Automobile cabin atmosphere adjusting method, computer device and storage medium

Abstract

This invention discloses a method, computer device, and storage medium for adjusting the atmosphere of a car cabin. The method includes determining the priority weights of each occupant in the car cabin, detecting the atmosphere requirements of each occupant, obtaining atmosphere requirement information for each occupant, generating target atmosphere adjustment parameters based on the atmosphere requirement information and priority weights, and adjusting the atmosphere of the car cabin space according to the target atmosphere adjustment parameters. This invention can synthesize multiple atmosphere requirement information based on the priority weights of multiple occupants to obtain target atmosphere adjustment parameters, and adjust the atmosphere of the car cabin space according to these parameters. This allows the adjusted atmosphere of the car cabin space to reach or approach an atmosphere type that satisfies the overall needs of multiple occupants, thus meeting the atmosphere needs of more occupants and improving the driving and riding experience. This invention has wide applications in the field of automotive technology.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and in particular to a method for adjusting the atmosphere of an automotive cabin, a computer device, and a storage medium. Background Technology

[0002] The atmosphere of a car's interior cabin is an environment that presents a specific visual, auditory, and sensory experience to the occupants. For example, current automotive technology uses ambient lighting to project specific lighting effects into the cabin, creating a particular ambiance. Furthermore, some technologies have made the lighting effects of ambient lighting controllable, thereby enhancing the sense of atmosphere and the overall experience. A car's cabin atmosphere creates a comfortable space for occupants, improving the driving and riding experience, and a good driving and riding experience also contributes to ensuring traffic safety.

[0003] However, current automotive cabin atmosphere control technology has poor adjustability and can generally only meet the needs of a few passengers, especially the owner. This means that when there are multiple passengers in the car, the cabin atmosphere control effect will ignore the needs of other passengers. On the one hand, there is still room for improvement in terms of driving and riding experience. On the other hand, because passengers interact, if the atmosphere needs of other passengers are not met, it is possible that the interaction of passengers may reduce the driving and riding experience of passengers whose atmosphere needs have been met, resulting in a negative effect such as a decrease in driving and riding experience instead of an increase.

[0004] Terminology Explanation:

[0005] IVI: In-Vehicle Infotainment;

[0006] CAN: Controller Area Network;

[0007] MIC: Microphone;

[0008] DMC: Driver Monitoring Camera;

[0009] CWC: Cabin Watch Camera;

[0010] SOC: System on Chip;

[0011] MCU: Micro Controller Unit. Summary of the Invention

[0012] In view of the technical problems of current automotive cabin atmosphere adjustment technology, such as the tendency to overlook the needs of some passengers and the potential for the driving and riding experience to decrease rather than increase, the purpose of this invention is to provide an automotive cabin atmosphere adjustment method, computer device and storage medium.

[0013] On one hand, embodiments of the present invention include a method for adjusting the atmosphere of a car cabin, the method comprising the following steps:

[0014] Determine the priority weight of each occupant in the car cabin;

[0015] The atmosphere needs of the people in each vehicle were detected separately to obtain the atmosphere needs information of each person in each vehicle.

[0016] Based on the aforementioned atmosphere requirement information and the aforementioned priority weights, generate the target parameters for atmosphere adjustment;

[0017] The atmosphere inside the car cabin is adjusted according to the aforementioned atmosphere adjustment target parameters.

[0018] Furthermore, determining the priority weight of each occupant in the vehicle cabin includes:

[0019] Acquire voice information from each person in the car's cabin;

[0020] Perform semantic recognition on each of the aforementioned voice information to obtain the semantic information corresponding to each of the aforementioned voice information;

[0021] Based on the semantic information provided, the priority weights of each occupant in the car cabin are determined.

[0022] Furthermore, determining the priority weight of each occupant in the vehicle cabin based on the semantic information includes:

[0023] Among the occupants in the car cabin, identify several target occupants;

[0024] The priority weight of the personnel in the target vehicle is set to a fixed value;

[0025] Based on the semantic information of the person in the target vehicle and the association between the semantic information of the people in other vehicles, the relative magnitude relationship between the priority weight of the person in the target vehicle and the priority weight of the people in other vehicles is determined.

[0026] The priority weights of other vehicle occupants are determined based on the priority weights of the target vehicle occupants and their relative size relationships.

[0027] Furthermore, the step of detecting the atmosphere needs of each person in the vehicle to obtain the atmosphere need information corresponding to each person in the vehicle includes:

[0028] During the same time period, behavioral detection was performed on the people in each vehicle to obtain their individual behavioral information;

[0029] The behavioral information is sorted according to the corresponding detection time;

[0030] Execute multiple rounds of iterative process; in any round of the iterative process, the following steps are performed:

[0031] When the current iteration process is the first iteration process, the behavioral information with the first order is obtained. Based on the behavioral information, the atmosphere demand information processed by the current iteration process is determined. The atmosphere demand information processed by the current iteration process is used to determine the atmosphere demand information corresponding to the person in the vehicle who generated the behavioral information.

[0032] If the current iteration is not the first iteration, obtain the behavioral information in the order corresponding to the number of iterations in the current iteration; adjust or maintain the atmosphere requirement information obtained from the previous iteration based on the behavioral information to obtain the atmosphere requirement information obtained from the current iteration; determine the atmosphere requirement information corresponding to the person in the vehicle who generated the behavioral information based on the behavioral information and the atmosphere requirement information obtained from the previous iteration.

[0033] Furthermore, adjusting or maintaining the atmosphere demand information obtained from the previous iteration based on the behavioral information includes:

[0034] Detect the type of the behavioral information;

[0035] When the behavioral information belongs to the voice information type, semantic recognition is performed on the behavioral information, and the atmosphere demand information obtained from the previous iteration process is adjusted or maintained based on the semantic recognition result.

[0036] When the behavioral information belongs to the action information type, the atmosphere demand information obtained from the previous iteration process is retained.

[0037] Further, generating the atmosphere adjustment target parameters based on each of the atmosphere demand information and each of the priority weights includes:

[0038] A weighted calculation is performed based on the priority weight corresponding to each of the aforementioned atmosphere requirement information;

[0039] The target parameters for atmosphere adjustment are determined based on the results of the weighted calculation.

[0040] Further, generating the atmosphere adjustment target parameters based on each of the atmosphere demand information and each of the priority weights includes:

[0041] Obtain the maximum priority weight among all the aforementioned priority weights;

[0042] The atmospheric adjustment target parameters are generated based on the atmospheric demand information corresponding to the maximum priority weight.

[0043] Furthermore, the step of adjusting the atmosphere of the vehicle cabin space according to the atmosphere adjustment target parameters includes:

[0044] Based on the stated atmospheric adjustment target parameters, generate atmospheric adjustment control commands;

[0045] According to the atmosphere adjustment control command, the atmosphere adjustment function module is controlled to adjust the atmosphere of the space inside the car cabin.

[0046] On the other hand, embodiments of the present invention also include a computer device, including a memory and a processor, the memory for storing at least one program, and the processor for loading at least one program to execute a method for adjusting the atmosphere of a car cabin according to the embodiments.

[0047] On the other hand, embodiments of the present invention also include a storage medium storing a processor-executable program, which, when executed by a processor, is used to perform a method for adjusting the atmosphere of a car cabin in the embodiments.

[0048] The beneficial effects of the present invention are as follows: The car cabin atmosphere adjustment method in the embodiments can obtain the atmosphere demand information of each person in the car, and according to the priority weight of each person in the car, integrate multiple atmosphere demand information to obtain atmosphere adjustment target parameters. The atmosphere of the space in the car cabin is adjusted according to the atmosphere adjustment target parameters, so that the atmosphere of the car cabin space after adjustment reaches or tends to meet the overall needs of multiple people in the car. This breaks through the limitation of only meeting the atmosphere demand of individual people in the car, and can meet the atmosphere demand of more people in the car, improve the adjustability of car cabin atmosphere adjustment, and enhance the driving experience. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of a vehicle system for which the vehicle cabin atmosphere adjustment method can be applied in the embodiment.

[0050] Figure 2 This is a flowchart illustrating the steps of the vehicle cabin atmosphere adjustment method in the embodiment;

[0051] Figure 3This is a schematic diagram illustrating the principle of the step in the embodiment whereby atmosphere needs are detected for each person in the vehicle, and atmosphere needs information for each person in the vehicle is obtained. Detailed Implementation

[0052] In this embodiment, the automotive cabin atmosphere adjustment method can be applied to... Figure 1 The vehicle system shown. (Refer to...) Figure 1 The automotive system includes a microphone (MIC), a camera, an infotainment system (IVI), as well as ambient lighting, speakers, and air conditioning, all of which are installed on the vehicle.

[0053] Reference Figure 1 The car's cabin includes four seats: front left, front right, rear left, and rear right. Figure 1 The left front microphone is installed in the left front seat to record the driver and other passengers in the vehicle. Similarly, the right front microphone, left rear microphone, and right rear microphone are installed in the right front seat, left rear seat, and right rear seat, respectively, to record the passengers in the right front seat, left rear seat, and right rear seat.

[0054] Reference Figure 1 The DMC camera can be installed on the steering wheel or dashboard to capture images of the driver. The CWC camera can be installed on the back of the front seat (or center console) or the right rear seat (or left rear seat) to capture images of the occupants.

[0055] In this embodiment, authorization from the driver and passengers can be obtained before using the microphone or camera, ensuring they are aware that the microphone and camera will record audio and video. If the driver and passengers do not authorize the use of the microphone or camera, the microphone or camera will not be used, thus protecting their privacy. Alternatively, the audio and video data obtained by using the microphone or camera to record audio and video of the driver and passengers is limited to the vehicle's internal environment and is only used during the execution of the vehicle cabin atmosphere adjustment method. The image and audio data are not backed up, played back, displayed, or uploaded to a server. Furthermore, the image and audio data are immediately deleted after the vehicle cabin atmosphere adjustment method is completed. This reduces or even eliminates the possibility of image and audio data leakage that could harm the privacy of the driver and passengers, thus protecting their privacy.

[0056] Reference Figure 2 The method for adjusting the atmosphere of a car cabin includes the following steps:

[0057] S1. Determine the priority weight of each occupant in the car cabin;

[0058] S2. Conduct atmosphere demand detection for each person in each vehicle to obtain the atmosphere demand information for each person in each vehicle;

[0059] S3. Generate target parameters for atmosphere adjustment based on the information on each atmosphere requirement and the priority weights;

[0060] S4. Adjust the atmosphere of the car cabin according to the target parameters for atmosphere adjustment.

[0061] It can be by Figure 1 The microcontroller unit (MCU) in the vehicle executes steps S1-S4. When certain data needs to be acquired to execute steps S1-S4, the MCU can obtain the corresponding data by calling on the vehicle components.

[0062] One application scenario for steps S1-S4 is: a car has one driver and at least one passenger, meaning there are multiple people in the car; the car is equipped with an atmosphere control module, specifically, see [link to relevant documentation]. Figure 1 The system uses ambient lighting, speakers, and air conditioning as its atmosphere control modules. Ambient lighting displays light with specific effects to create a visual atmosphere in the car cabin; speakers play music to create an auditory atmosphere; and air conditioning regulates the temperature inside the car cabin to create a sensory atmosphere.

[0063] During step S1, the microcontroller unit (MCU) can access the camera to perform facial recognition on each person in the vehicle, thereby recording their identity information. Specifically, for frequent passengers such as the vehicle owner, their family members, or company employees, facial registration can be done in advance. This means that the MCU records their facial features and names. When facial features are detected through facial recognition, the MCU can match the corresponding name and other identity information. For passengers who are not frequent passengers and have not been pre-registered, the MCU can generate a number based on the facial features detected by the camera as their identity information. Using this identity information, the MCU can distinguish between different passengers in the vehicle.

[0064] In step S1, the microcontroller unit (MCU) establishes a data table to record the correspondence between identity information and priority weights, thereby assigning a corresponding priority weight to each person in the vehicle. In this embodiment, the data type of the priority weights is data that can be judged in terms of magnitude, thus enabling comparison of the relative magnitudes of the priority weights corresponding to different people in the vehicle.

[0065] In step S2, the microcontroller unit (MCU) can call upon relevant vehicle components to detect the atmosphere requirements of each occupant. Detecting the atmosphere requirements of any occupant yields their corresponding atmosphere requirement information. This information indicates the type of car cabin atmosphere that best meets the needs of that occupant.

[0066] In step S3, the microcontroller unit (MCU) generates target air conditioning parameters based on the priority weights obtained in step S1 and the air conditioning demand information obtained in step S2. These target air conditioning parameters represent the type of car cabin atmosphere that, when determined by combining the air conditioning demand information according to the priority weights, can meet the overall needs of all occupants.

[0067] In step S4, the microcontroller unit (MCU) adjusts the atmosphere of the car cabin according to the atmosphere adjustment target parameters obtained in step S3. During step S4, the MCU can invoke the atmosphere adjustment function module to adjust the atmosphere of the car cabin until the atmosphere reaches the type corresponding to the atmosphere adjustment target parameters obtained in step S3.

[0068] In this embodiment, by executing steps S1-S4, the individual atmosphere requirements of each person in the vehicle can be obtained. Based on the priority weight of each person in the vehicle, the multiple atmosphere requirements are combined to obtain the atmosphere adjustment target parameters. The atmosphere of the space inside the car cabin is adjusted according to the atmosphere adjustment target parameters, so that the atmosphere inside the car cabin after adjustment can reach or tend to meet the overall atmosphere requirements of multiple people in the vehicle. This breaks through the limitation of only meeting the atmosphere requirements of a few people in the vehicle, and can meet the atmosphere requirements of more people in the vehicle, improve the adjustability of the car cabin atmosphere adjustment, and enhance the driving experience.

[0069] In this embodiment, when performing step S1, which is to determine the priority weight of each occupant in the car cabin, the following steps can be performed:

[0070] S101. Obtain voice information from each person in the vehicle's cabin;

[0071] S102. Perform semantic recognition on each speech information to obtain the semantic information corresponding to each speech information;

[0072] S103. Determine the priority weight of each occupant in the car cabin based on the semantic information.

[0073] In step S101, the microcontroller unit (MCU) can call... Figure 1 The microphones in the vehicle record the voice information of each person in the car. Specifically, Figure 1 All microphones in the system can be in recording mode and the recorded signals can be filtered to retain only human voices. If a microphone's recorded signal does not contain human voices, it indicates that no one is sitting in that seat, and in this case, the microphone will not send voice information to the microcontroller unit (MCU). If a microphone's recorded signal contains human voices, it can extract the human voice portion, generate voice information, and send the voice information to the MCU. The voice information can be represented using audio data such as audio.

[0074] In step S102, the microcontroller unit (MCU) acquires the voice information sent from each microphone and performs semantic recognition on each voice message. The MCU performs semantic recognition on each voice message to obtain the corresponding semantic information, which can be represented by data in the form of text or other formats.

[0075] In step S103, the microcontroller unit (MCU) analyzes the semantic relationships between the semantic information obtained in step S102 to determine the priority weight of each person in the car cabin.

[0076] Specifically, when executing step S103, the microcontroller unit (MCU) can perform the following steps:

[0077] S10301. Among the occupants in the vehicle's cabin, identify several target occupants;

[0078] S10302. Set the priority weight of the personnel on the target vehicle to a fixed value;

[0079] S10303. Based on the semantic information of the people in the target vehicle and the association between the semantic information of the people in other vehicles, determine the relative size relationship between the priority weight of the people in the target vehicle and the priority weight of the people in other vehicles;

[0080] S10304. Determine the priority weights of other vehicle personnel based on the priority weights and relative size relationships of the personnel in the target vehicle.

[0081] In step S10301, the microcontroller unit (MCU) can identify specific individuals in the vehicle, such as the vehicle owner, their family members, or employees of the vehicle owner's workplace, as target occupants. Specifically, after obtaining the facial features of each occupant, the MCU can compare them with pre-registered facial features to determine their identity. Those who have not registered their facial features are identified as strangers and excluded from the target occupant list; those who have registered their facial features are identified as target occupants. In this embodiment, identifying the vehicle owner as the target occupant is used as an example.

[0082] In step S10302, the microcontroller unit (MCU) sets the priority weight of the personnel in the target vehicle to a fixed value. Taking a positive number between 1 and 10 as an example, the priority weight of the personnel in the target vehicle can be set to 5.

[0083] In step S10303, the microcontroller unit (MCU) traverses all other passengers (i.e., passengers other than the target passenger), performs semantic association analysis on the semantic information of the target passenger and the semantic information of each other passenger, and determines the association relationship between the semantic information of the target passenger and the semantic information of any other passenger.

[0084] In step S10303, the microcontroller unit (MCU) can express any semantic information as a vector. For the semantic information of any other occupant, the MCU can execute a semantic distance algorithm to calculate the semantic distance between the semantic information of the target occupant and the semantic information of any other occupant, or execute a semantic opposition algorithm to calculate the semantic opposition between the semantic information of the target occupant and the semantic information of any other occupant. In other words, "semantic distance" or "semantic opposition" can be used as the quantification value of the "association relationship." Specifically, the "semantic distance" or "semantic opposition" can be negatively correlated with the "association relationship." That is, the smaller the semantic distance between the semantic information of the target occupant and the semantic information of any other occupant, the stronger the association relationship; the smaller the semantic opposition between the semantic information of the target occupant and the semantic information of any other occupant, the stronger the association relationship.

[0085] In step S10303, for the semantic information of any other person in the vehicle, the microcontroller unit (MCU) can count the number of conversations or the duration of conversations between this other person and the target person in the vehicle. That is, the "number of conversations" or "duration of conversations" is used as the quantifiable value of the "association relationship." Specifically, the "number of conversations" or "duration of conversations" and the "association relationship" can be determined to be positively correlated; that is, the more "number of conversations" or "duration of conversations" exist between the semantic information of the target person and the semantic information of any other person in the vehicle, the stronger the association relationship.

[0086] In step S10303, for any other passenger, the difference between the priority weight of this other passenger and the priority weight of the target passenger can be determined as a positive correlation with their semantic information. For example, if the semantic information of another passenger and the target passenger is "close," then the difference between the priority weight of this other passenger and the priority weight of the target passenger can be determined to be 3. Since the priority weight of the target passenger is fixed at 5, then by executing step S10304, the priority weight of this other passenger can be determined to be 5+3=8. If the semantic information of another other passenger and the target passenger is "neutral," then the difference between the priority weight of this other passenger and the priority weight of the target passenger can be determined to be 1. Since the priority weight of the target passenger is fixed at 5, then by executing step S10304, the priority weight of this other passenger can be determined to be 5+1=6.

[0087] In this embodiment, the principle of executing steps S10301-S10304 is as follows: the correlation between the semantic information of the target vehicle occupants and the semantic information of other vehicle occupants can represent the subjective importance that the target vehicle occupants (owner) place on other vehicle occupants. Therefore, based on this correlation, the relative size relationship between the priority weights of the target vehicle occupants and other vehicle occupants is determined. Further determining the priority weights of other vehicle occupants can convert the subjective intentions of the target vehicle occupants (owner) into control parameters used to control the car cabin atmosphere, thereby achieving intelligent control of the car cabin atmosphere. This is especially relevant when there are other vehicle occupants who require special care from the target vehicle occupants (owner) (e.g., children, VIPs). In the case of passengers in scenarios such as hotels and ride-hailing services, the semantic information relationship between other passengers in the vehicle and the target passenger is often closer than that between other types of passengers in the vehicle and the target passenger. Therefore, executing steps S10301-S10304 is beneficial for other passengers such as "persons who need special care from the target passenger (driver)" to obtain higher priority weights. As a result, the atmosphere adjustment target parameters generated in step S3 can better reflect the atmosphere needs of these other passengers, making the final atmosphere adjustment effect more inclined to meet the atmosphere needs of these other passengers, and realizing intelligent adjustment of the car cabin atmosphere.

[0088] In this embodiment, when performing step S2, which involves detecting the atmosphere requirements of each person in the vehicle and obtaining their respective atmosphere requirement information, the following steps can be performed:

[0089] S201. Within the same time period, conduct behavior detection on the occupants of each vehicle to obtain individual behavior information of each occupant;

[0090] S202. Sort the behavioral information according to the corresponding detection time;

[0091] S203. Perform multiple iterations; each iteration performs the following steps:

[0092] When this iteration process is the first iteration process, the behavioral information with the first order is obtained. Based on the behavioral information, the atmosphere demand information obtained in this iteration process is determined. The atmosphere demand information obtained in this iteration process is used to determine the atmosphere demand information corresponding to the people on the vehicle who generated the behavioral information.

[0093] If this iteration is not the first iteration, obtain the behavioral information in the order corresponding to the round number of this iteration; based on the behavioral information, adjust or maintain the atmosphere requirement information obtained from the previous iteration to obtain the atmosphere requirement information obtained from this iteration; based on the behavioral information and the atmosphere requirement information obtained from the previous iteration, determine the atmosphere requirement information corresponding to the person in the vehicle who generated the behavioral information.

[0094] The principle of steps S201-S203 is as follows: Figure 3 As shown.

[0095] In step S201, within the time period 0-T, behavior detection is performed on each person in the vehicle to obtain their individual behavior information. In this embodiment, the behavior information is information expressed by the language, facial expressions, or body movements of the people in the vehicle, specifically, it can be expressed as language information, facial expression information, and action information.

[0096] Reference Figure 3 Behavior detection is performed on passenger 1 to obtain behavior information 1; behavior detection is performed on passenger 2 to obtain behavior information 2; behavior detection is performed on passenger 3 to obtain behavior information 3... behavior detection is performed on passenger n to obtain behavior information n.

[0097] In step S202, the behavior information 1, behavior information 2, behavior information 3... behavior information n, etc., are sorted according to the time when the corresponding on-board personnel's behavior was detected. (Refer to...) Figure 3 Suppose that passenger 1 is the first to make a verbal, facial, or physical gesture, which is captured by the microcontroller unit (MCU) using the camera or microphone. Then, the corresponding behavioral information 1 is the first behavioral information. If passenger 2 makes a verbal, facial, or physical gesture after passenger 1, and this gesture is also captured by the MCU using the camera or microphone, then the corresponding behavioral information 2 is the second behavioral information…

[0098] In step S203, refer to Figure 3 It performs multiple rounds of iterative processes.

[0099] like Figure 3 As shown, in the first iteration, the behavioral information obtained first, namely behavioral information 1, is acquired. Based on behavioral information 1, the atmosphere requirement information 1' obtained in this iteration is determined. The atmosphere requirement information 1' obtained in this iteration is then used to determine the atmosphere requirement information 1 corresponding to the person in the vehicle who generated behavioral information 1. Specifically, when determining "atmosphere requirement information 1'" based on "behavioral information 1", the type of emotion or symptom expressed by the language, facial expressions, or body movements corresponding to "behavioral information 1" can be determined, and then the corresponding atmosphere requirement can be determined. For example, if "behavioral information 1" indicates that person 1 in the vehicle is yawning and says "I feel cold", then by looking up a table, it can be determined that person 1 is drowsy, and the atmosphere requirement for person 1 is "play soft music, reduce the brightness of the ambient lights, and increase the air conditioning temperature", thus determining "atmosphere requirement information 1'", which is the same as "atmosphere requirement information 1'".

[0100] like Figure 3 As shown, in any iteration process other than the first iteration process, taking the second iteration process as an example, the second behavioral information, i.e., behavioral information 2, is obtained. Based on behavioral information 2, the atmosphere demand information 1' obtained in the previous iteration process (i.e., the first iteration process) is adjusted or maintained to obtain the atmosphere demand information 2' obtained in the current iteration process. Specifically, if behavioral information 2 belongs to the voice information type, then semantic recognition is performed on behavioral information 2. Based on the semantic recognition result of behavioral information 2, the atmosphere demand information 1' obtained in the previous iteration process is adjusted or maintained. For example, since atmosphere demand information 1' already expresses the content "increase the air conditioning temperature" which can satisfy the semantic meaning of "I feel cold", if the semantic recognition result of behavioral information 2 includes semantics such as "I also feel cold", then atmosphere demand information 1' is maintained; otherwise, it is modified. If behavioral information 2 belongs to the action information type, then it is not easy to determine whether the person in the car 2 agrees with atmosphere demand information 1', then atmosphere demand information 1' is maintained.

[0101] like Figure 3As shown, in the second iteration, based on the behavioral information 2 and the atmosphere demand information 1' obtained from the previous iteration, the atmosphere demand information 2 corresponding to the person 2 in the vehicle who generated the behavioral information 2 is determined. For example, since the atmosphere demand information 1' already represents the content "increase the air conditioning temperature" which can satisfy the semantics of "I feel cold", if the semantic recognition result of the behavioral information 2 includes semantics such as "I also feel cold", then the atmosphere demand information 1' can be copied to obtain the atmosphere demand information 2; otherwise, the atmosphere demand information 1' is adjusted to obtain the atmosphere demand information 2.

[0102] Reference Figure 3 The principle behind execution steps S201-S203 is as follows: By executing multiple iterations, each iteration adjusts or maintains the atmosphere demand information obtained from the previous iteration based on the corresponding behavioral information, thus concentrating all behavioral information into the atmosphere demand information. By determining the atmosphere demand information of the corresponding passengers based on the atmosphere demand information obtained from the previous iteration and the corresponding behavioral information in the current iteration, the social correlation between the behavioral information of multiple passengers within the same time period can be fully utilized, thereby capturing and analyzing the atmosphere demand information expressed by passengers through language, facial expressions, and body language.

[0103] For example, suppose passenger 1 expresses "increase the air conditioning temperature" in their atmosphere needs through behavioral information 1. Passenger 3, after sensing "I feel cold" expressed by passenger 1 through behavioral information 1, makes a dissenting facial expression. By executing steps S201-S203, it is possible to analyze the temporal relationship between multiple behavioral information and the social connections therein, and thus determine that passenger 3's behavioral information 3 contains the meaning of "I don't feel cold". This is accurately reflected in passenger 3's corresponding atmosphere needs information 3, achieving the keen capture of the atmosphere needs information of multiple passengers.

[0104] In this embodiment, when performing step S3, which is to generate the target parameters for atmosphere adjustment based on the various atmosphere demand information and priority weights, the following steps can be specifically performed:

[0105] S301A. Perform a weighted calculation based on the priority weights of each atmospheric requirement information;

[0106] S302A. Determine the target parameters for atmosphere conditioning based on the results of the weighted calculation.

[0107] Steps S301A-S302A are the first execution method of step S3.

[0108] In step S301A, each atmosphere requirement information can be vectorized, multiplied by its corresponding priority weight, and then summed to obtain the weighted calculation result. For example, if passenger 1 corresponds to atmosphere requirement information 1 and priority weight 1, passenger 2 corresponds to atmosphere requirement information 2 and priority weight 2, and so on, passenger n corresponds to atmosphere requirement information n and priority weight n, then the weighted calculation result = atmosphere requirement information 1 × priority weight 1 + atmosphere requirement information 2 × priority weight 2 + ... + atmosphere requirement information n × priority weight n.

[0109] In step S302A, based on the atmosphere requirements represented by the weighted calculation results, the target parameters for atmosphere adjustment are determined, for example, for Figure 1 The system shown can use ambient lighting target parameters to represent the desired brightness of the ambient lights, the music played by the speakers, the target temperature of the air conditioner, etc.

[0110] By executing steps S301A-S302A, the atmosphere needs of multiple passengers can be fully considered, thereby achieving the effect of satisfying the overall atmosphere needs of multiple passengers.

[0111] In this embodiment, when performing step S3, which is to generate the target parameters for atmosphere adjustment based on the various atmosphere demand information and priority weights, the following steps can be specifically performed:

[0112] S301B. Obtain the maximum priority weight among all priority weights;

[0113] S302B. Generate atmospheric adjustment target parameters based on the atmospheric demand information corresponding to the maximum priority weight.

[0114] Steps S301B-S302B are the second execution method of step S3.

[0115] In step S301B, for example, passenger 1 corresponds to atmosphere requirement information 1 and priority weight 1, passenger 2 corresponds to atmosphere requirement information 2 and priority weight 2, and so on, passenger n corresponds to atmosphere requirement information n and priority weight n. Assuming that the largest priority weight, i.e. the maximum priority weight, is priority weight 2, then in step S302B, the atmosphere requirement information corresponding to the maximum priority weight, i.e. priority weight 2, is atmosphere requirement information 2. Therefore, other atmosphere requirement information can be ignored, and the atmosphere adjustment target parameter can be generated only based on atmosphere requirement information 2.

[0116] By executing steps S301B-S302B, in cases where there are passengers requiring special care (such as infants), target parameters for atmosphere adjustment can be generated based on the atmosphere needs information of such passengers, thereby prioritizing the satisfaction of the atmosphere needs of these passengers and achieving the effect of meeting the specific atmosphere needs of individual passengers.

[0117] In this embodiment, when performing step S4, which is to adjust the atmosphere of the car cabin space according to the target atmosphere adjustment parameters, the following steps can be performed:

[0118] S401. Generate atmosphere control commands based on the target atmosphere control parameters;

[0119] S402. According to the atmosphere adjustment control command, control the atmosphere adjustment function module to adjust the atmosphere of the space inside the car cabin.

[0120] In step S401, the ambient lighting target parameters represent information such as the desired brightness of the ambient lights, the music to be played by the speakers, and the desired temperature of the air conditioning. Based on this information, ambient lighting control commands are generated to control the ambient lighting, speakers, and air conditioning modules. In step S402, the microcontroller unit (MCU) sends the ambient lighting control commands to the ambient lighting, speakers, and air conditioning modules, thereby controlling these modules to adjust the atmosphere of the car cabin to achieve the desired effect as indicated by the ambient lighting target parameters.

[0121] A computer program for executing the car cabin atmosphere adjustment method in this embodiment can be written into a computer device or storage medium. When the computer program is read out and run, the car cabin atmosphere adjustment method in this embodiment is executed, thereby achieving the same technical effect as the car cabin atmosphere adjustment method in the embodiment.

[0122] It should be noted that, unless otherwise specified, when a feature is referred to as "fixed" or "connected" to another feature, it can be directly fixed or connected to the other feature, or indirectly fixed or connected to the other feature. Furthermore, the descriptions of "upper," "lower," "left," and "right" used in this disclosure are only relative to the relative positional relationships of the components of this disclosure in the accompanying drawings. The singular forms "a," "an," and "the" used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. Moreover, unless otherwise defined, all technical and scientific terms used in this embodiment have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this embodiment specification is only for describing particular embodiments and is not intended to limit the invention. The term "and / or" as used in this embodiment includes any combination of one or more of the associated listed items.

[0123] It should be understood that although the terms first, second, third, etc., may be used to describe various elements in this disclosure, these elements should not be limited to these terms. These terms are only used to distinguish elements of the same type from each other. For example, a first element may also be referred to as a second element without departing from the scope of this disclosure, and similarly, a second element may also be referred to as a first element. The use of any and all instances or exemplary language (“e.g.,” “such as,” etc.) provided in this embodiment is intended only to better illustrate embodiments of the invention and, unless otherwise required, does not impose a limitation on the scope of the invention.

[0124] It should be recognized that embodiments of the present invention can be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable storage medium. The method can be implemented using standard programming techniques—including a non-transitory computer-readable storage medium configured with a computer program, wherein such a storage medium causes the computer to operate in a specific and predefined manner—according to the methods and drawings described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. Furthermore, for this purpose, the program can run on a programmed application-specific integrated circuit (ASIC).

[0125] Furthermore, the procedures described in this embodiment can be performed in any suitable order unless otherwise indicated by this embodiment or clearly contradicted by the context. The procedures (or variations and / or combinations thereof) described in this embodiment can be executed under the control of one or more computer systems configured with executable instructions, and can be implemented by hardware or a combination thereof as code (e.g., executable instructions, one or more computer programs, or one or more applications) that commonly executes on one or more processors. A computer program includes multiple instructions executable by one or more processors.

[0126] Furthermore, the method can be implemented in any suitable type of computing platform, including but not limited to personal computers, minicomputers, mainframes, workstations, networked or distributed computing environments, standalone or integrated computer platforms, or in communication with charged particle tools or other imaging devices, etc. Aspects of the invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the processes described herein. Furthermore, the machine-readable code, or portions thereof, can be transmitted via wired or wireless networks. The invention of this embodiment includes these and other different types of non-transitory computer-readable storage media when such media comprises instructions or programs that implement the steps above in conjunction with a microprocessor or other data processor. When programmed according to the methods and techniques of the invention, the invention also includes the computer itself.

[0127] A computer program can be applied to input data to perform the functions of this embodiment, thereby transforming the input data to generate output data stored in non-volatile memory. The output information can also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including specific visual depictions of physical and tangible objects generated on the display.

[0128] The above are merely preferred embodiments of the present invention. The present invention is not limited to the above-described embodiments. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention, as long as they achieve the technical effects of the present invention by the same means, should be included within the scope of protection of the present invention. Within the scope of protection of the present invention, the technical solutions and / or implementation methods can have various modifications and variations.

Claims

1. A method of adjusting an atmosphere of a vehicle cabin, characterized by, The automobile cabin atmosphere adjusting method comprises: determining respective priority weights of each vehicle occupant in the automobile cabin; respectively detecting atmosphere demand of each vehicle occupant to obtain respective atmosphere demand information of each vehicle occupant; generating atmosphere adjusting target parameters according to the atmosphere demand information and the priority weights; adjusting the atmosphere of the space in the automobile cabin according to the atmosphere adjusting target parameters; the respective detection of the atmosphere demand of each vehicle occupant to obtain the respective atmosphere demand information of each vehicle occupant comprises: respectively detecting behaviors of each vehicle occupant in the same time period to obtain respective behavior information of each vehicle occupant; sorting the behavior information according to the corresponding detection time; performing a plurality of iteration processes; any round of the iteration process performs the following steps: when the iteration process of the current round is the first iteration process, obtaining the behavior information in the first order, determining the atmosphere demand information processed by the iteration process of the current round according to the behavior information, and determining the atmosphere demand information corresponding to the vehicle occupant generating the behavior information with the atmosphere demand information processed by the iteration process of the current round; when the iteration process of the current round is not the first iteration process, obtaining the behavior information in the order corresponding to the number of the iteration process of the current round; detecting the type of the behavior information, when the behavior information belongs to the voice information type, performing semantic recognition on the behavior information, adjusting or maintaining the atmosphere demand information processed by the iteration process of the last round according to the semantic recognition result, when the behavior information belongs to the action information type, maintaining the atmosphere demand information processed by the iteration process of the last round; obtaining the atmosphere demand information processed by the iteration process of the current round; determining the atmosphere demand information corresponding to the vehicle occupant generating the behavior information according to the behavior information and the atmosphere demand information processed by the iteration process of the last round.

2. The automotive cabin ambience adjustment method according to claim 1, characterized in that, the determination of the respective priority weights of each vehicle occupant in the automobile cabin comprises: obtaining voice information of each vehicle occupant in the automobile cabin; performing semantic recognition on each voice information to obtain respective semantic information corresponding to each voice information; determining the priority weights of each vehicle occupant in the automobile cabin according to the semantic information.

3. The automotive cabin atmosphere adjusting method according to claim 2, characterized by, the determination of the respective priority weights of each vehicle occupant in the automobile cabin according to the semantic information comprises: determining a plurality of target vehicle occupants among the vehicle occupants in the automobile cabin; setting the priority weights of the target vehicle occupants as fixed values; determining the relative size relationship between the priority weights of the target vehicle occupants and the priority weights of other vehicle occupants according to the association relationship between the semantic information of the target vehicle occupants and the semantic information of other vehicle occupants; determining the priority weights of other vehicle occupants according to the priority weights of the target vehicle occupants and the relative size relationship.

4. The automotive cabin ambience adjustment method of claim 1, wherein, the generation of the atmosphere adjusting target parameters according to the atmosphere demand information and the priority weights comprises: performing weighted operation on the respective priority weights of the atmosphere demand information. According to a result of the weighting operation, the atmosphere adjustment target parameter is determined.

5. The automotive cabin ambience adjustment method of claim 1, wherein, The atmosphere adjustment target parameter is generated according to each of the atmosphere demand information and each of the priority weights, including: A maximum value of the priority weights is obtained; According to the atmosphere demand information corresponding to the maximum value of the priority weights, the atmosphere adjustment target parameter is generated.

6. The automotive cabin ambience adjustment method of claim 1, wherein, According to the atmosphere adjustment target parameter, the space in the vehicle cabin is subjected to atmosphere adjustment, including: According to the atmosphere adjustment target parameter, an atmosphere adjustment control instruction is generated; According to the atmosphere adjustment control instruction, an atmosphere adjustment function module is controlled to perform atmosphere adjustment on the space in the vehicle cabin.

7. A computer apparatus, comprising: A memory and a processor are included, the memory is used to store at least one program, and the processor is used to load the at least one program to execute the vehicle cabin atmosphere adjustment method of any one of claims 1-6.

8. A computer-readable storage medium having stored therein a program that is executable by a processor, the program comprising instructions for causing the processor to perform the method of any one of claims 1 to 7. The program executable by the processor, when executed by the processor, is used to execute the vehicle cabin atmosphere adjustment method of any one of claims 1-6.

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