Air conditioner intelligent regulation and control method and device based on predicted airflow form

Abstract

The invention discloses an air conditioner intelligent regulation and control method and device based on a predicted airflow form. The method comprises the steps that a regulation and control operation instruction of a target user for an intelligent air conditioner is obtained and analyzed, and predicted airflow form information of the intelligent air conditioner is obtained; acquiring area information of a current area corresponding to the intelligent air conditioner; according to the predicted airflow form information and the area information, the expected influence effect of the intelligent air conditioner on the current area is analyzed, and whether the expected influence effect meets the area operation condition corresponding to the current area or not is judged; and if not, airflow form regulation and control parameters of the intelligent air conditioner are generated, the regulation and control operation instruction is updated according to the airflow form regulation and control parameters, and the intelligent air conditioner is controlled to execute regulation and control operation matched with the updated regulation and control operation instruction. It can be seen that by means of the method and device, the intelligence of adjusting and controlling the air conditioner airflow can be improved, the accuracy and reliability of adjusting and controlling the air conditioner airflow can be improved, and then the comfort degree and experience feeling of a user using the air conditioner can be improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent control method and device for air conditioning based on predicting airflow patterns. Background Art

[0002] In real life, air conditioning has become one of the indispensable devices in people's daily life. At present, most of the ways to control air conditioning are basically manual control by users through air conditioning remote controls or remote control devices (such as smart phones, smart tablets, etc.). The traditional air supply direction is manually controlled by the user himself, but if the user controls it improperly or makes a mistake, the air conditioning air will blow directly at the user, which will make the user's body temperature lower, reduce the user's experience and comfort, and easily affect the user's physical condition. It can be seen that it is particularly important to provide a new method for regulating air conditioning airflow to improve the accuracy and intelligence of airflow regulation and thus improve the user's comfort and experience. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an intelligent air-conditioning control method and device based on the prediction of airflow patterns, which can help improve the intelligence of the air-conditioning airflow control, as well as improve the accuracy and reliability of the air-conditioning airflow control, and further help improve the comfort and experience of users using air conditioning.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses an intelligent air conditioning control method based on predicting airflow patterns, the method comprising:

[0005] Acquire a control operation instruction of a target user for a smart air conditioner, analyze the control operation instruction, and obtain predicted airflow pattern information of the smart air conditioner, wherein the predicted airflow pattern information includes information corresponding to the airflow pattern outputted after the smart air conditioner performs an operation matching the control operation instruction;

[0006] Acquire area information of a current area corresponding to the smart air conditioner, wherein the area information includes biological information and object information in the current area;

[0007] Analyze the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information, and determine whether the expected impact meets the area operation condition corresponding to the current area;

[0008] When it is determined that the expected impact effect does not meet the regional operating conditions corresponding to the current area, the airflow shape control parameters of the intelligent air conditioner are generated, the control operation instructions are updated according to the airflow shape control parameters, and the intelligent air conditioner is controlled to perform the control operation matching the updated control operation instructions.

[0009] As an optional implementation, in the first aspect of the present invention, before analyzing the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information, the method further includes:

[0010] Acquire the real-time regional information of the current region, wherein the real-time regional information includes one or more of the real-time temperature information, real-time wind force information, real-time wind direction information, and real-time humidity information of the region;

[0011] According to the real-time information of the area, determining whether there is an airflow influencing factor in the current area that affects the airflow pattern of the intelligent air conditioner;

[0012] When it is determined that the airflow influencing factors exist in the current area, the predicted airflow pattern information of the intelligent air conditioner is updated according to all the airflow influencing factors, and the operation of analyzing the expected impact effect of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information and judging whether the expected impact effect meets the area operating conditions corresponding to the current area is triggered.

[0013] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0014] Performing a preset region division operation on the current region to obtain at least two target sub-regions corresponding to the current region;

[0015] The analyzing the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information includes:

[0016] Determining, based on the predicted airflow pattern information, the airflow pattern outputted after the intelligent air conditioner performs an operation matching the control operation instruction and the regional airflow influence information between each of the target sub-areas;

[0017] Determining airflow bearing effect information of each target sub-area according to the area information;

[0018] For each of the target sub-areas, based on the regional airflow impact information of the target sub-area and the airflow bearing effect information of the target sub-area, an expected regional impact effect of the target sub-area is generated, wherein the expected regional impact effect includes a predicted impact effect of the airflow output by the intelligent air conditioner on the target sub-area;

[0019] According to the expected regional impact effects of all the target sub-areas, the expected impact effect of the intelligent air conditioner on the current area is generated.

[0020] As an optional implementation manner, in the first aspect of the present invention, the determining whether the expected impact effect satisfies the regional operating condition corresponding to the current region includes:

[0021] For each of the target sub-regions, determining a matching parameter between the regional airflow impact information of the target sub-region and the airflow bearing effect information of the target sub-region according to the regional airflow impact information of the target sub-region and the airflow bearing effect information of the target sub-region;

[0022] Determining whether the matching parameters of all the target sub-regions meet the preset region matching conditions;

[0023] When it is determined that the matching parameters of all the target sub-regions satisfy the preset regional matching condition, determining that the expected impact effect satisfies the regional operating condition corresponding to the current region;

[0024] When it is determined that the matching parameters of all the target sub-regions do not satisfy the preset region matching condition, it is determined that the expected impact effect does not satisfy the region operation condition corresponding to the current region.

[0025] As an optional implementation, in the first aspect of the present invention, after generating the airflow pattern control parameters of the intelligent air conditioner, the method further includes:

[0026] Generate simulated airflow morphology parameters according to the airflow morphology control parameters and the area information;

[0027] Based on the simulated airflow shape parameters, a simulated airflow supply trajectory of the intelligent air conditioner is generated, and according to the airflow bearing effect information of each target sub-area, regional airflow bearing effect information of the current area is generated;

[0028] According to the simulated airflow supply trajectory and the regional airflow bearing effect information, determining whether the simulated airflow supply trajectory matches the regional airflow bearing effect information;

[0029] When it is determined that the simulated airflow supply trajectory matches the regional airflow bearing effect information, the control of the intelligent air conditioner to perform a control operation that matches the updated control operation instruction is triggered.

[0030] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0031] When it is determined that the simulated airflow supply trajectory does not match the regional airflow bearing effect information, at least one to-be-regulated area is determined from all the target sub-areas according to the simulated airflow supply trajectory and the airflow bearing effect information of each target sub-area, and the airflow bearing effect information of the to-be-regulated area does not match the simulated airflow supply trajectory;

[0032] Determine the regional position information of each of the areas to be regulated in the current area, and perform an update operation on the airflow pattern control parameters based on the regional position information of all the areas to be regulated, and trigger the execution of the control of the intelligent air conditioner to perform a control operation that matches the updated control operation instruction.

[0033] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0034] Determine the smart devices existing in each of the target sub-areas in the current area, and obtain device status information of the smart devices in the current area, wherein the number of the smart devices is at least one;

[0035] For each of the smart devices, determining the airflow requirement information of the smart device for the smart air conditioner according to the device status information of the smart device;

[0036] Wherein, determining the airflow bearing effect information of each target sub-area according to the area information includes:

[0037] According to the airflow requirement information of each of the smart devices and the area information, the airflow tolerance effect information of each of the target sub-areas is determined.

[0038] The second aspect of the present invention discloses an intelligent air conditioning control device based on predicting airflow patterns, the device comprising:

[0039] An acquisition module is used to obtain the control operation instructions of the target user for the smart air conditioner;

[0040] An analysis module, used to analyze the control operation instruction to obtain predicted airflow pattern information of the intelligent air conditioner, wherein the predicted airflow pattern information includes information corresponding to the airflow pattern output by the intelligent air conditioner after performing an operation matching the control operation instruction;

[0041] The acquisition module is further used to acquire the area information of the current area corresponding to the smart air conditioner, wherein the area information includes biological information and object information in the current area;

[0042] The analysis module is further used to analyze the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information;

[0043] A judgment module, used to judge whether the expected impact effect meets the regional operation condition corresponding to the current region;

[0044] A generating module, configured to generate airflow pattern control parameters of the intelligent air conditioner when the judging module judges that the expected impact effect does not meet the regional operating conditions corresponding to the current region;

[0045] An updating module, used for updating the control operation instruction according to the airflow shape control parameter;

[0046] The control module is used to control the intelligent air conditioner to perform a control operation that matches the updated control operation instruction.

[0047] As an optional implementation, in the second aspect of the present invention, the acquisition module is further used to acquire the regional real-time information of the current area before the analysis module analyzes the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the regional information, wherein the regional real-time information includes one or more of the real-time temperature information, real-time wind force information, real-time wind direction information, and real-time humidity information of the area;

[0048] The judgment module is further used to judge whether there is an airflow influencing factor in the current area that affects the airflow form of the intelligent air conditioner according to the real-time information of the area;

[0049] The update module is also used to update the predicted airflow pattern information of the intelligent air conditioner according to all the airflow influencing factors when the judgment module determines that the airflow influencing factors exist in the current area, and trigger the analysis module to execute the operation of analyzing the expected impact effect of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information, and trigger the judgment module to execute the operation of judging whether the expected impact effect meets the area operating conditions corresponding to the current area.

[0050] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0051] An execution module, configured to perform a preset region division operation on the current region to obtain at least two target sub-regions corresponding to the current region;

[0052] The specific manner in which the analysis module analyzes the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information includes:

[0053] Determining, based on the predicted airflow pattern information, the airflow pattern outputted after the intelligent air conditioner performs an operation matching the control operation instruction and the regional airflow influence information between each of the target sub-areas;

[0054] Determining airflow bearing effect information of each target sub-area according to the area information;

[0055] For each of the target sub-areas, based on the regional airflow impact information of the target sub-area and the airflow bearing effect information of the target sub-area, an expected regional impact effect of the target sub-area is generated, wherein the expected regional impact effect includes a predicted impact effect of the airflow output by the intelligent air conditioner on the target sub-area;

[0056] According to the expected regional impact effects of all the target sub-areas, the expected impact effect of the intelligent air conditioner on the current area is generated.

[0057] As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the judgment module judges whether the expected impact effect satisfies the regional operating condition corresponding to the current region includes:

[0058] For each of the target sub-regions, determining a matching parameter between the regional airflow impact information of the target sub-region and the airflow bearing effect information of the target sub-region according to the regional airflow impact information of the target sub-region and the airflow bearing effect information of the target sub-region;

[0059] Determining whether the matching parameters of all the target sub-regions meet the preset region matching conditions;

[0060] When it is determined that the matching parameters of all the target sub-regions satisfy the preset regional matching condition, determining that the expected impact effect satisfies the regional operating condition corresponding to the current region;

[0061] When it is determined that the matching parameters of all the target sub-regions do not satisfy the preset region matching condition, it is determined that the expected impact effect does not satisfy the region operation condition corresponding to the current region.

[0062] As an optional implementation, in the second aspect of the present invention, the generation module is further used to generate simulated airflow morphology parameters according to the airflow morphology control parameters and the regional information after generating the airflow morphology control parameters of the intelligent air conditioner; based on the simulated airflow morphology parameters, generate the simulated airflow supply trajectory of the intelligent air conditioner, and generate the regional airflow bearing effect information of the current area according to the airflow bearing effect information of each of the target sub-areas;

[0063] The judgment module is also used to judge whether the simulated airflow supply trajectory matches the regional airflow bearing effect information based on the simulated airflow supply trajectory and the regional airflow bearing effect information; when it is judged that the simulated airflow supply trajectory matches the regional airflow bearing effect information, the control module is triggered to execute the control of the intelligent air conditioner to perform the control operation matching the updated control operation instruction.

[0064] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0065] A determination module, for determining, when the judgment module determines that the simulated airflow supply trajectory does not match the regional airflow bearing effect information, at least one to-be-regulated area from all the target sub-areas according to the simulated airflow supply trajectory and the airflow bearing effect information of each target sub-area, wherein the airflow bearing effect information of the to-be-regulated area does not match the simulated airflow supply trajectory; and determining the regional position information of each to-be-regulated area in the current area;

[0066] The update module is also used to perform an update operation on the airflow pattern control parameters based on the regional location information of all the areas to be controlled, and trigger the control module to execute the control operation of controlling the intelligent air conditioner to perform the control operation matching the updated control operation instruction.

[0067] As an optional implementation, in the second aspect of the present invention, the determining module is further used to determine the smart devices existing in each of the target sub-areas in the current area;

[0068] The acquisition module is further used to acquire device status information of the smart device in the current area, wherein the number of the smart device is at least one;

[0069] The determination module is further configured to determine, for each of the smart devices, the airflow requirement information of the smart device for the smart air conditioner according to the device status information of the smart device;

[0070] The specific manner in which the determination module determines the airflow bearing effect information of each target sub-area according to the area information includes:

[0071] According to the airflow requirement information of each of the smart devices and the area information, the airflow tolerance effect information of each of the target sub-areas is determined.

[0072] The third aspect of the present invention discloses another intelligent air conditioning control device based on predicting airflow patterns, the device comprising:

[0073] A memory storing executable program code;

[0074] a processor coupled to the memory;

[0075] The processor calls the executable program code stored in the memory to execute the intelligent air conditioning control method based on predicted airflow patterns disclosed in the first aspect of the present invention.

[0076] 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 predicted airflow form disclosed in the first aspect of the present invention.

[0077] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0078] In the embodiment of the present invention, the control operation instructions of the target user for the smart air conditioner are obtained and analyzed to obtain the predicted airflow pattern information of the smart air conditioner; the regional information of the current area corresponding to the smart air conditioner is obtained; based on the predicted airflow pattern information and the regional information, the expected impact effect of the smart air conditioner on the current area is analyzed to determine whether the expected impact effect meets the regional operating conditions corresponding to the current area; if not, the airflow pattern control parameters of the smart air conditioner are generated, the control operation instructions are updated according to the airflow pattern control parameters, and the smart air conditioner is controlled to perform the control operation matching the updated control operation instructions. It can be seen that the implementation of the present invention can be beneficial to improving the intelligence of the air conditioning airflow control, as well as the accuracy and reliability of the air conditioning airflow control, and further, it is beneficial to improve the comfort and experience of users using the air conditioner. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] 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.

[0080] Figure 1 It is a scene schematic diagram of an air conditioning intelligent control scene based on predicted airflow form disclosed in an embodiment of the present invention;

[0081] Figure 2 It is a flow chart of an intelligent air conditioning control method based on predicting airflow patterns disclosed in an embodiment of the present invention;

[0082] Figure 3 It is a flow chart of another intelligent air conditioning control method based on predicting airflow patterns disclosed in an embodiment of the present invention;

[0083] Figure 4 It is a structural schematic diagram of an intelligent air conditioning control device based on predicted airflow form disclosed in an embodiment of the present invention;

[0084] Figure 5 It is a structural schematic diagram of another intelligent air conditioning control device based on predicted airflow form disclosed in an embodiment of the present invention;

[0085] Figure 6 It is a structural schematic diagram of another intelligent air-conditioning control device based on predicted airflow patterns disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0086] 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.

[0087] 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.

[0088] 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.

[0089] The present invention discloses an intelligent air conditioning control method and device based on the prediction of airflow patterns, which can help improve the intelligence of air conditioning airflow control, as well as the accuracy and reliability of air conditioning airflow control, and further help improve the comfort and experience of users using air conditioning. The following are detailed descriptions.

[0090] In order to better understand the method and device for intelligent air conditioning control based on predicted airflow form disclosed in the present invention, firstly, an intelligent air conditioning control scenario based on predicted airflow form is described. Specifically, an intelligent air conditioning control scenario based on predicted airflow form can be described as follows: Figure 1 As shown, Figure 1 The present invention discloses a scene schematic diagram of an air conditioning intelligent control scene based on the prediction of airflow form. Figure 1 As shown, the scene of intelligent air conditioning control based on predicted airflow form may include air conditioning, chairs, tables, and a piece of paper attached to the table; optionally, the scene of intelligent air conditioning control based on predicted airflow form may be a home scene, a shopping mall scene, a company scene, and other scenes, which are not specifically limited in the embodiment of the present invention. It should be noted that in this scene, the method of intelligent air conditioning control scene based on predicted airflow form may be to obtain predicted airflow form information of the intelligent air conditioner by acquiring and analyzing the control operation instructions of the target user for the intelligent air conditioner; to obtain the regional information of the current area corresponding to the intelligent air conditioner; according to the predicted airflow form information and the regional information, analyze the expected impact effect of the intelligent air conditioner on the current area, and judge whether the expected impact effect meets the regional operating conditions corresponding to the current area; if not, generate the airflow form control parameters of the intelligent air conditioner, update the control operation instructions according to the airflow form control parameters, and control the intelligent air conditioner to perform the control operation matching the updated control operation instructions, which can be beneficial to improve the intelligence of the air conditioning airflow control, and to improve the accuracy and reliability of the air conditioning airflow control, and further to improve the comfort and experience of the user using the air conditioner.

[0091] Further optionally, the real-time regional information of the current area is obtained, and it is determined whether there are airflow influencing factors in the current area that affect the airflow pattern of the smart air conditioner based on the real-time regional information. If so, the predicted airflow pattern information of the smart air conditioner is updated according to all airflow influencing factors, and the expected impact of the smart air conditioner on the current area is analyzed based on the predicted airflow pattern information and regional information, and it is determined whether the expected impact meets the regional operating conditions corresponding to the current area. The predicted airflow pattern information can be updated based on the airflow influencing factors on the smart air conditioner in the current area, which can improve the accuracy and execution of the prediction of the airflow pattern of the smart air conditioner.

[0092] Further optionally, multiple target sub-areas are obtained by performing a region division operation on the current area, and the airflow bearing effect information of each target sub-area is determined, thereby generating the expected regional impact effect of each target sub-area, and combining the expected regional impact effects of all target sub-areas to generate the expected impact effect of the current area, which is beneficial to improving the accuracy and reliability of the expected impact effect of the smart air conditioner on the current area.

[0093] Further optionally, the smart devices existing in each target sub-area in the current area are determined, and the device status information of the smart devices in the current area is obtained, so as to determine the airflow demand information of each smart device for the smart air conditioner, and determine the airflow tolerance information of the target sub-area according to the airflow demand information and the regional information of each smart device. The airflow demand information can be comprehensively generated in combination with the smart devices existing in the current area and the device status, which is conducive to improving the accuracy and reliability of generating the airflow demand information of each smart device, and is conducive to improving the accuracy and reliability of subsequently determining the airflow tolerance information of each target sub-area according to the airflow demand information and the regional information of each smart device, and is conducive to improving the accuracy and reliability of determining the expected regional impact effect of each target sub-area and the subsequent generation of the expected impact effect of the smart air conditioner on the current area.

[0094] For example, if Figure 1 As shown, Figure 1 It is a home scene, and the installation position of the smart air conditioner is located at the upper and rear side relative to the chair and the table, and the air conditioner is blowing air directly towards the chair and the table, and there is a piece of paper on the table. The analysis shows that the expected impact of the smart air conditioner on the current area is that the paper on the table is easily blown away by the airflow output by the air conditioner. Therefore, it is determined that the expected impact does not meet the regional operating conditions corresponding to the current area, thereby generating the airflow shape control parameters of the smart air conditioner and updating the control operation instructions of the air conditioner.

[0095] It should be noted that Figure 1The scenario architecture shown is only for representing the scenario used by the intelligent air conditioning control method based on the predicted airflow pattern. The tables, chairs, air conditioners, and paper involved are only schematically displayed. The specific structure / size / shape / location / installation method can be adaptively adjusted according to the actual scenario. Figure 1 The scene architecture shown is not limited to this.

[0096] The above describes the scenarios in which the method for intelligently controlling air conditioners based on predicted airflow patterns is used. The following describes in detail the method and device for intelligently controlling air conditioners based on predicted airflow patterns.

[0097] Embodiment 1

[0098] See also Figure 2 , Figure 2 This is a flow chart of an intelligent air conditioning control method based on airflow pattern prediction disclosed in an embodiment of the present invention. Figure 2 The described method for intelligent air conditioning control based on predicted airflow patterns can be applied to an intelligent air conditioning control device based on predicted airflow patterns, can also be applied to a local server or cloud server for intelligent air conditioning control based on predicted airflow patterns, and can also be applied to the intelligent air conditioner itself, which is not limited in the embodiments of the present invention. Figure 2 As shown, the intelligent air conditioning control method based on predicting airflow patterns may include the following operations:

[0099] 101. Obtain control operation instructions of a target user for the smart air conditioner, analyze the control operation instructions, and obtain predicted airflow pattern information of the smart air conditioner.

[0100] In an embodiment of the present invention, the predicted airflow pattern information includes information corresponding to the airflow pattern outputted after the intelligent air conditioner executes an operation matching the control operation instruction.

[0101] In an embodiment of the present invention, optionally, obtaining the control operation instructions of the target user for the smart air conditioner can be obtained in real time, can be obtained periodically according to a preset time period, or can be obtained when the airflow pattern of the smart air conditioner needs to be controlled, and the embodiment of the present invention does not make specific limitations.

[0102] In an embodiment of the present invention, optionally, the predicted airflow pattern information of the intelligent air conditioner includes one or more of airflow flow information, airflow trajectory information, and airflow velocity information output by the intelligent air conditioner.

[0103] 102. Obtain the area information of the current area corresponding to the smart air conditioner.

[0104] In the embodiment of the present invention, the area information includes biological information and object information in the current area.

[0105] In an embodiment of the present invention, optionally, the biological information in the current area includes one or more of user information, animal information, and plant information included in the current area, wherein the user information includes one or more of user location information, user attribute information, user gender information, user status information, and user age information of each user in the current area, and the animal information includes one or more of animal breed information, animal status information, and animal age information of each animal in the current area; the plant information includes one or more of plant breed information and plant status information of each plant in the current area; further optionally, the object information in the current area includes one or more of object location information and object attribute information of each object included in the current area.

[0106] In an embodiment of the present invention, optionally, the regional information of the current area corresponding to the smart air conditioner can be obtained at the same time as the control operation instructions of the target user for the smart air conditioner are obtained, or the regional information of the current area corresponding to the smart air conditioner can be obtained first and then the control operation instructions of the target user for the smart air conditioner are obtained, or the control operation instructions of the target user for the smart air conditioner can be obtained first and then the regional information of the current area corresponding to the smart air conditioner is obtained, which is not specifically limited in the embodiment of the present invention.

[0107] 103. Based on the predicted airflow pattern information and regional information, analyze the expected impact of the intelligent air conditioner on the current area, and determine whether the expected impact meets the regional operating conditions corresponding to the current area.

[0108] In the embodiment of the present invention, optionally, analyzing the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information may include:

[0109] The predicted airflow pattern information and regional information are input into a predetermined impact effect determination model to obtain a model output result, and the expected impact effect of the intelligent air conditioner on the current area is determined based on the model output result.

[0110] 104. When it is determined that the expected impact effect does not meet the regional operating conditions corresponding to the current area, the airflow shape control parameters of the intelligent air conditioner are generated, the control operation instructions are updated according to the airflow shape control parameters, and the intelligent air conditioner is controlled to perform the control operation matching the updated control operation instructions.

[0111] In the embodiment of the present invention, further optionally, the method further includes:

[0112] When it is determined that the expected impact effect meets the regional operating conditions corresponding to the current area, the intelligent air conditioner is controlled to perform a control operation that matches the control operation instruction.

[0113] In the embodiment of the present invention, optionally, the airflow shape control parameter includes one or more of an airflow velocity control parameter, an airflow direction control parameter, and an airflow flow control parameter.

[0114] In the embodiment of the present invention, optionally, the expected impact effect corresponding to the updated control operation instruction satisfies the regional operating conditions corresponding to the current region.

[0115] It can be seen that implementation Figure 2 The described intelligent air conditioning control method based on predicted airflow patterns can obtain and analyze the user's control operation instructions for the intelligent air conditioner to obtain the predicted airflow pattern information of the intelligent air conditioner, and obtain the regional information of the current area corresponding to the intelligent air conditioner. According to the predicted airflow pattern information and regional information, the expected impact of the intelligent air conditioner on the current area is analyzed, and it is judged whether the expected impact meets the regional operating conditions corresponding to the current area. If not, airflow pattern control parameters are generated, and the control operation instructions are updated according to the airflow pattern control parameters, and the intelligent air conditioner is controlled to perform the control operation matching the updated control operation instructions. The airflow pattern control parameters can be intelligently generated in combination with the regional information of the current area, and the matching degree between the generated airflow pattern control parameters and the current area can be improved, which is conducive to improving the accuracy and reliability of the generated airflow pattern control parameters. The control operation instructions are updated according to the airflow pattern control parameters, which is conducive to improving the accuracy and reliability of the updated control operation instructions, and is conducive to improving the accuracy and reliability of the subsequent control of the intelligent air conditioner to perform the corresponding control operation and output the matching airflow, thereby helping to improve the user experience and comfort.

[0116] Embodiment 2

[0117] See also Figure 3 , Figure 3 is a flow chart of another intelligent air conditioning control method based on predicting airflow patterns disclosed in an embodiment of the present invention. Figure 3 The described method for intelligent air conditioning control based on predicted airflow patterns can be applied to an intelligent air conditioning control device based on predicted airflow patterns, can also be applied to a local server or cloud server for intelligent air conditioning control based on predicted airflow patterns, and can also be applied to the intelligent air conditioner itself, which is not limited in the embodiments of the present invention. Figure 3 As shown, the intelligent air conditioning control method based on predicting airflow patterns may include the following operations:

[0118] 201. Obtain control operation instructions of a target user for an intelligent air conditioner, analyze the control operation instructions, and obtain predicted airflow pattern information of the intelligent air conditioner.

[0119] 202. Obtain area information of a current area corresponding to the smart air conditioner.

[0120] 203. Obtain the real-time regional information of the current region.

[0121] In the embodiment of the present invention, the regional real-time information includes one or more of the real-time temperature information, real-time wind force information, real-time wind direction information, and real-time humidity information of the region.

[0122] In the embodiment of the present invention, optionally, the real-time wind information includes one or more of wind flow information and wind speed information in the current area.

[0123] In an embodiment of the present invention, optionally, real-time regional information of the current area can be obtained by means of a temperature sensor, a wind sensor, a wind direction sensor, a humidity sensor, an infrared sensor, a radar sensor, a camera, etc., which is not specifically limited in the embodiment of the present invention.

[0124] 204. According to the regional real-time information, determine whether there is an airflow influencing factor in the current area that affects the airflow pattern of the intelligent air conditioner.

[0125] In the embodiment of the present invention, optionally, the number of airflow influencing factors may be one or more, which is not specifically limited in the embodiment of the present invention.

[0126] In the embodiment of the present invention, optionally, the airflow influencing factor includes one or more of an airflow flow influencing factor, an airflow velocity influencing factor, and an airflow direction influencing factor.

[0127] 205. When it is determined that there are airflow influencing factors in the current area, the predicted airflow pattern information of the intelligent air conditioner is updated according to all airflow influencing factors.

[0128] In the embodiment of the present invention, further optionally, when it is determined that there is no airflow influencing factor in the current area, step 206 is directly triggered to be executed.

[0129] In the embodiment of the present invention, optionally, updating the predicted airflow form information of the intelligent air conditioner according to all airflow influencing factors includes:

[0130] For each airflow influencing factor, analyze the influence of the airflow influencing factor on the airflow pattern of the intelligent air conditioner;

[0131] According to the influence results of all airflow influencing factors on the airflow shape of the intelligent air conditioner, the predicted airflow shape information of the intelligent air conditioner is updated.

[0132] 206. Analyze the expected impact of the intelligent air conditioner on the current area based on the predicted airflow pattern information and regional information, and determine whether the expected impact meets the regional operating conditions corresponding to the current area.

[0133] 207. When it is determined that the expected impact effect does not meet the regional operating conditions corresponding to the current area, the airflow pattern control parameters of the intelligent air conditioner are generated, the control operation instructions are updated according to the airflow pattern control parameters, and the intelligent air conditioner is controlled to perform a control operation that matches the updated control operation instructions.

[0134] In the embodiment of the present invention, for detailed descriptions of steps 201 to 202 and steps 206 to 207, please refer to other descriptions of steps 101 to 104 in the first embodiment, and the embodiment of the present invention will not be repeated here.

[0135] It can be seen that implementation Figure 3 The described intelligent air conditioning control method based on predicted airflow patterns can obtain regional implementation information of the current area, and determine whether there are airflow influencing factors that affect the airflow pattern of the intelligent air conditioner in the current area according to the regional implementation information. If so, the predicted airflow pattern information of the intelligent air conditioner is updated according to all airflow influencing factors. It can be combined with the regional real-time information of the current area to determine whether there are airflow influencing factors, which is conducive to improving the accuracy and reliability of determining whether there are airflow influencing factors. When it is determined that there are airflow influencing factors, the predicted airflow pattern information is updated based on all airflow influencing factors, which is conducive to improving the accuracy and reliability of generating predicted airflow pattern information, and further conducive to improving the accuracy and reliability of subsequent analysis of expected impact effects and generating airflow pattern control parameters.

[0136] In an optional embodiment, the method further comprises:

[0137] Performing a preset region division operation on the current region to obtain at least two target sub-regions corresponding to the current region;

[0138] Based on the predicted airflow pattern information and regional information, the expected impact of smart air conditioning on the current area is analyzed, including:

[0139] Determine, based on the predicted airflow pattern information, the airflow pattern outputted after the intelligent air conditioner performs an operation matching the control operation instruction and the regional airflow impact information between each target sub-area;

[0140] According to the regional information, determine the airflow bearing effect information of each target sub-region;

[0141] For each target sub-area, based on the regional airflow impact information of the target sub-area and the airflow bearing effect information of the target sub-area, the regional expected impact effect of the target sub-area is generated, and the regional expected impact effect includes the predicted impact effect of the airflow output by the intelligent air conditioner on the target sub-area;

[0142] According to the expected regional impact effects of all target sub-areas, the expected impact effect of the smart air conditioner on the current area is generated.

[0143] In this optional embodiment, the preset region division operation may optionally include a region area division operation and a region function division operation. For example, the region function division operation may determine the rest region as a target sub-region and the exercise region as a target sub-region.

[0144] In this optional embodiment, optionally, the airflow pattern output by the intelligent air conditioner after performing an operation matching the control operation instruction and the regional airflow impact information between each target sub-area includes one or more of the airflow direction impact information, airflow flow impact information, and airflow velocity impact information of the airflow pattern output by the intelligent air conditioner after performing an operation matching the control operation instruction on each target sub-area.

[0145] In this optional embodiment, optionally, determining the airflow bearing effect information of each target sub-area according to the area information includes:

[0146] According to the regional information, determine the airflow demand information of each target sub-region;

[0147] For each target sub-area, determining a maximum airflow tolerance effect parameter of the target sub-area according to the airflow demand information of the target sub-area, and determining the airflow tolerance effect information of the target sub-area based on the maximum airflow tolerance effect parameter of the target sub-area;

[0148] The maximum airflow tolerance effect parameter of each target sub-area includes one or more of a maximum airflow flow tolerance parameter and a maximum airflow velocity tolerance parameter of the target sub-area.

[0149] In this optional embodiment, optionally, the expected regional impact effect of each target sub-area includes one or more of an expected flow velocity effect, an expected flow effect, and an expected direction effect of the target sub-area.

[0150] In this optional embodiment, optionally, the expected impact of the intelligent air conditioner on the current area includes the expected regional impact of all target sub-areas.

[0151] It can be seen that the implementation of this optional embodiment can divide the current area into at least two target sub-areas, determine the regional airflow influence information between the airflow pattern output by the intelligent air conditioner after executing the operation matching the control operation instruction according to the predicted airflow pattern information, determine the airflow bearing effect information of each target sub-area according to the regional information, and generate the regional expected impact effect of each target sub-area according to the regional airflow influence information of each target sub-area and the airflow bearing effect information of each target sub-area, generate the expected impact effect of the intelligent air conditioner on the current area according to the regional expected impact effects of all target sub-areas, and can specifically determine the airflow bearing effect information of each target sub-area, which is conducive to improving the accuracy and reliability of determining the airflow bearing effect information of each target sub-area, and is also conducive to improving the intelligence of determining the airflow bearing effect information of each target sub-area, thereby helping to improve the accuracy and reliability of subsequently generating the expected impact effect of the intelligent air conditioner on the current area according to the regional expected impact effects of all target sub-areas, and is also conducive to improving the accuracy and reliability of subsequently generating the expected impact effect of the intelligent air conditioner on the current area based on the regional expected impact effects of all target sub-areas, and is also conducive to improving the accuracy and reliability of subsequently generating the airflow pattern control parameters based on the expected impact effect.

[0152] In another optional embodiment, determining whether the expected impact effect satisfies the regional operating condition corresponding to the current region includes:

[0153] For each target sub-region, determining a matching parameter between the regional airflow impact information of the target sub-region and the airflow bearing effect information of the target sub-region according to the regional airflow impact information of the target sub-region and the airflow bearing effect information of the target sub-region;

[0154] Determine whether the matching parameters of all target sub-regions meet the preset region matching conditions;

[0155] When it is determined that the matching parameters of all target sub-regions meet the preset regional matching conditions, it is determined that the expected impact effect meets the regional operating conditions corresponding to the current region;

[0156] When it is determined that the matching parameters of all target sub-regions do not satisfy the preset region matching condition, it is determined that the expected impact effect does not satisfy the region operation condition corresponding to the current region.

[0157] In this optional embodiment, optionally, the matching parameter of each target sub-region includes a matching degree between the regional airflow influence information of the target sub-region and the airflow bearing effect information of the target sub-region.

[0158] In this optional embodiment, optionally, determining whether the matching parameters of all target sub-regions satisfy a preset region matching condition includes:

[0159] Determine whether the matching degree of all target sub-regions is greater than or equal to a preset region matching degree threshold;

[0160] When it is determined whether the matching degrees of all target sub-regions are greater than or equal to a preset regional matching degree threshold, it is determined that the matching parameters of all target sub-regions meet the preset regional matching condition;

[0161] When it is determined that the matching degree inequality of all target sub-regions is greater than or equal to a preset region matching degree threshold, it is determined that the matching parameter inequality of all target sub-regions satisfies a preset region matching condition.

[0162] It can be seen that the implementation of this optional embodiment can determine the matching parameters between the regional airflow influence information of each target sub-area and the airflow bearing effect information of the target sub-area based on the regional airflow influence information of each target sub-area and the airflow bearing effect information of each target sub-area, and judge whether the target sub-area meets the preset regional matching conditions based on the matching parameters, which is conducive to improving the accuracy and reliability of judging whether the matching parameters of each target sub-area meet the regional matching conditions, thereby facilitating improving the accuracy and reliability of judging whether the expected impact effect meets the regional operating conditions corresponding to the current area, and further facilitating improving the accuracy and reliability of subsequently generating the airflow shape control parameters of the intelligent air conditioner and updating the control operation instructions, and is also conducive to improving the comfort and experience of users in each target sub-area for using the air conditioner.

[0163] In yet another optional embodiment, after generating the airflow pattern control parameters of the intelligent air conditioner, the method further includes:

[0164] Generate simulated airflow morphology parameters according to airflow morphology control parameters and regional information;

[0165] Based on the simulated airflow shape parameters, the simulated airflow supply trajectory of the intelligent air conditioner is generated, and according to the airflow bearing effect information of each target sub-area, the regional airflow bearing effect information of the current area is generated;

[0166] According to the simulated airflow supply trajectory and the regional airflow bearing effect information, it is determined whether the simulated airflow supply trajectory matches the regional airflow bearing effect information;

[0167] When it is determined that the simulated airflow supply trajectory matches the regional airflow bearing effect information, the execution control of the intelligent air conditioner is triggered to perform a control operation that matches the updated control operation instruction.

[0168] In this optional embodiment, optionally, generating simulated airflow morphology parameters according to the airflow morphology control parameters and the area information includes:

[0169] According to the airflow pattern control parameters and the regional information, the intelligent air conditioner is simulated to perform an airflow output operation that matches the airflow pattern control parameters to obtain an airflow output result, and simulated airflow pattern parameters are generated based on the airflow output result; wherein the simulated airflow pattern parameters include one or more of simulated airflow velocity parameters, simulated airflow flow parameters, and simulated airflow direction parameters.

[0170] In this optional embodiment, optionally, generating regional airflow tolerance effect information of the current area according to the airflow tolerance effect information of each target sub-area includes:

[0171] According to the airflow bearing effect information of each target sub-area and the simulated airflow supply trajectory of the intelligent air conditioner, the simulated airflow bearing effect corresponding to each target sub-area is determined, and based on the simulated airflow bearing effect corresponding to each target sub-area, the regional airflow bearing effect information of the current area is generated;

[0172] The simulated airflow effect corresponding to each target sub-area includes one or more of the simulated airflow wind speed effect, the simulated airflow wind direction effect, and the simulated airflow wind volume effect of the target sub-area.

[0173] In this optional embodiment, optionally, judging whether the simulated airflow supply trajectory matches the regional airflow bearing effect information according to the simulated airflow supply trajectory and the regional airflow bearing effect information includes:

[0174] Determine the airflow effect information corresponding to the simulated airflow trajectory, and calculate the effect difference between the airflow effect information and the regional airflow bearing effect information;

[0175] Determine whether the effect difference value is greater than or equal to a preset difference threshold;

[0176] When it is determined that the effect difference value is greater than or equal to a preset difference threshold, it is determined that the simulated airflow supply trajectory does not match the regional airflow bearing effect information;

[0177] When it is determined that the effect difference value is less than a preset difference threshold, it is determined that the simulated airflow supply trajectory matches the regional airflow bearing effect information.

[0178] It can be seen that the implementation of this optional embodiment can generate simulated airflow morphology parameters according to the airflow morphology control parameters and regional information, and then generate a simulated airflow supply trajectory of the intelligent air conditioner, and generate regional airflow bearing effect information of the current area according to the airflow bearing effect information of each target sub-area, and judge whether the simulated airflow supply trajectory matches the regional airflow bearing effect. If they match, the intelligent air conditioner is controlled to perform a control operation that matches the updated control operation instruction. Before actually controlling the intelligent air conditioner to perform the corresponding operation, the simulated airflow morphology parameters can be generated first, and the airflow bearing effect of each target sub-area can be determined according to the simulated simulated airflow supply trajectory, which is conducive to improving the accuracy and reliability of determining each airflow bearing effect information, and further conducive to improving the accuracy and reliability of generating regional airflow bearing effect information. The airflow morphology of the intelligent air conditioner can be simulated first, which can improve the accuracy and reliability of subsequent control of the intelligent air conditioner, avoid control errors due to control errors, and also help improve the intelligence and efficiency of the control of the intelligent air conditioner.

[0179] In yet another optional embodiment, the method further comprises:

[0180] When it is determined that the simulated airflow supply trajectory does not match the regional airflow bearing effect information, at least one to-be-regulated area is determined from all target sub-areas according to the simulated airflow supply trajectory and the airflow bearing effect information of each target sub-area, and the airflow bearing effect information of the to-be-regulated area does not match the simulated airflow supply trajectory;

[0181] Determine the regional position information of each area to be regulated in the current area, and update the airflow pattern control parameters based on the regional position information of all areas to be regulated, and trigger the execution control of the intelligent air conditioner to perform a control operation that matches the updated control operation instruction.

[0182] In this optional embodiment, the number of the areas to be regulated may be one or more, which is not specifically limited in the embodiment of the present invention.

[0183] In this optional embodiment, optionally, performing an update operation on the airflow shape control parameters based on the regional positions of all the regions to be controlled includes:

[0184] For each area to be regulated, determining the regional regulation parameters of the area to be regulated according to the regional position of the area to be regulated and the simulated airflow supply trajectory, wherein the regional regulation parameters include regional airflow direction regulation parameters;

[0185] According to the regional control parameters of all the areas to be controlled, the airflow pattern control parameters are updated so that the airflow bearing effect information corresponding to the area to be controlled matches the simulated airflow supply trajectory.

[0186] In this optional embodiment, further optionally, after performing an update operation on the airflow shape control parameters based on the area location information of all areas to be controlled, the method further includes:

[0187] According to the updated airflow shape control parameters, determine the updated simulated airflow supply trajectory, and judge whether the updated simulated airflow supply trajectory matches the regional airflow bearing effect information;

[0188] When it is determined that the updated simulated airflow supply trajectory matches the regional airflow bearing effect information, the intelligent air conditioner is triggered to execute a control operation that matches the updated control operation instruction;

[0189] When it is determined that the updated simulated airflow supply trajectory does not match the airflow bearing effect information, the steps of re-triggering the execution are as follows: at least one area to be regulated is determined from all target sub-areas according to the simulated airflow supply trajectory and the airflow bearing effect information of each target sub-area, the regional position information of each area to be regulated in the current area is determined, and the airflow morphology control parameters are updated based on the regional position information of all areas to be regulated.

[0190] It can be seen that the implementation of this optional embodiment can determine the area to be regulated when it is determined that the simulated airflow supply trajectory does not match the regional airflow bearing effect information, and perform an update operation on the airflow shape control parameters based on the regional position information of each area to be regulated. It can determine each area to be regulated in a targeted manner, which is beneficial to improving the accuracy and reliability of determining each area to be regulated, and perform an update operation on the airflow shape control parameters based on the regional position information of all areas to be regulated, which is beneficial to improving the accuracy and reliability of updating the airflow shape control parameters, and thus is beneficial to improving the accuracy and reliability of controlling the intelligent air conditioner, as well as improving the intelligence and efficiency of controlling the intelligent air conditioner.

[0191] In yet another optional embodiment, the method further comprises:

[0192] Determine the smart devices existing in each target sub-area in the current area, and obtain device status information of the smart devices in the current area, wherein the number of the smart devices is at least one;

[0193] For each smart device, determine the airflow requirement information of the smart device for the smart air conditioner according to the device status information of the smart device;

[0194] Wherein, according to the area information, the airflow bearing effect information of each target sub-area is determined, including:

[0195] According to the airflow demand information and area information of each smart device, the airflow tolerance effect information of each target sub-area is determined.

[0196] In this optional embodiment, the number of smart devices may be one or more, which is not limited in the embodiment of the present invention.

[0197] In this optional embodiment, optionally, the smart device can be one or more of a smart fan, a smart sweeping robot, a smart TV, a smart floor scrubber, and the like.

[0198] In this optional embodiment, optionally, the device status information of each smart device includes one or more of a use status, a pause status, a shutdown status, a heat dissipation status, and a heating status.

[0199] In this optional embodiment, optionally, for each smart device, determining the airflow requirement information of the smart device for the smart air conditioner according to the device status information of the smart device includes:

[0200] For each smart device, the device status information of the smart device is input into a predetermined airflow demand analysis model to obtain a demand analysis result, and the airflow demand information of the smart device for the smart air conditioner is determined based on the demand analysis result.

[0201] In this optional embodiment, optionally, the airflow demand information of each smart device for the smart air conditioner includes one or more of airflow temperature demand information, airflow humidity demand information, airflow velocity demand information, airflow flow demand information, and airflow direction demand information.

[0202] In this optional embodiment, optionally, for example, if there is an intelligent sweeping robot in the target sub-area A, and the intelligent sweeping robot is in a state where the motor temperature is high after just being used, then the airflow demand information of the intelligent sweeping robot for the intelligent air conditioner is determined to be airflow demand information with lower airflow temperature, higher airflow velocity and larger airflow flow rate.

[0203] It can be seen that the implementation of this optional embodiment can determine the airflow demand information of the smart devices for the smart air conditioner by determining the smart devices in each target sub-area and obtaining the device status information of each smart device. The airflow tolerance effect information of each target sub-area can be determined according to the airflow demand information and area information of each smart device. The airflow demand information can be generated in combination with the smart devices in each target sub-area and the device status information of each smart device, so as to achieve targeted determination of the airflow demand information of each smart device, which is conducive to improving the accuracy and reliability of determining the airflow demand information of each smart device for the smart air conditioner, and thus is conducive to improving the accuracy and reliability of determining the airflow tolerance effect information of each target sub-area. It is further conducive to the subsequent intelligence of determining the airflow tolerance effect information of each target sub-area, and thus is conducive to improving the accuracy and reliability of subsequently generating the expected impact effect of the smart air conditioner on the current area according to the expected regional impact effects of all target sub-areas, and is also conducive to improving the accuracy and reliability of subsequently generating the airflow morphology control parameters based on the expected impact effect.

[0204] Embodiment 3

[0205] See also Figure 4 , Figure 4 Schematic diagram of the structure of an intelligent air conditioning control device based on predicting airflow patterns disclosed in an embodiment of the present invention. Figure 4 As shown, the intelligent air conditioning control device based on predicting airflow patterns may include:

[0206] The acquisition module 301 is used to acquire the control operation instruction of the target user for the smart air conditioner;

[0207] The analysis module 302 is used to analyze the control operation instruction to obtain the predicted airflow pattern information of the intelligent air conditioner, and the predicted airflow pattern information includes information corresponding to the airflow pattern output after the intelligent air conditioner performs the operation matching the control operation instruction;

[0208] The acquisition module 301 is further used to acquire the area information of the current area corresponding to the smart air conditioner, where the area information includes biological information and item information in the current area;

[0209] The analysis module 302 is further used to analyze the expected impact of the intelligent air conditioner on the current area based on the predicted airflow pattern information and the area information;

[0210] A judgment module 303 is used to judge whether the expected impact effect meets the regional operation conditions corresponding to the current region;

[0211] The generating module 304 is used to generate the airflow pattern control parameters of the intelligent air conditioner when the judging module judges that the expected impact effect does not meet the regional operating conditions corresponding to the current region;

[0212] An updating module 305 is used to update the control operation instruction according to the airflow shape control parameter;

[0213] The control module 306 is used to control the intelligent air conditioner to perform a control operation that matches the updated control operation instruction.

[0214] It can be seen that implementation Figure 4 The described device can obtain and analyze the user's control operation instructions for the smart air conditioner to obtain the predicted airflow pattern information of the smart air conditioner, and obtain the regional information of the current area corresponding to the smart air conditioner. According to the predicted airflow pattern information and regional information, the expected impact effect of the smart air conditioner on the current area is analyzed, and it is judged whether the expected impact effect meets the regional operating conditions corresponding to the current area. If not, airflow pattern control parameters are generated, and the control operation instructions are updated according to the airflow pattern control parameters, and the smart air conditioner is controlled to perform the control operation matching the updated control operation instructions. The airflow pattern control parameters can be intelligently generated in combination with the regional information of the current area, and the matching degree between the generated airflow pattern control parameters and the current area can be improved, which is conducive to improving the accuracy and reliability of the generated airflow pattern control parameters. The control operation instructions are updated according to the airflow pattern control parameters, which is conducive to improving the accuracy and reliability of the updated control operation instructions, and is conducive to improving the accuracy and reliability of the subsequent control of the smart air conditioner to perform the corresponding control operation and output the matching airflow, thereby helping to improve the user experience and comfort.

[0215] In an optional embodiment, the acquisition module 301 is further used to acquire the regional real-time information of the current area before the analysis module 302 analyzes the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the regional information, wherein the regional real-time information includes one or more of the regional real-time temperature information, real-time wind force information, real-time wind direction information, and real-time humidity information;

[0216] The judgment module 303 is further used to judge whether there is an airflow influencing factor that affects the airflow form of the intelligent air conditioner in the current area according to the real-time regional information;

[0217] The update module 305 is also used to update the predicted airflow pattern information of the intelligent air conditioner according to all airflow influencing factors when the judgment module 303 determines that there are airflow influencing factors in the current area, and trigger the analysis module 302 to perform an operation of analyzing the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and regional information, and trigger the judgment module 303 to perform an operation of judging whether the expected impact meets the regional operating conditions corresponding to the current area.

[0218] It can be seen that implementation Figure 4The described device can obtain regional implementation information of the current area, and determine whether there are airflow influencing factors that affect the airflow pattern of the intelligent air conditioner in the current area based on the regional implementation information. If so, the predicted airflow pattern information of the intelligent air conditioner is updated based on all airflow influencing factors. It can determine whether there are airflow influencing factors in combination with the regional real-time information of the current area, which is conducive to improving the accuracy and reliability of determining whether there are airflow influencing factors. When it is determined that there are airflow influencing factors, the predicted airflow pattern information is updated based on all airflow influencing factors, which is conducive to improving the accuracy and reliability of generating predicted airflow pattern information, and further conducive to improving the accuracy and reliability of subsequent analysis of expected impact effects and generating airflow pattern control parameters.

[0219] In another optional embodiment, Figure 5 As shown, the device also includes:

[0220] An execution module 307 is used to perform a preset region division operation on the current region to obtain at least two target sub-regions corresponding to the current region;

[0221] The specific method in which the analysis module 302 analyzes the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information includes:

[0222] Determine, based on the predicted airflow pattern information, the airflow pattern outputted after the intelligent air conditioner performs an operation matching the control operation instruction and the regional airflow impact information between each target sub-area;

[0223] According to the regional information, determine the airflow bearing effect information of each target sub-region;

[0224] For each target sub-area, based on the regional airflow impact information of the target sub-area and the airflow bearing effect information of the target sub-area, the regional expected impact effect of the target sub-area is generated, and the regional expected impact effect includes the predicted impact effect of the airflow output by the intelligent air conditioner on the target sub-area;

[0225] According to the expected regional impact effects of all target sub-areas, the expected impact effect of the smart air conditioner on the current area is generated.

[0226] It can be seen that implementation Figure 5The described device can divide the current area into at least two target sub-areas, determine the regional airflow influence information between the airflow pattern output by the intelligent air conditioner after executing the operation matching the control operation instruction according to the predicted airflow pattern information and each target sub-area, determine the airflow bearing effect information of each target sub-area according to the regional information, and generate the regional expected impact effect of each target sub-area according to the regional airflow influence information of each target sub-area and the airflow bearing effect information of each target sub-area, generate the expected impact effect of the intelligent air conditioner on the current area according to the regional expected impact effects of all target sub-areas, and can determine the airflow bearing effect information of each target sub-area in a targeted manner, which is conducive to improving the accuracy and reliability of determining the airflow bearing effect information of each target sub-area, and is also conducive to improving the intelligence of determining the airflow bearing effect information of each target sub-area, thereby helping to improve the accuracy and reliability of subsequently generating the expected impact effect of the intelligent air conditioner on the current area according to the regional expected impact effects of all target sub-areas, and is also conducive to improving the accuracy and reliability of subsequently generating the airflow pattern control parameters based on the expected impact effect.

[0227] In yet another optional embodiment, Figure 5 As shown, the specific manner in which the judgment module 303 judges whether the expected impact effect meets the regional operating conditions corresponding to the current region includes:

[0228] For each target sub-region, determining a matching parameter between the regional airflow impact information of the target sub-region and the airflow bearing effect information of the target sub-region according to the regional airflow impact information of the target sub-region and the airflow bearing effect information of the target sub-region;

[0229] Determine whether the matching parameters of all target sub-regions meet the preset region matching conditions;

[0230] When it is determined that the matching parameters of all target sub-regions meet the preset regional matching conditions, it is determined that the expected impact effect meets the regional operating conditions corresponding to the current region;

[0231] When it is determined that the matching parameters of all target sub-regions do not satisfy the preset region matching condition, it is determined that the expected impact effect does not satisfy the region operation condition corresponding to the current region.

[0232] It can be seen that implementation Figure 5The described device can determine the matching parameters between the regional airflow influence information of each target sub-area and the airflow bearing effect information of the target sub-area based on the regional airflow influence information of each target sub-area and the airflow bearing effect information of each target sub-area, and judge whether the target sub-area meets the preset regional matching conditions based on the matching parameters, which is conducive to improving the accuracy and reliability of judging whether the matching parameters of each target sub-area meet the regional matching conditions, thereby facilitating improving the accuracy and reliability of judging whether the expected impact effect meets the regional operating conditions corresponding to the current area, and further facilitating improving the accuracy and reliability of subsequently generating the airflow shape control parameters of the intelligent air conditioner and updating the control operation instructions, and is also conducive to improving the comfort and experience of users in each target sub-area for using the air conditioner.

[0233] In yet another optional embodiment, Figure 5 As shown, the generating module 304 is further used to generate simulated airflow shape parameters according to the airflow shape control parameters and the regional information after the airflow shape control parameters of the intelligent air conditioner are generated; based on the simulated airflow shape parameters, generate simulated airflow supply trajectories of the intelligent air conditioner, and generate regional airflow bearing effect information of the current area according to the airflow bearing effect information of each target sub-area;

[0234] The judgment module 303 is also used to judge whether the simulated airflow supply trajectory matches the regional airflow bearing effect information based on the simulated airflow supply trajectory and the regional airflow bearing effect information; when it is judged that the simulated airflow supply trajectory matches the regional airflow bearing effect information, the control module 306 is triggered to control the intelligent air conditioner to perform a control operation that matches the updated control operation instruction.

[0235] It can be seen that implementation Figure 5 The described device can generate simulated airflow morphology parameters according to airflow morphology control parameters and regional information, and then generate a simulated airflow supply trajectory of the intelligent air conditioner, and generate regional airflow bearing effect information of the current area according to the airflow bearing effect information of each target sub-area, and judge whether the simulated airflow supply trajectory matches the regional airflow bearing effect. If they match, the intelligent air conditioner is controlled to execute a control operation matching the updated control operation instruction. The simulated airflow morphology parameters can be generated before actually controlling the intelligent air conditioner to execute the corresponding operation, and the airflow bearing effect of each target sub-area can be determined according to the simulated simulated airflow supply trajectory, which is conducive to improving the accuracy and reliability of determining each airflow bearing effect information, and further conducive to improving the accuracy and reliability of generating regional airflow bearing effect information. The airflow morphology of the intelligent air conditioner can be simulated first, which can improve the accuracy and reliability of subsequent control of the intelligent air conditioner, avoid control errors caused by control errors, and also help improve the intelligence and efficiency of the control of the intelligent air conditioner.

[0236] In yet another optional embodiment, Figure 5 As shown, the device also includes:

[0237] The determination module 308 is used to determine at least one area to be regulated from all target sub-areas according to the simulated airflow supply trajectory and the airflow bearing effect information of each target sub-area when the judgment module 303 determines that the simulated airflow supply trajectory does not match the regional airflow bearing effect information, and the airflow bearing effect information of the area to be regulated does not match the simulated airflow supply trajectory; determine the regional position information of each area to be regulated in the current area;

[0238] The updating module 305 is also used to perform an updating operation on the airflow pattern control parameters based on the regional location information of all the areas to be controlled, and trigger the control module 306 to control the intelligent air conditioner to perform a control operation matching the updated control operation instruction.

[0239] It can be seen that implementation Figure 5 The described device can determine the area to be regulated when it is determined that the simulated airflow supply trajectory does not match the regional airflow bearing effect information, and update the airflow shape control parameters based on the regional position information of each area to be regulated. It can determine each area to be regulated in a targeted manner, which is beneficial to improving the accuracy and reliability of determining each area to be regulated, and update the airflow shape control parameters based on the regional position information of all areas to be regulated, which is beneficial to improving the accuracy and reliability of updating the airflow shape control parameters, and thus is beneficial to improving the accuracy and reliability of controlling the intelligent air conditioner, as well as improving the intelligence and efficiency of controlling the intelligent air conditioner.

[0240] In yet another optional embodiment, Figure 5 As shown, the determination module 308 is further used to determine the smart devices existing in each target sub-area in the current area;

[0241] The acquisition module 301 is further used to acquire device status information of smart devices in the current area, wherein the number of the smart devices is at least one;

[0242] The determination module 308 is further configured to determine, for each smart device, the airflow requirement information of the smart device for the smart air conditioner according to the device status information of the smart device;

[0243] The specific manner in which the determination module 308 determines the airflow bearing effect information of each target sub-region according to the region information includes:

[0244] According to the airflow demand information and area information of each smart device, the airflow tolerance effect information of each target sub-area is determined.

[0245] It can be seen that implementation Figure 5 The described device can determine the smart devices in each target sub-area and obtain the device status information of each smart device to determine the airflow demand information of the smart devices for the smart air conditioner, and determine the airflow tolerance effect information of each target sub-area according to the airflow demand information and area information of each smart device. It can generate airflow demand information in combination with the smart devices in each target sub-area and the device status information of each smart device, so as to achieve targeted determination of the airflow demand information of each smart device, which is conducive to improving the accuracy and reliability of determining the airflow demand information of each smart device for the smart air conditioner, and thus is conducive to improving the accuracy and reliability of determining the airflow tolerance effect information of each target sub-area, and is also conducive to the subsequent determination of the intelligence of the airflow tolerance effect information of each target sub-area, and thus is conducive to improving the accuracy and reliability of the subsequent generation of the expected impact effect of the smart air conditioner on the current area based on the expected regional impact effects of all target sub-areas.

[0246] Embodiment 4

[0247] See also Figure 6 , Figure 6 FIG. 1 is a schematic diagram of another structure of an intelligent air conditioning control device based on predicting airflow patterns disclosed in an embodiment of the present invention. Figure 6 As shown, the intelligent air conditioning control device based on predicting airflow patterns may include:

[0248] A memory 401 storing executable program codes;

[0249] a processor 402 coupled to the memory 401;

[0250] 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 the prediction of airflow patterns described in the first embodiment of the present invention or the second embodiment of the present invention.

[0251] Embodiment 5

[0252] 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 in the intelligent air conditioning control method based on predicted airflow patterns described in Embodiment 1 or Embodiment 2 of the present invention.

[0253] Embodiment 6

[0254] 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 predicted airflow patterns described in Example 1 or Example 2.

[0255] 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.

[0256] 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.

[0257] Finally, it should be noted that the intelligent air conditioning control method and device based on the prediction of airflow patterns disclosed in the embodiments of the present invention only discloses the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical schemes described 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 schemes from the spirit and scope of the technical schemes of the embodiments of the present invention.

Claims

1. An intelligent air conditioning control method based on predicting airflow patterns, characterized in that: The method comprises: Acquire a control operation instruction of a target user for a smart air conditioner, analyze the control operation instruction, and obtain predicted airflow pattern information of the smart air conditioner, wherein the predicted airflow pattern information includes information corresponding to the airflow pattern outputted after the smart air conditioner performs an operation matching the control operation instruction; Acquire area information of a current area corresponding to the smart air conditioner, wherein the area information includes biological information and object information in the current area; Analyze the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information, and determine whether the expected impact meets the area operation condition corresponding to the current area; When it is determined that the expected impact effect does not meet the regional operating conditions corresponding to the current area, the airflow shape control parameters of the intelligent air conditioner are generated, the control operation instructions are updated according to the airflow shape control parameters, and the intelligent air conditioner is controlled to perform the control operation matching the updated control operation instructions.

2. The intelligent air conditioning control method based on airflow pattern prediction according to claim 1 is characterized in that: Before analyzing the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information, the method further includes: Acquire the real-time regional information of the current region, wherein the real-time regional information includes one or more of the real-time temperature information, real-time wind force information, real-time wind direction information, and real-time humidity information of the region; According to the real-time information of the area, determining whether there is an airflow influencing factor in the current area that affects the airflow pattern of the intelligent air conditioner; When it is determined that the airflow influencing factors exist in the current area, the predicted airflow pattern information of the intelligent air conditioner is updated according to all the airflow influencing factors, and the operation of analyzing the expected impact effect of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information and judging whether the expected impact effect meets the area operating conditions corresponding to the current area is triggered.

3. The intelligent air conditioning control method based on airflow pattern prediction according to claim 1 or 2, characterized in that: The method further comprises: Performing a preset region division operation on the current region to obtain at least two target sub-regions corresponding to the current region; The analyzing the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information includes: Determining, based on the predicted airflow pattern information, the airflow pattern outputted after the intelligent air conditioner performs an operation matching the control operation instruction and the regional airflow impact information between each of the target sub-areas; Determining airflow bearing effect information of each target sub-area according to the area information; For each of the target sub-areas, based on the regional airflow impact information of the target sub-area and the airflow bearing effect information of the target sub-area, generating the regional expected impact effect of the target sub-area, wherein the regional expected impact effect includes the predicted impact effect of the airflow output by the intelligent air conditioner on the target sub-area; According to the expected regional impact effects of all the target sub-areas, the expected impact effect of the intelligent air conditioner on the current area is generated.

4. The intelligent air conditioning control method based on airflow pattern prediction according to claim 3 is characterized in that: The determining whether the expected impact effect satisfies the regional operating condition corresponding to the current region includes: For each of the target sub-regions, determining a matching parameter between the regional airflow impact information of the target sub-region and the airflow bearing effect information of the target sub-region according to the regional airflow impact information of the target sub-region and the airflow bearing effect information of the target sub-region; Determining whether the matching parameters of all the target sub-regions meet the preset region matching conditions; When it is determined that the matching parameters of all the target sub-regions satisfy the preset regional matching condition, determining that the expected impact effect satisfies the regional operating condition corresponding to the current region; When it is determined that the matching parameters of all the target sub-regions do not satisfy the preset region matching condition, it is determined that the expected impact effect does not satisfy the region operation condition corresponding to the current region.

5. The intelligent air conditioning control method based on airflow pattern prediction according to claim 3 is characterized in that: After generating the airflow pattern control parameters of the intelligent air conditioner, the method further includes: Generate simulated airflow morphology parameters according to the airflow morphology control parameters and the area information; Based on the simulated airflow shape parameters, a simulated airflow supply trajectory of the intelligent air conditioner is generated, and according to the airflow bearing effect information of each target sub-area, regional airflow bearing effect information of the current area is generated; According to the simulated airflow supply trajectory and the regional airflow bearing effect information, determining whether the simulated airflow supply trajectory matches the regional airflow bearing effect information; When it is determined that the simulated airflow supply trajectory matches the regional airflow bearing effect information, the control of the intelligent air conditioner to perform a control operation that matches the updated control operation instruction is triggered.

6. The intelligent air conditioning control method based on airflow pattern prediction according to claim 5 is characterized in that: The method further comprises: When it is determined that the simulated airflow supply trajectory does not match the regional airflow bearing effect information, at least one to-be-regulated area is determined from all the target sub-areas according to the simulated airflow supply trajectory and the airflow bearing effect information of each target sub-area, and the airflow bearing effect information of the to-be-regulated area does not match the simulated airflow supply trajectory; Determine the regional position information of each of the areas to be regulated in the current area, and perform an update operation on the airflow pattern control parameters based on the regional position information of all the areas to be regulated, and trigger the execution of the control of the intelligent air conditioner to perform a control operation that matches the updated control operation instruction.

7. The intelligent air conditioning control method based on airflow pattern prediction according to claim 3 is characterized in that: The method further comprises: Determine the smart devices existing in each of the target sub-areas in the current area, and obtain device status information of the smart devices in the current area, wherein the number of the smart devices is at least one; For each of the smart devices, determining the airflow requirement information of the smart device for the smart air conditioner according to the device status information of the smart device; Wherein, determining the airflow bearing effect information of each target sub-area according to the area information includes: According to the airflow requirement information of each of the smart devices and the area information, the airflow tolerance effect information of each of the target sub-areas is determined.

8. An intelligent air conditioning control device based on predicting airflow patterns, characterized in that: The device comprises: An acquisition module is used to obtain the control operation instructions of the target user for the smart air conditioner; An analysis module, used to analyze the control operation instruction to obtain predicted airflow pattern information of the intelligent air conditioner, wherein the predicted airflow pattern information includes information corresponding to the airflow pattern output by the intelligent air conditioner after performing an operation matching the control operation instruction; The acquisition module is further used to acquire the area information of the current area corresponding to the smart air conditioner, wherein the area information includes biological information and object information in the current area; The analysis module is further used to analyze the expected impact of the intelligent air conditioner on the current area according to the predicted airflow pattern information and the area information; A judgment module, used to judge whether the expected impact effect meets the regional operation condition corresponding to the current region; A generating module, configured to generate airflow pattern control parameters of the intelligent air conditioner when the judging module judges that the expected impact effect does not meet the regional operating conditions corresponding to the current region; An updating module, used for updating the control operation instruction according to the airflow shape control parameter; The control module is used to control the intelligent air conditioner to perform a control operation that matches the updated control operation instruction.

9. An intelligent air conditioning control device based on predicting airflow patterns, 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 predicted airflow patterns 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 based on predicted airflow patterns as described in any one of claims 1-7.

Images