Air conditioner wind direction adjusting method and device based on environmental information

Abstract

The application discloses an air conditioner wind direction adjusting method and device based on environmental information, and the method comprises the following steps: collecting scene data of a target scene, and determining a scene type of the target scene and corresponding target scene data according to the scene data; the scene type comprises a static closed scene type in which a preset time variable meets a first time variable condition or a dynamic scene type in which a preset time variable meets a second time variable condition; determining position information of a target user included in the target scene and a blowing demand according to the target scene data; judging whether the air outlet parameter of the air conditioner matches the blowing demand; if not, generating a target adjustment parameter for the air conditioner according to the scene type, the target scene data, the position information and the blowing demand, and adjusting the air outlet parameter of the air conditioner according to the target adjustment parameter. It can be seen that the application can realize intelligent adjustment of the air outlet mode of the air conditioner and the user demand, improve the control intelligence of the air outlet of the air conditioner, and is favorable for improving the use comfort of the user.

Description

Technical Field

[0001] This invention relates to the field of electrical parameter control technology, and in particular to a method and device for adjusting the airflow direction of an air conditioner based on environmental information. Background Technology

[0002] Traditional air conditioners require users to manually adjust the airflow direction via remote control, based on their individual needs. However, these needs are dynamic; for example, a user might prefer direct airflow during one period (e.g., half an hour after exercise) and then dislike it later. This necessitates frequent manual adjustments, which is inconvenient if the user's needs change frequently within a short timeframe. Furthermore, even if a user's needs change, they might forget to adjust the remote control due to distractions, resulting in the airflow mode being completely unsuitable for their needs at certain times or periods, causing discomfort and reducing user comfort. Therefore, addressing the lack of intelligence in current air conditioner airflow technology and developing a solution to enhance its intelligentness is crucial. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an air conditioning air direction adjustment method and device based on environmental information, which can realize intelligent adjustment of air conditioning air outlet mode and user needs, improve the control intelligence of air conditioning air outlet, and help improve user comfort.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a method for adjusting air conditioning airflow direction based on environmental information, the method comprising:

[0005] Collect scene data corresponding to the target scene, and determine the scene type of the target scene and its corresponding target scene data based on the scene data. The scene type includes a static closed scene type in which the preset time variable satisfies a first time-varying condition or a dynamic scene type in which the preset time variable satisfies a second time-varying condition.

[0006] Based on the target scene data, determine the location information and air-drying needs of the target users included in the target scene;

[0007] Determine whether the air outlet parameters of the air conditioner match the air blowing demand. If the determination result is no, generate target adjustment parameters for the air conditioner based on the scene type, the target scene data, the location information and the air blowing demand, and adjust the air outlet parameters of the air conditioner according to the target adjustment parameters.

[0008] The target adjustment parameter is used to control at least one of the following: air outlet angle, air force value, air outlet temperature, and air outlet frequency of the air conditioner.

[0009] As an optional implementation, in the first aspect of the present invention, the preset time variables include the flow of people within a unit detection time and scene parameters, wherein the scene parameters include at least one sub-parameter among scene ventilation, scene temperature and humidity, and scene heat exchange.

[0010] When the flow of people is less than the first flow threshold, and all the sub-parameters are within the static monitoring threshold corresponding to the sub-parameters, it is determined that the preset variable satisfies the first time-varying condition.

[0011] When the pedestrian flow is greater than or equal to the first flow threshold, and at least one of the sub-parameters exceeds the dynamic monitoring threshold corresponding to the sub-parameter, it is determined that the preset time variable satisfies the second time-varying condition.

[0012] As an optional implementation, in the first aspect of the present invention, generating target adjustment parameters for the air conditioner based on the scene type, the target scene data, the location information, and the air blowing requirement includes:

[0013] When the scene type is the quiet and enclosed scene type, the historical stay records of the target user in the target scene are obtained, the historical stay records are analyzed, and a preset number of stay nodes are obtained. The stay nodes are the nodes corresponding to the target user's stay time in the target scene being longer than the preset monitoring time.

[0014] Based on the historical stay records, all the stay nodes are sorted according to the order of stay to obtain the sorting result of all the stay nodes;

[0015] Based on the sorting results of all the stopping nodes, and combined with the relative orientation information of each stopping node and the air conditioner, and the target scene data, node adjustment parameters for each stopping node are generated as target adjustment parameters for the air conditioner;

[0016] The relative orientation information of each dwell node and the air conditioner includes the relative angle and relative distance between the dwell node and the air conditioner; the node adjustment parameters of each dwell node include at least one of the following: outlet air temperature and humidity, wind force value, outlet air duration, and outlet air change frequency.

[0017] As an optional implementation, in the first aspect of the present invention, the step of generating node adjustment parameters for each of the dwelling nodes based on the sorting results of all the dwelling nodes, combined with the relative orientation information of each dwelling node and the air conditioner, and the target scene data, includes:

[0018] Based on the historical stay records and the target scene data, the predicted stay duration for each stay node and the target air blowing parameters that match the air blowing demand are determined; the target air blowing parameters include multiple different air blowing durations, and also include the air blowing temperature and / or wind force values ​​corresponding to each air blowing duration.

[0019] All the aforementioned dwelling nodes are divided into target dwelling nodes corresponding to each of the aforementioned blowing durations;

[0020] Based on the sorting results of all the dwelling nodes, and combined with the relative orientation information of each target dwelling node and the air conditioner, the predicted dwelling time corresponding to each target dwelling node, and the target blowing parameters, node adjustment parameters are generated for each target dwelling node.

[0021] As an optional implementation, in the first aspect of the present invention, the method further includes:

[0022] When the scene type is the dynamic scene type, multiple core nodes in the target scene are determined based on the target scene data. All the core nodes include docking nodes, airflow interaction nodes, and densely populated nodes. The docking node is the node where the target scene docks with other scenes. The airflow interaction node is the node with the highest airflow exchange rate in the target scene. The densely populated node is the node in the target scene where the crowd density is higher than the standard dense threshold.

[0023] Based on the relative position of each core node and the air conditioner and the target scene data, a dynamic equilibrium point is determined. The dynamic equilibrium point is the equilibrium point corresponding to the intertwined influence of all the core nodes.

[0024] Based on the dynamic equilibrium point and the dynamic airflow requirements corresponding to the dynamic scenario type, target adjustment parameters for the air conditioner are generated.

[0025] As an optional implementation, in the first aspect of the present invention, generating target adjustment parameters for the air conditioner based on the dynamic equilibrium point and the dynamic airflow demand corresponding to the dynamic scenario type includes:

[0026] Based on the dynamic equilibrium point and the dynamic airflow requirements corresponding to the dynamic scenario type, the initial adjustment parameters for controlling the air conditioner are determined.

[0027] Calculate the scene change data corresponding to the target scene after the initial adjustment parameters are introduced, and the demand error between it and the dynamic air blowing demand;

[0028] The initial adjustment parameters are corrected based on the required error to obtain the corrected adjustment parameters, which are then used as the target adjustment parameters for the target air conditioner.

[0029] As an optional implementation, in the first aspect of the present invention, when the scene type is the quiet and enclosed scene type, the target scene data corresponding to the scene type includes the item layout data of the target scene, the current scene temperature data, and the record data of the target user. The record data of the target user includes at least one of the following: the entry time of entering the target scene, the duration of entry, the entry movement trajectory, and the exit time of leaving the target scene.

[0030] When the scene type is the dynamic scene type, the target scene data corresponding to the scene type includes the flow of people entering and leaving the target scene within a preset monitoring period, the current scene temperature data, the scene heat exchange data corresponding to the flow of people, the entrance and exit locations of the target scene, and the distribution information of people in the target scene.

[0031] A second aspect of the present invention discloses an air conditioning airflow direction adjustment device based on environmental information, the device comprising:

[0032] The acquisition module is used to acquire scene data corresponding to the target scene;

[0033] The determination module is used to determine the scene type of the target scene and its corresponding target scene data based on the scene data. The scene type includes a static closed scene type in which the preset time variable satisfies a first time-varying condition or a dynamic scene type in which the preset time variable satisfies a second time-varying condition.

[0034] The determining module is also used to determine the location information and air-blowing needs of the target users included in the target scene based on the target scene data;

[0035] The judgment module is used to determine whether the air outlet parameters of the air conditioner match the air blowing requirements;

[0036] The generation module is used to generate target adjustment parameters for the air conditioner based on the scene type, the target scene data, the location information, and the air blowing requirement when the judgment result of the judgment module is negative.

[0037] The control module is used to adjust the air outlet parameters of the air conditioner according to the target adjustment parameters;

[0038] The target adjustment parameter is used to control at least one of the following: air outlet angle, air force value, air outlet temperature, and air outlet frequency of the air conditioner.

[0039] As an optional implementation, in the second aspect of the present invention, the preset time variables include the flow of people within a unit detection time and scene parameters, wherein the scene parameters include at least one sub-parameter among scene ventilation, scene temperature and humidity, and scene heat exchange.

[0040] When the flow of people is less than the first flow threshold, and all the sub-parameters are within the static monitoring threshold corresponding to the sub-parameters, it is determined that the preset variable satisfies the first time-varying condition.

[0041] When the pedestrian flow is greater than or equal to the first flow threshold, and at least one of the sub-parameters exceeds the dynamic monitoring threshold corresponding to the sub-parameter, it is determined that the preset time variable satisfies the second time-varying condition.

[0042] As an optional implementation, in a second aspect of the invention, the generation module includes:

[0043] The acquisition submodule is used to acquire the historical stay records of the target user in the target scene when the scene type is the quiet and enclosed scene type;

[0044] The analysis submodule is used to analyze the historical dwell records to obtain a preset number of dwell nodes, wherein the dwell nodes are nodes corresponding to the target user's dwell time in the target scene being longer than a preset monitoring time.

[0045] The sorting submodule is used to sort all the stopping nodes according to the order of stopping based on the historical stopping records, and obtain the sorting result of all the stopping nodes;

[0046] A generation submodule is used to generate node adjustment parameters for each of the dwelling nodes based on the sorting results of all the dwelling nodes, combined with the relative orientation information of each dwelling node and the air conditioner and the target scene data, as target adjustment parameters for the air conditioner;

[0047] The relative orientation information of each dwell node and the air conditioner includes the relative angle and relative distance between the dwell node and the air conditioner; the node adjustment parameters of each dwell node include at least one of the following: outlet air temperature and humidity, wind force value, outlet air duration, and outlet air change frequency.

[0048] As an optional implementation, in the second aspect of the present invention, the generation submodule generates node adjustment parameters for each of the dwelling nodes based on the sorting results of all the dwelling nodes, combined with the relative orientation information of each dwelling node and the air conditioner, and the target scene data. Specifically, this includes:

[0049] Based on the historical stay records and the target scene data, the predicted stay duration for each stay node and the target air blowing parameters that match the air blowing demand are determined; the target air blowing parameters include multiple different air blowing durations, and also include the air blowing temperature and / or wind force values ​​corresponding to each air blowing duration.

[0050] All the aforementioned dwelling nodes are divided into target dwelling nodes corresponding to each of the aforementioned blowing durations;

[0051] Based on the sorting results of all the dwelling nodes, and combined with the relative orientation information of each target dwelling node and the air conditioner, the predicted dwelling time corresponding to each target dwelling node, and the target blowing parameters, node adjustment parameters are generated for each target dwelling node.

[0052] As an optional implementation, in a second aspect of the invention, the generation module further includes:

[0053] The determination submodule is used to determine multiple core nodes in the target scene based on the target scene data when the scene type is the dynamic scene type. All the core nodes include docking nodes, airflow interaction nodes, and densely populated nodes. The docking nodes are the nodes where the target scene docks with other scenes. The airflow interaction nodes are the nodes with the highest airflow exchange rate in the target scene. The densely populated nodes are the nodes in the target scene where the crowd density is higher than the standard density threshold.

[0054] The determining submodule is further configured to determine a dynamic equilibrium point based on the relative position of each core node and the air conditioner and the target scene data. The dynamic equilibrium point is the equilibrium point corresponding to the interleaved influence of all the core nodes.

[0055] The generation submodule is further configured to generate target adjustment parameters for the air conditioner based on the dynamic equilibrium point and the dynamic airflow requirements corresponding to the dynamic scenario type.

[0056] As an optional implementation, in the second aspect of the present invention, the method by which the generation submodule generates target adjustment parameters for the air conditioner based on the dynamic equilibrium point and the dynamic airflow requirements corresponding to the dynamic scene type specifically includes:

[0057] Based on the dynamic equilibrium point and the dynamic airflow requirements corresponding to the dynamic scenario type, the initial adjustment parameters for controlling the air conditioner are determined.

[0058] Calculate the scene change data corresponding to the target scene after the initial adjustment parameters are introduced, and the demand error between it and the dynamic air blowing demand;

[0059] The initial adjustment parameters are corrected based on the required error to obtain the corrected adjustment parameters, which are then used as the target adjustment parameters for the target air conditioner.

[0060] As an optional implementation, in the second aspect of the present invention, when the scene type is the quiet and enclosed scene type, the target scene data corresponding to the scene type includes the item layout data of the target scene, the current scene temperature data, and the record data of the target user. The record data of the target user includes at least one of the following: the entry time of entering the target scene, the duration of entry, the entry movement trajectory, and the exit time of leaving the target scene.

[0061] When the scene type is the dynamic scene type, the target scene data corresponding to the scene type includes the flow of people entering and leaving the target scene within a preset monitoring period, the current scene temperature data, the scene heat exchange data corresponding to the flow of people, the entrance and exit locations of the target scene, and the distribution information of people in the target scene.

[0062] A third aspect of the present invention discloses another air conditioning airflow direction adjustment device based on environmental information, the device comprising:

[0063] Memory containing executable program code;

[0064] A processor coupled to the memory;

[0065] The processor calls the executable program code stored in the memory to execute the air conditioning air direction adjustment method based on environmental information disclosed in the first aspect of the present invention.

[0066] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the air conditioning airflow direction adjustment method based on environmental information disclosed in the first aspect of the present invention.

[0067] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0068] This invention provides a method for adjusting the airflow direction of an air conditioner based on environmental information. The method includes: collecting scene data corresponding to a target scene, and determining the scene type of the target scene and its corresponding target scene data based on the scene data. The scene type includes a static closed scene type where the preset time variable satisfies a first time-varying condition or a dynamic scene type where the preset time variable satisfies a second time-varying condition; determining the location information and airflow demand of the target user included in the target scene based on the target scene data; determining whether the air outlet parameters of the air conditioner match the airflow demand; if the determination result is negative, generating target adjustment parameters for the air conditioner based on the scene type, target scene data, location information, and airflow demand, and adjusting the air outlet parameters of the air conditioner based on the target adjustment parameters. As can be seen, implementing this invention can automatically determine the scene type of the target scene and extract the required target scene data based on the collected scene data, realizing preliminary analysis and screening of the scene data, which facilitates subsequent targeted processing based on the scene type and target scene data. Then, when it is determined that the air conditioner's air outlet parameters do not match the user's air blowing needs, it automatically generates targeted adjustment parameters based on the scene type, target scene data, and the determined location information and air blowing needs of the target user. This intelligently adjusts at least one of the air outlet angle, wind speed, air outlet temperature, and air outlet frequency of the air conditioner, realizing intelligent adaptation control between the air conditioner and the user's air blowing needs, improving the accuracy of the air conditioner's air outlet and the level of intelligent control, and helping to improve the user's comfort. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 This is a schematic diagram of the architecture of the air conditioning air direction adjustment method based on environmental information disclosed in the embodiments of the present invention.

[0071] Figure 2 This is a schematic flowchart of an air conditioning airflow direction adjustment method based on environmental information disclosed in an embodiment of the present invention;

[0072] Figure 3 This is a schematic flowchart of another air conditioning air direction adjustment method based on environmental information disclosed in an embodiment of the present invention;

[0073] Figure 4 This is a schematic diagram of the structure of an air conditioning air direction adjustment device based on environmental information disclosed in an embodiment of the present invention;

[0074] Figure 5 This is a schematic diagram of the structure of the generation module disclosed in an embodiment of the present invention;

[0075] Figure 6 This is a schematic diagram of another air conditioning air direction adjustment device based on environmental information disclosed in an embodiment of the present invention. Detailed Implementation

[0076] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0077] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0078] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0079] This invention discloses an air conditioning airflow direction adjustment method and device based on environmental information. It can automatically determine the scene type of the target scene and extract the required target scene data based on collected scene data, achieving preliminary analysis and filtering of the scene data to facilitate subsequent targeted processing based on the scene type and target scene data. Then, when it is determined that the air conditioning's air outlet parameters do not match the user's airflow needs, it automatically generates targeted adjustment parameters based on the scene type, target scene data, and the determined location information and airflow needs of the target user. This intelligently adjusts at least one of the air conditioning's air outlet angle, wind speed, air outlet temperature, and air outlet frequency, achieving intelligent adaptation control between the air conditioning and the user's airflow needs. This improves the accuracy and intelligence of the air conditioning's airflow control, ultimately enhancing user comfort. Detailed descriptions follow.

[0080] To better understand the air conditioning airflow direction adjustment method and apparatus based on environmental information described in this invention, the scenario architecture applicable to the air conditioning airflow direction adjustment method based on environmental information is first described. Specifically, this scenario architecture can be as follows: Figure 1 As shown, Figure 1 This is a schematic diagram of the scenario architecture for an air conditioning airflow direction adjustment method based on environmental information disclosed in an embodiment of the present invention. Figure 1 As shown, the scenario architecture may include:

[0081] Air conditioner 101 is used to regulate the temperature in the target scene, and at the same time output the corresponding wind power and air conditioning airflow according to the user's needs;

[0082] Entrance / exit 102 is used for users to enter and exit this target scene;

[0083] Several windows 103 are used to implement the ventilation design.

[0084] The target scenario may include multiple dwelling nodes. Specifically, when the target scenario is the target user's private residence, dwelling nodes A, B, and C may be the bedroom, living room, and bathroom, respectively. When the target scenario is a public place, such as a shopping mall, dwelling nodes A, B, and C may be the areas where different shops are located in the corresponding shopping mall. This embodiment of the invention does not impose any limitations.

[0085] Furthermore, when the target scene is a quiet and enclosed scene, after the user enters the target scene, the backend system can automatically identify the user's identity information and retrieve the user's historical stay records in the target scene, thereby generating targeted adjustment parameters for controlling the air conditioner 101; thus intelligently adjusting the output airflow direction of the air conditioner 101 so that the target user can feel a comfortable breeze.

[0086] When the target scene is a dynamic scene, such as a shopping mall, the system can automatically collect the population count at each stopping point. Then, combining this data with the distribution of entrances and exits, the distribution of ventilation openings, the ventilation conditions of the target scene, and the mall's set temperature, it intelligently generates corresponding adjustment parameters for each stopping point. For example, if the population density at stopping point B is too high, the airflow output needs to be increased; if the population density at stopping points A and C is low, the airflow output can be reduced; or if stopping point A is close to an entrance or exit and has a high airflow exchange rate, a larger airflow output is needed to ensure that the temperature at stopping point A remains at a preset constant temperature. Specific adjustments can be made according to the actual application of the target scene, and this embodiment of the invention does not limit the specific adjustments.

[0087] It should be noted that, Figure 1The scene architecture shown is only to illustrate the scenario to which the air conditioning airflow direction adjustment method based on environmental information is applicable. The air conditioner 101, entrance / exit 102, and window 103 involved are only schematic representations. The specific structure / size / shape / location / installation method, etc., can be adaptively adjusted according to the actual scenario. Figure 1 The scenario architecture shown is not limited in this respect.

[0088] The above describes the application scenarios applicable to the air conditioning air direction adjustment method based on environmental information. The following is a detailed description of the air conditioning air direction adjustment method and device based on environmental information.

[0089] Example 1

[0090] Please see Figure 2 , Figure 2 This is a flowchart illustrating an air conditioning airflow direction adjustment method based on environmental information disclosed in an embodiment of the present invention. Figure 2 The described air conditioning airflow direction adjustment method based on environmental information can be applied to air conditioning airflow direction adjustment devices based on environmental information, and the embodiments of the present invention are not limited thereto. Figure 2 As shown, the air conditioning airflow direction adjustment method based on environmental information may include the following operations:

[0091] 201. Collect scene data corresponding to the target scene, and determine the scene type of the target scene and its corresponding target scene data based on the scene data.

[0092] In this embodiment of the invention, the scene type includes a static closed scene type where the preset time variable satisfies a first time-varying condition or a dynamic scene type where the preset time variable satisfies a second time-varying condition.

[0093] In this embodiment of the invention, the preset time variables include the flow of people within a unit detection time and scene parameters. The scene parameters include at least one sub-parameter among scene ventilation, scene temperature and humidity, and scene heat exchange.

[0094] When the flow of people is less than the first flow threshold, and all sub-parameters are within the static monitoring threshold corresponding to the sub-parameter, it is determined that the preset variable satisfies the first time-varying condition.

[0095] When the flow of people is greater than or equal to the first flow threshold, and at least one sub-parameter exceeds the dynamic monitoring threshold corresponding to that sub-parameter, the preset time variable is determined to meet the second time-varying condition.

[0096] In this embodiment of the invention, when the scene type is a quiet and enclosed scene type, the target scene data corresponding to the scene type includes the item layout data of the target scene, the current scene temperature data, and the target user's record data. The target user's record data includes at least one of the following: the entry time of entering the target scene, the duration of entry, the entry movement trajectory, and the exit time of leaving the target scene.

[0097] When the scene type is dynamic, the target scene data corresponding to this scene type includes the flow of people entering and leaving the target scene within the preset monitoring time, the current scene temperature data, the scene heat exchange data corresponding to the flow of people, the location of the entrance and exit of the target scene, and the distribution information of people in the target scene.

[0098] 202. Determine the location information and air blowing needs of the target users included in the target scene based on the target scene data.

[0099] 203. Determine whether the air outlet parameters of the air conditioner match the air blowing demand. If the result is no, generate target adjustment parameters for the air conditioner based on the scene type, target scene data, location information and air blowing demand, and adjust the air outlet parameters of the air conditioner according to the target adjustment parameters.

[0100] In this embodiment of the invention, the target adjustment parameter is used to control at least one of the air outlet angle, wind speed, air outlet temperature, and air outlet frequency of the air conditioner.

[0101] It is evident that implementation Figure 2 The described air conditioning airflow direction adjustment method based on environmental information can automatically determine the scene type of the target scene and extract the required target scene data based on the collected scene data, realizing preliminary analysis and filtering of the scene data, which facilitates subsequent targeted processing based on the scene type and target scene data. Then, when it is determined that the air conditioning air outlet parameters do not match the user's air blowing needs, the method automatically generates targeted adjustment parameters based on the scene type, target scene data, and the determined location information and air blowing needs of the target user. This intelligently adjusts at least one of the air outlet angle, wind speed, air outlet temperature, and air outlet frequency of the air conditioning, realizing intelligent adaptation control between the air conditioning and the user's air blowing needs, improving the accuracy of air conditioning air outlet and the level of intelligent control, and helping to improve user comfort.

[0102] Example 2

[0103] Please see Figure 3 , Figure 3 This is a schematic flowchart of another air conditioning airflow direction adjustment method based on environmental information disclosed in an embodiment of the present invention. Figure 3 The described air conditioning airflow direction adjustment method based on environmental information can be applied to air conditioning airflow direction adjustment devices based on environmental information, and the embodiments of the present invention are not limited thereto. Figure 3 As shown, the air conditioning airflow direction adjustment method based on environmental information may include the following operations:

[0104] 301. Collect scene data corresponding to the target scene, and determine the scene type of the target scene and its corresponding target scene data based on the scene data.

[0105] 302. Determine the location information and air blowing needs of the target users included in the target scenario based on the target scenario data.

[0106] 303. Determine whether the air outlet parameters of the air conditioner match the air blowing demand. If the result is no, and the scenario type is a quiet and closed scenario, obtain the historical dwell records of the target user in the target scenario, analyze the historical dwell records, and obtain a preset number of dwell nodes.

[0107] In this embodiment of the invention, the dwell node is the node corresponding to the target user's dwell time in the target scene being longer than the preset monitoring time.

[0108] In this embodiment of the invention, the target scene corresponding to the quiet and enclosed scene type can be a private residence's bedroom, living room, or other scene where the flow of people is less than a first flow threshold.

[0109] 304. Based on the historical stop records, sort all stop nodes according to the order of stop to obtain the sorting result of all stop nodes.

[0110] 305. Based on the sorting results of all dwelling nodes, and combined with the relative orientation information of each dwelling node and the air conditioner, and the target scene data, generate node adjustment parameters for each dwelling node, which will serve as the target adjustment parameters for the air conditioner.

[0111] In this embodiment of the invention, the relative orientation information between each dwell node and the air conditioner includes the relative angle and relative distance between the dwell node and the air conditioner; the node adjustment parameters of each dwell node include at least one of the following: outlet air temperature and humidity, wind force value, outlet air duration, and outlet air change frequency.

[0112] 306. Adjust the air outlet parameters of the air conditioner according to the target.

[0113] For further descriptions of steps 301-302 and 306 in this embodiment of the invention, please refer to the other specific descriptions of steps 201-203 in Embodiment 1. These descriptions will not be repeated in this embodiment of the invention.

[0114] It is evident that implementation Figure 3The described air conditioning airflow direction adjustment method based on environmental information, for non-quiet and enclosed scenarios, can automatically analyze the historical dwell records of the target user in the target scenario (such as recording the target user's historical movement trajectory in the target scenario), thereby analyzing the target user's frequent dwelling nodes in the target scenario; then, according to the target user's habits, it sorts out the user's dwelling habits among all dwelling nodes one by one, and then generates node adjustment parameters for each dwelling node as target adjustment parameters. In this way, it can intelligently determine the location corresponding to the node where the target user will stay for a long time, and thus intelligently generate node adjustment parameters for the dwelling node with the dwelling node as the target, making the final target adjustment parameters of the air conditioning more accurate and precise, and improving the accuracy of target adjustment parameter generation in this quiet and enclosed scenario type.

[0115] In an optional embodiment, step 305, based on the sorting results of all dwelling nodes and combining the relative orientation information of each dwelling node to the air conditioner and the target scene data, specifically generates node adjustment parameters for each dwelling node, including:

[0116] Based on historical dwell records and target scenario data, determine the predicted dwell time for each dwell node and the target airflow parameters that match the airflow demand; the target airflow parameters include multiple different airflow durations, as well as the airflow temperature and / or wind force values ​​corresponding to each airflow duration.

[0117] All dwelling nodes are divided into target dwelling nodes corresponding to each blowing duration;

[0118] Based on the sorting results of all dwelling nodes, and combined with the relative orientation information of each target dwelling node and the air conditioner, the predicted dwell time corresponding to each target dwelling node, and the target blowing parameters, node adjustment parameters are generated for each target dwelling node.

[0119] In this optional embodiment, the method of determining the predicted dwell time for each dwell node and the target blowing parameters matching the blowing demand based on historical dwell records and target scene data specifically includes:

[0120] Extract the historical dwell time of the target user at each dwell node from historical dwell records;

[0121] Calculate the data difference between the target scene data and the historical scene data in the historical stay records;

[0122] Based on the target scenario data, determine the temperature, humidity, and ventilation corresponding to the target scenario; based on the target user's air blowing needs, determine the air blowing temperature and / or wind force values ​​that achieve user comfort, and the corresponding air blowing duration, as the target air blowing parameters that match the air blowing needs.

[0123] Based on the historical dwell time and the data difference, combined with the set dwell time error, the predicted dwell time for each dwell node is calculated.

[0124] In this optional embodiment, specifically, it is assumed that the historical stay record includes stay nodes A and B. In the historical stay record, after the target user enters the target scene (such as the target user's bedroom), he will stay at stay node A for 1 hour. During this 1 hour, the air conditioner is historically controlled to be in high fan speed mode for 30 minutes, low fan speed mode for 30 minutes, room temperature 30°C, and constant temperature control is adjusted to 25°C. The relative angle between the air conditioner deflector and stay node A is 30°. Afterward, the target user will move to stay node B and stay for 3 hours. The constant temperature control is 25°C. At this location, the air conditioner is controlled to be in low fan speed mode for 3 hours. The relative angle between the air conditioner deflector and stay node B is 60°. Therefore, based on the historical stay record, combined with the current room temperature, the difference between the current room temperature and the historical room temperature can be calculated. For example, if the current room temperature is 31℃, the difference between this and the historical room temperature of 30℃ is 1℃, which is within the allowable error range. Then, based on the historical record, the data difference, and the duration error, the predicted stay duration of the target user at each stay node can be calculated. For example, if the user stays at stay node A for 1 hour (error 1-3 minutes), during this 1 hour, the air conditioner was historically controlled in high fan speed mode for 30 minutes (error 1 minute), low fan speed mode for 30 minutes (error 1 minute), the room temperature was 31℃, the constant temperature control was adjusted to 25℃, and the relative angle between the air conditioner deflector and stay node A was 30°. Afterward, the target user will move to stay node B and stay for 3 hours (error 1-3 minutes), the constant temperature control is 25℃, and the air conditioner is controlled in low fan speed mode for 3 hours at this location, with the relative angle between the air conditioner deflector and stay node B being 60°.

[0125] As can be seen, in this optional embodiment, when generating node adjustment parameters for each dwell node, the predicted dwell time of the target user at each dwell node can be automatically determined. Thus, based on the blowing needs of the target user and the blowing time as the benchmark, target dwell nodes matching each blowing time are divided. At the same time, the sorting results of each dwell node are combined, and multiple parameters are combined to generate node adjustment parameters for each target dwell node, thereby improving the accuracy of generating node adjustment parameters.

[0126] In another alternative embodiment, the method further includes:

[0127] When the scene type is dynamic, multiple core nodes in the target scene are determined based on the target scene data. All core nodes include docking nodes, airflow interaction nodes, and densely populated nodes. Docking nodes are nodes where the target scene docks with other scenes. Airflow interaction nodes are nodes in the target scene with the highest airflow exchange rate. Densely populated nodes are nodes in the target scene where the crowd density is higher than the standard dense threshold.

[0128] Based on the relative position of each core node and the air conditioner, as well as the target scenario data, a dynamic equilibrium point is determined. The dynamic equilibrium point is the equilibrium point corresponding to the intertwined influence of all core nodes.

[0129] Based on the dynamic equilibrium point and the dynamic airflow requirements corresponding to the dynamic scenario type, target adjustment parameters for the air conditioner are generated.

[0130] In this optional embodiment, the target scene corresponding to the dynamic scene type can be a place with dense crowds such as a shopping mall; or a scene with high ventilation in a private residence after the air conditioner is turned on. This embodiment of the invention does not limit the scope.

[0131] In this optional embodiment, the method of generating target adjustment parameters for the air conditioner based on the dynamic equilibrium point and the dynamic airflow requirements corresponding to the dynamic scenario type specifically includes:

[0132] Based on the dynamic equilibrium point and the dynamic airflow requirements corresponding to the dynamic scenario type, determine the initial adjustment parameters for controlling the air conditioner;

[0133] Calculate the scene change data corresponding to the target scene after the initial adjustment parameters are intervened, and the demand error between it and the dynamic blowing demand.

[0134] The initial adjustment parameters are corrected based on the demand error to obtain the corrected adjustment parameters, which are then used as the target adjustment parameters for the target air conditioner.

[0135] As can be seen, in this optional embodiment, for dynamic scene types, multiple core nodes of the target scene can be automatically selected based on the characteristics of dense crowds in the dynamic scene type, and then the dynamic equilibrium point of these multiple core nodes can be determined. Based on the equilibrium dynamic point and combined with the user's dynamic air blowing needs in the scene, the initial adjustment parameters are determined in sequence, the demand error is calculated, and the initial adjustment parameters are corrected based on the demand error, finally obtaining the corrected adjustment parameters. The determination of the dynamic equilibrium point and the design of the correction of the initial adjustment parameters improve the accuracy and reliability of the determination of the target adjustment parameters for dynamic scene types.

[0136] Example 3

[0137] Please see Figure 4 , Figure 4This is a schematic diagram of an air conditioning airflow direction adjustment device based on environmental information, disclosed in an embodiment of the present invention. The air conditioning airflow direction adjustment device based on environmental information can be an air conditioning airflow direction adjustment terminal based on environmental information, an air conditioning airflow direction adjustment device based on environmental information, an air conditioning airflow direction adjustment system based on environmental information, or an air conditioning airflow direction adjustment server based on environmental information. The air conditioning airflow direction adjustment server based on environmental information can be a local server, a remote server, or a cloud server (also known as a cloud server). When the air conditioning airflow direction adjustment server based on environmental information is not a cloud server, it can communicate with the cloud server. This embodiment of the present invention does not impose any limitations. Figure 4 As shown, the air conditioning airflow direction adjustment device based on environmental information may include a data acquisition module 401, a determination module 402, a judgment module 403, a generation module 404, and a control module 405, wherein:

[0138] Acquisition module 401 is used to acquire scene data corresponding to the target scene;

[0139] The determination module 402 is used to determine the scene type of the target scene and its corresponding target scene data based on the scene data. The scene type includes a static closed scene type where the preset time variable satisfies the first time-varying condition or a dynamic scene type where the preset time variable satisfies the second time-varying condition.

[0140] In this embodiment of the invention, the preset time variables include the flow of people within a unit detection time and scene parameters. The scene parameters include at least one sub-parameter among scene ventilation, scene temperature and humidity, and scene heat exchange.

[0141] When the flow of people is less than the first flow threshold, and all sub-parameters are within the static monitoring threshold corresponding to the sub-parameter, it is determined that the preset variable satisfies the first time-varying condition.

[0142] When the flow of people is greater than or equal to the first flow threshold, and at least one sub-parameter exceeds the dynamic monitoring threshold corresponding to that sub-parameter, the preset time variable is determined to meet the second time-varying condition.

[0143] In this embodiment of the invention, when the scene type is a quiet and enclosed scene type, the target scene data corresponding to the scene type includes the item layout data of the target scene, the current scene temperature data, and the target user's record data. The target user's record data includes at least one of the following: the entry time of entering the target scene, the duration of entry, the entry movement trajectory, and the exit time of leaving the target scene.

[0144] When the scene type is dynamic, the target scene data corresponding to this scene type includes the flow of people entering and leaving the target scene within the preset monitoring time, the current scene temperature data, the scene heat exchange data corresponding to the flow of people, the location of the entrance and exit of the target scene, and the distribution information of people in the target scene.

[0145] The determining module 402 is also used to determine the location information and air blowing needs of the target users included in the target scene based on the target scene data;

[0146] The judgment module 403 is used to determine whether the air outlet parameters of the air conditioner match the air blowing demand;

[0147] The generation module 404 is used to generate target adjustment parameters for the air conditioner based on the scene type, target scene data, location information, and air blowing requirements when the judgment result of the judgment module 403 is negative.

[0148] The control module 405 is used to adjust the air outlet parameters of the air conditioner according to the target adjustment parameters;

[0149] Among them, the target adjustment parameters are used to control at least one of the air outlet angle, wind speed, air outlet temperature, and air outlet frequency of the air conditioner.

[0150] It is evident that implementation Figure 4 The described air conditioning airflow direction adjustment device based on environmental information can automatically determine the scene type of the target scene and extract the required target scene data based on the collected scene data. This enables preliminary analysis and filtering of the scene data, facilitating subsequent targeted processing based on the scene type and target scene data. Subsequently, when it is determined that the air conditioning's air outlet parameters do not match the user's airflow needs, the device automatically generates targeted adjustment parameters based on the scene type, target scene data, and the determined location information and airflow needs of the target user. This intelligently adjusts at least one of the air conditioning's air outlet angle, wind speed, air outlet temperature, and air outlet frequency, achieving intelligent adaptation control between the air conditioning and the user's airflow needs. This improves the accuracy and intelligence of the air conditioning's airflow control, thereby enhancing user comfort.

[0151] In an optional embodiment, such as Figure 5 As shown, the generation module 404 may include an acquisition submodule 4041, an analysis submodule 4042, a sorting submodule 4043, and a generation submodule 4044, wherein:

[0152] The acquisition submodule 4041 is used to acquire the historical dwell records of the target user in the target scene when the scene type is a static and enclosed scene type.

[0153] The analysis submodule 4042 is used to analyze historical dwell records and obtain a preset number of dwell nodes. Dwell nodes are nodes that correspond to the target user's dwell time in the target scene being longer than the preset monitoring time.

[0154] The sorting submodule 4043 is used to sort all the stop nodes according to the order of stop based on the historical stop records, and obtain the sorting result of all the stop nodes;

[0155] The generation submodule 4044 is used to generate node adjustment parameters for each station based on the sorting results of all stationary nodes, combined with the relative orientation information of each stationary node and the air conditioner and the target scene data, as the target adjustment parameters for the air conditioner;

[0156] The relative orientation information of each dwell node and the air conditioner includes the relative angle and relative distance between the dwell node and the air conditioner; the node adjustment parameters of each dwell node include at least one of the following: outlet air temperature and humidity, wind force value, outlet air duration, and outlet air change frequency.

[0157] It is evident that implementation Figure 5 The described air conditioning airflow direction adjustment device based on environmental information can automatically analyze the historical dwell records of the target user in the target scene (such as recording the target user's historical movement trajectory in the target scene) for non-quiet and enclosed scene types, thereby analyzing the dwelling nodes where the target user frequently stays in the target scene; then, according to the target user's habits, it sorts out the user's dwelling habits among all dwelling nodes one by one, and then generates node adjustment parameters for each dwelling node as target adjustment parameters. In this way, it can intelligently determine the location corresponding to the node where the target user will stay for a long time, and thus intelligently generate node adjustment parameters for the dwelling node with the dwelling node as the target, making the final target adjustment parameters of the air conditioning more accurate and precise, and improving the accuracy of target adjustment parameter generation in the quiet and enclosed scene type.

[0158] In another optional embodiment, the generation submodule 4044 generates node adjustment parameters for each stop node based on the sorting results of all stop nodes, combined with the relative orientation information of each stop node and the air conditioner, and the target scene data. Specifically, this includes:

[0159] Based on historical dwell records and target scenario data, determine the predicted dwell time for each dwell node and the target airflow parameters that match the airflow demand; the target airflow parameters include multiple different airflow durations, as well as the airflow temperature and / or wind force values ​​corresponding to each airflow duration.

[0160] All dwelling nodes are divided into target dwelling nodes corresponding to each blowing duration;

[0161] Based on the sorting results of all dwelling nodes, and combined with the relative orientation information of each target dwelling node and the air conditioner, the predicted dwell time corresponding to each target dwelling node, and the target blowing parameters, node adjustment parameters are generated for each target dwelling node.

[0162] As can be seen, in this optional embodiment, when generating node adjustment parameters for each dwell node, the predicted dwell time of the target user at each dwell node can be automatically determined. Thus, based on the blowing needs of the target user and the blowing time as the benchmark, target dwell nodes matching each blowing time are divided. At the same time, the sorting results of each dwell node are combined, and multiple parameters are combined to generate node adjustment parameters for each target dwell node, thereby improving the accuracy of generating node adjustment parameters.

[0163] In yet another alternative embodiment, such as Figure 5 As shown, the generation module 404 further includes a determination submodule 4045, wherein:

[0164] The determination submodule 4045 is used to determine multiple core nodes in the target scene based on the target scene data when the scene type is dynamic scene type. All core nodes include docking nodes, airflow interaction nodes, and densely populated nodes. Docking nodes are nodes that connect the target scene with other scenes. Airflow interaction nodes are nodes with the highest airflow exchange rate in the target scene. Densely populated nodes are nodes in the target scene with a crowd density higher than the standard density threshold.

[0165] Submodule 4045 is also used to determine the dynamic equilibrium point based on the relative position of each core node and the air conditioner and the target scene data. The dynamic equilibrium point is the equilibrium point corresponding to the interleaved influence of all core nodes.

[0166] The generation submodule 4044 is also used to generate target adjustment parameters for the air conditioner based on the dynamic equilibrium point and the dynamic air blowing requirements corresponding to the dynamic scene type.

[0167] In this optional embodiment, the generation submodule 4044 generates target adjustment parameters for the air conditioner based on the dynamic equilibrium point and the dynamic airflow requirements corresponding to the dynamic scene type, specifically including:

[0168] Based on the dynamic equilibrium point and the dynamic airflow requirements corresponding to the dynamic scenario type, determine the initial adjustment parameters for controlling the air conditioner;

[0169] Calculate the scene change data corresponding to the target scene after the initial adjustment parameters are intervened, and the demand error between it and the dynamic blowing demand.

[0170] The initial adjustment parameters are corrected based on the demand error to obtain the corrected adjustment parameters, which are then used as the target adjustment parameters for the target air conditioner.

[0171] It is evident that implementation Figure 5The described air conditioning airflow direction adjustment device based on environmental information, for dynamic scene types, can automatically filter out multiple core nodes of the target scene due to the high population density of the dynamic scene type, and then determine the dynamic balance point of these multiple core nodes. Based on the balance dynamic point, combined with the dynamic airflow demand of users in the scene, the initial adjustment parameters are determined in sequence, the demand error is calculated, and the initial adjustment parameters are corrected based on the demand error, finally obtaining the corrected adjustment parameters. The design of determining the dynamic balance point and correcting the initial adjustment parameters improves the accuracy and reliability of determining the target adjustment parameters for dynamic scene types.

[0172] Example 4

[0173] Please see Figure 6 , Figure 6 This is a schematic diagram of another air conditioning airflow direction adjustment device based on environmental information disclosed in an embodiment of the present invention. Figure 6 As shown, the air conditioning airflow direction adjustment device based on environmental information may include:

[0174] Memory 501 storing executable program code;

[0175] Processor 502 coupled to memory 501;

[0176] The processor 502 calls the executable program code stored in the memory 501 to execute the steps in the air conditioning air direction adjustment method based on environmental information as described in Embodiment 1 or Embodiment 2 of the present invention.

[0177] Example 5

[0178] This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the air conditioning airflow direction adjustment method based on environmental information described in Embodiment 1 or Embodiment 2 of this invention.

[0179] Example 6

[0180] This invention discloses a computer program product, which includes a non-transitory computer storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the air conditioning air direction adjustment method based on environmental information described in Embodiment 1 or Embodiment 2.

[0181] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0182] Through the detailed 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 necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0183] Finally, it should be noted that the air conditioning airflow direction adjustment method and device based on environmental information disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adjusting air conditioning airflow direction based on environmental information, characterized in that, The method includes: Collect scene data corresponding to the target scene, and determine the scene type of the target scene and its corresponding target scene data based on the scene data. The scene type includes a static closed scene type in which the preset time variable satisfies a first time-varying condition or a dynamic scene type in which the preset time variable satisfies a second time-varying condition. Based on the target scene data, determine the location information and air-drying needs of the target users included in the target scene; Determine whether the air outlet parameters of the air conditioner match the air blowing demand. If the determination result is no, generate target adjustment parameters for the air conditioner based on the scene type, the target scene data, the location information and the air blowing demand, and adjust the air outlet parameters of the air conditioner according to the target adjustment parameters. The target adjustment parameter is used to control at least one of the following: air outlet angle, air force value, air outlet temperature, and air outlet frequency of the air conditioner. The preset time variables include the flow of people and scene parameters within a unit detection time. The scene parameters include at least one sub-parameter among scene ventilation, scene temperature and humidity, and scene heat exchange. When the flow of people is less than the first flow threshold, and all the sub-parameters are within the static monitoring threshold corresponding to the sub-parameters, it is determined that the preset time variable satisfies the first time-varying condition. When the flow of people is greater than or equal to the first flow threshold, and at least one of the sub-parameters exceeds the dynamic monitoring threshold corresponding to the sub-parameter, it is determined that the preset time variable satisfies the second time-varying condition; The step of generating target adjustment parameters for the air conditioner based on the scene type, the target scene data, the location information, and the airflow requirement includes: When the scene type is the quiet and enclosed scene type, the historical stay records of the target user in the target scene are obtained, the historical stay records are analyzed, and a preset number of stay nodes are obtained. The stay nodes are the nodes corresponding to the target user's stay time in the target scene being longer than the preset monitoring time. Based on the historical stay records, all the stay nodes are sorted according to the order of stay to obtain the sorting result of all the stay nodes; Based on the sorting results of all the stopping nodes, and combined with the relative orientation information of each stopping node and the air conditioner, and the target scene data, node adjustment parameters for each stopping node are generated as target adjustment parameters for the air conditioner; The relative orientation information of each dwell node and the air conditioner includes the relative angle and relative distance between the dwell node and the air conditioner; the node adjustment parameters of each dwell node include at least one of the following: outlet air temperature and humidity, wind force value, outlet air duration, and outlet air change frequency. The method further includes: When the scene type is the dynamic scene type, multiple core nodes in the target scene are determined based on the target scene data. All the core nodes include docking nodes, airflow interaction nodes, and densely populated nodes. The docking node is the node where the target scene docks with other scenes. The airflow interaction node is the node with the highest airflow exchange rate in the target scene. The densely populated node is the node in the target scene where the crowd density is higher than the standard dense threshold. Based on the relative position of each core node and the air conditioner and the target scene data, a dynamic equilibrium point is determined. The dynamic equilibrium point is the equilibrium point corresponding to the intertwined influence of all the core nodes. Based on the dynamic equilibrium point and the dynamic airflow requirements corresponding to the dynamic scenario type, target adjustment parameters for the air conditioner are generated.

2. The air conditioning airflow direction adjustment method based on environmental information according to claim 1, characterized in that, The step of generating node adjustment parameters for each of the stopping nodes based on the sorting results of all the stopping nodes, combined with the relative orientation information of each stopping node and the air conditioner, and the target scene data, includes: Based on the historical stay records and the target scene data, the predicted stay duration for each stay node and the target air blowing parameters that match the air blowing demand are determined; the target air blowing parameters include multiple different air blowing durations, and also include the air blowing temperature and / or wind force values ​​corresponding to each air blowing duration. All the aforementioned dwelling nodes are divided into target dwelling nodes corresponding to each of the aforementioned blowing durations; Based on the sorting results of all the dwelling nodes, and combined with the relative orientation information of each target dwelling node and the air conditioner, the predicted dwelling time corresponding to each target dwelling node, and the target blowing parameters, node adjustment parameters are generated for each target dwelling node.

3. The air conditioning airflow direction adjustment method based on environmental information according to claim 1, characterized in that, The step of generating target adjustment parameters for the air conditioner based on the dynamic equilibrium point and the dynamic airflow requirements corresponding to the dynamic scenario type includes: Based on the dynamic equilibrium point and the dynamic airflow requirements corresponding to the dynamic scenario type, the initial adjustment parameters for controlling the air conditioner are determined. Calculate the scene change data corresponding to the target scene after the initial adjustment parameters are introduced, and the demand error between it and the dynamic air blowing demand; The initial adjustment parameters are corrected based on the required error to obtain the corrected adjustment parameters, which are then used as the target adjustment parameters for the air conditioner.

4. The air conditioning airflow direction adjustment method based on environmental information according to claim 1, characterized in that, When the scene type is the quiet and enclosed scene type, the target scene data corresponding to the scene type includes the item layout data of the target scene, the current scene temperature data, and the target user's record data. The target user's record data includes at least one of the following: the entry time of entering the target scene, the duration of entry, the entry movement trajectory, and the exit time of leaving the target scene. When the scene type is the dynamic scene type, the target scene data corresponding to the scene type includes the flow of people entering and leaving the target scene within a preset monitoring period, the current scene temperature data, the scene heat exchange data corresponding to the flow of people, the entrance and exit locations of the target scene, and the distribution information of people in the target scene.

5. An air conditioning airflow direction adjustment device based on environmental information, characterized in that, The apparatus is used to perform the air conditioning airflow direction adjustment method based on environmental information as described in any one of claims 1-4, and the apparatus comprises: The acquisition module is used to acquire scene data corresponding to the target scene; The determination module is used to determine the scene type of the target scene and its corresponding target scene data based on the scene data. The scene type includes a static closed scene type in which the preset time variable satisfies a first time-varying condition or a dynamic scene type in which the preset time variable satisfies a second time-varying condition. The determining module is also used to determine the location information and air-blowing needs of the target users included in the target scene based on the target scene data; The judgment module is used to determine whether the air outlet parameters of the air conditioner match the air blowing requirements; The generation module is used to generate target adjustment parameters for the air conditioner based on the scene type, the target scene data, the location information, and the air blowing requirement when the judgment result of the judgment module is negative. The control module is used to adjust the air outlet parameters of the air conditioner according to the target adjustment parameters; The target adjustment parameter is used to control at least one of the following: air outlet angle, air force value, air outlet temperature, and air outlet frequency of the air conditioner.

6. An air conditioning airflow direction adjustment device based on environmental information, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the air conditioning air direction adjustment method based on environmental information as described in any one of claims 1-4.

7. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the air conditioning airflow direction adjustment method based on environmental information as described in any one of claims 1-4.

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