Method and device for managing air quality in vehicle

Abstract

The invention provides a method and a device for managing air quality in a vehicle. The method for managing the air quality in the vehicle can comprise the steps that monitoring data of a sensor on the vehicle are obtained; according to the monitoring data, whether sudden peculiar smell appears in the vehicle or not is determined through an artificial intelligence model; and if the sudden peculiar smell appears in the vehicle, an air quality management strategy is executed to improve the air quality in the vehicle.

Description

Technical Field

[0001] The present application relates to the field of air quality management, and more specifically, to a method and device for managing the air quality inside a vehicle. Background Art

[0002] In today's society, as people's living standards and health awareness continue to improve, the quality of the in-vehicle environment has received more and more attention. When people are in a closed vehicle environment for a long time, various physiological and behavioral activities of passengers, such as burping, farting, smoking, etc., may produce some sudden odors, which will cause the air quality in the car to deteriorate. However, the existing technology may not be able to accurately distinguish between normal human body odor and these sudden odors in a timely manner, resulting in the in-vehicle air management device being unable to respond to these sudden odors in a timely manner, causing discomfort to passengers. Summary of the invention

[0003] The present application provides a method and device for managing the air quality inside a vehicle.

[0004] In a first aspect, a method for managing the air quality inside a vehicle is provided, comprising: obtaining monitoring data from sensors on the vehicle; determining whether a sudden odor occurs inside the vehicle through an artificial intelligence model based on the monitoring data; if a sudden odor occurs inside the vehicle, executing an air quality management strategy to improve the air quality inside the vehicle.

[0005] Optionally, the method may further include: receiving air quality preference information input by a user; and determining an air quality management strategy according to the air quality preference information.

[0006] Optionally, the air quality management strategy can be used to control the vehicle to perform at least one of the following operations: opening the windows, switching the circulation mode of the vehicle air conditioner, starting the air purification function of the vehicle air conditioner, and releasing aromatherapy.

[0007] Optionally, the method may further include: in response to the release time of the fragrance reaching a preset time length, turning off the fragrance; wherein the preset time length is a time length determined according to the olfactory sensitivity time of human beings.

[0008] Optionally, the sensors on the vehicle include one or more of the following: a gas sensor, an odor sensor, and an environmental sensor.

[0009] Optionally, the method may further include: receiving physiological indicator data sent by health monitoring equipment of passengers in the vehicle; and adjusting the air quality management strategy according to the monitoring data and the physiological indicator data.

[0010] Optionally, the method may further include: receiving navigation data of an on-vehicle navigation system; predicting the air quality of a road section ahead of the vehicle based on the navigation data; and adjusting an air quality management strategy based on the air quality of the road section ahead of the vehicle.

[0011] In a second aspect, an in-vehicle air quality management device is provided, including: an acquisition module for acquiring monitoring data from sensors on the vehicle; a determination module for determining whether a sudden odor occurs inside the vehicle through an artificial intelligence model based on the monitoring data; and an execution module for executing an air quality management strategy to improve the air quality inside the vehicle if a sudden odor occurs inside the vehicle.

[0012] In a third aspect, an in-vehicle air quality management device is provided, comprising: a memory for storing an executable program; and a processor for calling and executing the executable program to implement the method described in any method embodiment of the first aspect.

[0013] In a fourth aspect, a computer-readable storage medium is provided for storing a program, which, when executed by a computer, enables the computer to execute the method according to any method embodiment of the first aspect.

[0014] In a fifth aspect, a chip is provided, comprising a processor, which can be used to call and run a computer program from a memory, so that a device equipped with the chip executes the method described in any method embodiment of the first aspect.

[0015] According to a sixth aspect, a computer program product is provided, the computer program product comprising a program, the program enabling a computer to execute the method according to any method embodiment of the first aspect.

[0016] Through the embodiments of the present application, sudden odors in the car can be identified more accurately, and air quality management actions can be taken in time when sudden odors occur to purify the air in the car and improve the comfort of the passengers in the car. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A method for managing the air quality in a vehicle provided by an embodiment of the present application is shown.

[0018] Figure 2 The figure shows a structural block diagram of an intelligent analysis and decision-making module provided in one embodiment of the present application.

[0019] Figure 3 A schematic diagram of performing feature extraction through a convolutional neural network (CNN) provided in an embodiment of the present application is shown.

[0020] Figure 4 A schematic diagram of performing classification by a support vector machine (SVM) provided in an embodiment of the present application is shown.

[0021] Figure 5 A schematic diagram of a deep learning model training method provided in an embodiment of the present application is shown.

[0022] Figure 6 A schematic flowchart of triggering and executing an air purification program provided by an embodiment of the present application is shown.

[0023] Figure 7 A structural block diagram of an in-vehicle air quality management device provided in one embodiment of the present application is shown.

[0024] Figure 8 A structural block diagram of an in-vehicle air quality management device provided in one embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] In order 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 in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present application should fall within the scope of protection of the present application.

[0026] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0027] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention; the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance; in addition, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection or a detachable connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0028] In today's society, as people's living standards and health awareness continue to improve, the quality of the in-vehicle environment has received more and more attention. When people are in a closed vehicle environment for a long time, various physiological and behavioral activities of passengers, such as burping, farting, smoking, etc., may produce some sudden odors, which will cause the air quality in the car to deteriorate. However, the existing technology may not be able to accurately distinguish between normal human body odor and these sudden odors in a timely manner, resulting in the inability of the vehicle air management device to respond to these sudden odors in a timely manner, causing discomfort to passengers.

[0029] This application proposes an intelligent in-car air quality monitoring and adjustment system, which can automatically detect whether there is a sudden odor in the car, such as passengers farting, smoking, etc., and automatically purify the air in the car by intelligently adjusting the windows, air conditioning system and aromatherapy, thereby improving the passenger experience without manual intervention by the driver or passengers.

[0030] The technical solution in this application will be described below in conjunction with the accompanying drawings.

[0031] Figure 1 FIG. 1 shows a method 100 for managing the air quality in a vehicle provided by an embodiment of the present application. Figure 1 As shown, the method 100 includes steps S110 to S130.

[0032] Step S110: Acquire monitoring data from sensors on the vehicle.

[0033] Step S120: Determine whether there is a sudden odor inside the vehicle based on the monitoring data through an artificial intelligence model.

[0034] Step S130: If a sudden odor occurs inside the vehicle, an air quality management strategy is executed to improve the air quality inside the vehicle.

[0035] In some embodiments, the sensor in step S110 may be a multimodal sensor, in particular a multimodal sensor array. For example, in some embodiments, the multimodal sensor may include a gas sensor, an odor sensor and / or an environmental sensor. The gas sensor may include a total volatile organic compound (TVOC) sensor, a carbon dioxide (CO2) sensor, and the like. Volatile organic compounds may include, but are not limited to, formaldehyde, benzene, toluene, xylene, and the like. The TVOC sensor may be a sensor using, for example, metal oxide semiconductor technology or photoionization detector (PID) technology, for monitoring the concentration of volatile organic compounds in the air. The CO2 sensor may be a sensor using, for example, non-dispersive infrared technology, for detecting the concentration of CO2 in the air. The odor sensor may be used to monitor specific odor molecules in the environment, such as ammonia, nicotine, and the like. The odor sensor may be a sensor using, for example, metal oxide semiconductor technology, electrochemical sensor technology, and the like, or a sensor integrating multiple different types of sensors to simultaneously monitor multiple odor molecules. The environmental sensor may include a temperature sensor, a humidity sensor, a particle sensor, and the like, for monitoring the surrounding environment. The particle sensor may be a sensor using, for example, a laser scattering particle counter, etc., for monitoring the particles in the air, such as PM2.5.

[0036] In some embodiments, sensors on the vehicle may all be high-sensitivity sensors.

[0037] In some embodiments, the multimodal sensors on the vehicle can be arranged at various key locations inside the vehicle to fully cover the interior space of the vehicle, so as to timely monitor sudden odors in various corners of the vehicle. The key locations inside the vehicle may include, for example, near the driver's seat, the rear seat area, the air intake, etc.

[0038] In some embodiments, the data obtained by the sensor can be transmitted to a control unit, such as a central processing unit (CPU), wirelessly or wired. In some embodiments, an advanced signal processing algorithm can be embedded in the CPU, and the signal processing algorithm can effectively filter noise signals to ensure the accuracy of the monitored data, thereby helping to timely and accurately monitor sudden odors in the vehicle.

[0039] In some embodiments, the sensor may be equipped with self-diagnostic functions and may be capable of periodic automatic calibration, thereby ensuring the long-term stability and accuracy of the sensor system.

[0040] Traditional vehicles often lack effective means of monitoring the air quality inside the vehicle, and there is the problem of inaccurate monitoring. The odors generated by passengers farting, smoking, etc. cannot be identified and handled in a timely and accurate manner, resulting in poor air quality and affecting the riding experience. Through some embodiments of the present application, the in-vehicle environment can be fully monitored through a combination of sensors, which can not only identify harmful gases, but also detect specific odor analysis and changes in the in-vehicle environment, so as to accurately determine whether there is a sudden odor in the vehicle and handle it in a timely manner.

[0041] In some embodiments, the artificial intelligence model in step S120 can be a model obtained by using annotated valid data in historical monitoring data through machine learning, such as training through a deep learning model, which can determine whether a sudden odor occurs in the vehicle based on the monitoring data currently detected by the sensor.

[0042] Sudden odors can be compared to normal human body odors. Under normal circumstances, people have certain odors on their bodies, such as sweat and breath. These odors belong to normal human body odors and usually do not cause discomfort to people around them. They are acceptable odors. In contrast, sudden odors are caused by certain physiological activities or occasional actions of people, such as farting, burping, smoking, etc. These odors are often sudden and will make people around them feel obviously uncomfortable for a short time, especially in the confined space inside the car.

[0043] However, the existing technology may not be able to accurately and timely distinguish between normal body odor and sudden odor. For example, in some cases, the existing technology may mistake normal body odor for sudden odor and thus activate the in-vehicle air purification system for a long time, causing the entire in-vehicle air quality management system to be insufficiently energy-efficient. On the contrary, in other cases, the existing technology may not be able to identify some sudden odors in time, and therefore fail to quickly activate the in-vehicle air purification, causing passengers to feel uncomfortable due to sudden odors.

[0044] In order to solve the above problems, an artificial intelligence model is provided, which can be based on machine learning (such as deep learning algorithms) and trained with a large amount of data, so that it can accurately distinguish between normal human body odor and sudden odors, and make timely decisions accordingly to automatically adjust the air environment in the car, avoid sudden odors causing discomfort to passengers, and ultimately significantly improve the riding experience and the health standards of the in-car environment.

[0045] In some embodiments, the artificial intelligence model can be implemented as an intelligent analysis and decision-making module. The intelligent analysis and decision-making module can receive data from a multimodal sensor and accurately identify and quickly respond to various air pollution events, such as sudden odors, based on the received data. However, it should be understood by those skilled in the art that the artificial intelligence model in this application can also be implemented in other ways, and this application is not limited to this.

[0046] Figure 2 FIG. 2 shows a block diagram of the intelligent analysis and decision-making module 200 provided in an embodiment of the present application. Figure 2 As shown, the intelligent analysis and decision module 200 may include a data collection submodule 210 , a data analysis and processing submodule 220 , a decision generation submodule 230 and a feedback adjustment submodule 240 .

[0047] In some embodiments, the data acquisition submodule 220 may include a sensor interface and a pre-processing unit.

[0048] In some embodiments, the sensor interface can collect raw monitoring data from a multimodal sensor, wherein the raw monitoring data may include information such as gas concentration, temperature, humidity, PM2.5, etc. The sensor interface can support multiple communication protocols, such as serial peripheral interface (SPI), internal integrated circuit bus (I2C), universal asynchronous receiver and transmitter (UART), etc., to accommodate different types of sensors.

[0049] In some embodiments, the pre-processing unit may perform preliminary processing on the raw monitoring data collected through the sensor interface, such as noise filtering, data format conversion, etc., to ensure the accuracy and efficiency of subsequent analysis.

[0050] In some embodiments, the data analysis processing submodule 220 may include a feature extraction unit and a pattern recognition unit.

[0051] In some embodiments, the feature extraction unit may use various signal processing techniques to extract useful features from the raw monitoring data, such as concentration variation trends of specific gases, temperature fluctuations, etc.

[0052] In some embodiments, the pattern recognition unit may use a deep learning algorithm, such as a convolutional neural network (CNN), to perform feature extraction. Figure 3 A schematic diagram of performing feature extraction by CNN provided in an embodiment of the present application is shown. Those skilled in the art should recognize that in addition to CNN, other feature recognition algorithms may also be used to perform feature recognition, such as recurrent neural network (RNN), generative adversarial network (GAN), etc., and the present application does not limit this.

[0053] In some embodiments, for features extracted by a deep learning algorithm, a support vector machine (SVM) can be used to reclassify the extracted features to identify different sudden odor events, such as passengers farting, smoking, etc. Figure 4A schematic diagram of performing classification by SVM provided in an embodiment of the present application is shown. SVM attempts to find an optimal decision boundary, which is farthest from the nearest samples of the two categories, that is, SVM maximizes the margin. Those skilled in the art should recognize that in addition to vector machines, other classification methods can also be used to classify sudden odor events, such as decision tree method, random forest method, etc., which are not limited in the present application.

[0054] In some embodiments, the data analysis and processing submodule 220 may also include an anomaly detection unit. The anomaly detection unit may detect abnormal data points in the monitoring data using, for example, statistical methods and / or machine learning models, thereby preventing or reducing false positives and false negatives, and improving the accuracy of monitoring sudden odors. Among them, statistical methods may include Z-Score methods, interquartile range (IQR) methods, and the like. Machine learning models may include methods such as isolation forests, autoencoders, variational autoencoders, and generative adversarial networks. It should be understood by those skilled in the art that the anomaly detection unit may use various methods for detecting abnormal data points to detect abnormal data points, and this application does not limit this.

[0055] In some embodiments, the data analysis and processing submodule 220 may also include a trend prediction unit. The trend prediction unit may use a time series analysis method based on historical monitoring data to predict the trend of changes in the in-vehicle air quality in the future, thereby helping the in-vehicle air quality management device to prepare in advance, for example, switching the internal and external circulation mode of the vehicle air conditioner, turning on the negative ion generator, starting the high-efficiency particulate air filter (HEPA) filter purification, etc. The time series analysis method may include an autoregressive integrated moving average model (ARIMA), exponential smoothing method, state space model, vector autoregression model, etc. Those skilled in the art may select a suitable time series analysis method according to specific needs, and this application does not limit this.

[0056] In some embodiments, method 100 may further include the following steps: receiving navigation data of an on-board navigation system; predicting the air quality of a road section ahead of the vehicle based on the navigation data; and adjusting the air quality management strategy based on the air quality of the road section ahead of the vehicle.

[0057] For example, the navigation data of the on-board navigation may indicate that the vehicle will pass through a specific road section ahead, and the air quality around the specific road section is usually poor. In this case, the in-vehicle air quality management device can switch the circulation mode of the on-board air conditioner to the internal circulation in advance. As an example, the specific road section may be a tunnel. As is well known, due to the low air exchange rate, the air quality in the tunnel is often poor, so the on-board air conditioner can be switched to the internal circulation in advance before reaching the tunnel to avoid taking in the air in the tunnel. Similarly, the air quality of some construction sections or dilapidated sections is often not ideal, so before the vehicle passes these sections, the circulation mode of the on-board air conditioner can be switched to the internal circulation in advance to avoid reducing the air quality in the car. Those skilled in the art can easily think of other situations of specific sections, and this application is not further defined herein.

[0058] In some embodiments, the data analysis and processing submodule 220 can use historical monitoring data to train models based on machine learning algorithms (e.g., support vector machines, neural networks, etc.) to more accurately predict air quality change trends.

[0059] In some embodiments, the decision generation submodule 230 may include a rule engine, a dynamic adjustment unit, and a multi-objective optimization unit.

[0060] The rule engine can preset a series of air quality management strategies, and automatically select the most appropriate air quality management strategy according to the current air quality status and / or the prediction results of the trend prediction unit. For example, when it is detected that the CO2 concentration in the car exceeds a predetermined threshold, the rule engine can automatically select a specific air quality management strategy, and the air quality management strategy can include automatically adjusting the vehicle air conditioning circulation mode to external circulation and turning on the negative ion generator. For another example, when it is detected that the current concentration of a specific odor gas molecule is at a normal level, but its trend of change is that the concentration is increasing, although the current concentration of the odor gas detected is not enough to trigger the air purification action, the action engine will still select a specific air quality management strategy, and the strategy will start the air purification action in advance to avoid the concentration of the odor gas molecules from further increasing and causing discomfort to the passengers.

[0061] The dynamic adjustment unit can dynamically adjust the parameters in the rule engine based on real-time monitoring data to ensure that it is always in the best working state. For example, the dynamic adjustment unit can continuously optimize the threshold settings through online learning to improve the response accuracy and timeliness of the rule engine.

[0062] In some cases, it may be necessary to balance multiple air purification goals, for example, reducing PM2.5 concentration and maintaining low CO2 concentration levels at the same time. The multi-objective optimization unit can use a multi-objective optimization algorithm such as a non-dominated sorting genetic algorithm II (NSGA-II) to find the best balance point. It should be understood by those skilled in the art that other multi-objective optimization algorithms in the art can also be used instead of NSGA-II, and this application is not limited to this.

[0063] In some embodiments, the feedback regulation submodule 240 may include an execution control unit, an effect evaluation unit, and a user feedback unit.

[0064] The execution control unit can control the automation control unit (described in detail below) to perform corresponding operations according to the instructions output by the decision generation submodule 230. In some embodiments, the operation may include at least one of the following operations: opening the window, switching the circulation mode of the vehicle air conditioner, starting the air purification function of the vehicle air conditioner, and releasing aromatherapy.

[0065] The effect evaluation unit can collect the monitoring data of the air in the vehicle again, evaluate the change of air quality after the operation is performed, and verify the effectiveness of the air quality management strategy. If the verification effect is not ideal, the intelligent analysis and decision module 200 can re-analyze, generate and apply a new air quality management strategy.

[0066] The user feedback unit can allow users to provide feedback through various means such as mobile phone APP, vehicle-mounted central control screen, etc. The in-vehicle air quality management device can further optimize the air quality management strategy with reference to the user's feedback to improve the user experience.

[0067] Existing vehicle air management systems fail to integrate intelligent analysis and response mechanisms, and are unable to automatically adjust the vehicle interior environment based on real-time monitored air quality changes, such as automatically opening windows, adjusting air conditioning modes, or releasing aromatherapy.

[0068] In some embodiments of the present application, an intelligent analysis and decision-making module based on an artificial intelligence model is provided. Through an artificial intelligence (AI) algorithm, it can accurately distinguish between normal human odor and sudden odor, and accurately determine when to start the air purification program, avoiding false alarms and missed alarms, ensuring accurate responses, and improving the intelligence and reliability of in-vehicle air quality management.

[0069] Figure 5 FIG. 5 is a schematic diagram of a deep learning model training method 500 provided in an embodiment of the present application. Figure 5 As shown, the deep learning model training method 500 includes steps S510 to S540.

[0070] Step S510: Dataset construction. Specifically, a large amount of in-vehicle air quality monitoring data is collected, including information such as gas concentration, temperature, humidity, PM2.5, etc. in different scenarios, and corresponding labels, such as "normal smell", "sudden odor", "smoke", etc. It should be understood that the listed monitoring data and labels are only examples, and those skilled in the art can select other types of monitoring data and corresponding labels as needed, and this application does not limit this.

[0071] Step S520: Model selection and training. Specifically, a suitable deep learning model can be selected, such as a long short-term memory network (LSTM0), a gated recurrent unit (GRU), etc., and large-scale training can be performed on a graphics processing unit (GPU) cluster to ensure that the model has good generalization ability and robustness. It should be understood that the deep learning models listed are only examples, and those skilled in the art can select other suitable deep learning models as needed, and this application does not limit this.

[0072] Step S530: Model evaluation. Specifically, the performance of the model can be evaluated using a method such as cross-validation to ensure that the model performs stably and reliably on different test sets. It should be understood that the cross-validation method listed is only an example, and those skilled in the art can select other appropriate validation methods as needed, and this application does not limit this.

[0073] Step S540: Model deployment. Specifically, the trained model can be deployed on edge computing devices such as vehicle-mounted computing devices to achieve real-time data processing and decision generation.

[0074] In some embodiments, the intelligent analysis and decision module 200 may have real-time data processing capabilities and data caching and storage capabilities. Specifically, the intelligent analysis and decision module 200 may have a streaming data processing framework, such as Apache Kafka (Apache Kafka) or Spark Streaming (Spark Streaming) and other streaming data processing frameworks, so that sensor data can be processed in real time to ensure low latency and high throughput. In addition, the intelligent analysis and decision module 200 can use memory databases such as Redis to cache recent data, and store historical data in distributed storage systems such as Hadoop HDFS or AWS S3 for subsequent analysis and backtracking. It should be understood that the streaming data processing framework, memory database, and distributed storage system listed here are only examples, and those skilled in the art can select other settings as needed, and this application is not limited to this.

[0075] In addition to machine learning algorithms, by further combining real-time data processing technology, it can accurately identify and quickly respond to various sudden odor events, thereby significantly improving the riding experience and the health standards of the in-car environment.

[0076] In some embodiments, the intelligent analysis and decision module 200 can be seamlessly integrated with other systems of the vehicle, such as a navigation system, an entertainment system, etc., through a controller area network (CAN) bus or other vehicle communication protocols to achieve data sharing and collaborative work.

[0077] In some embodiments, the intelligent analysis and decision module 200 can be connected to a cloud platform to offload some computing tasks to the cloud, so that the cloud can use powerful computing resources to perform complex data analysis and model training, while maintaining real-time communication with the vehicle system through an application programming interface (API).

[0078] Through some embodiments of the present application, the intelligent analysis and decision-making module can achieve accurate monitoring and intelligent adjustment of the air quality in the car by integrating advanced machine learning algorithms and real-time data processing technology. This module can not only quickly respond to various air pollution events including sudden odor events, but also dynamically adjust the air quality management strategy according to user needs and environmental changes to provide users with a comfortable and healthy riding environment. In addition, through continuous learning and optimization, performance can be continuously improved to better meet user needs.

[0079] In some embodiments, the air quality management strategy in step S130 can be executed by an automated control unit. The automated control unit can implement intelligent window control, vehicle air conditioning adjustment, and intelligent aromatherapy release, etc. For example, when a sudden odor is detected in the car, the automated control unit can immediately perform at least one of the following operations: open the window, switch the circulation mode of the vehicle air conditioning, start the air purification function of the vehicle air conditioning, and release aromatherapy.

[0080] The onboard air conditioning system and window adjustment of traditional vehicles usually rely on manual operation by passengers, which often reacts slowly to sudden odors and poses safety risks when operated manually during driving. The above-mentioned automated control unit can quickly improve the air quality in the car without direct intervention from passengers or drivers, thus overcoming the defects in traditional vehicles.

[0081] In some embodiments, method 100 may also include: receiving air quality preference information input by the user; determining an air quality management strategy based on the air quality preference information. For example, a user can receive an air quality report, a custom sensitivity threshold, and an aromatherapy preference through an interactive interface such as a mobile phone APP. The user can also view the in-car air quality index, understand the type of pollution source, and the concentration level through the in-car central control screen or mobile phone APP. In addition, through the in-car central control screen or mobile phone APP, personalized air quality target setting can be provided to the user. For example, the user can adjust the working priority of the in-car air quality management device according to his or her own air quality preferences or needs, such as giving priority to reducing PM2.5 concentration or maintaining a low CO2 level. Furthermore, when it is detected that the air quality exceeds the standard, the user can be informed through an in-car voice prompt or a mobile phone notification, and appropriate air improvement measures can be recommended, such as opening the window for ventilation.

[0082] Different passengers may have different sensitivities and preferences for air quality. Existing vehicles often lack personalized setting options and are difficult to meet diverse needs. Through some embodiments of the present application, corresponding air quality management strategies can be determined according to the user's air quality preferences, thereby achieving personalized settings for different users and improving user experience.

[0083] In some embodiments, method 100 may further include: in response to the release time of the aromatherapy reaching a preset duration, turning off the aromatherapy, wherein the preset duration is a duration determined according to the sensitivity time of human sense of smell. As is well known, human sense of smell has a sensitive time to a specific smell, and the smell cannot be perceived after this time. For example, assuming that the sensitivity time of humans to car aromatherapy is 3 minutes, the aromatherapy fragrance cannot be perceived after 3 minutes. The present application utilizes this principle to set a preset duration for the release time of the aromatherapy, and the aromatherapy is turned off after this duration. By setting a preset duration for the release time of the aromatherapy, the consumption of energy and aromatherapy can be reduced, which is in line with the concepts of environmental protection and energy saving.

[0084] In some embodiments, the vehicle air conditioner can be started only when necessary to reduce unnecessary energy consumption and improve energy efficiency. For example, if the air quality outside the vehicle is good and the air temperature outside the vehicle is low, only the external circulation of the vehicle air conditioner can be turned on to cool the air inside the vehicle without starting the cooling function of the vehicle air conditioner. Therefore, energy consumption can be reduced, which is in line with the concept of green travel.

[0085] In some embodiments, the method 100 may further include: receiving physiological index data sent by a health monitoring device of a passenger in the vehicle; and adjusting the air quality management strategy according to the monitoring data and the physiological index data. For example, the physiological index data of the passenger may be received from a health monitoring device (such as a smart watch, a smart bracelet, smart glasses, etc.) worn by the passenger, and a suitable air quality management strategy may be determined and executed according to the monitoring data and the physiological index data to provide a personalized in-vehicle air quality management strategy suitable for the passenger and improve the comfort of the passenger.

[0086] In summary, the existing technology may have the following defects: insufficient monitoring accuracy, the existing technology may not be able to accurately identify sudden odors, such as the odor produced by passengers farting or smoking; slow response speed, traditional vehicle-mounted air quality management devices may rely on manual operation or have slow response speeds, and cannot respond to sudden deterioration in air quality in a timely manner; lack of intelligence, the existing technology basically does not have intelligent analysis capabilities, and cannot automatically select appropriate air purification strategies according to specific circumstances; low degree of personalization, the existing technology often fails to provide sufficient personalized settings, and cannot meet the different needs of different users for air quality; low energy efficiency, the existing technology may consume too much energy during the air purification process, which does not meet the requirements of energy saving and environmental protection.

[0087] In order to solve the above technical problems, the present application provides an intelligent in-vehicle air quality monitoring and adjustment method. By integrating a multimodal sensor array, an intelligent analysis and decision-making module, an automatic control unit, and user feedback and personalized settings, it realizes real-time monitoring, intelligent analysis, automatic adjustment and personalized services for sudden odors in the car, thereby significantly improving the riding experience and ensuring the comfort and health of the in-vehicle environment.

[0088] More specifically, through some embodiments of the present application, the following advantages can be obtained: high-precision monitoring, due to the use of multimodal sensors, it is possible to accurately identify a variety of odor molecules, thereby improving the monitoring accuracy and range; instant response, through the integrated intelligent analysis and decision-making module, it is possible to quickly respond to changes in air quality and automatically execute air purification programs without human intervention, thereby improving response speed; intelligent operation, through the application of artificial intelligence models, it is possible to accurately identify sudden odors in the car, and make the best decisions based on real-time data to optimize air purification strategies; personalized experience, users can make personalized settings through, for example, mobile phone APPs to meet the specific needs of different users and provide customized air quality management solutions; energy saving and environmental protection, only starting related equipment when necessary, reducing energy waste, in line with the principles of green travel.

[0089] In summary, the embodiments of the present application can solve the key technical problems of in-vehicle air quality monitoring and regulation by integrating advanced sensor technology, intelligent algorithms and automated control mechanisms, and provide an intelligent, efficient and personalized in-vehicle environment management solution.

[0090] Figure 6 FIG. 1 shows a schematic flow chart of triggering and executing an air purification program provided by an embodiment of the present application. Figure 6 As shown, the triggering and execution of the air purification program includes steps S610 to S670.

[0091] Step S610: The in-vehicle air quality management system is started.

[0092] Step S620: The sensor starts detection.

[0093] Step S630: The sensor detects a sudden odor.

[0094] Step S640: The intelligent analysis and decision-making module is started.

[0095] Step S650: The automation control unit receives the instruction.

[0096] Step S660: Execute air purification program.

[0097] Step S670: The system returns to standby mode.

[0098] In addition to the above embodiments, the present application may also include the following optional solutions.

[0099] In some embodiments, the natural purification ability of plants or microorganisms can also be used to design an in-vehicle ecological filtration system. For example, a small hydroponic plant wall or a biofilter can be installed, which can absorb CO2, release oxygen, and degrade certain harmful gases and odors.

[0100] In some embodiments, nanomaterials with air purification functions can also be used, such as a photocatalyst layer, which can decompose harmful gases such as volatile organic compounds such as formaldehyde and benzene, as well as bacteria and viruses under light, thereby purifying the air inside the car.

[0101] In some embodiments, negative ions or positive ions can be released to combine with pollutants in the air, so that the pollutants are settled or attached to the surface, reducing suspended particulate matter and odor, thereby improving air quality.

[0102] In some embodiments, the layout of the ventilation ducts in the vehicle can also be optimized, and an intelligent air duct control system can be used to intelligently distribute fresh air and improve air circulation efficiency based on the differences in air quality in different areas of the vehicle.

[0103] In some embodiments, it can also be linked with health monitoring devices worn by passengers, such as smart phones, to monitor passengers' physiological indicators, such as heart rate, breathing rate, etc., and combine with the in-car air quality data to intelligently adjust the in-car environment, such as adopting appropriate air quality management strategies to provide a more personalized comfort experience.

[0104] In some embodiments, the principle of high-frequency sound wave vibration can also be used to remove tiny particles and odor molecules in the air. This method does not have any chemical residues and is relatively friendly to both humans and pets.

[0105] In some embodiments, big data and AI prediction technology can also be used to predict possible air quality problems in the car based on historical monitoring data and environmental factors such as weather and traffic conditions, so that measures can be taken in advance to prevent the occurrence of air quality problems.

[0106] Each of the above solutions has its own characteristics and usage scenarios. Technical personnel in this field can comprehensively consider factors such as cost, effect, and passenger needs when applying them, and select the most appropriate solution or combine multiple solutions to optimize the in-vehicle air quality management effect.

[0107] Combine the following Figures 7 and 8 The device embodiment of the present application is described in detail. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment, so the parts not described in detail can refer to the previous method embodiment.

[0108] Figure 7 FIG. 1 shows a structural block diagram of a vehicle air quality management device 700 provided in an embodiment of the present application. Figure 7 As shown, the in-vehicle air quality management device 700 may include an acquisition module 710, a determination module 720, and an execution module 730. The acquisition module 710 may acquire monitoring data from sensors on the vehicle. The determination module 720 may determine whether a sudden odor occurs inside the vehicle through an artificial intelligence model based on the monitoring data. The execution module 730 may execute an air quality management strategy to improve the air quality inside the vehicle when a sudden odor occurs inside the vehicle.

[0109] In some embodiments, the in-vehicle air quality management device 700 may further include a receiving module for receiving air quality preference information input by a user. The determination module 720 may determine an air quality management strategy according to the air quality preference information.

[0110] In some embodiments, the air quality management strategy can be used to control the vehicle to perform at least one of the following operations: opening the windows, switching the circulation mode of the vehicle air conditioner, starting the air purification function of the vehicle air conditioner, and releasing aromatherapy.

[0111] In some embodiments, the execution module 730 may turn off the fragrance in response to the release time of the fragrance reaching a preset duration; wherein the preset duration is a duration determined according to the olfactory sensitivity time of humans.

[0112] In some embodiments, the sensors on the vehicle include one or more of: a gas sensor, an odor sensor, and an environmental sensor.

[0113] In some embodiments, the receiving module can be used to receive physiological index data sent by the health detection device of the passengers in the vehicle. The determination module 720 can adjust the air quality management strategy according to the monitoring data and the physiological index data, and the execution module 730 can execute the adjusted air quality management strategy.

[0114] In some embodiments, the in-vehicle air quality management device 700 may further include a prediction module. The receiving module may be used to receive navigation data of the vehicle navigation. The prediction module may predict the air quality of the road ahead of the vehicle based on the navigation data. The determination module 720 may adjust the air quality management strategy based on the air quality of the road ahead of the vehicle.

[0115] Figure 8 FIG. 8 is a block diagram of a vehicle air quality management device 800 provided in an embodiment of the present application. Figure 8 As shown, the in-vehicle air quality management device 800 may include a memory 810 and a processor 820. The memory 810 may store an executable program. The processor 820 may call and execute the executable program stored in the memory 810 to implement the various method embodiments described above.

[0116] The present application also provides a chip in an embodiment, including a processor, which can be used to call and run a computer program from a memory, so that a device equipped with the chip performs the method described in the above method embodiment. It is understood that the processor can be any type of processor. It is understood that the memory can be independent of the chip or integrated in the chip.

[0117] The embodiments of the present application also provide a computer-readable storage medium for storing a program, and the program enables a computer to execute the methods in the embodiments of the present application.

[0118] The embodiment of the present application also provides a computer program product, which includes a program, and the program enables a computer to execute the method in each embodiment of the present application.

[0119] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present disclosure is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a machine-readable storage medium, or transmitted from one machine-readable storage medium to another machine-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The machine-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0120] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments of the present disclosure can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present disclosure.

[0121] In the several embodiments provided in the present disclosure, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0122] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0123] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0124] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for managing the air quality in a vehicle, characterized in that: include: Obtain monitoring data from sensors on the vehicle; Determine whether a sudden odor occurs inside the vehicle through an artificial intelligence model based on the monitoring data; If a sudden odor occurs inside the vehicle, an air quality management strategy is executed to improve the air quality inside the vehicle.

2. The method according to claim 1, characterized in that The method further comprises: receiving air quality preference information input by a user; The air quality management strategy is determined according to the air quality preference information.

3. The method according to claim 1 or 2, characterized in that: The air quality management strategy is used to control the vehicle to perform at least one of the following operations: Open the car window, switch the car air conditioner's circulation mode, start the car air conditioner's air purification function, and release the aroma.

4. The method according to claim 3, characterized in that The method further comprises: In response to the release time of the aroma reaching a preset time length, turning off the aroma; The preset duration is determined according to the olfactory sensitivity of human beings.

5. The method according to claim 1 or 2, characterized in that: The sensors on the vehicle include one or more of the following: a gas sensor, an odor sensor, and an environmental sensor.

6. The method according to claim 1 or 2, characterized in that: The method further comprises: Receive physiological index data sent by the health detection equipment of passengers in the car; The air quality management strategy is adjusted according to the monitoring data and the physiological indicator data.

7. The method according to claim 1 or 2, characterized in that: The method further comprises: Receive navigation data from vehicle navigation; predicting the air quality of the road ahead of the vehicle based on the navigation data; The air quality management strategy is adjusted according to the air quality of the road section ahead of the vehicle.

8. A vehicle interior air quality management device, characterized in that: include: An acquisition module, which acquires monitoring data from sensors on the vehicle; A determination module, which determines whether a sudden odor occurs inside the vehicle through an artificial intelligence model according to the monitoring data; as well as The execution module executes an air quality management strategy to improve the air quality inside the vehicle if a sudden odor occurs inside the vehicle.

9. A vehicle interior air quality management device, characterized in that: include: A memory for storing executable programs; as well as A processor, configured to call and execute the executable program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium for storing a program, characterized in that: When the program is executed by a computer, the program causes the computer to execute the method according to any one of claims 1 to 7.

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