Vehicle-mounted intelligent fragrance control method and system based on health management and vehicle

Abstract

The invention discloses a vehicle-mounted intelligent fragrance control method and system based on health management and a vehicle, and relates to the technical field of vehicle-mounted fragrance, and the method comprises the steps: obtaining vehicle interior environment parameters, vehicle exterior environment information, passenger state data and vehicle travel information; inputting the data into a preset decision model, wherein the decision model comprises a series of trigger conditions and logic rules corresponding to the health risk; judging whether a health risk scene needing to be responded exists or not at present by running the decision model, and outputting a corresponding scene identifier and an intervention measure; generating a control instruction according to the output scene identifier and the intervention measure so as to drive a fragrance generation device to release corresponding fragrance; the control instruction at least specifies one target fragrance type, release concentration and release mode. According to the invention, the risk scene can be actively identified, the risk scene and the fragrance intervention scheme can be accurately matched, and the health demand of the user can be actively adapted.

Description

Technical Field

[0001] This invention relates to the field of in-vehicle fragrance technology, and in particular to an in-vehicle intelligent fragrance control method, system, and vehicle based on health management. Background Technology

[0002] As automobiles transform from mere transportation tools into mobile living spaces, consumers' demands for in-car comfort and health continue to rise, leading to steady growth in the in-car fragrance system market. The current market exhibits a trend towards intelligent connectivity and health-oriented development, but existing products largely focus on mood regulation and personalized experiences, with functions limited to passive selection or simple energizing.

[0003] Existing technologies include fragrance products based on health control, with some solutions attempting to adjust fragrance by combining environmental and human conditions. However, these products mostly employ passive adjustment logic, only switching fragrances when occupants experience significant discomfort. They cannot proactively identify health risks, resulting in a low degree of alignment between fragrance control interventions and health needs, making it difficult to meet users' needs for proactive health protection in in-vehicle scenarios. Summary of the Invention

[0004] To address the problem that existing fragrance control technologies cannot proactively identify health risks, resulting in low matching between fragrance control interventions and health needs, and failing to meet users' proactive health protection needs in in-vehicle scenarios, this invention provides an in-vehicle intelligent fragrance control method, system, and vehicle based on health management. This method can proactively identify risk scenarios, accurately match risk scenarios with fragrance intervention solutions, and proactively adapt to users' health needs. The specific technical solution is as follows: In a first aspect, the present invention provides a vehicle-mounted intelligent fragrance control method based on health management, comprising the following steps: Acquire in-vehicle environmental parameters, external environmental information, occupant status data, and vehicle travel information; The data is input into a preset decision model, which includes a series of triggering conditions and logical rules corresponding to health risks. By running the decision model, it is determined whether there is a health risk scenario that needs to be responded to, and the corresponding scenario identifier and intervention measures are output. Control commands are generated based on the output scene identifier and intervention measures to drive the fragrance generator to release the corresponding fragrance; the control commands specify at least one target fragrance type, release concentration and release mode.

[0005] Preferably, the in-vehicle environmental parameters include chemical pollutant concentration indicators, suspended particulate matter concentration indicators, and temperature and humidity environmental indicators used to assess air quality; the external environmental information includes air quality index and regional pollution data.

[0006] Preferably, the occupant's current physiological state identifier is obtained through at least one of the following methods: Analyze video streams from driver-facing cameras to identify facial fatigue features; Process the audio stream from the vehicle's microphone to identify characteristics of voice fatigue; or Read data from biosensors integrated into the steering wheel or seat to obtain heart rate variability metrics.

[0007] Preferably, the decision model includes a first pollution protection rule, the execution logic of which is as follows: Acquire the concentration indicators of key chemical pollutants in the in-vehicle environmental parameters and the air quality index of the out-of-vehicle environmental information; If the concentration of the key chemical pollutant exceeds a first safety threshold preset based on the occupant's tolerance, and / or the air quality index exceeds a preset quality standard threshold, then it is determined to be a first pollution risk scenario, and an intervention measure to activate the first purification fragrance is output.

[0008] Preferably, the decision model includes a second pollution prevention rule, the execution logic of which is as follows: Obtain the suspended particulate matter concentration index from the in-vehicle environmental parameters and the air quality index from the out-of-vehicle environmental information; If the concentration of suspended particulate matter exceeds a preset second safety threshold, and / or the air quality index exceeds a preset quality standard threshold, it is determined to be a second pollution risk scenario, and intervention measures to activate a second purification fragrance are output.

[0009] Preferably, the decision model includes external pollution pre-control rules, the execution logic of which is as follows: Obtain regional pollution data from the vehicle's external environment information and path planning from the vehicle's travel information; If the vehicle's planned route will pass through the pollution-affected area indicated by the air quality index in the future, it will be identified as an external pollution exposure risk scenario, and an intervention measure to activate the third type of purification fragrance will be output.

[0010] Preferably, the decision model includes a fatigue driving prediction rule, the execution logic of which is as follows: Obtain the physiological fatigue index from the occupant status data and the road complexity index from the vehicle travel information; If the physiological fatigue index exceeds the individual baseline and the road complexity index is below the minimum threshold required to maintain attention, it is determined to be a fatigue driving risk scenario, and intervention measures such as activating energizing fragrances are output.

[0011] Preferably, a method for controlling in-vehicle intelligent fragrance based on health management further includes: Monitor the trend of changes in the occupant status data within a preset time period after the intervention, and evaluate the intervention effect score based on the trend of changes; When the intervention effect score is lower than the effective threshold, the trigger threshold of the corresponding risk scenario in the decision model or the fragrance concentration parameter in the intervention measures is adjusted.

[0012] Secondly, the present invention provides an in-vehicle intelligent fragrance control system based on health management, which applies the aforementioned in-vehicle intelligent fragrance control method based on health management, including: The data acquisition unit is used to acquire in-vehicle environmental parameters, external environmental information, occupant status data, and vehicle travel information. A fragrance generating device, comprising multiple fragrance storage and release units for storing and releasing various different types of fragrances; and The central controller is communicatively connected to both the data acquisition unit and the fragrance generating device. The central controller is configured to: receive in-vehicle environmental parameters, external environmental information, occupant status data, and vehicle travel information; input the received data into a preset decision model; determine whether a health risk scenario requiring response exists by running the decision model; output corresponding scenario identifiers and intervention measures; and generate control commands based on the output scenario identifiers and intervention measures to drive the fragrance generating device to release the corresponding fragrance. The decision model includes a series of triggering conditions and logical rules corresponding to health risks; the control commands specify at least one target fragrance type, release concentration, and release mode.

[0013] Thirdly, the present invention provides a vehicle including the aforementioned in-vehicle intelligent fragrance control system based on health management.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention discloses an in-vehicle intelligent fragrance control method based on health management. By integrating in-vehicle environmental parameters, external environmental information, occupant status data, and vehicle travel information, a comprehensive health risk monitoring system is constructed. This system can proactively identify risk scenarios at the initial stage of health problems such as occupant fatigue or discomfort, or before the in-vehicle environment deteriorates. Compared to traditional passive fragrance adjustment, this method intervenes in health management earlier, improving the timeliness of health protection. Simultaneously, by pre-setting a decision-making model and health risk triggering logic, it accurately matches risk scenarios with fragrance intervention plans, solving the problems of simple decision-making logic and lack of targeted intervention in traditional solutions, thus proactively adapting to the user's health needs. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0016] Figure 1 This is a flowchart of an in-vehicle intelligent fragrance control method based on health management according to the present invention.

[0017] Figure 2 This is a schematic diagram of an in-vehicle intelligent fragrance control system based on health management according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be understood that, when used in this specification, the terms “comprising” and “including” indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0020] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0021] It should also be further understood that the term "and / or" as used in this specification refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes such combinations.

[0022] like Figure 2 As shown, this embodiment of the invention provides an in-vehicle intelligent fragrance control system based on health management, comprising: The data acquisition unit is used to acquire in-vehicle environmental parameters, external environmental information, occupant status data, and vehicle travel information. The data acquisition unit includes: The in-vehicle environment sensing module consists of a group of high-precision miniature sensors distributed in key locations such as the dashboard, roof, and behind the seats. It monitors the concentrations of formaldehyde, TVOC, PM2.5, and carbon dioxide, as well as temperature and humidity, in real time to form a digital profile of the in-vehicle air quality.

[0023] The vehicle external environment acquisition module communicates in real time with the cloud data platform through the vehicle-mounted T-BOX. The cloud data platform integrates meteorological, environmental protection, and geographic information services to acquire meteorological data and air quality index (AQI) along the vehicle's current location and planned route.

[0024] The occupant status monitoring module employs non-invasive multimodal perception technology. It utilizes a driver-facing DMS camera and a passenger-facing OMS camera to analyze facial fatigue features in real time using computer vision algorithms.

[0025] The vehicle trip information module directly obtains real-time dynamic information such as vehicle speed, location, navigation route, road type, and traffic events ahead from the vehicle's CAN bus, GPS / BeiDou navigation module, or in-vehicle navigation software.

[0026] A fragrance generating device, comprising multiple fragrance storage and release units for storing and releasing various different types of fragrances; and The fragrance device is a modular structure comprising multiple independent fragrance capsules, each storing a specific type of fragrance base liquid. The control instructions include a command specifying the target capsule number and mixing ratio. The fragrance device, according to these instructions, mixes the fragrance base liquids from different capsules in the specified ratio and then atomizes and releases the mixture. Each unit encapsulates a basic fragrance ingredient, such as peppermint extract, sandalwood essential oil, or chamomile extract.

[0027] The capsules use a standardized interface, allowing users to easily replace them. By recording the usage of each fragrance capsule, and sending a replenishment reminder to the user via the vehicle's infotainment system or a linked mobile application when the remaining amount of a specific fragrance falls below a replenishment threshold.

[0028] The central controller is communicatively connected to both the data acquisition unit and the fragrance generating device. The central controller is configured to: receive in-vehicle environmental parameters, external environmental information, occupant status data, and vehicle travel information; input the received data into a preset decision model; determine whether a health risk scenario requiring response exists by running the decision model; output corresponding scenario identifiers and intervention measures; and generate control commands based on the output scenario identifiers and intervention measures to drive the fragrance generating device to release the corresponding fragrance. The decision model includes a series of triggering conditions and logical rules corresponding to health risks; the control commands specify at least one target fragrance type, release concentration, and release mode.

[0029] The central controller is typically implemented based on the powerful computing capabilities of the vehicle's domain controller, such as a high-performance MCU or SoC, to run the core decision-making algorithm. The preset decision model exists as software within the controller and is essentially a configurable policy engine. It includes a structured database mapping scenes, rules, and actions. After receiving synchronized data from the data acquisition unit, the controller inputs it into the decision model and performs rapid matching and logical operations against the triggering conditions of all rules. Successfully matched rules are triggered, generating an output containing clear scene identifiers and structured intervention measures.

[0030] The fragrance generator incorporates a microfluidic control system. Upon receiving instructions from the central controller, its miniature precision pumps extract precise volumes of fragrance concentrate from different capsules according to the specified ratio, mixing them uniformly in the mixing chamber. The mixture is then transformed into ultrafine mist particles via piezoelectric atomizing plates or low-temperature evaporation technology, and blown into the air conditioning duct or directly into the cabin by a low-noise variable frequency fan at a specified wind speed. The mist can be configured for continuous, uniform flow, intermittent pulses, or gradual intensification to suit different needs.

[0031] It should be noted that while generating fragrance control commands, the central controller can also send coordinated commands to the air conditioning system, window controller, ambient lighting system, and audio system via the vehicle network based on the decision results, so as to realize linked operations such as closing windows, switching air circulation modes, and adjusting lights and music.

[0032] This invention discloses an in-vehicle intelligent fragrance control system based on health management. By integrating in-vehicle environmental parameters, external environmental information, occupant status data, and vehicle travel information, it can proactively identify risk scenarios at the initial stage of health risks such as fatigue and discomfort experienced by occupants, or before the in-vehicle environment deteriorates, thus intervening in health management in advance and improving the timeliness of health protection. Simultaneously, by pre-setting a decision-making model and health risk triggering logic, it accurately matches risk scenarios with fragrance intervention plans, solving the problems of simple decision-making logic and lack of targeted intervention in traditional solutions, and achieving proactive adaptation to the health needs of different travel itineraries.

[0033] For the composition principle of the in-vehicle intelligent fragrance control system based on the foregoing embodiments, please refer to [link / reference needed]. Figure 1 This invention provides a method for controlling in-vehicle intelligent fragrance based on health management, comprising the following steps: Step S1: Obtain in-vehicle environmental parameters, external environmental information, occupant status data, and vehicle travel information; By pre-deploying data acquisition terminals in the vehicle system, various sensors, vehicle controllers and external devices are integrated to complete equipment calibration and communication protocol adaptation, while a data caching module is built.

[0034] Specifically, the in-vehicle environmental parameters include chemical pollutant concentration indicators, suspended particulate matter concentration indicators, and temperature and humidity environmental indicators used to assess air quality; the external environmental information includes air quality index and regional pollution data.

[0035] In this embodiment, data is collected in real time using an onboard temperature and humidity sensor, PM2.5 sensor, VOC sensor, and carbon dioxide sensor at different or the same sampling frequencies. The collected data includes temperature and humidity, PM2.5 concentration, formaldehyde concentration, and carbon dioxide concentration. Formaldehyde and carbon dioxide concentrations are indicators of chemical pollutant concentrations; PM2.5 concentration is an indicator of suspended particulate matter concentration.

[0036] In this embodiment, data is acquired through communication between the vehicle-mounted T-BOX and a cloud service platform. The cloud platform integrates real-time meteorological data from the meteorological bureau, AQI (Air Quality Index) data from the environmental protection department, sub-indices for various pollutants, and geotagged pollution area data. The pollution area data includes concentrated pollution sources such as industrial zones, transportation hubs, and energy production areas. Early pollution areas typically have higher AQIs than surrounding areas.

[0037] Specifically, the occupant's current physiological state identifier is obtained through at least one of the following methods: Analyze video streams from driver-facing cameras to identify facial fatigue features; A built-in near-infrared camera, located behind the steering wheel or on the A-pillar, continuously captures a video stream of the driver's face at a certain frame rate. Face detection and tracking are performed to ensure the target area remains stable. Image preprocessing and facial landmark localization are then conducted. From the stable facial landmark sequence, dynamic features strongly correlated with fatigue, including eye, mouth, and head features, are calculated and extracted. These extracted features are then input into a lightweight classifier trained on a large amount of labeled fatigue and wakefulness video data. The classifier outputs a comprehensive facial fatigue index or directly indicates a state of mild, moderate, or severe fatigue.

[0038] Process the audio stream from the vehicle's microphone to identify characteristics of voice fatigue; The system infers a driver's mental state by analyzing changes in their voice during voice interaction or natural conversation. An in-vehicle microphone array collects in-vehicle audio. Using sound source localization and beamforming techniques, the system obtains the speech signal in the driver's direction. Speech segments are accurately segmented from the continuous audio stream. Short-time Fourier transform and other analyses are performed on the clean speech segments to extract features reflecting changes in the control of the vocal organs and neural excitability, including fundamental frequency correlation features, energy and formant features, speech rate and pause features, and articulation features. The extracted acoustic feature vectors are input into a pre-trained speech state recognition model, which outputs a speech fatigue index or a corresponding state classification.

[0039] Real-time vehicle status data can be obtained through the vehicle's ECU (Electronic Control Unit) or in-vehicle navigation software, including vehicle speed, driving time, cumulative mileage, road type, current gear, and whether the vehicle is climbing or driving at high speed.

[0040] Step S2: Input the data into a preset decision model, which includes a series of triggering conditions and logical rules corresponding to health risks; by running the decision model, determine whether there is a health risk scenario that needs to be responded to, and output the corresponding scenario identifier and intervention measures; In this embodiment, the decision model can be a rule base, an integrated lightweight machine learning model, or a hybrid of both. The core of the rule base is to transform the experience of domain experts and scientific research conclusions into a structured (IF-THEN) set of rules. The core of the data-driven lightweight machine learning model is to train a statistical model using historical data, enabling it to learn the mapping relationship between complex, high-dimensional input features and output scenarios.

[0041] When the decision-making model is based on a rule base, for example, it might immediately trigger and release a purifying fragrance once the concentration of chemical pollutants exceeds a threshold. When the decision-making model is based on a machine learning model, for example, it might determine whether a driver's fatigue level is mild or moderate. Such nuanced judgments are handled by machine learning models, allowing for a better user experience.

[0042] Whether based on rule bases, lightweight machine learning models, or a hybrid of both, the core principle they share is to systematically transform multi-source real-time sensing data into quantitative assessments and optimal intervention strategies for specific health risk scenarios through formalized and computable methods. This differs from the simple mechanisms in existing technologies that rely solely on single sensor threshold triggers.

[0043] The model input is the multi-dimensional feature vector fused in step S1, and the output is the risk scenario classification and the corresponding intervention strategy.

[0044] Step S3: Generate control instructions based on the output scene identifier and intervention measures to drive the fragrance generator to release the corresponding fragrance; the control instructions specify at least one target fragrance type, release concentration and release mode.

[0045] In this embodiment, the fragrance generating device is a sophisticated mechatronic module that includes multiple independent and replaceable fragrance capsules or liquid storage units. Each capsule encapsulates a base fragrance, such as a purifying fragrance, an anti-allergic fragrance, an invigorating fragrance, and a soothing fragrance.

[0046] The central controller compiles the intervention measures output in step S2 into low-level instructions executable by the fragrance generator. Upon receiving the instructions, the fragrance generator's internal micro-metering pump precisely extracts the fragrance base liquid from different capsules in a specified ratio. After uniform mixing in the mixing chamber, the mixture is sent to the ultrasonic atomizing plate or the micro-heating evaporator. The fan speed is adjustable to control the diffusion rate of the fragrance within the vehicle.

[0047] The release modes include: Continuous diffusion: Suitable for scenarios where a certain concentration needs to be maintained, such as purifying fragrances.

[0048] Pulse impact: Suitable for scenarios that require quickly attracting attention and breaking the current state, such as energizing fragrances.

[0049] It should be noted that when the preset decision model is a lightweight machine learning model deployed on the vehicle domain controller, its parameters can be updated from the cloud server via OTA to optimize the accuracy of risk identification or add the ability to identify new health risk scenarios.

[0050] This invention provides a vehicle-mounted intelligent fragrance control method based on health management. By integrating in-vehicle environmental parameters, external environmental information, occupant status data, and vehicle travel information, it constructs a comprehensive health risk monitoring system. This system can proactively identify risk scenarios at the initial stage of health risks such as occupant fatigue or discomfort, or before the in-vehicle environment deteriorates. Compared to traditional passive fragrance adjustment, this method intervenes in health management earlier, improving the timeliness of health protection. Simultaneously, by pre-setting a decision-making model and health risk triggering logic, it accurately matches risk scenarios with fragrance intervention plans, solving the problems of simple decision-making logic and lack of targeted intervention in traditional methods, and achieving proactive adaptation to the health needs of different travel itineraries.

[0051] Specifically, in a preferred embodiment of this application, the decision model includes a first pollution protection rule, the execution logic of which is as follows: Acquire the concentration indicators of key chemical pollutants in the in-vehicle environmental parameters and the air quality index of the out-of-vehicle environmental information; If the concentration of the key chemical pollutant exceeds a first safety threshold preset based on the occupant's tolerance, and / or the air quality index exceeds a preset quality standard threshold, then it is determined to be a first pollution risk scenario, and an intervention measure to activate the first purification fragrance is output.

[0052] The rules set forth in this optimization implementation target volatile organic compounds (VOCs) released from in-vehicle interior materials, adhesives, etc., characterized by slowly changing but persistent concentrations, posing a chronic health threat. Specifically, the key chemical pollutant concentration indicators refer to formaldehyde and total VOC concentration data collected in real time by high-sensitivity electrochemical sensors or photoionization detectors deployed within the cabin. The air quality index (AQI) for the external environment refers to refined AQI data, including photochemical pollutant indicators such as ozone and nitrogen dioxide, obtained from the cloud via the vehicle-to-everything (V2X) network and matched to the vehicle's current and future routes.

[0053] In practice, two judgments are performed: Judgment A: Compare the real-time monitored formaldehyde or TVOC concentration with a preset first safety threshold. This threshold can be set differently based on occupant information or set uniformly.

[0054] Judgment B: If the photochemical pollution component index is abnormally high when analyzing external AQI data, and navigation information predicts that the vehicle will be exposed to the area, then it is considered a risk of external chemical pollution intrusion.

[0055] If either condition A or condition B is met, the system is identified as the first pollution risk scenario. Once triggered, the system will execute the first purification fragrance intervention package, which releases a fragrance based on the principle of activated carbon adsorption, supplemented by plant essential oils such as tea tree and eucalyptus that have natural odor-decomposing effects. The fragrance is released in a medium concentration and continuous diffusion mode to maintain the concentration of purification molecules in the cabin.

[0056] Based on the first pollution protection rule, it can respond immediately when the concentration of formaldehyde, TVOC and other substances just exceeds the safety line, avoiding occupants' long-term exposure to low doses of harmful chemicals and effectively reducing respiratory irritation and long-term health risks.

[0057] Specifically, in a preferred embodiment of this application, the decision model includes a second pollution prevention rule, the execution logic of which is as follows: Obtain the suspended particulate matter concentration index from the in-vehicle environmental parameters and the air quality index from the out-of-vehicle environmental information; If the concentration of suspended particulate matter exceeds a preset second safety threshold, and / or the air quality index exceeds a preset quality standard threshold, it is determined to be a second pollution risk scenario, and intervention measures to activate a second purification fragrance are output.

[0058] The setting rules of this preferred embodiment mainly address inhalable particulate matter such as PM2.5 and PM10, which have wide sources including vehicle exhaust, dust, and industrial emissions. They can quickly enter the vehicle interior with airflow, directly impacting the respiratory and cardiovascular systems. The suspended particulate matter concentration index refers to data monitored in real time by a laser scattering PM2.5 / PM10 sensor inside the vehicle. This sensor is typically installed at the air conditioning vents or in the center of the cabin to reflect the particulate matter level in the occupant's breathing zone.

[0059] In practice, two judgments are performed: Judgment C: Whether the PM2.5 concentration inside the vehicle exceeds the second safety threshold based on the World Health Organization or national standards; Judgment D: Analyze the external PM2.5 index, and pay special attention to situations where the vehicle navigation indicates that it is about to enter an industrial area, a traffic congestion area, or an area affected by sandstorms. Even if the current concentration inside the vehicle does not exceed the standard, it is judged as high risk based on the prediction model.

[0060] If either criterion C or D is met, the scenario is classified as the second pollution risk scenario. Upon triggering, the second purification-type fragrance intervention package is activated. This releases a fragrance containing ingredients simulated by a negative ion generator and fresh woody essential oils such as pine needles and cedar. Negative ions help to cause suspended particles in the air to settle, while the woody notes evoke a sense of cleanliness and openness. Simultaneously, in the event of a sudden pollution event or when entering a high-risk area, a higher concentration and pulsed release are used to quickly establish a purified atmosphere; under continuous pollution conditions, the release transitions to a stable and continuous pattern.

[0061] It reacts rapidly to sudden increases in particulate matter pollution, and its fragrance can reduce the concentration of particulate matter in the breathing zone, protecting respiratory health. Through predictive capabilities based on geographical location and external data, it enables occupants to initiate protective measures before they actually experience pollution, achieving a shift from passive response to proactive health management.

[0062] The first and second rules together constitute protection against both chemical and physical particulate pollution. Each data source focuses on in-vehicle emissions, working together to ensure timely and effective proactive management of in-vehicle air quality regardless of the pollution environment, thus enhancing the health and comfort of drivers and passengers.

[0063] Specifically, in a preferred embodiment of this application, the decision model includes external pollution pre-control rules, the execution logic of which is as follows: Obtain regional pollution data from the vehicle's external environment information and path planning from the vehicle's travel information; If the vehicle's planned route will pass through the pollution-affected area indicated by the air quality index in the future, it will be identified as an external pollution exposure risk scenario, and an intervention measure to activate the third type of purification fragrance will be output.

[0064] In this preferred embodiment, rules are set to achieve proactive protection, initiating defensive measures before the vehicle actually enters the polluted area. The pollution-affected areas are identified by the air quality index. Combined with geographical information and meteorological conditions, the pollutant concentration distribution in each area is obtained. A dynamic travel plan with estimated arrival timestamps for each waypoint is obtained from the navigation system. The system overlays the dynamic path with spatial pollution data for calculation. The system calculates the geographical location the vehicle will reach at a future point in time based on its current travel plan. It then queries pollution dispersion to obtain the predicted pollutant concentration at that location and time. If the vehicle's estimated exposure time in that area exceeds the effective exposure time, it is classified as an external pollution exposure risk scenario. Upon triggering, the system will execute a third-level purification fragrance intervention package. This releases a complex fragrance containing antioxidants and a gentle herbal scent with mucosal protective properties, creating a mild, protective atmosphere in the occupant's breathing zone.

[0065] This embodiment achieves true proactive health protection by advancing the response from after pollution occurs to before pollution exposure, thereby reducing the inhaled dose of pollutants for occupants.

[0066] Specifically, in a preferred embodiment of this application, the decision model includes a fatigue driving prediction rule, the execution logic of which is as follows: Obtain the physiological fatigue index from the occupant status data and the road complexity index from the vehicle travel information; If the physiological fatigue index exceeds the individual baseline and the road complexity index is below the minimum threshold required to maintain attention, it is determined to be a fatigue driving risk scenario, and intervention measures such as activating energizing fragrances are output.

[0067] The goal of setting the rules in this preferred embodiment is to identify early signs of accumulated fatigue and to provide proactive and flexible intervention.

[0068] The system uses DMS cameras to calculate in real time the percentage of eyelid closure time, blinking frequency, and the duration and frequency of gaze deviation from the center of the road. It establishes a personalized fatigue baseline for each driver. The current real-time calculated physiological fatigue index score is compared to this dynamic baseline. Simultaneously, the current road complexity index is calculated. If this index is below a preset attention maintenance threshold, it indicates that the road environment itself is insufficiently stimulating for the driver, easily inducing fatigue. Only when the physiological fatigue index exceeds the personal baseline and the road complexity index is below the threshold is it identified as a fatigue driving risk scenario. This avoids accidental triggering due to brief blinks in congested urban areas. After triggering, a refreshing aromatherapy intervention package is executed, the core of which is gentle stimulation and awakening perception. By releasing a fresh, cool fragrance rich in limonene and menthol, such as lemon, grapefruit, mint, and basil, it directly stimulates the olfactory nerve, transmitting signals to the brain's alertness center.

[0069] Unlike piercing alarm sounds, the multi-sensory awakening method centered on fragrance is gentler and more acceptable, and can effectively improve the driver's alertness level without increasing the driver's stress response.

[0070] Specifically, in a preferred embodiment of this application, an in-vehicle intelligent fragrance control method based on health management further includes: Monitor the trend of changes in the occupant status data within a preset time period after the intervention, and evaluate the intervention effect score based on the trend of changes; When the intervention effect score is lower than the effective threshold, the trigger threshold of the corresponding risk scenario in the decision model or the fragrance concentration parameter in the intervention measures is adjusted.

[0071] After each fragrance intervention is initiated, the system automatically opens a dynamic monitoring window of a preset duration. The duration of this window is intelligently set according to the type of risk scenario. For example, in a scenario involving fatigued driving, the monitoring window may be a period of time after the intervention to observe the immediate response of the energizing effect.

[0072] Within the monitoring window, the system reactivates and focuses on a subset of occupant status data strongly correlated with the intervention objective. The effectiveness is quantified by calculating the rate of change, stability, and trend of key indicators before and after the intervention. If the system determines that the effectiveness score of the intervention is below a preset effective threshold, it considers the current intervention strategy to have not achieved optimal results for the current occupant in this specific situation, and enters the parameter optimization process. Adjustments follow these principles: The system addresses the triggering conditions for the decision-making model. For example, if a refreshing aromatherapy product is ineffective for a user, the system will appropriately lower their personal fatigue baseline, allowing intervention to be triggered at earlier signs of fatigue in the future, in order to observe the effectiveness of early intervention.

[0073] Regarding the intervention measures themselves, for example, if the current concentration of soothing fragrance fails to effectively alleviate a user's anxiety symptoms, the system will slightly increase the fragrance release concentration or change the release mode from continuous to intermittent pulses in the next similar scenario to test the effects of different stimulation methods.

[0074] Through feedback adjustments, the system can continuously learn and adapt to each passenger's unique physiological responses and odor preferences, ultimately providing a customized optimal health intervention plan. As usage time increases, the system becomes increasingly adept at understanding users, resulting in more precise and personalized interventions. This effectively prevents users from abandoning the product due to unsatisfactory initial results, greatly enhancing the product's long-term competitiveness and user dependence.

[0075] It should be noted that, based on the rules in the aforementioned embodiments, when a scenario is determined to be a chemical pollution risk, particulate matter pollution risk, or external pollution exposure risk, an instruction to control the vehicle's air conditioning system to switch to internal circulation mode is generated simultaneously.

[0076] The linkage control logic is a key collaborative mechanism in the pollution protection system, achieved through the joint action of the environmental control system and the fragrance system. When the model outputs a scenario identifier for chemical pollution risk, particulate matter pollution risk, or external pollution exposure risk, the central controller issues this collaborative instruction and the fragrance control instruction within the same execution cycle and with the same priority. The instruction is sent to the vehicle domain controller or a separate air conditioning controller via the vehicle controller's local area network bus. Upon receiving the instruction, the air conditioning system executes the following sequence of actions, driving the damper actuator to switch the air conditioning intake mode from external circulation or automatically to internal circulation.

[0077] By physically blocking the continuous input of polluted external air through internal circulation, the purification efficiency is greatly improved. In the closed-loop environment, the purifying ingredients released by the fragrance system can fully circulate and function inside the vehicle, avoiding dilution by the constantly incoming new polluted air and extending the effective working time.

[0078] This invention also provides a vehicle that includes the aforementioned in-vehicle intelligent fragrance control system based on health management.

[0079] The technical effects of this embodiment are the same as those of the vehicle-mounted intelligent fragrance control system based on health management in Embodiment 1, and will not be repeated here.

[0080] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0081] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0082] Furthermore, the functional units in the various embodiments of the present invention 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.

[0083] 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 the present invention, 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 the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the specification of the present invention.

Claims

1. A health management-based in-vehicle intelligent fragrance control method, characterized by, The method comprises the following steps: obtaining in-vehicle environmental parameters, external environmental information, passenger state data, and vehicle travel information; inputting the obtained data into a preset decision model, the decision model comprising a series of trigger conditions and logical rules corresponding to health risks; determining whether a health risk scenario needs to be responded to by running the decision model, and outputting corresponding scenario identification and intervention measures; generating control instructions according to the output scenario identification and intervention measures to drive the fragrance generating device to release corresponding fragrances; the control instructions at least specifying a target fragrance type, a release concentration, and a release mode.

2. The vehicle-mounted intelligent fragrance control method based on health management according to claim 1, characterized in that, The in-vehicle environmental parameters include chemical pollutant concentration indicators, suspended particulate matter concentration indicators, and temperature and humidity environmental indicators for evaluating air quality; the external environmental information includes an air quality index and regional pollution data.

3. The vehicle-mounted intelligent fragrance control method based on health management according to claim 2, characterized in that, The current physiological state identification of the passenger is obtained by at least one of the following methods: analyzing the video stream of the driver-facing camera to identify facial fatigue features; processing the audio stream of the vehicle-mounted microphone to identify voice burnout features; or reading the biological sensor data integrated in the steering wheel or seat to obtain heart rate variability indicators.

4. The vehicle-mounted intelligent fragrance control method based on health management according to claim 2, characterized in that, The decision model comprises a first pollution prevention rule, the execution logic of which is as follows: obtaining the key chemical pollutant concentration indicator in the in-vehicle environmental parameters and the air quality index in the external environmental information; if the key chemical pollutant concentration indicator exceeds the first safety threshold value preset based on the passenger's tolerance, and / or the air quality index exceeds the preset quality standard threshold value, it is determined as a first pollution risk scenario, and an intervention measure of starting a first purification type fragrance is output.

5. The vehicle-mounted intelligent fragrance control method based on health management according to claim 2, characterized in that, The decision model comprises a second pollution prevention rule, the execution logic of which is as follows: obtaining the suspended particulate matter concentration indicator in the in-vehicle environmental parameters and the air quality index in the external environmental information; if the suspended particulate matter concentration indicator exceeds the second safety threshold value, and / or the air quality index exceeds the preset quality standard threshold value, it is determined as a second pollution risk scenario, and an intervention measure of starting a second purification type fragrance is output.

6. The vehicle-mounted intelligent fragrance control method based on health management according to claim 2, characterized in that, The decision model comprises an external pollution pre-control rule, the execution logic of which is as follows: obtaining the regional pollution data in the external environmental information and the path planning in the vehicle travel information; if the planned path of the vehicle will pass through the pollution affected area identified by the air quality index in the future period, it is determined as an external pollution exposure risk scenario, and an intervention measure of starting a third purification type fragrance is output.

7. The vehicle-mounted intelligent fragrance control method based on health management according to claim 3, characterized in that, The decision model comprises a fatigue driving pre-judgment rule, the execution logic of which is as follows: obtaining the physiological fatigue indicator in the passenger state data and the road complexity indicator in the vehicle travel information; if the physiological fatigue indicator exceeds the personal baseline, and the road complexity indicator is lower than the minimum threshold required to maintain attention, it is determined as a fatigue driving risk scenario, and an intervention measure of starting a refreshing type fragrance is output.

8. The vehicle-mounted intelligent fragrance control method based on health management according to claim 1, characterized in that, Further comprising: monitoring the change trend of the passenger state data in a preset period of time after the intervention, and evaluating the intervention effect score based on the change trend; When the intervention effect score is lower than the effective threshold, adjusting a triggering threshold of a corresponding risk scenario in the decision model or a fragrance concentration parameter in the intervention measure.

9. A health management based in-vehicle intelligent fragrance control system, characterized by, The application discloses a vehicle-mounted intelligent fragrance control method based on health management, and belongs to the field of vehicle-mounted intelligent fragrance control. A data acquisition unit is configured to acquire in-vehicle environmental parameters, external environmental information, passenger state data and vehicle trip information. A fragrance generating device includes a plurality of fragrance storage and release units for storing and releasing a plurality of different types of fragrances. A central controller is in communication connection with the data acquisition unit and the fragrance generating device, respectively, and is configured to receive in-vehicle environmental parameters, external environmental information, passenger state data and vehicle trip information, input the received data into a preset decision model, determine whether a health risk scenario needs to be responded to by running the decision model, output a corresponding scenario identifier and intervention measure, and generate a control instruction according to the output scenario identifier and intervention measure to drive the fragrance generating device to release a corresponding fragrance.

10. A vehicle characterized by comprising: The application discloses a vehicle-mounted intelligent fragrance control system based on health management. The application discloses a vehicle-mounted intelligent fragrance control method based on health management, and belongs to the field of vehicle-mounted intelligent fragrance control.

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