Vehicle air conditioner control method, vehicle and storage medium

Abstract

The embodiment of the invention provides a vehicle air conditioner control method, a vehicle and a storage medium, the method comprises the steps that user feature data and environmental condition data associated with a target vehicle are obtained, the user feature data are used for representing passenger attribute information corresponding to the target vehicle, and the environmental condition data are used for representing passenger attribute information corresponding to the target vehicle; the environmental condition data is used for representing external environmental information and internal environmental information of the target vehicle; decision analysis is carried out based on the user feature data and the environmental condition data, a decision analysis result is obtained, and the decision analysis result is used for determining the working mode of the vehicle-mounted air conditioner of the target vehicle; target control parameters of the vehicle-mounted air conditioner are determined based on the decision analysis result, and the target control parameters are used for adjusting the running state of the vehicle-mounted air conditioner; and the vehicle-mounted air conditioner is controlled through the target control parameters. The technical problems that in the related technology, the control efficiency of the vehicle air conditioner is low, and the user experience is poor are solved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more specifically, to a vehicle air conditioning control method, a vehicle, and a storage medium. Background Technology

[0002] With the rapid development of the automotive industry, especially the popularization of new energy vehicles, intelligent functions have gradually become key to improving the driving and riding experience. As the core of in-vehicle environment regulation, the air conditioning system directly affects passenger comfort. Traditional air conditioning systems mainly rely on manual adjustments by the user. Whether it's temperature, airflow, or airflow direction, users need to adjust these settings in real time based on their personal feelings. This method is not only cumbersome but also often fails to reflect the user's actual needs in a timely manner. For example, when the external environment changes rapidly, such as a sudden shift from hot summer to cool weather, or when multiple passengers with different body temperature preferences are in the vehicle, traditional air conditioning systems struggle to quickly and accurately adjust parameters to meet the comfort requirements of all passengers. While some high-end models are equipped with automatic air conditioning, this is limited to basic temperature adjustments based on signals from in-vehicle temperature sensors, resulting in low control efficiency and a poor user experience.

[0003] There is currently no good solution to the above problems. Summary of the Invention

[0004] This application provides a vehicle air conditioning control method, a vehicle, and a storage medium to at least solve the technical problems of low control efficiency and poor user experience of vehicle air conditioning in related technologies.

[0005] According to one aspect of the embodiments of this application, a vehicle air conditioning control method is provided, comprising: acquiring user characteristic data and environmental condition data associated with a target vehicle, wherein the user characteristic data is used to represent passenger attribute information corresponding to the target vehicle, and the environmental condition data is used to represent external environmental information and internal environmental information of the target vehicle; performing decision analysis based on the user characteristic data and environmental condition data to obtain a decision analysis result, wherein the decision analysis result is used to determine the operating mode of the vehicle air conditioning; determining target control parameters of the vehicle air conditioning based on the decision analysis result, wherein the target control parameters are used to adjust the operating state of the vehicle air conditioning; and controlling the vehicle air conditioning using the target control parameters.

[0006] Optionally, obtaining user feature data associated with the target vehicle includes: in response to receiving key positioning information, obtaining first image data, wherein the first image data is used to represent external user image data associated with the target vehicle; in response to receiving door opening information, obtaining second image data, wherein the second image data is used to represent internal user image data of the target vehicle; and performing feature extraction on the first image data and the second image data to obtain user feature data.

[0007] Optionally, feature extraction from the first image data and the second image data to obtain user feature data includes: extracting features from the first image data to obtain human body envelope data, wherein the human body envelope data is used to represent the visual feature information of the target user; extracting features from the second image data to obtain facial feature data of the target user; and determining user feature data based on the human body envelope data and the facial feature data.

[0008] Optionally, decision analysis based on user feature data and environmental condition data is performed to obtain decision analysis results, including: verifying the user feature data and environmental condition data using a preset pattern database to obtain verification results, wherein the verification results are used to determine whether there is prior pattern information corresponding to the user feature data and environmental condition data in the preset pattern database; and performing decision analysis based on the verification results to obtain decision analysis results.

[0009] Optionally, decision analysis is performed based on the verification processing results, and the decision analysis results include: the response determines that prior pattern information exists in the preset pattern database based on the verification processing results, and determines the decision analysis results based on the prior pattern information; the response determines that prior pattern information does not exist in the preset pattern database based on the verification processing results, and creates a pattern based on user feature data and environmental condition data to obtain the decision analysis results.

[0010] Optionally, pattern creation based on user feature data and environmental condition data to obtain decision analysis results includes: analyzing and processing user feature data and environmental condition data using a target state prediction network model to obtain target state prediction results, wherein the target state prediction results are used to represent passenger state information of the target vehicle; and creating patterns based on the target state prediction results and a preset mapping relationship to obtain decision analysis results, wherein the preset mapping relationship is used to record the mapping relationship between preset passenger state and preset air conditioning mode.

[0011] Optionally, the vehicle air conditioning control method further includes: responding to a target user's touch operation on the air conditioning setting control, obtaining prior mode information corresponding to the target user, wherein the prior mode information is used to represent the target user's air conditioning preference mode information; and constructing a preset mode database based on the prior mode information.

[0012] Optionally, the target control parameters include at least one of the following: function mode information, temperature information, air volume information, and air outlet angle information.

[0013] According to another aspect of the embodiments of this application, a vehicle air conditioning control device is also provided, comprising: an acquisition module, configured to acquire user characteristic data and environmental condition data associated with a target vehicle, wherein the user characteristic data is used to represent passenger attribute information corresponding to the target vehicle, and the environmental condition data is used to represent external environmental information and internal environmental information of the target vehicle; an analysis module, configured to perform decision analysis based on the user characteristic data and environmental condition data to obtain a decision analysis result, wherein the decision analysis result is used to determine the operating mode of the vehicle air conditioning; a determination module, configured to determine target control parameters of the vehicle air conditioning based on the decision analysis result, wherein the target control parameters are used to adjust the operating state of the vehicle air conditioning; and a control module, configured to control the vehicle air conditioning using the target control parameters.

[0014] Optionally, the acquisition module is further configured to: in response to receiving key positioning information, acquire first image data, wherein the first image data is used to represent external user image data associated with the target vehicle; in response to receiving door opening information, acquire second image data, wherein the second image data is used to represent internal user image data of the target vehicle; and perform feature extraction on the first image data and the second image data to obtain user feature data.

[0015] Optionally, the acquisition module is further configured to: extract features from the first image data to obtain human body envelope data, wherein the human body envelope data is used to represent the visual feature information of the target user; extract features from the second image data to obtain the facial feature data of the target user; and determine user feature data based on the human body envelope data and the facial feature data.

[0016] Optionally, the analysis module is also used to: perform verification processing on user feature data and environmental condition data using a preset pattern database to obtain verification processing results, wherein the verification processing results are used to determine whether there is prior pattern information corresponding to user feature data and environmental condition data in the preset pattern database; and perform decision analysis based on the verification processing results to obtain decision analysis results.

[0017] Optionally, the analysis module is also used to: respond to determine that prior pattern information exists in the preset pattern database based on the verification processing results, and determine the decision analysis results based on the prior pattern information; respond to determine that prior pattern information does not exist in the preset pattern database based on the verification processing results, and create a pattern based on user feature data and environmental condition data to obtain the decision analysis results.

[0018] Optionally, the analysis module is also used to: analyze and process user feature data and environmental condition data using a target state prediction network model to obtain target state prediction results, wherein the target state prediction results are used to represent passenger state information of the target vehicle; and create a pattern based on the target state prediction results and a preset mapping relationship to obtain decision analysis results, wherein the preset mapping relationship is used to record the mapping relationship between preset passenger state and preset air conditioning mode.

[0019] Optionally, the acquisition module is further configured to: in response to a target user's touch operation on the air conditioning settings control, acquire prior mode information corresponding to the target user, wherein the prior mode information is used to represent the target user's air conditioning preference mode information; the vehicle air conditioning control device further includes: a construction module, configured to construct a preset mode database based on the prior mode information.

[0020] Optionally, the target control parameters include at least one of the following: function mode information, temperature information, air volume information, and air outlet angle information.

[0021] According to another aspect of the embodiments of this application, a vehicle is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0022] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0023] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0024] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0025] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0026] In this embodiment, by acquiring user characteristic data and environmental condition data associated with the target vehicle, and then performing decision analysis based on the user characteristic data and environmental condition data, the target control parameters of the vehicle air conditioner are determined based on the decision analysis results. Finally, the vehicle air conditioner is controlled using the target control parameters, achieving the goal of personalized and health-oriented air conditioning control through intelligent analysis, thereby improving passenger comfort and health protection. Through deep learning and big data analysis technologies, comprehensive perception and intelligent decision-making regarding user characteristics and environmental conditions are achieved. This allows for rapid adjustment of air conditioning modes and parameters, ensuring that air conditioning control better meets passenger needs, improving the overall passenger experience, and thus solving the technical problems of low control efficiency and poor user experience in related technologies. Attached Figure Description

[0027] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0028] Figure 1 This is a flowchart of a vehicle air conditioning control method according to an embodiment of this application;

[0029] Figure 2 This is a schematic diagram of a feature extraction process according to an embodiment of this application;

[0030] Figure 3 This is a schematic diagram of the architecture of an intelligent air conditioning control system according to an embodiment of this application;

[0031] Figure 4 This is a schematic diagram of a vehicle air conditioning control method according to an embodiment of this application;

[0032] Figure 5 This is a schematic diagram of another vehicle air conditioning control method according to an embodiment of this application;

[0033] Figure 6 This is a structural block diagram of a vehicle air conditioning control device according to an embodiment of this application. Detailed Implementation

[0034] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 this application 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.

[0036] According to an embodiment of this application, a method embodiment for controlling a vehicle air conditioning system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] This embodiment provides a vehicle air conditioning control method. Figure 1 This is a flowchart of a vehicle air conditioning control method according to an embodiment of this application, such as... Figure 1 As shown, the process includes the following steps:

[0038] Step S11: Obtain user feature data and environmental condition data associated with the target vehicle. The user feature data is used to represent the passenger attribute information corresponding to the target vehicle, and the environmental condition data is used to represent the external environment information and the internal environment information of the target vehicle.

[0039] Step S12: Decision analysis is performed based on user characteristic data and environmental condition data to obtain decision analysis results, wherein the decision analysis results are used to determine the working mode of the target vehicle's air conditioning.

[0040] Step S13: Determine the target control parameters of the vehicle air conditioner based on the decision analysis results, wherein the target control parameters are used to adjust the operating status of the vehicle air conditioner;

[0041] Step S14: Control the vehicle air conditioner using the target control parameters.

[0042] The user characteristic data associated with the target vehicle can be collected by cameras on the vehicle, such as surround-view / panoramic cameras, driver monitoring system (DMS) cameras, and other sensors, such as infrared temperature sensors, which are related to the physical characteristics and biometric data of the current passengers in the target vehicle, such as height, age, gender, health status, activity level, and emotional state.

[0043] The aforementioned environmental condition data covers environmental parameters inside and outside the target vehicle, including but not limited to information such as temperature, humidity, light intensity, and air quality outside the vehicle, as well as indicators such as temperature distribution, humidity level, and air quality index inside the vehicle. Environmental condition data can be obtained through the vehicle's internal and external temperature sensors, humidity sensors, light sensors, and air quality sensors.

[0044] User characteristic data and environmental condition data can be collected by various sensors on the vehicle through the in-car control center (ICC) to ensure the comprehensiveness and real-time nature of the data, providing accurate reference for subsequent decision analysis.

[0045] Decision analysis is performed based on collected user characteristic data and environmental condition data to generate decision analysis results. This process involves complex deep learning computations, aiming to determine the optimal operating mode of the air conditioning by analyzing passengers' physical condition and environmental needs, in order to achieve an optimal balance between comfort and energy consumption. The above decision analysis results, derived from a comprehensive analysis of user characteristic data and environmental condition data, can indicate the most suitable operating mode for the target vehicle's air conditioning, such as automatic mode, powerful cooling, rapid heating, airflow adjustment, and temperature setting, to meet passengers' comfort and health requirements.

[0046] Based on the decision analysis results, the target control parameters of the vehicle air conditioning system can be further clarified. This involves translating the decision analysis results into specific operational instructions, such as setting the air conditioning temperature to 22℃, the fan speed to medium, and using a combination of internal and external circulation to ensure the air conditioning system can respond quickly and adjust to the ideal state. Target control parameters are the operating parameters of the vehicle air conditioning system, including but not limited to set temperature, fan speed, air circulation mode, and airflow mode (such as direct airflow to passengers or automatic air sweeping). These parameters are used to precisely adjust the operating status of the vehicle air conditioning system to adapt to the current passenger needs and environmental conditions.

[0047] The target control parameters determined in the decision analysis results are transmitted to the vehicle's air conditioning system through the vehicle's air conditioning system, thereby executing corresponding adjustments, such as changing the temperature setting, increasing or decreasing the air volume, switching between internal and external circulation, etc., to ensure that the in-vehicle environment reaches the ideal comfort state predicted in the decision analysis stage.

[0048] Based on steps S11 to S14 above, by acquiring user characteristic data and environmental condition data associated with the target vehicle, and then performing decision analysis based on this data, the target control parameters for the vehicle's air conditioning are determined. Finally, the vehicle's air conditioning is controlled using these target control parameters. This achieves the goal of personalized and health-oriented air conditioning control through intelligent analysis, thereby improving passenger comfort and health protection. Through deep learning and big data analytics, comprehensive perception and intelligent decision-making regarding user characteristics and environmental conditions are achieved. This allows for rapid adjustment of air conditioning modes and parameters, ensuring that air conditioning control better meets passenger needs and improves the overall passenger experience. This solves the technical problems of low control efficiency and poor user experience associated with vehicle air conditioning in related technologies.

[0049] The vehicle air conditioning control method in the embodiments of this application will be further described below.

[0050] In one optional embodiment, obtaining user characteristic data associated with the target vehicle includes:

[0051] In response to receiving key positioning information, first image data is acquired, wherein the first image data is used to represent external user image data associated with the target vehicle;

[0052] In response to receiving door opening information, second image data is acquired, wherein the second image data is used to represent in-vehicle user image data of the target vehicle;

[0053] Feature extraction is performed on the first image data and the second image data to obtain user feature data.

[0054] The key location information mentioned above is provided by the digital key control module and includes relative position data between the smart key and the vehicle, such as Bluetooth signal strength indicator (RSSI), which is used to trigger external camera data acquisition to identify the characteristics of people approaching the vehicle.

[0055] The aforementioned first image data can be images of users outside the vehicle captured by surround-view / panoramic-view cameras. It is mainly used to analyze the appearance information and preliminary physical characteristics of the personnel, such as height, body shape, and clothing, to provide estimated data for subsequent precise positioning and depth analysis by in-vehicle cameras.

[0056] The aforementioned door opening information can be monitored and fed back by the area controller, indicating that the door is open and triggering the DMS camera to start working and collect facial details of the user inside the vehicle.

[0057] The aforementioned second image data refers to the in-vehicle user images captured by the DMS camera, focusing on the capture and analysis of facial features, such as facial expressions and whether the user is sweating, which helps to understand the user's current emotions and physiological state more deeply.

[0058] Upon receiving key location information, the external camera is activated to collect first image data, which includes the appearance information of the person approaching the vehicle, providing preliminary visual data for subsequent feature extraction and user identification. Upon receiving door opening information, the in-vehicle DMS camera is activated to collect second image data, namely a high-resolution image of the user's face inside the vehicle, to further analyze the user's emotions and physiological state. Furthermore, deep feature extraction is performed on the first and second image data, including but not limited to the application of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) algorithms, to obtain user feature data, including body envelope data and facial feature data, for intelligent decision-making and personalized adjustment of the air conditioning control system.

[0059] Based on the above optional embodiments, in response to receiving key positioning information, first image data is acquired, and then in response to receiving door opening information, second image data is acquired. Finally, feature extraction is performed on the first and second image data to obtain user feature data. This achieves comprehensive and automated collection and analysis of user feature data, providing a solid data foundation for intelligent air conditioning control and ensuring that the air conditioning system can make rapid and accurate adjustment responses based on the passenger's real-time physical characteristics and emotional state.

[0060] In one optional embodiment, feature extraction is performed on the first image data and the second image data to obtain user feature data, including:

[0061] Feature extraction is performed on the first image data to obtain human body envelope data, wherein the human body envelope data is used to represent the visual feature information of the target user;

[0062] Feature extraction is performed on the second image data to obtain the facial feature data of the target user;

[0063] User characteristic data is determined based on human body envelope data and facial feature data.

[0064] The aforementioned human body envelope data can be a set of user body features extracted from the first image data, including but not limited to body contours, size, height, and limb posture. This data is used to quickly predict the user's basic body characteristics, such as whether they are a child, pregnant woman, or elderly person. This data is crucial for the initial selection of the air conditioning mode. Acquiring human body envelope data can quickly determine the user's basic type, providing rough guidance for subsequent, more precise adjustments, reducing the air conditioning system's response time, and improving adjustment efficiency.

[0065] The aforementioned facial feature data can be specific facial features extracted from the second image data, such as facial temperature, sweat distribution, and skin color changes. These features reflect the user's emotional state and physiological needs, playing a crucial role in the refined control of intelligent air conditioning. Compared to the preliminary judgment based on body envelope data, facial feature data extraction is more precise, capturing subtle physiological reactions and emotional changes in the user. For example, sweating indicates overheating, while paleness may indicate cold or discomfort. This feature limitation allows the air conditioning system to adjust according to the user's immediate physiological needs, greatly enhancing personalized adjustment capabilities and improving the user experience.

[0066] Figure 2 This is a schematic diagram of a feature extraction process according to an embodiment of this application, such as... Figure 2 As shown, when images or videos are used as input sources into a deep learning algorithm, the multimedia data is first fed into convolutional neural networks and recurrent neural networks for detailed feature extraction to parse the user's body appearance and other key visual information. Subsequently, 3D human keypoint detection technology is used to perform in-depth analysis of the extracted features, accurately locating various important parts of the body, such as elbows, knees, and the head, constructing a three-dimensional human structural model. Next, through detailed analysis of the relative positions between joints and limbs, the topological structure between joints is established to ensure an accurate understanding of human posture and movement, maintaining consistency and reliability in recognition even in complex environments. Based on this, the built-in point-by-point feasibility analyzer performs real-time tracking, keenly capturing minute changes in the human body's position and determining whether there are occlusion issues to maintain the continuity and integrity of image analysis. Ultimately, it obtains human body envelope data, which includes detailed information such as the user's body size, gender classification, age group, and whether they are in a special condition (such as a child, elderly person, or pregnant woman). This data will be used by the entertainment host ICC as the core basis for adaptive air conditioning adjustment, thereby enabling refined and personalized adjustment of air conditioning parameters to meet the unique needs of different users in different scenarios and improve the comfort and intelligence of the riding experience.

[0067] Based on the above optional embodiments, feature extraction is performed on the first image data to obtain human body envelope data, and then feature extraction is performed on the second image data to obtain the facial feature data of the target user. Finally, user feature data is determined based on the human body envelope data and facial feature data, realizing in-depth mining and accurate extraction of user feature data, which further significantly improves the intelligence level of air conditioning adjustment and user experience, especially the applicability and accuracy under different population groups and complex environmental conditions.

[0068] In one optional embodiment, decision analysis is performed based on user characteristic data and environmental condition data to obtain decision analysis results including:

[0069] The user feature data and environmental condition data are validated using a preset pattern database to obtain the validation results. The validation results are used to determine whether there is prior pattern information corresponding to the user feature data and environmental condition data in the preset pattern database.

[0070] Decision analysis is performed based on the verification results to obtain the decision analysis results.

[0071] The above verification process involves the system's preliminary comparison and evaluation of user characteristic data and environmental condition data to determine whether prior mode information matching the current scenario can be found in the preset mode database. Prior mode information refers to existing air conditioning control modes within the preset mode database, obtained based on historical big data analysis. These prior mode information corresponds to specific user characteristics and environmental conditions and can be directly used to guide the intelligent adjustment of the air conditioning system. The preset mode database stores a series of air conditioning control modes formed through learning and historical data accumulation, aiming to cover the optimal air conditioning parameter settings under various combinations of user characteristics and environmental conditions. Through continuous learning and optimization, the preset mode database contains optimal air conditioning parameter settings for various combinations of user characteristics and environmental conditions, enabling rapid search and application of the most suitable control mode.

[0072] The above decision analysis results can be a set of parameters that are finally generated after the verification process or the pattern creation process to guide the actual operation of the air conditioning system, including but not limited to temperature setting, air volume, air direction control and other function adjustment information.

[0073] Based on the above optional embodiments, by using a preset pattern database to verify user feature data and environmental condition data, a verification result is obtained. Then, a decision analysis is performed based on the verification result to obtain the decision analysis result. This achieves high-precision and personalized adjustment of the air conditioning system control. It can quickly retrieve or generate matching air conditioning control modes according to the user's body type, emotional state, and real-time conditions such as external weather changes and in-vehicle temperature, significantly improving the comfort and convenience of the vehicle air conditioning.

[0074] In one optional embodiment, decision analysis is performed based on the verification processing results, and the resulting decision analysis includes:

[0075] The response determines that prior pattern information exists in the preset pattern database based on the verification processing results, and determines the decision analysis results based on the prior pattern information.

[0076] The response determines that there is no prior pattern information in the preset pattern database based on the verification process results. Pattern creation is performed based on user feature data and environmental condition data to obtain decision analysis results.

[0077] Specifically, when prior mode information exists in the preset mode database, the entertainment host ICC immediately retrieves the relevant prior mode information to generate decision analysis results and adjusts various parameters of the air conditioning system accordingly. When no prior mode information exists in the preset mode database, the system enters the mode creation stage. The entertainment host ICC combines user characteristic data and environmental condition data, using deep learning technology to create a new air conditioning control mode. This mode can more accurately reflect the current user's personalized needs and environmental characteristics. Subsequently, this mode, as part of the decision analysis results, is used to guide the intelligent adjustment of the air conditioning.

[0078] Based on the above optional embodiments, by responding to the verification processing results to determine that prior pattern information exists in the preset pattern database, determining the decision analysis result based on the prior pattern information, and responding to the verification processing results to determine that prior pattern information does not exist in the preset pattern database, a pattern is created based on user feature data and environmental condition data to obtain the decision analysis result, which significantly enhances the response speed, decision accuracy and user adaptability of the air conditioning intelligent control system.

[0079] In one optional embodiment, pattern creation is performed based on user characteristic data and environmental condition data to obtain decision analysis results, including:

[0080] The target state prediction network model is used to analyze and process user feature data and environmental condition data to obtain target state prediction results, which are used to represent the passenger state information of the target vehicle.

[0081] Based on the target state prediction results and the preset mapping relationship, a pattern is created to obtain the decision analysis results. The preset mapping relationship is used to record the mapping relationship between the preset passenger state and the preset air conditioning mode.

[0082] The aforementioned target state prediction network model can be a deep learning model specifically designed to predict the state of passengers in a vehicle. The entertainment system (ICC) uses this model to comprehensively analyze collected user characteristic data and environmental condition data, assessing passengers' current feelings, such as cold, heat, and comfort levels, as well as potential health conditions, such as fatigue and anxiety. This results in a target state prediction, which characterizes the passenger's potential comfort level or health condition under specific environmental conditions and serves as a crucial basis for subsequent air conditioning mode adjustments.

[0083] The aforementioned preset mapping relationship is an association rule that defines the pairing relationship between various passenger states and the most suitable air conditioning operating mode. For example, high body temperature is mapped to the cooling mode, and fatigue is associated with the mild ventilation mode.

[0084] By combining the target state prediction results and the preset mapping relationship, the entertainment host ICC can create or select an appropriate air conditioning mode and form specific air conditioning parameter adjustment instructions, i.e. decision analysis results, including temperature setting, fan speed control, air purification and other function configurations.

[0085] Based on the above optional embodiments, by using a target state prediction network model to analyze and process user feature data and environmental condition data, a target state prediction result is obtained. Then, based on the target state prediction result and a preset mapping relationship, a pattern is created to obtain a decision analysis result, which effectively improves the pertinence and flexibility of air conditioning control, and further improves the efficiency of air conditioning control and the user's riding experience.

[0086] In an optional embodiment, the vehicle air conditioning control method in this application further includes:

[0087] In response to the target user's touch operation on the air conditioner settings control, the prior mode information corresponding to the target user is obtained, wherein the prior mode information is used to represent the target user's air conditioner preference mode information;

[0088] A predefined pattern database is constructed based on prior pattern information.

[0089] In the smart cockpit of this application embodiment, the target user can manually operate the air conditioning settings via the air conditioning control on the vehicle's large screen, such as adjusting the temperature, fan speed, and mode, thereby reflecting the target user's immediate preference for air conditioning comfort. The entertainment host (ICC) can perform deep learning and pattern recognition on the target user's touch operations to construct an information set reflecting the target user's historical air conditioning usage preferences and habits, including but not limited to the target user's preferred air conditioning temperature, fan speed level, airflow direction selection, and air conditioning setting habits under specific environmental conditions.

[0090] The aforementioned preset mode database is used to store prior mode information for all target users (including repeat users and new users), so that when the target user uses the vehicle again, the corresponding air conditioning preference mode can be quickly invoked to achieve personalized and intelligent air conditioning control.

[0091] Upon detecting a target user's touch operation of the air conditioning settings controls via the vehicle's infotainment screen, the data acquisition and processing flow is immediately initiated. After the target user completes the touch operation, the infotainment unit's ICC analyzes the operation details, combines them with the target user's historical data, extracts and updates prior mode information, ensuring that the database content remains consistent with the target user's latest preferences. The extracted and updated prior mode information is stored or updated to a preset mode database, forming a collection containing all target users' personalized air conditioning preference modes, providing a data foundation for future intelligent air conditioning control.

[0092] Based on the above optional embodiments, by responding to the target user's touch operation on the air conditioning setting control, the prior mode information corresponding to the target user is obtained, and then a preset mode database is constructed based on the prior mode information. This significantly improves the personalized service level and operating efficiency of the air conditioning intelligent control system. When the target user uses the vehicle again, this information can be quickly called up and the air conditioning parameters can be automatically adjusted to meet the target user's personalized needs without the target user having to make repeated settings, thereby greatly improving the convenience and comfort of riding in the vehicle.

[0093] In one optional embodiment, the target control parameters include at least one of the following: functional mode information, temperature information, air volume information, and air outlet angle information.

[0094] Functional mode information covers various operating modes offered by the air conditioning system, including but not limited to cooling, heating, switching between internal and external circulation, and air purification, and is one of the core components of intelligent air conditioning control. Temperature information refers to the target temperature value that the air conditioning system should be set, which can be intelligently adjusted based on the temperature difference between the inside and outside of the vehicle and the passengers' personal preferences to achieve the ideal comfortable temperature. Airflow information involves the speed and intensity of the airflow emitted from the air conditioning system's vents, which can be dynamically adjusted according to passenger location, number of passengers, and the current ventilation needs of the environment. Airflow angle information refers to the direction and distribution of the airflow from the air conditioning system, including automatic sweeping, direct airflow to people, and free-flow modes, designed to ensure that the airflow can evenly cover or precisely direct the passenger area.

[0095] In this embodiment, the target control parameters, including but not limited to function mode information, temperature information, air volume information, and air outlet angle information, are automatically generated by the entertainment host ICC. This enables intelligent and personalized adjustment of the air conditioning system, which can automatically optimize the air conditioning function according to the passenger's immediate needs and personalized preferences, providing a more comfortable and energy-saving riding environment without the need for frequent manual adjustments by the passenger, greatly improving the quality and convenience of the riding experience.

[0096] Figure 3 This is a schematic diagram of the architecture of an intelligent air conditioning control system according to an embodiment of this application, such as... Figure 3 As shown, the surround-view camera and DMS camera are connected to the infotainment unit (ICC) via Low Voltage Differential Signaling (LVDS) protocol to capture images of people inside and outside the vehicle, including human appearance, facial features, and key body information. The ICC communicates with the Zone Control Unit (ZCU) via Controller Area Network Flexible Data Rate (CANFD). The ZCU is responsible for vehicle unlocking and providing power mode information for the entire vehicle. The Digital Key Module (BNCM) is also connected to the ZCU via CANFD to control the welcome unlocking and disembarking functions, and simultaneously sends key location information to the infotainment unit. The Temperature Management System (TMS) interacts with the ZCU via CANFD, receiving air conditioning function parameter adjustment commands from the infotainment unit. The electric air vent module connects to the TMS via a Local Interconnect Network (LIN) to adjust airflow direction and volume according to TMS commands.

[0097] Figure 4 This is a schematic diagram of a vehicle air conditioning control method according to an embodiment of this application, as shown below. Figure 4As shown, when a target user approaches the vehicle with the BNCM digital key, the vehicle's welcome function is triggered, activating the surround-view camera for personnel feature recognition. As the user further opens the door and enters the vehicle, the DMS camera activates, performing efficient facial recognition to confirm the user's identity and quickly retrieve associated personalized air conditioning parameters. Next, the infotainment system (ICC), acting as the processing center, comprehensively analyzes user feature data from the DMS camera and real-time environmental data collected by external sensors (including but not limited to weather and temperature sensors). Using deep learning algorithms, it generates a precise set of air conditioning adjustment commands to match the current passenger status and environmental conditions. Subsequently, the air conditioning control module (TMS) receives the commands from the ICC and immediately executes them, adjusting the air conditioning system's operating mode, including temperature, airflow, and airflow direction, to quickly create a suitable in-car environment. After automatic adjustment, the user can still manually adjust the air conditioning at any time. In this case, the ICC will further learn the user's personalized preferences, updating and optimizing the big data model in real time to ensure more appropriate and personalized air conditioning services in the future, allowing users to enjoy an ideal and comfortable in-car climate every time they ride.

[0098] Figure 5 This is a schematic diagram of another vehicle air conditioning control method according to an embodiment of this application, as shown below. Figure 5 As shown, the moment a target user approaches the vehicle with the digital key (BNCM), the vehicle's welcome function is immediately activated. The Zone Controller (ZCU) sensitively captures the signal from the digital key (BNCM), thereby activating the surround-view camera to begin collecting image data and initially identifying the person's height, type, and other characteristics. As the target user further opens the car door, the more detailed DMS camera activates, focusing on monitoring the driver's or passenger's facial features and current state, such as signs of fatigue or changes in body temperature, such as sweating. Next, the infotainment system (ICC) first attempts to identify whether the target user is a known, authenticated user based on the information collected by the DMS camera. For authenticated users, the ICC quickly matches their personal account and retrieves personalized air conditioning parameters bound to the user ID from the preset mode database, including preferred temperature, fan speed level, and airflow mode. For new users encountered for the first time, a series of feature recognition and ID creation processes are initiated to establish their unique air conditioning adjustment records.

[0099] Whether for new or existing users, the infotainment system (ICC) employs deep learning algorithms, comprehensively considering human characteristic data and real-time environmental conditions to intelligently generate or optimize air conditioning settings. This demonstrates the adaptive control capability of the air conditioning system, ensuring it can fine-tune according to each passenger's specific needs, thus providing the most suitable in-car environment. Even after the system has automatically adjusted the air conditioning parameters, users can still manually adjust them based on personal preference, such as if the airflow is too strong or the temperature setting is uncomfortable. The ICC records every manual operation by the user, integrating it into prior mode information, continuously updating and improving the user model in the preset mode database, laying a solid foundation for the subsequent riding experience, and ensuring that the air conditioning control system can provide increasingly personalized services that closely match user expectations in future use.

[0100] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0101] According to an embodiment of this application, an apparatus embodiment for a vehicle air conditioning control method is provided. It should be noted that the apparatus can be used to execute the above-described vehicle air conditioning control method.

[0102] Figure 6 This is a structural block diagram of a vehicle air conditioning control device according to an embodiment of this application, such as... Figure 6 As shown, the device includes:

[0103] The acquisition module 601 is used to acquire user characteristic data and environmental condition data associated with the target vehicle. The user characteristic data represents passenger attribute information corresponding to the target vehicle, and the environmental condition data represents external and internal environmental information of the target vehicle. The analysis module 602 is used to perform decision analysis based on the user characteristic data and environmental condition data to obtain decision analysis results. The decision analysis results are used to determine the operating mode of the vehicle's air conditioning. The determination module 603 is used to determine the target control parameters of the vehicle air conditioning based on the decision analysis results. The target control parameters are used to adjust the operating state of the vehicle air conditioning. The control module 604 is used to control the vehicle air conditioning using the target control parameters.

[0104] Optionally, the acquisition module 601 is further configured to: in response to receiving key positioning information, acquire first image data, wherein the first image data is used to represent external user image data associated with the target vehicle; in response to receiving door opening information, acquire second image data, wherein the second image data is used to represent internal user image data of the target vehicle; and perform feature extraction on the first image data and the second image data to obtain user feature data.

[0105] Optionally, the acquisition module 601 is further configured to: extract features from the first image data to obtain human body envelope data, wherein the human body envelope data is used to represent the visual feature information of the target user; extract features from the second image data to obtain the facial feature data of the target user; and determine user feature data based on the human body envelope data and the facial feature data.

[0106] Optionally, the analysis module 602 is further configured to: perform verification processing on user feature data and environmental condition data using a preset pattern database to obtain verification processing results, wherein the verification processing results are used to determine whether there is prior pattern information corresponding to user feature data and environmental condition data in the preset pattern database; and perform decision analysis based on the verification processing results to obtain decision analysis results.

[0107] Optionally, the analysis module 602 is further configured to: respond to determining, based on the verification processing results, that prior pattern information exists in the preset pattern database, and determine the decision analysis result based on the prior pattern information; respond to determining, based on the verification processing results, that prior pattern information does not exist in the preset pattern database, and create a pattern based on user feature data and environmental condition data to obtain the decision analysis result.

[0108] Optionally, the analysis module 602 is further configured to: analyze and process user feature data and environmental condition data using a target state prediction network model to obtain a target state prediction result, wherein the target state prediction result is used to represent the passenger state information of the target vehicle; and create a pattern based on the target state prediction result and a preset mapping relationship to obtain a decision analysis result, wherein the preset mapping relationship is used to record the mapping relationship between the preset passenger state and the preset air conditioning mode.

[0109] Optionally, the acquisition module 601 is further configured to: in response to the target user's touch operation on the air conditioning setting control, acquire the prior mode information corresponding to the target user, wherein the prior mode information is used to represent the target user's air conditioning preference mode information; the vehicle air conditioning control device further includes: a construction module 605, configured to construct a preset mode database based on the prior mode information.

[0110] Optionally, the target control parameters include at least one of the following: function mode information, temperature information, air volume information, and air outlet angle information.

[0111] Embodiments of this application also provide a vehicle, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods described in various embodiments of this application when it runs.

[0112] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0113] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0114] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0115] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of this application.

[0116] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0118] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0120] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0121] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A vehicle air conditioning control method, characterized in that, include: Acquire user feature data and environmental condition data associated with the target vehicle, wherein the user feature data is used to represent passenger attribute information corresponding to the target vehicle, and the environmental condition data is used to represent external environmental information and internal environmental information of the target vehicle; Decision analysis is performed based on the user characteristic data and the environmental condition data to obtain decision analysis results, wherein the decision analysis results are used to determine the working mode of the target vehicle's air conditioning. Based on the decision analysis results, target control parameters for the vehicle air conditioner are determined, wherein the target control parameters are used to adjust the operating status of the vehicle air conditioner. The vehicle air conditioner is controlled using the target control parameters.

2. The method according to claim 1, characterized in that, Obtaining the user feature data associated with the target vehicle includes: In response to receiving key location information, first image data is acquired, wherein the first image data is used to represent external user image data associated with the target vehicle; In response to receiving door opening information, second image data is acquired, wherein the second image data is used to represent in-vehicle user image data of the target vehicle; Feature extraction is performed on the first image data and the second image data to obtain the user feature data.

3. The method according to claim 2, characterized in that, Feature extraction is performed on the first image data and the second image data to obtain the user feature data, which includes: Feature extraction is performed on the first image data to obtain human body envelope data, wherein the human body envelope data is used to represent the visual feature information of the target user; Feature extraction is performed on the second image data to obtain the facial feature data of the target user; The user feature data is determined based on the human body envelope data and the facial feature data.

4. The method according to claim 1, characterized in that, The decision analysis results obtained based on the user characteristic data and the environmental condition data include: The user feature data and the environmental condition data are verified using a preset pattern database to obtain a verification result. The verification result is used to determine whether there is prior pattern information corresponding to the user feature data and the environmental condition data in the preset pattern database. Decision analysis is performed based on the verification results to obtain the decision analysis results.

5. The method according to claim 4, characterized in that, Based on the verification process results, a decision analysis is performed, and the decision analysis results include: The response determines, based on the verification process result, that the prior pattern information exists in the preset pattern database, and determines the decision analysis result based on the prior pattern information. The response determines, based on the verification process result, that the prior pattern information does not exist in the preset pattern database, and performs pattern creation based on the user feature data and the environmental condition data to obtain the decision analysis result.

6. The method according to claim 5, characterized in that, Pattern creation based on the user characteristic data and the environmental condition data yields the following decision analysis results: The user feature data and the environmental condition data are analyzed and processed using a target state prediction network model to obtain a target state prediction result, wherein the target state prediction result is used to represent the passenger state information of the target vehicle. Based on the target state prediction results and the preset mapping relationship, a pattern is created to obtain the decision analysis results. The preset mapping relationship is used to record the mapping relationship between the preset passenger state and the preset air conditioning mode.

7. The method according to claim 4, characterized in that, The method further includes: In response to a target user's touch operation on the air conditioner settings controls, prior mode information corresponding to the target user is obtained, wherein the prior mode information is used to represent the target user's air conditioner preference mode information; The preset pattern database is constructed based on the prior pattern information.

8. The method according to claim 1, characterized in that, The target control parameters include at least one of the following: functional mode information, temperature information, air volume information, and air outlet angle information.

9. A vehicle, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 8.

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