Intelligent air conditioner control method and device applied to airflow dispersion scene

Abstract

The invention discloses an intelligent air conditioner control method and device applied to an airflow dispersion scene, and the method comprises the steps that whether the current scene condition of an area where an intelligent air conditioner is located meets a preset scene condition or not is judged, and the scene contained in the current scene condition is the scene where people exist in the area; if it is judged that the current scene condition of the area where the intelligent air conditioner is located meets the preset scene condition, it is determined that the operation mode of the intelligent air conditioner is an oscillatory wave flow mode; based on the oscillatory wave flow mode, target control parameters of the intelligent air conditioner are generated, the target control parameters are parameters for controlling the intelligent air conditioner to execute corresponding control operation, and the control operation at least comprises airflow dispersion operation corresponding to the oscillatory wave flow mode. Therefore, the control accuracy of the intelligent air conditioner can be improved, and the intelligent requirement of a user for the intelligent air conditioner can be met.

Description

Technical Field

[0001] The present invention relates to the field of intelligent air conditioning technology, and in particular to an intelligent air conditioning control method and device applied to air flow diffusion scenarios. Background Art

[0002] With the rapid development of science and technology and the improvement of people's living standards, users' demand for intelligent air conditioners is increasing.

[0003] Currently, when it is detected that the indoor environment conditions meet the conditions for turning on the air conditioner, the smart air conditioner will automatically turn on and work to adjust the indoor environment. However, in real life, the current smart air conditioner simply works according to the indoor environment conditions. Once the indoor user is in the target scene (such as: leisure scene / close-range scene), it is easy to cause the user's intelligent demand for the smart air conditioner to not be met, and its intelligence needs to be improved. It can be seen that it is particularly important to propose a technical solution to improve the control accuracy of the smart air conditioner when the user is in the target scene. Summary of the invention

[0004] The present invention provides an intelligent air-conditioning control method and device applied to an air flow diffusion scenario, which can improve the control accuracy of the intelligent air-conditioning and help meet the intelligent needs of users for the intelligent air-conditioning.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses an intelligent air conditioning control method applied to an air flow diffusion scene, the method comprising:

[0006] Determine whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition, where the scene included in the current scene condition is a scene where there are people in the area;

[0007] If it is determined that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition, determining that the operation mode of the smart air conditioner is an oscillating wave flow mode;

[0008] Based on the oscillating wave flow pattern, a target control parameter of the intelligent air conditioner is generated, wherein the target control parameter is a parameter for controlling the intelligent air conditioner to perform a corresponding control operation, and the control operation at least includes an airflow diffusion operation corresponding to the oscillating wave flow pattern.

[0009] As an optional implementation, in the first aspect of the present invention, generating the target control parameter of the intelligent air conditioner based on the oscillating wave flow mode includes:

[0010] Acquire regional environmental information of the area where the smart air conditioner is located, where the regional environmental information of the area at least includes regional airflow information of the area;

[0011] Determining an airflow path of airflow in the area according to the area airflow information;

[0012] Analyzing at least one target control parameter type for the oscillating wave flow pattern based on the oscillating wave flow pattern, the regional environment information, and the airflow flow path;

[0013] Determine a specific control parameter of each target control parameter type according to the regional environment information, the airflow path, all the target control parameter types, and the preset control configuration information corresponding to each target control parameter type;

[0014] The target control parameter of the intelligent air conditioner is used to represent a specific control parameter of each target control parameter type among all the target control parameter types.

[0015] As an optional implementation manner, in the first aspect of the present invention, the specific control parameter of each target control parameter type is determined according to the regional environment information, the airflow path, all the target control parameter types, and the preset control configuration information corresponding to each target control parameter type, including:

[0016] For each of the target control parameter types, a target airflow position matching the target control parameter type is selected from all airflow positions included in the airflow path;

[0017] According to the target airflow positions corresponding to all the target control parameter types, the regional environment information is grouped to obtain a plurality of sub-regional environment information groups, wherein all sub-regional environment information in the same sub-regional environment information group corresponds to the same target airflow position;

[0018] For each of the target control parameter types, generating a parameter type control requirement corresponding to the target control parameter type according to the target airflow flow position corresponding to the target control parameter type and the sub-region environment information group corresponding to the target airflow flow position;

[0019] For each of the target control parameter types, specific control parameters of the target control parameter type are determined according to a parameter type control requirement corresponding to the target control parameter type and preset control configuration information corresponding to the target control parameter type.

[0020] As an optional implementation, in the first aspect of the present invention, the analyzing at least one target control parameter type for the oscillating wave flow pattern based on the oscillating wave flow pattern, the regional environment information and the airflow path includes:

[0021] Analyzing the airflow diffusion condition corresponding to each of all the airflow flow positions according to the regional environmental information;

[0022] Generate an airflow diffusion requirement for the area where the smart air conditioner is located according to the airflow diffusion conditions corresponding to all the airflow flow positions and the regional environmental information;

[0023] According to the airflow diffusion demand of the area where the smart air conditioner is located, at least one of the preset initial control parameter type sets whose matching degree with the airflow diffusion demand is greater than or equal to a preset matching degree threshold is searched as the target control parameter type for the oscillating wave flow mode. The initial control parameter type set includes the initial control parameter type corresponding to each sub-initial control parameter in the initial control parameters of the smart air conditioner.

[0024] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0025] Obtaining the initial airflow diffusion condition of the area before generating the target control parameter;

[0026] According to the target control parameter and the initial airflow diffusion condition, predict the airflow diffusion change trend of the area, wherein the airflow diffusion change trend is the change trend of the airflow diffusion condition of the area within a future target preset time period obtained when simulating the airflow diffusion operation;

[0027] According to the airflow diffusion change trend, predicting the predicted perception situation of the airflow diffusion change trend that the personnel need to perceive;

[0028] Determining whether the predicted perception of the personnel meets the preset personnel comfort conditions;

[0029] When it is determined that the predicted perception of the person meets the preset person comfort condition, the intelligent air conditioner is controlled to perform the airflow diffusion operation according to the target control parameter.

[0030] As an optional implementation, in the first aspect of the present invention, the determining whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition includes:

[0031] Acquire personnel information of personnel in the area where the smart air conditioner is located, wherein the personnel information of the personnel at least includes the personnel status of the personnel and the personnel location of the personnel;

[0032] According to the personnel information of the personnel in the area where the smart air conditioner is located, determining whether the current scene where the personnel is located is at least one preset target scene, wherein the target scene includes a first scene corresponding to the personnel state of the personnel being a leisure state and / or a second scene corresponding to the distance between the personnel position of the personnel and the air conditioning position of the smart air conditioner being less than or equal to a preset distance;

[0033] If it is determined that the current scene in which the person is located is at least one of all the target scenes, it is determined that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition.

[0034] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0035] If it is determined that the current scene where the person is located is neither the first scene nor the second scene, it is determined whether the acquired historical operation times of the smart air conditioner are greater than or equal to a preset number, where the historical operation times are the number of times the operation mode of the smart air conditioner is the oscillating wave flow mode when the person is in the area where the smart air conditioner is located within a preset time period in the past;

[0036] If it is determined that the historical operation times of the smart air conditioner are less than the preset times, it is determined that the current scene condition of the area where the smart air conditioner is located does not meet the preset scene condition;

[0037] If it is determined that the historical operation times of the smart air conditioner are greater than or equal to the preset times, it is determined that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition.

[0038] A second aspect of the present invention discloses an intelligent air conditioning control device applied to an air flow diffusion scenario, the device comprising:

[0039] A judgment module, used to judge whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition, and the scene included in the current scene condition is the scene where there are people in the area;

[0040] a determination module, configured to determine that the operation mode of the smart air conditioner is an oscillating wave flow mode if the determination module determines that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition;

[0041] A generation module is used to generate target control parameters of the intelligent air conditioner based on the oscillating wave flow pattern, wherein the target control parameters are parameters for controlling the intelligent air conditioner to perform corresponding control operations, and the control operations at least include airflow diffusion operations corresponding to the oscillating wave flow pattern.

[0042] As an optional implementation, in the second aspect of the present invention, the generation module generates the target control parameters of the intelligent air conditioner based on the oscillating wave flow mode in a manner that specifically includes:

[0043] Acquire regional environmental information of the area where the smart air conditioner is located, where the regional environmental information of the area at least includes regional airflow information of the area;

[0044] Determining an airflow path of airflow in the area according to the area airflow information;

[0045] Analyzing at least one target control parameter type for the oscillating wave flow pattern based on the oscillating wave flow pattern, the regional environment information, and the airflow flow path;

[0046] Determine a specific control parameter of each target control parameter type according to the regional environment information, the airflow path, all the target control parameter types, and the preset control configuration information corresponding to each target control parameter type;

[0047] The target control parameter of the intelligent air conditioner is used to represent a specific control parameter of each target control parameter type among all the target control parameter types.

[0048] As an optional implementation, in the second aspect of the present invention, the generation module determines the specific control parameter of each target control parameter type according to the regional environment information, the airflow path, all the target control parameter types, and the preset control configuration information corresponding to each target control parameter type, specifically including:

[0049] For each of the target control parameter types, a target airflow position matching the target control parameter type is selected from all airflow positions included in the airflow path;

[0050] According to the target airflow positions corresponding to all the target control parameter types, the regional environment information is grouped to obtain a plurality of sub-regional environment information groups, wherein all sub-regional environment information in the same sub-regional environment information group corresponds to the same target airflow position;

[0051] For each of the target control parameter types, generating a parameter type control requirement corresponding to the target control parameter type according to the target airflow flow position corresponding to the target control parameter type and the sub-region environment information group corresponding to the target airflow flow position;

[0052] For each of the target control parameter types, specific control parameters of the target control parameter type are determined according to a parameter type control requirement corresponding to the target control parameter type and preset control configuration information corresponding to the target control parameter type.

[0053] As an optional implementation, in the second aspect of the present invention, the generation module analyzes at least one target control parameter type for the oscillating wave flow pattern based on the oscillating wave flow pattern, the regional environment information, and the airflow path, specifically including:

[0054] Analyzing the airflow diffusion condition corresponding to each of all the airflow flow positions according to the regional environmental information;

[0055] Generate an airflow diffusion requirement for the area where the smart air conditioner is located according to the airflow diffusion conditions corresponding to all the airflow flow positions and the regional environmental information;

[0056] According to the airflow diffusion demand of the area where the smart air conditioner is located, at least one of the preset initial control parameter type sets whose matching degree with the airflow diffusion demand is greater than or equal to a preset matching degree threshold is searched as the target control parameter type for the oscillating wave flow mode. The initial control parameter type set includes the initial control parameter type corresponding to each sub-initial control parameter in the initial control parameters of the smart air conditioner.

[0057] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0058] An acquisition module, used for acquiring the initial airflow diffusion condition of the area before generating the target control parameter;

[0059] A prediction module, used for predicting the change trend of the airflow diffusion in the area according to the target control parameter and the initial airflow diffusion condition, wherein the change trend of the airflow diffusion condition in the area within a future target preset time period obtained by simulating the airflow diffusion operation;

[0060] The prediction module is further used to predict the predicted perception situation of the airflow diffusion change trend that the personnel need to perceive according to the airflow diffusion change trend;

[0061] The judgment module is further used to judge whether the predicted perception of the personnel meets the preset personnel comfort condition;

[0062] The control module is used to control the intelligent air conditioner to perform the airflow diffusion operation according to the target control parameters when the judgment module determines that the predicted perception of the person meets the preset person comfort condition.

[0063] As an optional implementation, in the second aspect of the present invention, the judgment module judges whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition in a specific manner including:

[0064] Acquire personnel information of personnel in the area where the smart air conditioner is located, wherein the personnel information of the personnel at least includes the personnel status of the personnel and the personnel location of the personnel;

[0065] According to the personnel information of the personnel in the area where the smart air conditioner is located, determining whether the current scene where the personnel is located is at least one preset target scene, wherein the target scene includes a first scene corresponding to the personnel state of the personnel being a leisure state and / or a second scene corresponding to the distance between the personnel position of the personnel and the air conditioning position of the smart air conditioner being less than or equal to a preset distance;

[0066] If it is determined that the current scene in which the person is located is at least one of all the target scenes, it is determined that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition.

[0067] As an optional implementation, in the second aspect of the present invention, the judgment module is further used to judge whether the acquired historical operation times of the smart air conditioner is greater than or equal to a preset number if it is judged that the current scene where the person is located is neither the first scene nor the second scene, wherein the historical operation times are the times that the operation mode of the smart air conditioner is the oscillating wave flow mode when the person is in the area where the smart air conditioner is located within a preset time period in the past;

[0068] The determination module is further configured to determine that the current scene condition of the area where the smart air conditioner is located does not meet the preset scene condition if the determination module determines that the historical operation times of the smart air conditioner are less than the preset times;

[0069] The determination module is further configured to determine that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition if the judgment module determines that the historical operation times of the smart air conditioner is greater than or equal to the preset times.

[0070] The third aspect of the present invention discloses another intelligent air conditioning control device applied to an air flow diffusion scene, the device comprising:

[0071] A memory storing executable program code;

[0072] a processor coupled to the memory;

[0073] The processor calls the executable program code stored in the memory to execute the intelligent air-conditioning control method for air flow diffusion scenarios disclosed in the first aspect of the present invention.

[0074] 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 for air flow diffusion scenarios disclosed in the first aspect of the present invention.

[0075] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0076] In the embodiment of the present invention, it is determined whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition, and the scene included in the current scene condition is a scene where there are people in the area; if it is determined that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition, the operation mode of the smart air conditioner is determined to be the oscillating wave flow mode; based on the oscillating wave flow mode, the target control parameter of the smart air conditioner is generated, and the target control parameter is a parameter for controlling the smart air conditioner to perform a corresponding control operation, and the control operation at least includes an airflow diffusion operation corresponding to the oscillating wave flow mode. It can be seen that the implementation of the present invention can determine that the operation mode of the smart air conditioner is the oscillating wave flow mode when the current scene condition of the area where the smart air conditioner is located meets the preset scene condition, and generate the target control parameter of the smart air conditioner based on the oscillating wave flow mode, which can improve the generation accuracy of the target control parameter of the smart air conditioner, thereby facilitating the subsequent improvement of the control accuracy of the smart air conditioner through the accurately generated target control parameter, and then facilitating the improvement of the adjustment accuracy of the environmental parameters of the area where the smart air conditioner is located through the control operation performed by the smart air conditioner, and can meet the user's intelligent needs for the smart air conditioner through the accurately adjusted environmental parameters, improve the intelligence level of the smart air conditioner and enhance the user's usage viscosity. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] 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.

[0078] Figure 1 It is a schematic diagram of a scenario to which an intelligent air-conditioning control method applied to an air flow diffusion scenario disclosed in an embodiment of the present invention is applicable;

[0079] Figure 2 It is a flow chart of an intelligent air-conditioning control method applied to an air flow diffusion scenario disclosed in an embodiment of the present invention;

[0080] Figure 3 It is a flow chart of another intelligent air conditioning control method applied to air flow diffusion scene disclosed in an embodiment of the present invention;

[0081] Figure 4 It is a structural schematic diagram of an intelligent air-conditioning control device applied to an air flow diffusion scenario disclosed in an embodiment of the present invention;

[0082] Figure 5 is a structural schematic diagram of another intelligent air conditioning control device applied to air flow diffusion scenarios disclosed in an embodiment of the present invention;

[0083] Figure 6 It is a structural schematic diagram of another intelligent air-conditioning control device applied to air flow diffusion scenarios disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0084] 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.

[0085] 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.

[0086] 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.

[0087] The present invention discloses a smart air conditioner control method and device applied to air flow diffusion scenes, which can determine that the operation mode of the smart air conditioner is an oscillating wave flow mode when the current scene conditions in the area where the smart air conditioner is located meet the preset scene conditions, and generate target control parameters of the smart air conditioner based on the oscillating wave flow mode, which can improve the generation accuracy of the target control parameters of the smart air conditioner, thereby facilitating the subsequent improvement of the control accuracy of the smart air conditioner through accurately generated target control parameters, and further facilitating the improvement of the adjustment accuracy of the environmental parameters in the area where the smart air conditioner is located through the control operation performed by the smart air conditioner, and can meet the user's intelligent needs for the smart air conditioner through accurately adjusted environmental parameters, thereby improving the intelligence level of the smart air conditioner and enhancing the user's usage viscosity. The following are detailed descriptions.

[0088] In order to better understand the intelligent air conditioning control method and device for air flow diffusion scenarios described in the present invention, the scenarios to which the intelligent air conditioning control method for air flow diffusion scenarios is applicable are first described. The scenarios to which the method is applicable are specifically scenarios where the user parameters of users in the area where the air conditioner is located need to be adjusted through the air conditioner, such as a home scenario. Specifically, taking the home scenario 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 for air flow diffusion scenario disclosed in an embodiment of the present invention is applicable. Figure 1 As shown, the scene schematic diagram takes the bedroom area in the home scene as an example for illustration, in which a smart air conditioner is placed and a user is in the bedroom area. Furthermore, furniture, such as a bed, a wardrobe, etc., can also be placed in the bedroom area.

[0089] It should be noted that Figure 1 The scene diagram shown is only for showing one of the scenes applicable to the intelligent air conditioning control method and device applied to the air flow diffusion scene, and it is not used to limit other scenes applicable to the intelligent air conditioning control method and device applied to the air flow diffusion scene, and Figure 1 The smart air conditioner involved in Figure 1 The scenario schematic diagram shown does not limit the shape, size, installation location, etc. of the smart air conditioner; Figure 1 There are no restrictions on the existence of the users involved, the existence of beds and wardrobes, and their existence forms.

[0090] The above describes an example of one of the scenarios to which the intelligent air-conditioning control method and device applied to the air-flow diffusion scenario are applicable. The following describes the intelligent air-conditioning control method and device applied to the air-flow diffusion scenario in detail.

[0091] Embodiment 1

[0092] See also Figure 2 , Figure 2 : is a flow chart of an intelligent air conditioning control method applied to air flow diffusion scenarios disclosed in an embodiment of the present invention. Figure 2 The intelligent air conditioning control method for air flow diffusion scenarios described above can be applied to an intelligent air conditioning control device for air flow diffusion scenarios, wherein the device may include an air conditioning control device or an air conditioning control server, wherein the air conditioning control server may include a cloud server or a local server, which is not limited in the embodiments of the present invention. Figure 2 As shown, the intelligent air conditioning control method applied to the air flow diffusion scene may include the following operations:

[0093] 101. Determine whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition.

[0094] In the embodiment of the present invention, the current scene condition includes a scene where there are people in the area. Optionally, the preset scene condition may be at least one of a condition corresponding to a preset leisure scene (e.g., a user resting in bed) and a condition corresponding to a preset close-range scene (e.g., a user is close to the air conditioner), which is not limited in the embodiment of the present invention.

[0095] In the embodiment of the present invention, if the judgment result of step 101 is yes, step 102 is executed; if the judgment result of step 101 is no, step 104 is executed, or the present process is terminated.

[0096] 102. Determine that the operation mode of the intelligent air conditioner is the oscillating wave flow mode.

[0097] In the embodiment of the present invention, the operation mode of the intelligent air conditioner can optionally include at least an oscillating wave flow mode. The oscillating wave flow mode is an operation mode corresponding to the purpose of rapid cooling by causing the tail wing plate of the intelligent air conditioner to vibrate to generate a fluctuating airflow ring, which is not limited in the embodiment of the present invention.

[0098] 103. Generate target control parameters of intelligent air conditioner based on oscillating wave flow mode.

[0099] In an embodiment of the present invention, the target control parameter is a parameter for controlling the intelligent air conditioner to perform a corresponding control operation. Among them, the target control parameter may at least include an airflow diffusion control parameter. Among them, the airflow diffusion control parameter is a parameter for controlling the airflow of the intelligent air conditioner to diffuse rapidly. The control operation may at least include an airflow diffusion operation corresponding to the oscillating wave flow mode. Among them, the airflow diffusion operation may specifically be: adjusting the amplitude / frequency / initial angle and the wind speed of the tail wing panel so that the airflow falls to the designated target area for diffusion. Among them, the airflow speed is ≤0.3m / s, which is not limited in the embodiment of the present invention.

[0100] 104. Control the intelligent air conditioner to perform corresponding default control operations according to the acquired default control parameters.

[0101] It can be seen that implementation Figure 2 The described intelligent air-conditioning control method applied to the air flow diffusion scenario can determine that the operating mode of the intelligent air-conditioner is an oscillating wave flow mode when the current scene conditions in the area where the intelligent air-conditioner is located meet the preset scene conditions, and generate target control parameters of the intelligent air-conditioner based on the oscillating wave flow mode, which can improve the generation accuracy of the target control parameters of the intelligent air-conditioner, thereby facilitating the subsequent improvement of the control accuracy of the intelligent air-conditioner through accurately generated target control parameters, and further facilitating the improvement of the adjustment accuracy of the environmental parameters of the area where the intelligent air-conditioner is located through controlling the control operations performed by the intelligent air-conditioner, and can meet the user's intelligent needs for the intelligent air-conditioner through accurately adjusted environmental parameters, thereby improving the intelligence level of the intelligent air-conditioner and enhancing the user's usage stickiness.

[0102] In an optional embodiment, after generating the target control parameters of the intelligent air conditioner based on the oscillating wave flow mode in the above step 103, the method may further include:

[0103] Get the historical control parameters of the smart air conditioner;

[0104] According to the historical control parameters of the intelligent air conditioner, the target control parameters of the intelligent air conditioner are calibrated to obtain the calibrated target control parameters;

[0105] According to the calibrated target control parameters, the intelligent air conditioner is controlled to perform corresponding control operations.

[0106] In the embodiment of the present invention, the operation mode corresponding to the historical control parameter is the oscillating wave flow mode, and the condition similarity between the regional historical conditions corresponding to the historical control parameter and the current scene conditions of the region is greater than or equal to the preset similarity threshold. The preset similarity threshold can be 80%, 70%, or any other set value, which is not limited in the embodiment of the present invention.

[0107] It can be seen that this optional embodiment can calibrate the target control parameters of the smart air conditioner according to the acquired historical control parameters of the smart air conditioner, and can improve the calibration accuracy of the target control parameters, thereby facilitating improving the accuracy of controlling the smart air conditioner through accurately calibrated target control parameters and reducing the occurrence of air conditioning control errors caused by the lack of parameter calibration.

[0108] In another optional embodiment, when the intelligent air conditioner is controlled to perform the airflow diffusion operation corresponding to the oscillating wave flow mode according to the airflow diffusion control parameter in the target control parameter, the method may further include:

[0109] Obtaining the airflow diffusion influencing factor of the airflow diffusion control parameter in the scene parameters of the area;

[0110] Generate an influence coefficient corresponding to the airflow diffusion influence factor according to the airflow diffusion influence factor and the preset influence factor correlation relationship;

[0111] According to the influence coefficient corresponding to the airflow diffusion influence factor, the airflow diffusion control parameter is corrected to obtain the corrected airflow diffusion control parameter, and the intelligent air conditioner is controlled to continue to perform the airflow diffusion operation corresponding to the oscillating wave flow mode according to the corrected airflow diffusion control parameter.

[0112] In an embodiment of the present invention, the air flow diffusion influencing factor may be an air parameter in the scene parameter of the area where the smart air conditioner is located that affects the air flow diffusion operation. The air parameter may include the types of gases in the air, and at least one of the concentration of each gas type and the density of each gas type. The preset influencing factor association relationship is a relationship corresponding to a correlation degree between each gas type and the remaining gas types among all gas types except the gas type being greater than a preset correlation degree (e.g., 80%), which is not limited in the embodiment of the present invention.

[0113] It can be seen that this optional embodiment can accurately generate the influence coefficient corresponding to the airflow diffusion influence factor based on the acquired airflow diffusion influence factor and the preset influence factor association relationship, and correct the airflow diffusion control parameters according to the influence coefficient corresponding to the airflow diffusion influence factor, which can improve the accuracy and timeliness of the correction of the airflow diffusion control parameters, and improve the intelligence level of the adaptive correction of the smart air conditioner when correction is needed, which is beneficial to improve the accuracy and efficiency of the subsequent execution of the airflow diffusion operation.

[0114] Embodiment 2

[0115] See also Figure 3 , Figure 3 : is a flow chart of an intelligent air conditioning control method applied to air flow diffusion scenarios disclosed in an embodiment of the present invention. Figure 3 The intelligent air conditioning control method for air flow diffusion scenarios described above can be applied to an intelligent air conditioning control device for air flow diffusion scenarios, wherein the device may include an air conditioning control device or an air conditioning control server, wherein the air conditioning control server may include a cloud server or a local server, which is not limited in the embodiments of the present invention. Figure 3 As shown, the intelligent air conditioning control method applied to the air flow diffusion scene may include the following operations:

[0116] 201. Determine whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition.

[0117] In the embodiment of the present invention, if the judgment result of step 201 is yes, step 202 is executed; if the judgment result of step 201 is no, step 207 is executed, or the present process is terminated.

[0118] 202. Determine that the operation mode of the intelligent air conditioner is an oscillating wave flow mode.

[0119] 203. Obtain regional environmental information of the area where the smart air conditioner is located.

[0120] In an embodiment of the present invention, the regional environmental information of a region may at least include the regional airflow information of the region. The regional environmental information of a region may also include the temperature parameters of the region. The temperature parameters may include the temperature value of each temperature acquisition time segment in a plurality of preset temperature acquisition time segments and the temperature change of all temperature acquisition time segments, which is not limited in the embodiment of the present invention.

[0121] 204. Determine an airflow path of airflow in the area according to the area airflow information.

[0122] In the embodiment of the present invention, optionally, the airflow path may be an airflow ring (eg, air in the area circulates in the airflow ring).

[0123] 205. Analyze at least one target control parameter type for the oscillating wave flow pattern based on the oscillating wave flow pattern, regional environment information, and airflow path.

[0124] In an embodiment of the present invention, all target control parameter types may include a combination of one or more of the amplitude control type of the tail wing panel, the frequency control type of the tail wing panel, the angle control type of the tail wing panel, and the wind speed control type of the air outlet at the tail wing panel, which is not limited in the embodiment of the present invention.

[0125] 206. Determine specific control parameters of each target control parameter type according to the regional environment information, the airflow path, all target control parameter types, and preset control configuration information corresponding to each target control parameter type.

[0126] For example, if the preset control configuration information corresponding to the angle control type of the tail wing panel is that the initial angle control is 15°, and after analyzing the regional environmental information and the airflow path, it is concluded that the angle of the tail wing panel needs to be 45°, then the angle control type of the tail wing panel can be to control the angle of the tail wing panel to continue rotating 30° in the current rotation direction (the current rotation direction is the rotation direction corresponding to the above initial angle).

[0127] In the embodiment of the present invention, the target control parameter of the intelligent air conditioner is used to represent a specific control parameter of each target control parameter type among all target control parameter types.

[0128] 207. Control the intelligent air conditioner to perform corresponding default control operations according to the acquired default control parameters.

[0129] It can be seen that implementation Figure 3 The described intelligent air-conditioning control method applied to the air flow diffusion scenario can determine that the operating mode of the intelligent air-conditioner is an oscillating wave flow mode when the current scene conditions in the area where the intelligent air-conditioner is located meet the preset scene conditions, and generate target control parameters of the intelligent air-conditioner based on the oscillating wave flow mode, which can improve the generation accuracy of the target control parameters of the intelligent air-conditioner, thereby facilitating the subsequent improvement of the control accuracy of the intelligent air-conditioner through accurately generated target control parameters, and further facilitating the improvement of the adjustment accuracy of the environmental parameters of the area where the intelligent air-conditioner is located through controlling the control operations performed by the intelligent air-conditioner, and can meet the user's intelligent needs for the intelligent air-conditioner through accurately adjusted environmental parameters, thereby improving the intelligence level of the intelligent air-conditioner and enhancing the user's usage stickiness. In addition, it is also possible to accurately determine the airflow path of the airflow in the area based on the regional airflow information contained in the acquired regional environmental information, and analyze at least one target control parameter type for the oscillating wave flow pattern based on the oscillating wave flow pattern, the regional environmental information and the airflow path, which can improve the analysis accuracy and reliability of the target control parameter type, and determine the specific control parameters of each target control parameter type based on the regional environmental information, the airflow path and the preset control configuration information corresponding to each accurately analyzed target control parameter type, which can improve the determination accuracy of the specific control parameters of each target control parameter type, thereby facilitating improving the generation accuracy and reliability of the target control parameters through all accurately determined target control parameter types and their specific control parameters.

[0130] In an optional embodiment, the step 206 above, based on the regional environment information, the airflow path, all target control parameter types, and the preset control configuration information corresponding to each target control parameter type, determines the specific control parameter of each target control parameter type, which may include:

[0131] For each target control parameter type, a target airflow position matching the target control parameter type is selected from all airflow positions included in the airflow path;

[0132] According to the target airflow flow positions corresponding to all target control parameter types, the regional environmental information is grouped to obtain a plurality of sub-regional environmental information groups;

[0133] For each target control parameter type, generating a parameter type control requirement corresponding to the target control parameter type according to the target airflow flow position corresponding to the target control parameter type and the sub-region environment information group corresponding to the target airflow flow position;

[0134] For each target control parameter type, a specific control parameter of the target control parameter type is determined according to a parameter type control requirement corresponding to the target control parameter type and preset control configuration information corresponding to the target control parameter type.

[0135] In the embodiment of the present invention, all sub-region environmental information in the same sub-region environmental information group corresponds to the same target airflow flow position. For example, assuming that the parameter type control requirement corresponding to the wind speed control type of the air outlet at the tail wing plate is to adjust the wind speed to 0.3m / s, and the preset control configuration information corresponding to the wind speed control type includes configuration information that the initial wind speed is 0.1m / s, then the specific control parameters of the wind speed control type may include the control parameters corresponding to adjusting the initial wind speed from 0.1m / s to 0.3m / s.

[0136] It can be seen that this optional embodiment can, for each target control parameter type, screen out target airflow positions that match the target control parameter type from all airflow positions included in the airflow path, thereby reducing the occurrence of subsequent grouping processing of target airflow positions that do not need to be screened, and group the regional environmental information according to the target airflow positions corresponding to all accurately screened target control parameter types to obtain multiple sub-regional environmental information groups, which can improve the grouping accuracy and reliability of the sub-regional environmental information groups, and for each target control parameter type, accurately generate the parameter type control requirement corresponding to the target control parameter type according to the target airflow positions corresponding to the target control parameter type and the sub-regional environmental information groups corresponding to the accurately grouped target airflow positions, and determine the specific control parameters of the target control parameter type according to the accurately generated parameter type control requirement corresponding to the target control parameter type and the preset control configuration information corresponding to the target control parameter type, which can improve the determination accuracy and reliability of the specific control parameters of the target control parameter type.

[0137] In another optional embodiment, the step 205 of analyzing at least one target control parameter type for the oscillating wave flow pattern based on the oscillating wave flow pattern, the regional environment information, and the airflow path may include:

[0138] Analyze the airflow diffusion status corresponding to each of all airflow locations according to the regional environmental information;

[0139] Generate the airflow diffusion requirements of the area where the smart air conditioner is located based on the airflow diffusion conditions corresponding to all airflow locations and regional environmental information;

[0140] According to the airflow diffusion demand of the area where the smart air conditioner is located, at least one of the preset initial control parameter type sets whose matching degree with the airflow diffusion demand is greater than or equal to the preset matching degree threshold is searched as the target control parameter type for the oscillating wave flow mode. The initial control parameter type set includes the initial control parameter type corresponding to each sub-initial control parameter in the initial control parameters of the smart air conditioner.

[0141] In the embodiment of the present invention, optionally, the air flow diffusion requirement may include a combination of one or more of an air flow diffusion area requirement, an air flow diffusion speed requirement, and an air flow diffusion volume requirement, which is not limited in the embodiment of the present invention.

[0142] It can be seen that this optional embodiment can accurately analyze the airflow diffusion conditions corresponding to each of all airflow flow positions according to the regional environmental information, and generate the airflow diffusion demand of the area where the smart air conditioner is located according to the accurately analyzed airflow diffusion conditions corresponding to all airflow flow positions and the regional environmental information, which can improve the accuracy of generating the airflow diffusion demand, and according to the accurately generated airflow diffusion demand, search for the target control parameter type whose matching degree with the airflow diffusion demand is greater than or equal to the preset matching degree threshold from the preset initial control parameter type set, which can improve the searching accuracy and reliability of the target control parameter type, and help to reduce the occurrence of target control parameter type searching errors caused by the failure to accurately generate the airflow diffusion demand.

[0143] In yet another optional embodiment, the method may further include:

[0144] Obtaining the initial airflow diffusion conditions in the area before generating the target control parameters;

[0145] According to the target control parameters and the initial airflow dispersion, the airflow dispersion change trend of the forecast area is predicted;

[0146] According to the change trend of air flow dispersion, the forecast personnel need to perceive the predicted perception of the change trend of air flow dispersion;

[0147] Determine whether the predicted perception of personnel meets the preset personnel comfort conditions;

[0148] When it is determined that the predicted perception of the personnel meets the preset personnel comfort conditions, the intelligent air conditioner is controlled to perform airflow diffusion operations according to the target control parameters.

[0149] In the embodiment of the present invention, the airflow diffusion change trend may be the change trend of the airflow diffusion situation of the area in the future target preset time period obtained when simulating the airflow diffusion operation. Optionally, the predicted perception of the personnel may include the predicted comfort of the personnel, and the preset personnel comfort condition may be a condition corresponding to the preset comfort. The preset comfort level may be 60%, 75%, or any other set value, which is not limited in the embodiment of the present invention.

[0150] In an embodiment of the present invention, specifically, when it is determined that the predicted perception of the personnel does not meet the preset personnel comfort conditions, the above-mentioned operation of correcting the airflow diffusion control parameters in the target control parameters of the intelligent air conditioner can be performed, and the embodiment of the present invention is not limited thereto.

[0151] It can be seen that this optional embodiment can accurately predict the airflow diffusion change trend of the area based on the target control parameters and the initial airflow diffusion conditions of the area obtained before the target control parameters are generated, and based on the accurately predicted airflow diffusion change trend, predict the predicted perception of the airflow diffusion change trend that the personnel need to perceive, which can improve the accuracy and reliability of the prediction of the personnel's predicted perception conditions, and when it is predicted that the personnel may feel comfortable, control the intelligent air conditioner to perform airflow diffusion operations according to the target control parameters, which can improve the control flexibility of the intelligent air conditioner to perform airflow diffusion operations.

[0152] In another optional embodiment, the step 201 of determining whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition includes:

[0153] Obtaining personnel information of personnel in the area where the smart air conditioner is located;

[0154] According to the personal information of the person in the area where the smart air conditioner is located, determining whether the current scene where the person is located is at least one preset target scene;

[0155] If it is determined that the current scene where the person is located is at least one of all target scenes, it is determined that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition.

[0156] In an embodiment of the present invention, the personnel information of a person may include at least the personnel status of the person and the personnel location of the person. Among them, the target scene may include a first scene corresponding to the personnel status of the person being a leisure state and / or a second scene corresponding to the distance between the personnel location of the person and the air conditioning position of the smart air conditioner being less than or equal to a preset distance. Among them, the leisure state may be a state where the person is resting in bed, a state where the person is drinking tea, or a state where the person is moving slowly, which is not limited in the embodiment of the present invention.

[0157] It can be seen that this optional embodiment can obtain the personal information of people in the area where the smart air conditioner is located, and judge whether the current scene where the people are located is at least one preset target scene based on the personal information of the people in the area where the smart air conditioner is located, and when the current scene where the people are located is the target scene, it is determined that the current scene conditions in the area where the smart air conditioner is located meet the preset scene conditions, which can improve the accuracy of determining whether the preset scene conditions are met, and is conducive to the smart air conditioner selecting a suitable operating mode.

[0158] In this optional embodiment, as an optional implementation, the method may further include:

[0159] If it is determined that the current scene in which the person is located is neither the first scene nor the second scene, determining whether the acquired historical operation times of the smart air conditioner is greater than or equal to a preset number;

[0160] If it is determined that the historical operation times of the smart air conditioner are less than the preset times, it is determined that the current scene condition of the area where the smart air conditioner is located does not meet the preset scene condition;

[0161] If it is determined that the historical operation times of the smart air conditioner are greater than or equal to the preset times, it is determined that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition.

[0162] In the embodiment of the present invention, the historical operation times may be the times when the operation mode of the smart air conditioner is the oscillating wave flow mode when a person is in the area where the smart air conditioner is located within a preset time period in the past. For example, if the user has been in the target scene for the past three consecutive days and the number of times the smart air conditioner selects the oscillating wave flow mode reaches five times, which exceeds the prescribed three times, then it is determined that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition.

[0163] It can be seen that this optional implementation method can, when it is determined that the person is not in the first scene and the second scene, further determine that the smart air conditioner has selected the oscillating wave flow mode more times when the person was in the area in the recent period, and still determine that the current scene conditions in the area where the smart air conditioner is located meet the preset scene conditions. This can enrich the determination method of meeting the preset scene conditions and improve the flexibility of determining the preset scene conditions.

[0164] Embodiment 3

[0165] See also Figure 4 , Figure 4 : is a schematic diagram of the structure of an intelligent air conditioning control device applied to air flow diffusion scenarios disclosed in an embodiment of the present invention. Figure 4The described intelligent air conditioning control device applied to the air flow diffusion scene may include an air conditioning control device or an air conditioning control server, wherein the air conditioning control server may include a cloud server or a local server, which is not limited in the embodiment of the present invention. Figure 4 As shown, the intelligent air conditioning control device applied to the air flow diffusion scene may include:

[0166] The judgment module 301 is used to judge whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition, and the scene included in the current scene condition is the scene where there are people in the area.

[0167] The determination module 302 is used to determine that the operation mode of the smart air conditioner is the oscillating wave flow mode if the determination module 301 determines that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition.

[0168] The generation module 303 is used to generate target control parameters of the intelligent air conditioner based on the oscillating wave flow mode. The target control parameters are parameters for controlling the intelligent air conditioner to perform corresponding control operations, and the control operations at least include airflow diffusion operations corresponding to the oscillating wave flow mode.

[0169] It can be seen that implementation Figure 4 The described intelligent air-conditioning control device applied to the air flow diffusion scenario can determine that the operating mode of the intelligent air-conditioner is an oscillating wave flow mode when the current scene conditions in the area where the intelligent air-conditioner is located meet the preset scene conditions, and generate target control parameters of the intelligent air-conditioner based on the oscillating wave flow mode. This can improve the generation accuracy of the target control parameters of the intelligent air-conditioner, which is beneficial to subsequently improve the control accuracy of the intelligent air-conditioner through accurately generated target control parameters, and further help to improve the adjustment accuracy of the environmental parameters of the area where the intelligent air-conditioner is located by controlling the control operations performed by the intelligent air-conditioner, and can meet the user's intelligent needs for the intelligent air-conditioner through accurately adjusted environmental parameters, thereby improving the intelligence level of the intelligent air-conditioner and enhancing the user's usage stickiness.

[0170] In an optional embodiment, the generation module 303 generates the target control parameters of the intelligent air conditioner based on the oscillating wave flow mode, which specifically includes:

[0171] Acquire regional environmental information of the area where the smart air conditioner is located, where the regional environmental information of the area at least includes regional airflow information of the area;

[0172] Determine an airflow path of airflow in the area according to the area airflow information;

[0173] Analyzing at least one target control parameter type for the oscillating wave flow pattern based on the oscillating wave flow pattern, regional environmental information, and airflow flow path;

[0174] Determine the specific control parameters of each target control parameter type according to the regional environment information, the airflow path, all target control parameter types and the preset control configuration information corresponding to each target control parameter type;

[0175] The target control parameter of the intelligent air conditioner is used to represent the specific control parameter of each target control parameter type among all target control parameter types.

[0176] It can be seen that this optional embodiment can accurately determine the airflow path of the airflow in the area based on the regional airflow information contained in the acquired regional environmental information, and analyze at least one target control parameter type for the oscillating wave flow pattern based on the oscillating wave flow pattern, the regional environmental information and the airflow path, which can improve the analysis accuracy and reliability of the target control parameter type, and determine the specific control parameters of each target control parameter type based on the regional environmental information, the airflow path and the preset control configuration information corresponding to each accurately analyzed target control parameter type, which can improve the determination accuracy of the specific control parameters of each target control parameter type, thereby facilitating improving the generation accuracy and reliability of the target control parameters through accurately determining all target control parameter types and their specific control parameters.

[0177] In this optional embodiment, as an optional implementation, the generation module 303 determines the specific control parameters of each target control parameter type according to the regional environment information, the airflow path, all target control parameter types and the preset control configuration information corresponding to each target control parameter type, and the method may specifically include:

[0178] For each target control parameter type, a target airflow position matching the target control parameter type is selected from all airflow positions included in the airflow path;

[0179] According to the target airflow flow positions corresponding to all target control parameter types, the regional environmental information is grouped to obtain a plurality of sub-regional environmental information groups, wherein all sub-regional environmental information in the same sub-regional environmental information group corresponds to the same target airflow flow position;

[0180] For each target control parameter type, generating a parameter type control requirement corresponding to the target control parameter type according to the target airflow flow position corresponding to the target control parameter type and the sub-region environment information group corresponding to the target airflow flow position;

[0181] For each target control parameter type, a specific control parameter of the target control parameter type is determined according to a parameter type control requirement corresponding to the target control parameter type and preset control configuration information corresponding to the target control parameter type.

[0182] It can be seen that this optional implementation method can, for each target control parameter type, screen out the target airflow flow positions that match the target control parameter type from all the airflow flow positions included in the airflow flow path, thereby reducing the occurrence of subsequent grouping processing of the target airflow flow positions that do not need to be screened, and group the regional environmental information according to the target airflow flow positions corresponding to all the accurately screened target control parameter types to obtain multiple sub-regional environmental information groups, which can improve the grouping accuracy and reliability of the sub-regional environmental information groups, and for each target control parameter type, accurately generate the parameter type control requirement corresponding to the target control parameter type according to the target airflow flow positions corresponding to the target control parameter type and the sub-regional environmental information groups corresponding to the accurately grouped target airflow flow positions, and determine the specific control parameters of the target control parameter type according to the accurately generated parameter type control requirement corresponding to the target control parameter type and the preset control configuration information corresponding to the target control parameter type, which can improve the determination accuracy and reliability of the specific control parameters of the target control parameter type.

[0183] In this optional embodiment, as another optional implementation, the generation module 303 analyzes at least one target control parameter type for the oscillating wave flow pattern based on the oscillating wave flow pattern, the regional environment information, and the airflow path, specifically including:

[0184] Analyze the airflow diffusion status corresponding to each of all airflow locations according to the regional environmental information;

[0185] Generate the airflow diffusion requirements of the area where the smart air conditioner is located based on the airflow diffusion conditions corresponding to all airflow locations and regional environmental information;

[0186] According to the airflow diffusion demand of the area where the smart air conditioner is located, at least one of the preset initial control parameter type sets whose matching degree with the airflow diffusion demand is greater than or equal to the preset matching degree threshold is searched as the target control parameter type for the oscillating wave flow mode. The initial control parameter type set includes the initial control parameter type corresponding to each sub-initial control parameter in the initial control parameters of the smart air conditioner.

[0187] It can be seen that this optional implementation method can accurately analyze the airflow diffusion conditions corresponding to each of all airflow flow positions according to the regional environmental information, and generate the airflow diffusion demand of the area where the smart air conditioner is located based on the accurately analyzed airflow diffusion conditions corresponding to all airflow flow positions and the regional environmental information, which can improve the accuracy of generating the airflow diffusion demand, and according to the accurately generated airflow diffusion demand, search for the target control parameter type whose matching degree with the airflow diffusion demand is greater than or equal to the preset matching degree threshold from the preset initial control parameter type set, which can improve the search accuracy and reliability of the target control parameter type, and help to reduce the occurrence of target control parameter type search errors caused by the failure to accurately generate the airflow diffusion demand.

[0188] In another optional embodiment, Figure 5 As shown, the device may also include:

[0189] An acquisition module 304 is used to acquire the initial airflow diffusion condition of the area before generating the target control parameter;

[0190] The prediction module 305 is used to predict the airflow diffusion change trend of the area according to the target control parameters and the initial airflow diffusion situation. The airflow diffusion change trend is the change trend of the airflow diffusion situation of the area in the future target preset time period obtained when simulating the airflow diffusion operation;

[0191] The prediction module 305 is also used to predict the predicted perception situation of the airflow diffusion change trend that the personnel need to perceive according to the airflow diffusion change trend;

[0192] The judgment module 301 is also used to judge whether the predicted perception of the personnel meets the preset personnel comfort condition;

[0193] The control module 306 is used to control the intelligent air conditioner to perform airflow diffusion operation according to the target control parameters when the judgment module 301 judges that the predicted perception of the personnel meets the preset personnel comfort conditions.

[0194] It can be seen that this optional embodiment can accurately predict the airflow diffusion change trend of the area based on the target control parameters and the initial airflow diffusion conditions of the area obtained before the target control parameters are generated, and based on the accurately predicted airflow diffusion change trend, predict the predicted perception of the airflow diffusion change trend that the personnel need to perceive, which can improve the accuracy and reliability of the prediction of the personnel's predicted perception conditions, and when it is predicted that the personnel may feel comfortable, control the intelligent air conditioner to perform airflow diffusion operations according to the target control parameters, which can improve the control flexibility of the intelligent air conditioner to perform airflow diffusion operations.

[0195] In another optional embodiment, the method in which the judgment module 301 judges whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition may specifically include:

[0196] Acquire personnel information of personnel in the area where the smart air conditioner is located, where the personnel information of the personnel at least includes the personnel status and the personnel location of the personnel;

[0197] According to the personal information of the person in the area where the smart air conditioner is located, it is determined whether the current scene where the person is located is at least one preset target scene, where the target scene includes a first scene corresponding to the person's personal status being a leisure state and / or a second scene corresponding to the distance between the person's personal position and the air conditioning position of the smart air conditioner being less than or equal to a preset distance;

[0198] If it is determined that the current scene where the person is located is at least one of all target scenes, it is determined that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition.

[0199] It can be seen that this optional embodiment can obtain the personal information of people in the area where the smart air conditioner is located, and judge whether the current scene where the people are located is at least one preset target scene based on the personal information of the people in the area where the smart air conditioner is located, and when the current scene where the people are located is the target scene, it is determined that the current scene conditions in the area where the smart air conditioner is located meet the preset scene conditions, which can improve the accuracy of determining whether the preset scene conditions are met, and is conducive to the smart air conditioner selecting a suitable operating mode.

[0200] In this optional embodiment, as an optional implementation, the judgment module 301 is further used to judge whether the acquired historical operation times of the smart air conditioner are greater than or equal to the preset times if it is judged that the current scene where the person is located is neither the first scene nor the second scene, and the historical operation times are the times that the operation mode of the smart air conditioner is the oscillating wave flow mode when the person is in the area where the smart air conditioner is located within the preset time period in the past;

[0201] The determination module 302 is further configured to determine that the current scene condition of the area where the smart air conditioner is located does not meet the preset scene condition if the determination module 301 determines that the historical operation times of the smart air conditioner are less than the preset times;

[0202] The determination module 302 is further used to determine whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition if the determination module 301 determines that the historical operation times of the smart air conditioner are greater than or equal to the preset times.

[0203] It can be seen that this optional implementation method can, when it is determined that the person is not in the first scene and the second scene, further determine that the smart air conditioner has selected the oscillating wave flow mode more times when the person was in the area in the recent period, and still determine that the current scene conditions of the area where the smart air conditioner is located meet the preset scene conditions. This can enrich the determination method of meeting the preset scene conditions and improve the flexibility of determining the preset scene conditions.

[0204] Embodiment 4

[0205] See also Figure 5 , Figure 5 FIG. 1 is a schematic diagram of the structure of another intelligent air conditioning control device applied to air flow diffusion scenarios disclosed in an embodiment of the present invention. Figure 5 As shown, the intelligent air conditioning control device applied to the air flow diffusion scene may include:

[0206] A memory 401 storing executable program codes;

[0207] a processor 402 coupled to the memory 401;

[0208] The processor 402 calls the executable program code stored in the memory 401 to execute the steps of the intelligent air conditioning control method applied to the air flow diffusion scenario described in the first embodiment of the present invention or the second embodiment of the present invention.

[0209] Embodiment 5

[0210] 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 applied to the air flow diffusion scenario described in Embodiment 1 or Embodiment 2 of the present invention.

[0211] Embodiment 6

[0212] 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 applied to the air flow diffusion scenario described in Example 1 or Example 2.

[0213] 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.

[0214] 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.

[0215] Finally, it should be noted that the intelligent air-conditioning control method and device applied to air flow diffusion scenarios disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, which are 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 it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to replace some of the technical features therein 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 applied to air flow diffusion scenarios, characterized in that: The method comprises: Determine whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition, where the scene included in the current scene condition is a scene where there are people in the area; If it is determined that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition, determining that the operation mode of the smart air conditioner is an oscillating wave flow mode; Based on the oscillating wave flow pattern, a target control parameter of the intelligent air conditioner is generated, wherein the target control parameter is a parameter for controlling the intelligent air conditioner to perform a corresponding control operation, and the control operation at least includes an airflow diffusion operation corresponding to the oscillating wave flow pattern.

2. The intelligent air conditioning control method applied to air flow diffusion scenarios according to claim 1 is characterized in that: The step of generating target control parameters of the intelligent air conditioner based on the oscillating wave flow mode includes: Acquire regional environmental information of the area where the smart air conditioner is located, where the regional environmental information of the area at least includes regional airflow information of the area; Determining an airflow path of airflow in the area according to the area airflow information; Analyzing at least one target control parameter type for the oscillating wave flow pattern based on the oscillating wave flow pattern, the regional environment information, and the airflow flow path; Determine a specific control parameter of each target control parameter type according to the regional environment information, the airflow path, all the target control parameter types, and the preset control configuration information corresponding to each target control parameter type; The target control parameter of the intelligent air conditioner is used to represent a specific control parameter of each target control parameter type among all the target control parameter types.

3. The intelligent air conditioning control method applied to air flow diffusion scenarios according to claim 2 is characterized in that: Determining the specific control parameter of each target control parameter type according to the regional environment information, the airflow path, all the target control parameter types, and the preset control configuration information corresponding to each target control parameter type includes: For each of the target control parameter types, a target airflow position matching the target control parameter type is selected from all airflow positions included in the airflow path; According to the target airflow positions corresponding to all the target control parameter types, the regional environment information is grouped to obtain a plurality of sub-regional environment information groups, wherein all sub-regional environment information in the same sub-regional environment information group corresponds to the same target airflow position; For each of the target control parameter types, generating a parameter type control requirement corresponding to the target control parameter type according to the target airflow flow position corresponding to the target control parameter type and the sub-region environment information group corresponding to the target airflow flow position; For each of the target control parameter types, specific control parameters of the target control parameter type are determined according to a parameter type control requirement corresponding to the target control parameter type and preset control configuration information corresponding to the target control parameter type.

4. The intelligent air conditioning control method applied to air flow diffusion scene according to claim 3 is characterized in that: The analyzing at least one target control parameter type for the oscillating wave flow pattern based on the oscillating wave flow pattern, the regional environment information, and the airflow path includes: Analyzing the airflow diffusion condition corresponding to each of all the airflow flow positions according to the regional environmental information; Generate an airflow diffusion requirement for the area where the smart air conditioner is located according to the airflow diffusion conditions corresponding to all the airflow flow positions and the regional environmental information; According to the airflow diffusion demand of the area where the smart air conditioner is located, at least one of the preset initial control parameter type sets whose matching degree with the airflow diffusion demand is greater than or equal to a preset matching degree threshold is searched as the target control parameter type for the oscillating wave flow mode. The initial control parameter type set includes the initial control parameter type corresponding to each sub-initial control parameter in the initial control parameters of the smart air conditioner.

5. The intelligent air conditioning control method applied to air flow diffusion scenarios according to any one of claims 1 to 4, characterized in that: The method further comprises: Obtaining the initial airflow diffusion condition of the area before generating the target control parameter; According to the target control parameter and the initial airflow diffusion condition, predict the airflow diffusion change trend of the area, wherein the airflow diffusion change trend is the change trend of the airflow diffusion condition of the area within a future target preset time period obtained when simulating the airflow diffusion operation; According to the airflow diffusion change trend, predicting the predicted perception situation of the airflow diffusion change trend that the personnel need to perceive; Determining whether the predicted perception of the personnel meets the preset personnel comfort conditions; When it is determined that the predicted perception of the person meets the preset person comfort condition, the intelligent air conditioner is controlled to perform the airflow diffusion operation according to the target control parameter.

6. The intelligent air conditioning control method applied to air flow diffusion scenarios according to any one of claims 1 to 4, characterized in that: The determining whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition includes: Acquire personnel information of personnel in the area where the smart air conditioner is located, wherein the personnel information of the personnel at least includes the personnel status of the personnel and the personnel location of the personnel; According to the personnel information of the personnel in the area where the smart air conditioner is located, determining whether the current scene where the personnel is located is at least one preset target scene, wherein the target scene includes a first scene corresponding to the personnel state of the personnel being a leisure state and / or a second scene corresponding to the distance between the personnel position of the personnel and the air conditioning position of the smart air conditioner being less than or equal to a preset distance; If it is determined that the current scene in which the person is located is at least one of all the target scenes, it is determined that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition.

7. The intelligent air conditioning control method applied to air flow diffusion scenarios according to claim 6 is characterized in that: The method further comprises: If it is determined that the current scene where the person is located is neither the first scene nor the second scene, it is determined whether the acquired historical operation times of the smart air conditioner are greater than or equal to a preset number, where the historical operation times are the number of times the operation mode of the smart air conditioner is the oscillating wave flow mode when the person is in the area where the smart air conditioner is located within a preset time period in the past; If it is determined that the historical operation times of the smart air conditioner are less than the preset times, it is determined that the current scene condition of the area where the smart air conditioner is located does not meet the preset scene condition; If it is determined that the historical operation times of the smart air conditioner are greater than or equal to the preset times, it is determined that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition.

8. An intelligent air conditioning control device applied to air flow diffusion scenes, characterized in that: The device comprises: A judgment module, used to judge whether the current scene condition of the area where the smart air conditioner is located meets the preset scene condition, and the scene included in the current scene condition is the scene where there are people in the area; a determination module, configured to determine that the operation mode of the smart air conditioner is an oscillating wave flow mode if the determination module determines that the current scene condition of the area where the smart air conditioner is located meets the preset scene condition; A generation module is used to generate target control parameters of the intelligent air conditioner based on the oscillating wave flow pattern, wherein the target control parameters are parameters for controlling the intelligent air conditioner to perform corresponding control operations, and the control operations at least include airflow diffusion operations corresponding to the oscillating wave flow pattern.

9. An intelligent air conditioning control device applied to air flow diffusion scenarios, 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 applied to the air flow diffusion scenario as described in any one of claims 1-7.

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 applied to the air flow diffusion scene as described in any one of claims 1-7.

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