A vehicle-mounted air conditioner control method and device, vehicle and medium

Abstract

The application discloses a vehicle-mounted air conditioner control method and device, a vehicle and a medium, and relates to the technical field of air conditioner control. The method comprises the following steps: when an air conditioner adjustment condition is met, acquiring in-vehicle detection data; determining individualized adjustment parameters of different targets in the vehicle according to the in-vehicle detection data and a pre-constructed database, wherein the pre-constructed database records standard behavior parameters, air conditioner control habit parameters and physiological standard parameters of different targets under a comfortable condition; and determining final adjustment parameters according to the air conditioner category of the vehicle-mounted air conditioner and the individualized adjustment parameters, and adjusting the vehicle-mounted air conditioner. The in-vehicle detection data is combined with the pre-constructed database constructed for the vehicle, the current state of the target in the vehicle is tracked to determine the individualized adjustment parameters of different targets, and then the final adjustment parameters are determined to adjust the vehicle-mounted air conditioner. The dynamic determination of the control parameters of different targets is realized, the dynamic individualized automatic control of the air conditioner is realized, and the overall vehicle experience is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air conditioner control, and particularly relates to a vehicle-mounted air conditioner control method and device, a vehicle and a medium. BACKGROUND

[0002] With the development of automobile intelligence, the demand for in-vehicle thermal comfort of passengers is increasing. Traditional vehicle-mounted air conditioning systems mostly use unified temperature control mode, for example, relying on a preset temperature sensor (usually located near the instrument panel) to control the air conditioner of the whole vehicle with single-point temperature data. A few high-end models support zoned temperature control (such as dual temperature zones), and passengers in different areas can manually adjust the air conditioner buttons for temperature control.

[0003] However, the thermal comfort of the human body varies from person to person, and the current unified temperature control mode and zoned temperature control mode cannot adapt to the personalized needs of different passengers, and passengers need to manually adjust the parameters, which distracts the driving attention. SUMMARY

[0004] The present application provides a vehicle-mounted air conditioner control method, device, vehicle and medium to determine the personalized air conditioner adjustment parameters of different passengers.

[0005] According to a first aspect of the present application, a vehicle-mounted air conditioner control method is provided, comprising:

[0006] When the air conditioner adjustment condition is met, obtaining in-vehicle detection data;

[0007] According to the in-vehicle detection data and a pre-constructed database, determining the personalized adjustment parameters of different targets in the vehicle, wherein the pre-constructed database records standard behavior parameters, air conditioner control habit parameters and physiological standard parameters of different targets under comfortable conditions;

[0008] According to the air conditioner category of the vehicle-mounted air conditioner and each personalized adjustment parameter, determining the final adjustment parameter and adjusting the vehicle-mounted air conditioner.

[0009] According to a second aspect of the present application, a vehicle-mounted air conditioner control device is provided, comprising:

[0010] A data acquisition module is configured to obtain in-vehicle detection data when the air conditioner adjustment condition is met;

[0011] A parameter determination module is configured to determine the personalized adjustment parameters of different targets in the vehicle according to the in-vehicle detection data and a pre-constructed database, wherein the pre-constructed database records standard behavior parameters, air conditioner control habit parameters and physiological standard parameters of different targets under comfortable conditions;

[0012] The air conditioner adjustment module is used for determining final adjustment parameters and adjusting the vehicle-mounted air conditioner according to the air conditioner category of the vehicle-mounted air conditioner and the individualized adjustment parameters.

[0013] According to a third aspect of the present application, an electronic device is provided, the electronic device comprising:

[0014] at least one controller; and

[0015] a memory in communication connection with the at least one controller; wherein,

[0016] the memory stores a computer program executable by the at least one controller, and the computer program is executed by the at least one controller to enable the at least one controller to perform the vehicle-mounted air conditioner control method according to any one of the embodiments of the present application.

[0017] According to a fourth aspect of the present application, a computer readable storage medium is provided, the computer readable storage medium stores computer instructions for enabling a controller to perform the vehicle-mounted air conditioner control method according to any one of the embodiments of the present application.

[0018] According to a fifth aspect of the present application, the embodiments of the present application further provide a computer program product, the computer program product comprises a computer program, and the computer program, when executed by a controller, implements the vehicle-mounted air conditioner control method according to any one of the embodiments of the present application.

[0019] The technical solution of the embodiments of the present application comprises the following steps: obtaining in-vehicle detection data when air conditioner adjustment conditions are met; determining individualized adjustment parameters of different targets in the vehicle according to the in-vehicle detection data and a pre-constructed database, the pre-constructed database recording standard behavior parameters, air conditioner control habit parameters and physiological standard parameters of different targets under comfortable conditions; determining final adjustment parameters and adjusting the vehicle-mounted air conditioner according to the air conditioner category of the vehicle-mounted air conditioner and the individualized adjustment parameters. The in-vehicle detection data is combined with the pre-constructed database constructed for the vehicle to track the current state of the targets in the vehicle to determine the individualized adjustment parameters of different targets, and then the final adjustment parameters are determined to adjust the vehicle-mounted air conditioner. The dynamic determination of the adjustment parameters of different targets is realized, the dynamic individualized automatic adjustment of the air conditioner is realized, and the overall driving experience is improved.

[0020] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0021] 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.

[0022] Figure 1 This is a flowchart of a vehicle air conditioning control method according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a vehicle air conditioning control method according to Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of the structure of a vehicle air conditioning control device according to Embodiment 3 of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device that implements an embodiment of the present invention. Detailed Implementation

[0026] 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 should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Example 1

[0029] Figure 1This is a flowchart of a vehicle air conditioning control method provided in Embodiment 1 of the present invention. This embodiment is applicable to the control of vehicle air conditioning systems. The method can be executed by a vehicle air conditioning control device, which can be implemented in hardware and / or software and can be configured in a vehicle. Figure 1 As shown, the method includes:

[0030] S110. When the air conditioning adjustment conditions are met, acquire in-vehicle detection data.

[0031] In this embodiment, the air conditioning adjustment conditions can be understood as the triggering conditions for adjusting the air conditioning. For example, these may include turning on the air conditioning, a timed threshold after the air conditioning is turned on (such as every 5 minutes), and detecting specified behaviors (such as taking off and putting on clothes) or abnormal states (such as fever and excessive sweating) of the occupants in the vehicle. The in-vehicle detection data can be understood as data on the state of the occupants and the in-vehicle environment, such as data on temperature, respiratory and heart rate, sweating, and fatigue.

[0032] Specifically, when the air conditioning adjustment conditions are met, the infrared camera installed in the vehicle can detect the facial and body surface temperature of the occupants, the millimeter-wave radar installed in the vehicle can detect breathing and heart rate, the camera can detect sweating and fatigue, and the in-vehicle environment can be detected by in-vehicle temperature and humidity sensors, sunlight sensors, and carbon dioxide concentration sensors. The controller can receive and synthesize the in-vehicle detection data.

[0033] S120. Based on in-vehicle detection data and a pre-built database, determine the personalized adjustment parameters for different targets inside the vehicle. The pre-built database records the standard behavioral parameters, air conditioning control habit parameters, and physiological standard parameters for different targets to achieve comfort conditions.

[0034] In this embodiment, the pre-built database can be understood as storing standard behavioral parameters, air conditioning control habit parameters, and physiological standard parameters of different targets determined through machine learning to achieve comfortable conditions. Comfort conditions can be determined through machine learning and other methods. Standard behavioral parameters can be understood as characterizing the standard behaviors of different targets in different environments, such as different temperatures and weather conditions and corresponding clothing indices. Air conditioning control habit parameters can be understood as air conditioning control habits corresponding to different environments, such as air conditioning control parameters for different targets under different temperatures and weather conditions (e.g., air outlet temperature, air outlet volume, air outlet mode, and internal / external circulation, and may also include fragrance status and air purification status). Physiological standard parameters can be understood as physiological state parameters under comfortable conditions, such as standard heart rate, respiratory rate, facial temperature status, body surface temperature, sweating status, and fatigue status.

[0035] In this embodiment, the target can be understood as the driver and passengers inside the vehicle. Personalized adjustment parameters can be understood as the air conditioning control parameters that need to be adjusted to achieve comfort levels for different targets.

[0036] Specifically, the controller can perform facial recognition based on in-vehicle detection data to distinguish different targets and determine their physiological health and behavioral information. By combining the parameters corresponding to the target in a pre-built database, it can determine the personalized adjustment parameters for different targets in the vehicle.

[0037] S130. Based on the air conditioning type and various personalized adjustment parameters of the vehicle air conditioner, determine the final adjustment parameters and adjust the vehicle air conditioner.

[0038] In this embodiment, the vehicle air conditioner can be understood as a device that regulates the air inside the vehicle cabin. The air conditioner category is used to characterize the zoning of the air conditioning system; for example, it may include single-zone and multi-zone air conditioning. The final adjustment parameters can be understood as parameters used to adjust the vehicle air conditioner.

[0039] Specifically, since a vehicle may contain multiple targets simultaneously, in order to determine which target's personalized adjustment parameters to adjust, it is first necessary to determine the vehicle's air conditioning category, then determine the target in the area covered by the air conditioning based on the air conditioning category, and then determine the priority of the target in the area through various methods. The personalized adjustment parameters of the target with the highest priority are used as the final adjustment parameters for that air conditioning area, and the vehicle's air conditioning is adjusted accordingly.

[0040] The technical solution of this invention involves acquiring in-vehicle detection data when air conditioning adjustment conditions are met; determining personalized adjustment parameters for different in-vehicle targets based on the in-vehicle detection data and a pre-built database. The pre-built database records standard behavioral parameters, air conditioning control habit parameters, and physiological standard parameters for different targets to achieve comfort conditions; and determining the final adjustment parameters and adjusting the in-vehicle air conditioning based on the air conditioning type and each personalized adjustment parameter. By combining in-vehicle detection data with a pre-built database specifically for the vehicle, the current state of in-vehicle targets is tracked to determine personalized adjustment parameters for different targets, thereby determining the final adjustment parameters and adjusting the in-vehicle air conditioning. This achieves dynamic determination of control parameters for different targets, enabling dynamic personalized automatic control of the air conditioning and improving the overall user experience.

[0041] As a first optional embodiment of this embodiment, based on the above embodiment, the construction steps of the pre-built database can be refined as follows:

[0042] Acquire first in-vehicle detection data and first air conditioning adjustment behavior during each ride; classify targets based on the first in-vehicle detection data to determine different target types; determine the physiological standard parameters for each target based on the physiological state data in the first in-vehicle detection data; learn comfort perception and air conditioning setting methods through machine learning based on the first in-vehicle detection data and the first air conditioning adjustment behavior to determine the air conditioning control habit parameters for each target; learn and generate standard behavioral parameters based on the behavior and clothing data in the first in-vehicle detection data; generate a pre-built database based on the target type and the corresponding physiological standard parameters, air conditioning control habit parameters, and standard behavioral parameters.

[0043] In this embodiment, the vehicle ride can be understood as the process of turning on the air conditioner and then turning it off after the vehicle starts. The first in-vehicle detection data can be understood as the detection data of the in-vehicle environment and occupants during the ride. The first air conditioning adjustment behavior can be understood as the air conditioning adjustment during the ride. Target type can be understood as classifying the target into categories such as vehicle owner, non-owner, and pet. Physiological state data is used to characterize the target's physiological state, such as heart rate, respiration, facial temperature, body surface temperature, sweating status, and fatigue status.

[0044] Specifically, the controller can acquire the first in-vehicle detection data and the first air conditioning adjustment behavior during each ride. Based on the first in-vehicle detection data, it classifies targets to determine different target types, such as car owners and non-car owners. This can be achieved by statistically analyzing driving frequency or by allowing users to select a method. The controller can also determine the physiological standard parameters for each target based on the physiological state data in the first in-vehicle detection data. For example, for non-car owners, the average value of each physiological state data point can be used as the standard value, or the number of data records can be maintained at or below a set limit (e.g., 10). If the limit is exceeded, the record with the largest standard deviation is deleted, and the average of the remaining values ​​is used as the standard value. Furthermore, the controller can use machine learning to learn comfort perception and air conditioning setting methods based on the first in-vehicle detection data and the first air conditioning adjustment behavior. For example, it can learn the correspondence between ambient temperature, air conditioning set temperature, and air conditioning fan speed setting, and learn the adjustment of air conditioning temperature and fan speed after a set time (e.g., 20 minutes) to reach stability, thus determining the air conditioning control habit parameters for each target. The controller can generate a clothing index curve based on behavioral and clothing data detected in the first in-vehicle data to determine dressing habits. Combined with ambient temperature and other factors, it can learn and generate standard behavioral parameters. The controller can also generate a pre-built database based on the target type and its corresponding physiological standard parameters, air conditioning control habit parameters, and standard behavioral parameters.

[0045] For example, parameters for both car owners and non-car owners can be stored up to a set limit. For instance, information on 8 car owners and 30 non-car owners can be saved. If a non-car owner becomes the 31st person, the least frequent non-car owner can be replaced and stored. Storing non-car owner information reduces the parameter learning time when non-car owners become car owners. For non-car owner information, only their physiological standard parameters can be analyzed and stored, or all parameters can be analyzed and stored, depending on storage capacity. For car owner information, their physiological standard parameters, air conditioning control habit parameters, and standard behavioral parameters can be saved.

[0046] In the first optional embodiment of this embodiment, by recording the usage habits, physiological state and behavior of each passenger, the standard parameters corresponding to each target are generated through machine learning analysis, and a pre-built database for personalized parameter determination is generated, providing a basis for subsequent parameter determination.

[0047] As a second optional embodiment of this first embodiment, after determining the final adjustment parameters and adjusting the vehicle air conditioner according to the air conditioning type and various personalized adjustment parameters, the method further includes:

[0048] Acquire the second in-vehicle detection data, the second air conditioning adjustment behavior, and the final adjustment parameter set for a set duration; update the pre-built database based on the second in-vehicle detection data, the second air conditioning adjustment behavior, and the final adjustment parameter set.

[0049] In this embodiment, the set duration can be understood as the duration for data extraction, such as one month or one week. The second in-vehicle detection data can be understood as the in-vehicle detection data under the set duration. The second air conditioning adjustment behavior can be understood as the air conditioning adjustment behavior under the set duration. The final adjustment parameter set can be understood as all the final adjustment parameters determined by this method under the set duration.

[0050] Specifically, the controller can acquire second in-vehicle detection data, second air conditioning adjustment behavior, and the final adjustment parameter set for a set duration. Based on this data, the controller can further learn the parameters of the generated target, update the pre-built database, and add parameters for targets that have not yet been generated.

[0051] In the second optional embodiment of this first embodiment, the parameters in the pre-built database are continuously optimized by learning from actual data, thereby improving the accuracy of the standard parameters and thus improving the accuracy of the personalized parameters determined based on the standard parameters.

[0052] As a third optional embodiment of this first embodiment, based on the above embodiments, it further includes:

[0053] Based on in-vehicle detection data, identify abnormal target states, determine abnormal indicators, and issue alerts.

[0054] In this embodiment, abnormal indicators can be understood as abnormal conditions related to health.

[0055] Specifically, the controller can compare health-related data from in-vehicle detection with standard parameters for that target in a pre-built database to determine if there are any significant deviations. Indicators with excessive deviations can be directly displayed on the vehicle's screen or alerted via sound. It can also compare indicators with excessive deviations with a pre-built database of typical medical findings to identify medical judgments as abnormal indicators and issue alerts. For example, if the identified target is a driver with a fever, the generated alert will be "Driver has a high fever"; if the identified target is a rear-seat passenger experiencing cardiac arrest, the alert will be "Rear-seat passenger experiences cardiac arrest" and further actions will be taken.

[0056] The third optional embodiment of this first embodiment achieves timely alerts of abnormal risks to the target by identifying and alerting the target in an abnormal state, thereby improving the overall driving experience and safety.

[0057] Example 2

[0058] Figure 2 This is a flowchart of a vehicle air conditioning control method provided in Embodiment 2 of the present invention. This embodiment is a further refinement of the above embodiments. Figure 2 As shown, the method includes:

[0059] S201. When the air conditioning adjustment conditions are met, obtain in-vehicle detection data.

[0060] S202. Determine the basic air conditioning control parameters based on the in-vehicle environment detection information.

[0061] In this embodiment, the in-vehicle environment detection information can be understood as information affecting the air conditioning adjustment, such as temperature, humidity, and carbon dioxide concentration inside the vehicle. The basic air conditioning adjustment parameters can be understood as the air conditioning adjustment under normal judgment.

[0062] Specifically, the controller can determine basic air conditioning adjustment parameters based on the in-vehicle environment detection information and corresponding algorithms or models. These parameters may include basic target air outlet temperature, basic target air outlet volume, basic target air outlet mode, basic target internal and external circulation mode, fragrance status, and air purification status.

[0063] S203. Based on the target recognition results, behavior recognition results, and pre-built database, determine the first correction factor for the target to reach the comfort condition.

[0064] In this embodiment, the target recognition result is used to distinguish different targets for target lookup from a pre-built database. The behavior recognition result can be understood as the result of recognizing behaviors of the target that affect air conditioning adjustments, such as dressing / undressing behaviors and clothing. The first correction factor is the air conditioning adjustment method determined through behavior.

[0065] Specifically, the controller can determine the first correction factor for the target to reach a comfortable condition based on the target recognition results, behavior recognition results, and a pre-built database.

[0066] Furthermore, based on the above embodiments, the step of determining the first correction factor for achieving the target's comfort condition based on the target recognition results, behavior recognition results, and the pre-built database can be refined as follows:

[0067] Based on the target identification results, the target air conditioning control habit parameters and target standard behavior parameters of the target are determined from the pre-built database; based on the target standard behavior parameters, behavior identification results and in-vehicle environment detection information, the clothing influence coefficient of the target is determined; based on the clothing influence coefficient and the target air conditioning control habit parameters, the first correction factor for the target to reach the comfort condition is determined.

[0068] In this embodiment, the clothing influence coefficient can be understood as the target clothing situation characterized by a coefficient.

[0069] Specifically, the controller can determine the target's air conditioning control habit parameters and standard behavior parameters from a pre-built database based on the target recognition results. The controller can then determine the corresponding standard clothing condition from the target's standard behavior parameters based on in-vehicle environment detection information, and compare this with the current clothing condition in the behavior identification results to determine the target's clothing influence coefficient. Based on the clothing influence coefficient and the target's air conditioning control habit parameters, the controller can determine the first correction factor to achieve the target's comfort conditions.

[0070] Based on the above embodiments, the steps for determining the clothing influence coefficient of the target according to the target standard behavioral parameters, behavioral identification results, and in-vehicle environment detection information can be refined as follows:

[0071] Based on the ambient temperature and target standard behavior parameters in the in-vehicle environment detection information, determine the standard clothing index at the ambient temperature; based on the weather indicators in the in-vehicle environment detection information, determine the clothing correction coefficient; based on the behavior recognition results and ambient temperature, determine the current outer clothing index; based on the standard clothing index, clothing correction coefficient, and current outer clothing index, determine the clothing influence coefficient.

[0072] In this embodiment, the standard clothing index can be understood as the clothing index of the target at that ambient temperature. For example, the standard clothing index is 1.6 at -20 degrees Celsius, 1.3 at -7 degrees Celsius, 1.2 at 15 degrees Celsius, 0.7 at 20 degrees Celsius, 0.6 at 35 degrees Celsius, and 0.4 at 40 degrees Celsius. Weather indicators reflect the weather conditions in the area where the vehicle is currently located and affect the adjustment of the air conditioning temperature. The current outerwear index can be understood as an index used to characterize the warmth of outerwear.

[0073] Specifically, the controller can look up the target standard behavior parameters based on the ambient temperature from the in-vehicle environment detection information to determine the standard clothing index Kcloth at that ambient temperature. The controller can also look up weather indicators from the in-vehicle environment detection information to determine the clothing correction factor. For example, corrections are needed in windy, rainy, and snowy conditions. Corresponding clothing correction factors can be preset for different weather conditions. For instance, the clothing correction factor Kweather is 1.02 times when there is light rain / light snow / light wind and the ambient temperature is -20 degrees Celsius; 1.03 times when there is moderate rain / moderate snow / moderate wind and the ambient temperature is -20 degrees Celsius; 1.01 times when there is light rain / light snow / light wind and the ambient temperature is -7 degrees Celsius; and 1.02 times when there is moderate rain / moderate snow / moderate wind and the ambient temperature is -7 degrees Celsius. The controller can determine whether a target is putting on or taking off clothing based on behavior recognition results. It can then determine the current outer clothing index (Koutclo) by looking up a table based on the ambient temperature. This table can be learned and set for different targets; for example, target A's outer clothing index is 0.7 at -20 degrees Celsius. Based on the standard clothing index, clothing correction factor, and the current outer clothing index, the clothing influence coefficient (KcloNow) is determined.

[0074] For example, the clothing influence coefficient can be determined in different ways based on the results of dressing and undressing behavior recognition. When there is undressing behavior, the clothing influence coefficient KcloNOW = standard clothing index Kcloth - current outerwear index Koutclo; when there is dressing behavior, the clothing influence coefficient KcloNOW = standard clothing index Kcloth + 0.1; when there is no dressing / undressing behavior, the clothing influence coefficient KcloNOW = standard clothing index Kcloth.

[0075] S204. Based on comprehensive physiological state information and the target's physiological standard parameters, determine the second correction factor related to the target's physiological health.

[0076] In this embodiment, the second correction factor can be understood as a correction factor related to physiological health.

[0077] Specifically, the controller can calculate and determine a second correction factor related to the target's physiological health based on comprehensive physiological state information and the target's standard physiological parameters. For example, the coefficient can be determined by dividing the comprehensive physiological state information by the corresponding standard physiological parameter.

[0078] For example, the second correction factor may include: a heart rate change coefficient Khr determined by the current heart rate / standard heart rate; a respiratory rate change coefficient Kbt determined by the current respiratory rate / standard respiratory rate; and a temperature change coefficient Kht determined by the current facial temperature / standard facial temperature at the current ambient temperature × 70% + current upper body temperature / standard lower body temperature at the current ambient temperature × 30%.

[0079] S205. Based on the first correction factor and the second correction factor, the basic air conditioning control parameters are corrected to determine the personalized adjustment parameters for different targets inside the vehicle.

[0080] Specifically, the controller can determine the corresponding air conditioning correction item and the corresponding correction value based on the first correction factor and the second correction factor by looking up a table or other means, so as to correct the basic air conditioning control parameters through the correction value and obtain personalized adjustment parameters for different targets in the vehicle.

[0081] For example, if the second correction factor determines that the coefficient of change in heart rate and respiratory rate is greater than 1 (i.e., higher than the standard value), and it is currently summer, then the corresponding air conditioning correction items are determined by looking up the coefficient and ambient temperature in a table: target air outlet temperature and air volume. The temperature correction value is -2, and the air volume is +2. If it is winter, the corresponding temperature correction value is -2, and the air volume is -2. As another example, if the clothing influence coefficient in the first correction factor is too low, after looking up the table, the corresponding air conditioning correction items are determined to be target air outlet temperature and air volume. The temperature correction value is +2, and the air volume is -2. If it is winter, the corresponding temperature correction value is +3, and the air volume is +2. For example, if the second correction factor determines fatigue driving, then after looking up the table, it is determined that the target air outlet temperature should be lowered and the air volume increased. If there is an electric air vent, it can be moved to an angle that directly blows on the fatigued target's face. The second correction factor determines that if the facial temperature coefficient is greater than 1 (i.e., higher than the standard value), and it is currently summer, the corresponding adjustment is to lower the target air outlet temperature and increase the airflow. If it is winter, the corresponding adjustment is to lower the target air outlet temperature and decrease the airflow. If it is a high fever, the target air outlet temperature will be increased and the airflow decreased in summer, and the target air outlet temperature and airflow will be increased in winter. If an infant or pet is staying in the room, the air conditioner will be turned on and automatically adjusted to the optimal temperature (e.g., 22 degrees Celsius).

[0082] S206. If the air conditioning type of the vehicle air conditioner is a single-zone air conditioner, the highest priority target shall be determined from each target according to the preset priority list. The preset priority list includes a target priority sublist, a behavior priority sublist, and a special event priority sublist.

[0083] In this embodiment, a single-zone air conditioner can be understood as an air conditioner that only supports uniform temperature control of the entire space. A preset priority list is used to assist in determining target priority. The target priority sub-list can be understood as a priority list divided according to target type; for example, the priority for the car owner and driver is higher than the car owner, higher than the driver, higher than the front passenger, and higher than the rear passengers. The behavior priority sub-list can be understood as a priority list set for behaviors such as dressing, undressing, wiping sweat, and sneezing. The special event priority sub-list can be understood as a priority list set for high fever, infant confinement, and sweat levels, etc.

[0084] Specifically, if the vehicle's air conditioning system is a single-zone system, the controller can determine the highest priority target from a preset priority list. This preset priority list includes a target priority sublist, a behavior priority sublist, and a special event priority sublist. The highest priority target within each sublist can be determined, thus identifying the overall highest priority target. For example, the highest priority target in a sublist containing special events will be prioritized as the highest priority target. This can be configured according to requirements.

[0085] S207. If the air conditioning type is multi-zone air conditioning, then for each air conditioning zone, determine the zone targets under each zone range, and determine the highest priority target from the zone targets according to the preset priority list.

[0086] In this embodiment, multi-zone air conditioning can be understood as an air conditioner that allows independent temperature control in different zones. Zone range can be understood as the area covered by the air conditioner, such as the front row area and the rear row area. Zone target can be understood as all targets within the zone.

[0087] Specifically, if the air conditioning type is a multi-zone air conditioning system, then for each air-conditioned zone, the regional targets under each zone range are determined, and the highest priority target is determined from the regional targets according to the preset priority list.

[0088] S208. Use the personalized adjustment parameters of the highest priority target as the final adjustment parameters, and adjust the vehicle air conditioning accordingly.

[0089] The technical solution of this invention first determines basic air conditioning control parameters through in-vehicle detection data. Then, by identifying in-vehicle occupants and combining this with a pre-built database, standard parameters for different targets are determined. Combined with real-time physiological state information, personalized adjustment parameters are determined for different targets, achieving dynamic determination of control parameters for different targets. Furthermore, by determining the type and priority of the in-vehicle air conditioning, the final adjustment parameters of the in-vehicle air conditioning are determined to appropriately correct the basic air conditioning control parameters, thereby achieving intelligent control of the air conditioning system. This realizes the linkage between health status and air conditioning control, covers adjustments for different types of in-vehicle air conditioning, reduces operating costs for occupants, improves comfort, and ultimately enhances driving safety and the overall user experience.

[0090] Example 3

[0091] Figure 3 This is a schematic diagram of the structure of a vehicle air conditioning control device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a data acquisition module 31, a parameter determination module 32, and an air conditioning adjustment module 33.

[0092] Data acquisition module 31 is used to acquire in-vehicle detection data when the air conditioning adjustment conditions are met;

[0093] The parameter determination module 32 is used to determine personalized adjustment parameters for different targets inside the vehicle based on the in-vehicle detection data and the pre-built database. The pre-built database records standard behavioral parameters, air conditioning control habit parameters, and physiological standard parameters for different targets to achieve comfort conditions.

[0094] The air conditioning adjustment module 33 is used to determine the final adjustment parameters and adjust the vehicle air conditioning according to the air conditioning type of the vehicle air conditioning and the personalized adjustment parameters.

[0095] The technical solution of this invention involves acquiring in-vehicle detection data when air conditioning adjustment conditions are met; determining personalized adjustment parameters for different in-vehicle targets based on the in-vehicle detection data and a pre-built database. The pre-built database records standard behavioral parameters, air conditioning control habit parameters, and physiological standard parameters for different targets to achieve comfort conditions; and determining the final adjustment parameters and adjusting the in-vehicle air conditioning based on the air conditioning type and each personalized adjustment parameter. By combining in-vehicle detection data with a pre-built database specifically for the vehicle, the current state of in-vehicle targets is tracked to determine personalized adjustment parameters for different targets, thereby determining the final adjustment parameters and adjusting the in-vehicle air conditioning. This achieves dynamic determination of control parameters for different targets, enabling dynamic personalized automatic control of the air conditioning and improving the overall user experience.

[0096] Furthermore, the in-vehicle detection data includes in-vehicle environmental detection information and comprehensive physiological state information; correspondingly, the parameter determination module 32 includes:

[0097] The first determining unit is used to determine basic air conditioning control parameters based on the in-vehicle environment detection information.

[0098] The second determining unit is used to determine a first correction factor for the target to reach the comfort condition based on the target recognition result, the behavior recognition result, and the pre-built database.

[0099] The third determining unit is used to determine a second correction factor related to the physiological health of the target based on the comprehensive physiological state information and the physiological standard parameters of the target.

[0100] The fourth determining unit is used to correct the basic air conditioning control parameters according to the first correction factor and the second correction factor, and determine the personalized adjustment parameters for different targets inside the vehicle.

[0101] The second determining unit includes:

[0102] The first determining subunit is used to determine the target air conditioning control habit parameters and target standard behavior parameters of the target from the pre-built database based on the target identification results.

[0103] The second determining subunit is used to determine the clothing influence coefficient of the target based on the target standard behavior parameters, the behavior identification results and the in-vehicle environment detection information;

[0104] The third determining subunit is used to determine the first correction factor for the target to reach the comfort condition based on the clothing influence coefficient and the target air conditioning control habit parameters.

[0105] Specifically, the second determining subunit is used for:

[0106] Based on the ambient temperature and the target standard behavior parameters in the in-vehicle environment detection information, determine the standard clothing index at the ambient temperature.

[0107] Based on the weather indicators in the in-vehicle environment detection information, determine the clothing correction factor;

[0108] Based on the behavior recognition results and the ambient temperature, determine the current outerwear index;

[0109] The clothing influence coefficient is determined based on the standard clothing index, the clothing correction coefficient, and the current outerwear index.

[0110] Furthermore, the air conditioning adjustment module 33 is specifically used for:

[0111] If the vehicle air conditioner is a single-zone air conditioner, the highest priority target is determined from the targets according to the preset priority list, which includes a target priority sublist, a behavior priority sublist, and a special event priority sublist.

[0112] If the air conditioning type is a multi-zone air conditioning, then for each air conditioning zone, the regional target under each zone range is determined, and the highest priority target is determined from the regional target according to the preset priority list;

[0113] The personalized adjustment parameters of the highest priority target are used as the final adjustment parameters, and the vehicle air conditioning is adjusted accordingly.

[0114] Optionally, the device may also include:

[0115] The database building module is specifically used for:

[0116] Acquire the first in-vehicle detection data and the first air conditioning adjustment behavior during each ride;

[0117] Based on the first in-vehicle detection data, targets are classified to determine different target types;

[0118] Based on the physiological state data in the first in-vehicle detection data, determine the physiological standard parameters of each target;

[0119] Based on the first in-vehicle detection data and the first air conditioning adjustment behavior, comfort perception and air conditioning setting methods are learned through machine learning to determine the air conditioning control habit parameters for each of the targets.

[0120] Based on the behavior and clothing data in the first in-vehicle detection data, standard behavior parameters are learned and generated.

[0121] A pre-built database is generated based on the target type and the corresponding physiological standard parameters, air conditioning control habit parameters, and standard behavioral parameters.

[0122] Optionally, the device may also include:

[0123] The database update module is specifically used for:

[0124] After determining the final adjustment parameters and adjusting the vehicle air conditioner according to the air conditioner type and the personalized adjustment parameters, the second in-vehicle detection data, the second air conditioner adjustment behavior, and the final adjustment parameter set for a set duration are obtained; the pre-built database is updated according to the second in-vehicle detection data, the second air conditioner adjustment behavior, and the final adjustment parameter set.

[0125] Optionally, the device may also include:

[0126] The anomaly alert module is used to identify target anomalies based on the in-vehicle detection data, determine anomaly indicators, and issue alerts.

[0127] The vehicle air conditioning control device provided in the embodiments of the present invention can execute the vehicle air conditioning control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0128] Example 4

[0129] Figure 4 This is a structural schematic diagram of a vehicle provided in Embodiment 4 of the present invention, as shown below. Figure 4 As shown, the vehicle includes a controller 41, a memory 42, an input device 43, and an output device 44; the number of controllers 41 in the vehicle can be one or more. Figure 4 Taking a controller 41 as an example; the controller 41, memory 42, input device 43, and output device 44 in the vehicle can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0130] The memory 42, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the vehicle air conditioning control method in this embodiment of the invention (e.g., data acquisition module 31, parameter determination module 32, and air conditioning adjustment module 33). The controller 41 executes various vehicle functions and data processing by running the software programs, instructions, and modules stored in the memory 42, thereby realizing the aforementioned vehicle air conditioning control method.

[0131] The memory 42 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, the memory 42 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the memory 42 may further include memory remotely configured relative to the controller 41, which can be connected to the vehicle via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0132] Input device 43 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the cloud platform. Output device 44 may include display devices such as a display screen and vehicle air conditioning.

[0133] Example 5

[0134] Embodiment 5 of the present invention also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a vehicle air conditioning control method, including:

[0135] When the air conditioning adjustment conditions are met, obtain in-vehicle detection data;

[0136] Based on the in-vehicle detection data and the pre-built database, personalized adjustment parameters for different targets in the vehicle are determined. The pre-built database records standard behavioral parameters, air conditioning control habit parameters, and physiological standard parameters for different targets to achieve comfort conditions.

[0137] Based on the air conditioning type of the vehicle air conditioner and the personalized adjustment parameters described above, the final adjustment parameters are determined and the vehicle air conditioner is adjusted accordingly.

[0138] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, 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-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0139] It is worth noting that in the above embodiments of the vehicle air conditioning control device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0140] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the vehicle air conditioning control method of any embodiment of the present invention.

[0141] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0142] Note that the above are merely preferred embodiments and the technical principles employed in this invention. Those skilled in the art will understand that this invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this invention. Therefore, although the invention has been described in detail through the above embodiments, this invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this invention, the scope of which is determined by the scope of the appended claims. In one embodiment, this invention further includes a computer program product, which includes a computer program that, when executed by a controller, implements the vehicle air conditioning control method of any embodiment of this invention.

[0143] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0144] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0145] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A vehicle air-conditioning control method characterized by comprising: The application relates to a method for adjusting a vehicle air conditioner, comprising the following steps: When an air conditioner adjustment condition is met, obtaining in-vehicle detection data; According to the in-vehicle detection data and a pre-constructed database, determining individualized adjustment parameters of different targets in the vehicle, wherein the pre-constructed database records standard behavior parameters, air conditioner control habit parameters and physiological standard parameters of the different targets under a comfortable condition; According to the air conditioner type of the vehicle air conditioner and the individualized adjustment parameters, determining final adjustment parameters and adjusting the vehicle air conditioner; The in-vehicle detection data comprises in-vehicle environment detection information and physiological state comprehensive information, and correspondingly, the step of determining the individualized adjustment parameters of different targets in the vehicle according to the in-vehicle detection data and the pre-constructed database comprises the following steps: According to the in-vehicle environment detection information, determining basic air conditioner control parameters; According to the target recognition result, the behavior recognition result and the pre-constructed database, determining a first correction factor of the target under the comfortable condition; According to the physiological state comprehensive information and the physiological standard parameters of the target, determining a second correction factor related to the physiological health of the target; According to the first correction factor and the second correction factor, correcting the basic air conditioner control parameters to determine the individualized adjustment parameters of different targets in the vehicle; The construction steps of the pre-constructed database comprise the following steps: Obtaining first in-vehicle detection data and first air conditioner adjustment behavior in each vehicle riding process; According to the target classification of the first in-vehicle detection data, determining different target types; According to the physiological state data in the first in-vehicle detection data, determining physiological standard parameters of each target; According to the first in-vehicle detection data and the first air conditioner adjustment behavior, learning the comfort perception and air conditioner setting mode to determine the air conditioner control habit parameters of each target; According to the behavior and clothing data in the first in-vehicle detection data, learning to generate standard behavior parameters; According to the target type and the corresponding physiological standard parameters, air conditioner control habit parameters and standard behavior parameters, generating a pre-constructed database.

2. The method of claim 1, wherein, The step of determining the first correction factor of the target under the comfortable condition according to the target recognition result, the behavior recognition result and the pre-constructed database comprises the following steps: According to the target recognition result, determining the target air conditioner control habit parameters and target standard behavior parameters of the target from the pre-constructed database; According to the target standard behavior parameters, the behavior recognition result and the in-vehicle environment detection information, determining a clothing influence coefficient of the target; According to the clothing influence coefficient and the target air conditioner control habit parameters, determining the first correction factor of the target under the comfortable condition.

3. The method of claim 2, wherein, The step of determining the clothing influence coefficient of the target according to the target standard behavior parameters, the behavior recognition result and the in-vehicle environment detection information comprises the following steps: According to the environmental temperature in the in-vehicle environment detection information and the target standard behavior parameters, determining a standard clothing index under the environmental temperature; According to the weather index in the in-vehicle environment detection information, determining a clothing correction coefficient; According to the behavior recognition result and the environmental temperature, determining a current clothing index; A clothing influence coefficient is determined according to the standard clothing index, the clothing correction coefficient and the current outerwear index.

4. The method of claim 1, wherein, The final adjustment parameter is determined according to the air conditioner category of the vehicle-mounted air conditioner and each of the individualized adjustment parameters, and the vehicle-mounted air conditioner is adjusted, including: If the air conditioner category of the vehicle-mounted air conditioner is a single-zone air conditioner, a highest priority target is determined from each of the targets according to a preset priority list, and the preset priority list includes a target priority sub-list, a behavior priority sub-list and a special event priority sub-list; If the air conditioner category is a multi-zone air conditioner, a regional target under a regional range is determined for each air conditioner region, and a highest priority target is determined from the regional targets according to the preset priority list; The individualized adjustment parameter of the highest priority target is taken as the final adjustment parameter, and the vehicle-mounted air conditioner is adjusted.

5. The method of claim 1, wherein, After the final adjustment parameter is determined according to the air conditioner category of the vehicle-mounted air conditioner and each of the individualized adjustment parameters, and the vehicle-mounted air conditioner is adjusted, the method further includes: Second in-vehicle detection data, a second air conditioner adjustment behavior and a final adjustment parameter set in a set time period are obtained; The pre-constructed database is updated according to the second in-vehicle detection data, the second air conditioner adjustment behavior and the final adjustment parameter set.

6. The method of claim 1, wherein, The method further includes: An abnormal state of a target is identified based on the in-vehicle detection data, an abnormal index is determined and a reminder is given.

7. A vehicle air-conditioning control device characterized by comprising: The method includes: A data acquisition module is configured to obtain in-vehicle detection data when an air conditioner adjustment condition is met; A parameter determination module is configured to determine individualized adjustment parameters of different targets in a vehicle according to the in-vehicle detection data and a pre-constructed database, and the pre-constructed database records standard behavior parameters, air conditioner control habit parameters and physiological standard parameters of different targets under a comfortable condition; An air conditioner adjustment module is configured to determine a final adjustment parameter according to an air conditioner category of a vehicle-mounted air conditioner and each of the individualized adjustment parameters, and adjust the vehicle-mounted air conditioner; The in-vehicle detection data includes in-vehicle environment detection information and physiological state comprehensive information, and correspondingly, the parameter determination module includes: A first determination unit is configured to determine a basic air conditioner control parameter according to the in-vehicle environment detection information; A second determination unit is configured to determine a first correction factor of a target under a comfortable condition according to a target recognition result, a behavior recognition result and the pre-constructed database; A third determination unit is configured to determine a second correction factor related to physiological health of the target according to the physiological state comprehensive information and a physiological standard parameter of the target; A fourth determination unit is configured to correct the basic air conditioner control parameter according to the first correction factor and the second correction factor, and determine individualized adjustment parameters of different targets in a vehicle; The device further includes a database construction module, which is specifically configured to: First in-vehicle detection data and a first air conditioner adjustment behavior of each ride are obtained; Different target types are determined according to the first in-vehicle detection data; Physiological standard parameters of each target are determined according to physiological state data in the first in-vehicle detection data; According to the first in-vehicle detection data and the first air conditioner adjustment behavior, comfort perception and air conditioner setting mode learning are performed through machine learning to determine air conditioner control habit parameters of each target; According to the behavior and clothing data in the first in-vehicle detection data, standard behavior parameters are learned and generated; According to the target type and the corresponding physiological standard parameters, air conditioner control habit parameters and standard behavior parameters, a pre-constructed database is generated.

8. A vehicle characterized by comprising: The vehicle comprises: at least one controller; and a memory in communication connection with the at least one controller; wherein, The memory stores a computer program executed by the at least one controller, and the computer program is executed by the at least one controller to enable the at least one controller to perform the vehicle-mounted air conditioner control method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the controller to perform the vehicle-mounted air conditioner control method of any one of claims 1-6 when executed.

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