature corresponding to the scene feature.

[0124] For example, it is assumed that the scene characteristics of one of the historical control scenes are the living room function (the corresponding operating requirement is that the air outlet range covers the living room) and the person is in motion (the corresponding operating requirement is that the ambient temperature is adjusted to a temperature suitable for the human body and does not blow directly on the person), and the control data characteristics corresponding to the historical control scene include mode control characteristics (such as: the control mode is set to dehumidification mode, that is, the corresponding operating effect is to reduce the ambient humidity), temperature control characteristics (such as: the temperature control data is set to 26°C, that is, the corresponding operating effect is to adjust the ambient temperature to 26 degrees Celsius) and air outlet control characteristics (such as: the air outlet range is set to the living room area, that is, the corresponding operating effect is that the air outlet range covers the entire living room area; the air outlet direction is set to avoid the person, that is, the corresponding operating effect is not to blow directly on the person in the living room area); after analysis, it is concluded that the living room function has a high correlation with the effect of the air outlet range control characteristic, and the person is in motion and has a high correlation with the effect of the temperature control characteristic and the air outlet direction control characteristic, and the target control data characteristics corresponding to each scene characteristic are determined.

[0125] It can be seen that this optional embodiment can also analyze the air-conditioning operation effect corresponding to each control data feature and the air-conditioning operation demand corresponding to each scene feature, and analyze the effect correlation between the air-conditioning operation effect corresponding to each control data feature and the air-conditioning operation demand corresponding to each scene feature, and determine the control data feature corresponding to each scene feature whose effect correlation is higher than or equal to the correlation threshold as the target control data feature corresponding to the scene feature, which can improve the accuracy of the analysis of the correlation relationship between the control data feature and the scene feature, thereby improving the accuracy of determining the target control data feature, which in turn is conducive to improving the accuracy of determining the control habits, and is conducive to improving the accuracy of analyzing the air-conditioning control needs.

[0126] In this optional embodiment, further optionally, the method may also include the following operations:

[0127] Counting the control frequency corresponding to each control data feature and the control time corresponding to each control data feature;

[0128] Filtering out at least one high-frequency manipulation feature having a manipulation frequency higher than a preset manipulation frequency from all manipulation data features;

[0129] According to the control moments corresponding to all the high-frequency control features, all the high-frequency control features are divided into a plurality of high-frequency control feature sets;

[0130] Determine the air conditioning control parameters corresponding to each high-frequency control feature set;

[0131] According to the air-conditioning control parameters corresponding to each high-frequency control feature set, a user habit control mode corresponding to each high-frequency control feature set is generated.

[0132] It can be seen that this optional embodiment can filter out at least one high-frequency control feature whose control frequency is higher than the preset control frequency from all control data features, and divide it into multiple high-frequency control feature sets according to the control moments corresponding to all high-frequency control features, and determine the user habitual control mode corresponding to each high-frequency control feature set according to the air-conditioning control parameters corresponding to each high-frequency control feature set, thereby simplifying the intelligent control operation of the air conditioner, and can improve the accuracy of generating the user habitual control mode, thereby facilitating users to operate quickly, so as to improve the convenience of air-conditioning control, and further help to improve users' satisfaction with the use of smart air conditioners.

[0133] Embodiment 2

[0134] See also Figure 3 , Figure 3 1 is a flow chart of an intelligent air conditioning control method based on historical control data disclosed in an embodiment of the present invention. Figure 3 The described air conditioning intelligent control method based on historical control data can be applied to an air conditioning intelligent control device based on historical control data, which may include a control device, a control terminal, a control system and a server, wherein the server includes a cloud server or a local server, and the device can be applied to a smart air conditioner or a control device corresponding to the smart air conditioner; the method can also be applied to a smart home platform or a smart home system, and can also be applied to an air conditioning control application program, which is not limited in the embodiments of the present invention. Figure 3 As shown, the air conditioning intelligent control method based on historical control data may include the following operations:

[0135] 201. Analyze the user's control habit information corresponding to various historical control scenarios of the smart air conditioner based on the acquired historical control data of the user on the smart air conditioner.

[0136] 202. Collect regional multimodal data of a target area corresponding to the user.

[0137] In an embodiment of the present invention, the target area corresponding to the user may be the area where the user is located, or may be the area where the user needs to control the air conditioner, which is not limited in the embodiment of the present invention; the regional multimodal data may include a combination of one or more of image data, video data, audio data, text data, radar point cloud data and sensor data about the target area.

[0138] 203. Analyze the historical scenario type of each historical manipulation scenario.

[0139] In an embodiment of the present invention, the historical scene type may include a combination of one or more of the scene function type, the personnel behavior state type, the personnel configuration type and the time period type. Exemplarily, the scene function type may include one of the kitchen type, the study type, the living room type, the bedroom type and the washroom type; the personnel behavior state type may include one or more of the personnel sleep state type, the personnel exercise state type, the personnel entertainment state type, the personnel clean and hygienic state type and the personnel eating state type; the personnel configuration type may include one or more of the types divided by age (for example: the configuration type with more elderly people and fewer children), the types divided by gender (for example: the configuration type with a male-female ratio), the types divided by health conditions, etc.; the time period type may include one of the morning time period type, the afternoon time period type, the evening time period type, the early morning time period type, the late night time period type, etc., and may also include any time period (for example: 7:45 am to 15:17 pm), which is not limited in the embodiment of the present invention.

[0140] 204. Determine a current scene type corresponding to the target area from all historical scene types according to the regional multimodal data of the target area and the historical scene types of all historical manipulation scenes.

[0141] 205. Analyze the control demand degree corresponding to each air conditioning control parameter according to the current scene type corresponding to the target area and the control habit information corresponding to all historical control scenes.

[0142] 206. According to the control demand degrees corresponding to all the air conditioning control parameters, determine the target control parameters corresponding to the target air conditioner in the target area from all the air conditioning control parameters.

[0143] 207. According to the target control parameters corresponding to the target air conditioner, control the target air conditioner to perform the control operation corresponding to the target control parameters.

[0144] In the embodiment of the present invention, for other detailed descriptions of step 201 and step 206-step 207, please refer to the detailed description of step 101 and step 103-step 104 in the first embodiment, and the embodiment of the present invention will not be repeated here.

[0145] It can be seen that the method described in the embodiment of the present invention can obtain the control habit information corresponding to various historical control scenarios according to the historical control data of the user for the smart air conditioner according to the acquired analysis, and then determine the control demand corresponding to each air conditioner control parameter according to the target area, all historical control scenarios and all control habit information, and then determine the target control parameter corresponding to the target air conditioner from all air conditioner control parameters according to the control demand, so as to control the target air conditioner to perform the control operation corresponding to the target control parameter, and realize the intelligent control of the air conditioner operation based on the user's historical control data, which can improve the user's analysis flexibility for the air conditioner control habit, thereby improving the analysis flexibility of the air conditioner control demand, so as to improve the flexibility of determining the target control parameter, and then improve the flexibility of air conditioner control, which is conducive to improving the accuracy of air conditioner control, and then is conducive to improving the adaptability of air conditioner operation and user needs, so as to improve the user's satisfaction with the use of the smart air conditioner. In addition, it is also possible to determine the current scene type corresponding to the target area according to the collected regional multimodal data of the target area and the historical scene type of the historical control scene, and analyze the control demand corresponding to each air conditioner control parameter according to the current scene type and the control habit information corresponding to all historical control scenes, which can improve the analysis accuracy of the current scene type, thereby improving the accuracy of determining the air conditioner control demand, and then is conducive to improving the accuracy of determining the target control parameter.

[0146] In an optional embodiment, analyzing the historical scenario type of each historical manipulation scenario may include the following operations:

[0147] For each historical manipulation scenario, according to the determined scenario features corresponding to the historical manipulation scenario, the scenario feature correlation between each scenario feature corresponding to the historical manipulation scenario is analyzed;

[0148] For each historical manipulation scene, according to the correlation of all scene features corresponding to the historical manipulation scene, determine the target scene feature for each scene feature type in the historical manipulation scene, where the scene feature type includes a combination of one or more of an environment feature type, a personnel feature type, and a layout feature type, and the scene feature correlation between each target scene feature is higher than or equal to a preset scene feature correlation;

[0149] For each historical manipulation scene, the historical scene type of the historical manipulation scene is determined according to the target scene features for each scene feature type in the historical manipulation scene.

[0150] It can be seen that this optional embodiment can analyze the scene feature correlation between each scene feature of each historical control scene according to the scene features, and determine the target scene features for each scene feature type in the historical control scene according to the scene feature correlation, and determine the historical scene type according to the target scene features for each scene feature type in the historical control scene, which can improve the analysis accuracy of the historical scene type, thereby facilitating improving the determination accuracy of the current scene type, and further facilitating improving the determination accuracy of the air-conditioning control needs.

[0151] In another optional embodiment, according to the regional multimodal data of the target area and the historical scene types of all historical manipulation scenes, determining the current scene type corresponding to the target area from all historical scene types may include the following operations:

[0152] Identify regional information of the target area according to the regional multimodal data of the target area, where the regional information of the target area includes current environment information of the target area and current personnel information corresponding to the target area;

[0153] According to the area information of the target area and the historical scene types of all historical control scenes, the scene matching degree between the target area and each historical control scene is analyzed;

[0154] Filter out target scene matching degrees that are higher than or equal to a preset matching degree from all scene matching degrees;

[0155] Determine the current scene type corresponding to the target area according to the historical control scene corresponding to the target scene matching degree and the area information of the target area;

[0156] Among them, the current environmental information of the target area includes one or more combinations of the current air information of the target area, the spatial dimension information of the target area and the layout information of the target area; the current personnel information corresponding to the target area includes one or more combinations of the personnel behavior status of the current personnel in the target area, the personnel position of the current personnel, the number of the current personnel, the personnel type of the current personnel and the corresponding number ratio of each personnel type.

[0157] Exemplarily, the personnel type may include a combination of one or more of age type, health condition type, gender type, etc.

[0158] It can be seen that this optional embodiment can identify the regional information of the target area based on the regional multimodal data, and analyze the scene matching degree between the target area and each historical control scene based on the regional information and all historical scene types, and then screen out the target scene matching degree, and determine the current scene type corresponding to the target area based on the historical control scenes and regional information corresponding to the target scene matching degree, thereby realizing intelligent analysis of scene types based on regional multimodal data, which can improve the analysis flexibility of regional multimodal data, thereby improving the analysis flexibility and analysis accuracy of the current scene type, which is beneficial to improving the analysis accuracy of the adjustment and control needs.

[0159] In this optional embodiment, optionally, analyzing the control demand corresponding to each air conditioning control parameter according to the current scene type corresponding to the target area and the control habit information corresponding to all historical control scenes may include the following operations:

[0160] Analyze the user's air conditioning control needs for the target area based on the current scene type corresponding to the target area and the area information of the target area;

[0161] According to the user's corresponding air conditioning control needs, the control habit information corresponding to all historical control scenes, and the scene matching degree between the target area and each historical control scene, the user's control tendency information corresponding to the target area is determined;

[0162] According to the control tendency information corresponding to the target area, the control demand degree corresponding to each air-conditioning control parameter is determined.

[0163] For example, if the current scene type includes a bedroom type and the person is in a sleeping state type, and the area information of the target area indicates that the ambient temperature corresponding to the target area is 30°C, then the user's air conditioning control demand may be to adjust the temperature to 26°C and the air outlet mode to a soft mode, and the control habit information corresponding to the historical control scene with the highest matching degree with the current scene type indicates that the user is accustomed to adjusting the temperature to 24°C and the air outlet mode to a soft mode in the sleeping state, then the control tendency information is determined to be to adjust the temperature to 24°C and the air outlet mode to a soft mode, and then according to the control tendency information, the control demand of the air outlet temperature and the air outlet mode in the air conditioning control parameters is determined, and the control demand of other air conditioning control parameters is adjusted;

[0164] Exemplarily, if the current scene type includes a person's cleaning and hygienic status type (for example, a person is mopping the floor), and the area information of the target area indicates that the humidity corresponding to the target area is relatively high, then the air conditioning control demand may be to blow air to the part of the floor that has been mopped and to set the air conditioning function mode to a dehumidification mode. The control habit information corresponding to the historical control scene with the highest matching degree with the current scene type indicates that the user is accustomed to setting the air outlet mode to a strong wind mode and the air conditioning function mode to a dehumidification mode in a clean and hygienic state. Then, the control tendency information is determined to be to blow air to the part of the floor that has been mopped, to set the air conditioning function mode to a dehumidification mode, and to set the air outlet mode to a strong wind mode. Then, based on the control tendency information, the control demand for the air conditioning function mode, the air conditioning outlet mode, and the air conditioning outlet range in the air conditioning control parameters is determined, as well as the control demand for adjusting other air conditioning control parameters.

[0165] It can be seen that this optional embodiment can also analyze the user's air-conditioning control needs according to the current scene type and area information, and determine the user's control tendency information according to the air-conditioning control needs, all control habit information and the scene matching degree between the target area and each historical control scene, so as to determine the control demand corresponding to each air-conditioning control parameter, which can improve the analysis accuracy of the control tendency information, thereby improving the demand analysis accuracy of the air-conditioning control parameters, and further improving the analysis accuracy of the air-conditioning control needs, which is conducive to improving the accuracy of air-conditioning control.

[0166] In yet another optional embodiment, according to the control requirements corresponding to all the air conditioning control parameters, determining the target control parameter corresponding to the target air conditioner in the target area from all the air conditioning control parameters may include the following operations:

[0167] Obtaining historical control information corresponding to each air conditioning control parameter, the historical control information including historical control demand and historical control weight;

[0168] According to all historical control information, the corresponding relationship between the control demand and the control weight of each air conditioning control parameter is analyzed;

[0169] Determine the current control weight corresponding to each air conditioning control parameter according to the control demand degrees corresponding to all air conditioning control parameters and the corresponding relationship between the control demand degrees and the control weights of all air conditioning control parameters;

[0170] Filter out at least one target control weight that is higher than or equal to a preset control weight from all current control weights;

[0171] At least one target control parameter corresponding to the target air conditioner in the target area is determined according to the air conditioning control parameters corresponding to all the target control weights.

[0172] For example, if the control demand of the air conditioning outlet temperature is 5, and the control demand of other air conditioning control parameters are all 1, then the current control weight corresponding to the air conditioning outlet temperature is 1, and the current control weights of other air conditioning control parameters are 0.

[0173] It can be seen that this optional embodiment can analyze the correspondence between the control demand and the control weight of each air-conditioning control parameter according to the historical control information corresponding to all the acquired air-conditioning control parameters, so as to determine the current control weight corresponding to each air-conditioning control parameter in combination with the control demand corresponding to all the air-conditioning control parameters, and determine the target control parameter according to the air-conditioning control parameter corresponding to the target control weight that meets the conditions, which can improve the analysis accuracy of the control weight, thereby improving the screening accuracy of the target control parameter, and further helping to improve the air-conditioning control accuracy.

[0174] Embodiment 3

[0175] See also Figure 4 , Figure 4 1 is a schematic diagram of the structure of an intelligent air conditioning control device based on historical control data disclosed in an embodiment of the present invention. Figure 4 The described air conditioning intelligent control device based on historical control data may include one of a control device, a control terminal, a control system and a server, wherein the server includes a cloud server or a local server. The device may be applied to a smart air conditioner or a control device corresponding to the smart air conditioner, and the embodiment of the present invention does not limit this. Figure 4 As shown, the air conditioning intelligent control device based on historical control data may include:

[0176] The analysis module 301 is used to analyze the user's control habit information corresponding to multiple historical control scenarios of the smart air conditioner according to the acquired historical control data of the user on the smart air conditioner, and the smart air conditioner includes at least one smart air conditioner;

[0177] A determination module 302 is used to determine the control demand degree corresponding to each air conditioning control parameter in the target area according to the target area corresponding to the user, all historical control scenes and the control habit information corresponding to all historical control scenes;

[0178] The determination module 302 is further used to determine the target control parameter corresponding to the target air conditioner in the target area from all the air conditioning control parameters according to the control demand degrees corresponding to all the air conditioning control parameters;

[0179] The control module 303 is used to control the target air conditioner to perform the control operation corresponding to the target control parameter according to the target control parameter corresponding to the target air conditioner.

[0180] It can be seen that the device described in the embodiment of the present invention can obtain control habit information corresponding to various historical control scenarios based on the acquired historical control data of the user for the smart air conditioner, and then determine the control demand corresponding to each air-conditioning control parameter according to the target area, all historical control scenarios and all control habit information, and then determine the target control parameters corresponding to the target air-conditioner from all air-conditioning control parameters according to the control demand, so as to control the target air-conditioner to perform the control operation corresponding to the target control parameters, thereby realizing intelligent control of air-conditioning operation based on the user's historical control data, which can improve the user's analysis flexibility for air-conditioning control habits, thereby improving the analysis flexibility for air-conditioning control needs, so as to improve the flexibility in determining target control parameters, and further improve the flexibility of air-conditioning control, which is conducive to improving the accuracy of air-conditioning control, and further conducive to improving the adaptability of air-conditioning operation and user needs, so as to improve user satisfaction with the use of smart air conditioners.

[0181] In an optional embodiment, the specific method of analyzing the user's control habit information corresponding to multiple historical control scenarios of the smart air conditioner according to the acquired historical control data of the user on the smart air conditioner may include:

[0182] Analyze the acquired historical control data of the user on the smart air conditioner to obtain the control data features corresponding to each historical control scenario in the historical control data;

[0183] Analyze each historical control scenario to obtain the scenario characteristics corresponding to each historical control scenario;

[0184] Determine the target control data feature corresponding to each scene feature according to the control data features corresponding to all historical control scenes and the scene features corresponding to all historical control scenes;

[0185] According to the target control data characteristics corresponding to all scene characteristics, the user's control habit information corresponding to each historical control scene of the smart air conditioner is determined.

[0186] It can be seen that the device described in the implementation of this optional embodiment can analyze historical control data to obtain control data characteristics corresponding to each historical control scenario, and analyze historical control scenarios to obtain scene characteristics corresponding to each historical control scenario, determine the target control data characteristics corresponding to each scene characteristic based on the control data characteristics and the scene characteristics, and determine the control habit information corresponding to each historical control scenario based on the target control data characteristics corresponding to all scene characteristics, thereby realizing intelligent analysis of the control habits corresponding to the scene based on the corresponding connection between the control data and the scene, which can improve the analysis accuracy of the control data characteristics and the scene characteristics, thereby improving the determination accuracy of the control habits, which in turn is beneficial to improving the analysis accuracy of the air-conditioning control needs, and is beneficial to improving the air-conditioning control accuracy.

[0187] In this optional embodiment, optionally, the specific manner in which the analysis module 301 determines the target manipulation data feature corresponding to each scene feature according to the manipulation data features corresponding to all historical manipulation scenes and the scene features corresponding to all historical manipulation scenes may include:

[0188] According to the control data characteristics corresponding to all historical control scenarios, analyze the air conditioning operation effect corresponding to each control data characteristic;

[0189] According to the scene characteristics corresponding to all historical control scenes, analyze the air conditioning operation requirements corresponding to each scene characteristic;

[0190] Analyze the effect correlation between the air conditioning operation effect corresponding to each control data feature and the air conditioning operation demand corresponding to each scene feature;

[0191] For each scene feature, at least one target effect correlation degree that is greater than or equal to a correlation degree threshold is selected from all effect correlation degrees corresponding to the scene feature;

[0192] For each scene feature, the manipulation data feature corresponding to each target effect correlation degree is determined as the target manipulation data feature corresponding to the scene feature.

[0193] It can be seen that the device described in the implementation of this optional embodiment can also analyze the air-conditioning operation effect corresponding to each control data feature and the air-conditioning operation demand corresponding to each scene feature, and analyze the effect correlation between the air-conditioning operation effect corresponding to each control data feature and the air-conditioning operation demand corresponding to each scene feature, and determine the control data feature corresponding to each scene feature whose effect correlation is higher than or equal to the correlation threshold as the target control data feature corresponding to the scene feature, which can improve the accuracy of the analysis of the correlation relationship between the control data feature and the scene feature, thereby improving the accuracy of determining the target control data feature, which is beneficial to improving the accuracy of determining the control habits, and is beneficial to improving the accuracy of analyzing the air-conditioning control needs.

[0194] In another optional embodiment, the specific manner in which the determination module 302 determines the control demand degree corresponding to each air conditioning control parameter in the target area according to the target area corresponding to the user, all historical control scenes, and the control habit information corresponding to all historical control scenes may include:

[0195] Collecting regional multimodal data of the target area corresponding to the user;

[0196] Analyze the historical scenario types for each historical manipulation scenario;

[0197] Determine a current scene type corresponding to the target area from all historical scene types according to the regional multimodal data of the target area and the historical scene types of all historical manipulation scenes;

[0198] According to the current scene type corresponding to the target area and the control habit information corresponding to all historical control scenes, the control demand corresponding to each air-conditioning control parameter is analyzed.

[0199] It can be seen that the device described in the implementation of this optional embodiment can determine the current scene type corresponding to the target area based on the collected regional multimodal data of the target area and the historical scene types of the historical control scenes, and analyze the control demand corresponding to each air-conditioning control parameter based on the current scene type and the control habit information corresponding to all historical control scenes, which can improve the analysis accuracy of the current scene type, thereby improving the accuracy of determining the air-conditioning control demand, which in turn is conducive to improving the accuracy of determining the target control parameters.

[0200] In this optional embodiment, optionally, the specific manner in which the determination module 302 determines the current scene type corresponding to the target area from all historical scene types according to the regional multimodal data of the target area and the historical scene types of all historical manipulation scenes may include:

[0201] Identify regional information of the target area according to the regional multimodal data of the target area, where the regional information of the target area includes current environment information of the target area and current personnel information corresponding to the target area;

[0202] According to the area information of the target area and the historical scene types of all historical control scenes, the scene matching degree between the target area and each historical control scene is analyzed;

[0203] Filter out target scene matching degrees that are higher than or equal to a preset matching degree from all scene matching degrees;

[0204] Determine the current scene type corresponding to the target area according to the historical control scene corresponding to the target scene matching degree and the area information of the target area;

[0205] Among them, the historical scene type includes one or more combinations of scene function type, personnel behavior status type, personnel configuration type and time period type; the current environmental information of the target area includes one or more combinations of the current air information of the target area, the spatial dimension information of the target area and the layout information of the target area; the current personnel information corresponding to the target area includes one or more combinations of the personnel behavior status of the current personnel in the target area, the personnel position of the current personnel, the number of the current personnel, the personnel type of the current personnel and the corresponding number ratio of each personnel type.

[0206] It can be seen that the device described in the implementation of this optional embodiment can also identify the regional information of the target area based on the regional multimodal data, and analyze the scene matching degree between the target area and each historical control scene based on the regional information and all historical scene types, and then screen out the target scene matching degree, and determine the current scene type corresponding to the target area based on the historical control scenes and regional information corresponding to the target scene matching degree, thereby realizing intelligent analysis of scene types based on regional multimodal data, which can improve the analysis flexibility of regional multimodal data, thereby improving the analysis flexibility and analysis accuracy of the current scene type, and further facilitating improving the analysis accuracy of adjustment and control needs.

[0207] In this optional embodiment, further optionally, the specific manner in which the determination module 302 analyzes the control demand corresponding to each air conditioning control parameter according to the current scene type corresponding to the target area and the control habit information corresponding to all historical control scenes may include:

[0208] Analyze the user's air conditioning control needs for the target area based on the current scene type corresponding to the target area and the area information of the target area;

[0209] According to the user's corresponding air conditioning control needs, the control habit information corresponding to all historical control scenes, and the scene matching degree between the target area and each historical control scene, the user's control tendency information corresponding to the target area is determined;

[0210] According to the control tendency information corresponding to the target area, the control demand degree corresponding to each air-conditioning control parameter is determined.

[0211] It can be seen that the device described in the implementation of this optional embodiment can also analyze the user's air-conditioning control needs according to the current scene type and area information, and determine the user's control tendency information according to the air-conditioning control needs, all control habit information and the scene matching degree between the target area and each historical control scene, so as to determine the control demand corresponding to each air-conditioning control parameter, which can improve the analysis accuracy of the control tendency information, thereby improving the demand analysis accuracy of the air-conditioning control parameters, and further improving the analysis accuracy of the air-conditioning control needs, which is conducive to improving the accuracy of air-conditioning control.

[0212] In another optional embodiment, the specific manner in which the determination module 302 determines the target control parameter corresponding to the target air conditioner in the target area from all the air conditioning control parameters according to the control demand degrees corresponding to all the air conditioning control parameters may include:

[0213] Obtaining historical control information corresponding to each air conditioning control parameter, the historical control information including historical control demand and historical control weight;

[0214] According to all historical control information, the corresponding relationship between the control demand and the control weight of each air conditioning control parameter is analyzed;

[0215] Determine the current control weight corresponding to each air conditioning control parameter according to the control demand degrees corresponding to all air conditioning control parameters and the corresponding relationship between the control demand degrees and the control weights of all air conditioning control parameters;

[0216] Filter out at least one target control weight that is higher than or equal to a preset control weight from all current control weights;

[0217] At least one target control parameter corresponding to the target air conditioner in the target area is determined according to the air conditioning control parameters corresponding to all the target control weights.

[0218] It can be seen that the device described in the implementation of this optional embodiment can analyze the corresponding relationship between the control demand and the control weight of each air-conditioning control parameter according to the historical control information corresponding to all the air-conditioning control parameters obtained, so as to determine the current control weight corresponding to each air-conditioning control parameter in combination with the control demand corresponding to all the air-conditioning control parameters, and determine the target control parameter according to the air-conditioning control parameter corresponding to the target control weight that meets the conditions, which can improve the analysis accuracy of the control weight, thereby improving the screening accuracy of the target control parameter, and further helping to improve the air-conditioning control accuracy.

[0219] Embodiment 4

[0220] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the structure of another intelligent air conditioning control device based on historical control data disclosed in an embodiment of the present invention. Figure 5 As shown, the air conditioning intelligent control device based on historical control data may include:

[0221] A memory 401 storing executable program codes;

[0222] a processor 402 coupled to the memory 401;

[0223] The processor 402 calls the executable program code stored in the memory 401 to execute the steps of the intelligent air conditioning control method based on historical control data described in the first embodiment of the present invention or the second embodiment of the present invention.

[0224] Embodiment 5

[0225] An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps of the intelligent air conditioning control method based on historical control data described in Embodiment 1 or Embodiment 2 of the present invention.

[0226] Embodiment 6

[0227] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the intelligent air conditioning control method based on historical control data described in Example 1 or Example 2.

[0228] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0229] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0230] Finally, it should be noted that the intelligent air conditioning control method and device based on historical control data disclosed in the embodiment of the present invention only discloses the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that the technical solutions recorded in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent air conditioning control method based on historical control data, characterized in that: The method comprises: Analyzing the user's control habit information corresponding to various historical control scenarios of the smart air conditioner according to the acquired historical control data of the user on the smart air conditioner; Determine the control demand degree corresponding to each air conditioning control parameter in the target area according to the target area corresponding to the user, all the historical control scenes and the control habit information corresponding to all the historical control scenes; According to the control demand degrees corresponding to all the air conditioning control parameters, determining a target control parameter corresponding to a target air conditioner in the target area from among all the air conditioning control parameters; According to the target control parameters corresponding to the target air conditioner, the target air conditioner is controlled to perform the control operation corresponding to the target control parameters.

2. The intelligent air conditioning control method based on historical control data according to claim 1, characterized in that: The step of analyzing the user's control habit information corresponding to a plurality of historical control scenarios of the smart air conditioner according to the acquired historical control data of the user on the smart air conditioner includes: Analyze the acquired historical control data of the user on the smart air conditioner to obtain control data features corresponding to each historical control scenario in the historical control data; Analyze each of the historical control scenarios to obtain scene features corresponding to each of the historical control scenarios; Determining a target control data feature corresponding to each of the scene features according to the control data features corresponding to all the historical control scenes and the scene features corresponding to all the historical control scenes; According to the target control data features corresponding to all the scene features, the control habit information of the user corresponding to each historical control scene of the smart air conditioner is determined.

3. The intelligent air conditioning control method based on historical control data according to claim 2 is characterized in that: The determining, based on the control data features corresponding to all the historical control scenes and the scene features corresponding to all the historical control scenes, the target control data features corresponding to each scene feature includes: According to the control data features corresponding to all the historical control scenarios, analyzing the air conditioning operation effect corresponding to each of the control data features; According to the scene features corresponding to all the historical control scenes, analyzing the air conditioning operation requirements corresponding to each of the scene features; Analyze the effect correlation between the air conditioning operation effect corresponding to each of the control data features and the air conditioning operation demand corresponding to each of the scene features; For each of the scene features, at least one target effect correlation degree that is greater than or equal to a correlation degree threshold is selected from all effect correlation degrees corresponding to the scene feature; For each of the scene features, the manipulation data features corresponding to each of the target effect correlation degrees are determined as the target manipulation data features corresponding to the scene feature.

4. The intelligent air conditioning control method based on historical control data according to claim 1, characterized in that: The determining, according to the target area corresponding to the user, all the historical control scenes and the control habit information corresponding to all the historical control scenes, the control demand degree corresponding to each air conditioning control parameter in the target area includes: Collecting regional multimodal data of a target area corresponding to the user; Analyzing a historical scenario type for each of the historical manipulation scenarios; Determining a current scene type corresponding to the target area from all the historical scene types according to the regional multimodal data of the target area and the historical scene types of all the historical manipulation scenes; According to the current scene type corresponding to the target area and the control habit information corresponding to all the historical control scenes, the control demand degree corresponding to each air-conditioning control parameter is analyzed.

5. The intelligent air conditioning control method based on historical control data according to claim 4 is characterized in that: The determining, based on the regional multimodal data of the target area and the historical scene types of all the historical manipulation scenes, the current scene type corresponding to the target area from all the historical scene types includes: Identifying, according to the regional multimodal data of the target area, regional information of the target area, wherein the regional information of the target area includes current environment information of the target area and current personnel information corresponding to the target area; Analyzing the scene matching degree between the target area and each of the historical manipulation scenes according to the area information of the target area and the historical scene types of all the historical manipulation scenes; Filtering out a target scene matching degree that is higher than or equal to a preset matching degree from all the scene matching degrees; Determining a current scene type corresponding to the target area according to the historical control scene corresponding to the target scene matching degree and the area information of the target area; Among them, the historical scene type includes a combination of one or more of scene function type, personnel behavior status type, personnel configuration type and time period type; the current environmental information of the target area includes a combination of one or more of the current air information of the target area, the spatial dimension information of the target area and the layout information of the target area; the current personnel information corresponding to the target area includes a combination of one or more of the personnel behavior status of the current personnel in the target area, the personnel position of the current personnel, the number of the current personnel, the personnel type of the current personnel and the corresponding number ratio of each personnel type.

6. The intelligent air conditioning control method based on historical control data according to claim 5, characterized in that: The step of analyzing the control demand degree corresponding to each air conditioning control parameter according to the current scene type corresponding to the target area and the control habit information corresponding to all the historical control scenes includes: Analyzing the user's air conditioning control needs for the target area according to the current scene type corresponding to the target area and the area information of the target area; Determining the user's control tendency information corresponding to the target area according to the user's corresponding air conditioning control demand, the control habit information corresponding to all the historical control scenes, and the scene matching degree between the target area and each of the historical control scenes; According to the control tendency information corresponding to the target area, the control demand degree corresponding to each air-conditioning control parameter is determined.

7. The intelligent air conditioning control method based on historical control data according to any one of claims 1 to 6, characterized in that: The step of determining a target control parameter corresponding to a target air conditioner in the target area from among all the air conditioning control parameters according to the control demand degrees corresponding to all the air conditioning control parameters includes: Acquire historical control information corresponding to each of the air conditioning control parameters, wherein the historical control information includes a historical control demand degree and a historical control weight; Analyzing the corresponding relationship between the control demand and the control weight of each air conditioning control parameter according to all the historical control information; Determine the current control weight corresponding to each of the air conditioning control parameters according to the control demand degrees corresponding to all the air conditioning control parameters and the corresponding relationship between the control demand degrees and the control weights of all the air conditioning control parameters; Filtering out at least one target control weight that is higher than or equal to a preset control weight from all the current control weights; At least one target control parameter corresponding to the target air conditioning in the target area is determined according to the air conditioning control parameters corresponding to all the target control weights.

8. An intelligent air conditioning control device based on historical control data, characterized in that: The device comprises: an analysis module, configured to analyze, based on the acquired historical control data of the user on the smart air conditioner, the control habit information of the user corresponding to the smart air conditioner with respect to a plurality of historical control scenarios, wherein the smart air conditioner includes at least one smart air conditioner; A determination module, configured to determine the control demand degree corresponding to each air conditioning control parameter in the target area according to the target area corresponding to the user, all the historical control scenes and the control habit information corresponding to all the historical control scenes; The determination module is further configured to determine, from among all the air conditioning control parameters, a target control parameter corresponding to a target air conditioner in the target area according to the control demand degrees corresponding to all the air conditioning control parameters; The control module is used to control the target air conditioner to perform a control operation corresponding to the target control parameter according to the target control parameter corresponding to the target air conditioner.

9. An intelligent air conditioning control device based on historical control data, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent air conditioning control method based on historical control data as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the intelligent air conditioning control method based on historical control data as described in any one of claims 1-7.

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