Intelligent cabin environment adjusting method and system based on solar term perception and vehicle

Abstract

The invention provides an intelligent cabin environment adjusting method and system based on solar term perception and a vehicle, and relates to the technical field of intelligent cabins, and the method can dynamically adjust the decision weight according to the matching degree of solar terms and actual weather, generates a target contextual model which fits the natural rhythm and gives consideration to the actual conditions, and improves the user experience. On the basis, different areas in the cabin are subjected to partition cooperative adjustment of multiple elements such as temperature, humidity, wind, light and fragrance, and the problems that a traditional system is single in adjustment and lacks context awareness and individuation are effectively solved; meanwhile, by continuously recording and analyzing user feedback behaviors, the system can adaptively optimize a weight allocation strategy and contextual model parameters, and continuously improve the accuracy of environment adjustment and the user satisfaction, thereby effectively enhancing the comfort, culture affinity and intelligent level of the intelligent cockpit.

Description

Technical Field

[0001] This invention relates to the field of intelligent cockpit technology, and in particular to an intelligent cockpit environment regulation method, system and vehicle based on seasonal sensing. Background Technology

[0002] With the rapid development of intelligent cockpit technology in automobiles, users' demands for in-vehicle comfort and personalized experiences are constantly increasing. However, existing in-vehicle air conditioning systems generally suffer from limited functionality and rudimentary adjustment: most systems can only perform simple closed-loop temperature control, failing to comprehensively sense and coordinate the adjustment of multi-dimensional environmental parameters such as humidity, air quality, fragrance concentration, and light intensity; they also lack zonal control capabilities, making it difficult to cope with temperature differences and passenger distribution variations in different areas of the cabin; more importantly, existing technologies generally ignore the natural climate patterns and humanistic experiences inherent in the traditional Chinese twenty-four solar terms, failing to provide gentle, intelligent, and contextualized environmental adjustments that conform to the characteristics of each solar term during seasonal changes, resulting in a fragmented user experience and a lack of cultural identity and emotional resonance. Summary of the Invention

[0003] This invention aims to solve the technical problems existing in the above-mentioned related technologies, and proposes an intelligent cockpit environment adjustment method, system and vehicle based on solar term perception. It can integrate solar term information and real-time weather data, dynamically generate and execute multi-dimensional cockpit environment adjustment schemes that conform to natural rhythms and user preferences, and realize the collaborative optimization and adaptive evolution of multi-dimensional environmental parameters.

[0004] The solution to the technical problem of this invention is as follows: This invention provides an intelligent cockpit environment adjustment method based on solar season perception, comprising the following steps: It can obtain the solar term information corresponding to the current date, real-time weather information outside the cabin, and multi-dimensional environmental parameters inside the cabin. The solar term information includes the temperature and humidity ranges associated with each solar term; the multi-dimensional environmental parameters include cabin temperature, humidity, air quality, light intensity, and fragrance concentration. Based on the matching relationship between the solar term information and the real-time weather information, the weights of the two in environmental regulation decisions are dynamically allocated, and a target scenario model is generated by fusing the weights. Based on the target scenario mode and multi-dimensional environmental parameters, the environment of different areas in the cockpit is adjusted in a zoned and coordinated manner. Record user feedback on the adjustment results, and adaptively optimize the weight allocation strategy and the target scenario mode based on the feedback.

[0005] Furthermore, the step of dynamically allocating the weights of the solar term information and the real-time weather information in environmental regulation decisions based on their matching relationship, and generating a target scenario model based on the weighted fusion, includes the following steps: The ambient temperature and humidity in the real-time weather information are compared with the temperature and humidity range associated with the current solar term; If the ambient temperature and humidity in the real-time weather information both fall within the range corresponding to the current solar term, then the solar term and weather are determined to match, the weight of the solar term information in the fusion calculation is increased, and the weight of the real-time weather information is reduced accordingly. If either the ambient temperature or humidity in the real-time weather information exceeds the range corresponding to the current solar term and the deviation exceeds a preset threshold, it is determined that the solar term and the weather do not match, the weight ratio of the solar term information in the fusion calculation is reduced, and the weight ratio of the real-time weather information is increased accordingly. Based on the adjusted weights, the scenario parameter templates corresponding to the solar term information and the environmental parameters recommended based on real-time weather information are fused to generate a target scenario model for guiding cabin environment adjustment.

[0006] Furthermore, the recommendation of environmental parameters based on real-time weather information is achieved in the following way: Based on the real-time ambient temperature and humidity outside the cabin, and combined with the preset human thermal comfort model, the appropriate target cabin temperature and humidity are determined. Determine whether the current situation is one of strong sunlight or cloudy based on the intensity of external light, and recommend corresponding cabin ambient lighting color temperature and sunshade strategies accordingly; In hot and humid weather, it is recommended to prioritize enhancing the cooling and dehumidification capabilities of the air conditioner and activate the air purification function in conjunction with it. In cold and dry weather, it is recommended to increase the cabin temperature, turn on the humidification function, and select a warm fragrance type; The above recommendations are used as weather-driven environmental parameter suggestions for weighted fusion with seasonal scenario parameters.

[0007] Furthermore, the step of performing zoned and coordinated adjustment of the environment in different areas of the cockpit based on the target scenario mode and multi-dimensional environmental parameters specifically includes: The cabin is divided into multiple adjustment zones based on the distribution of passengers within the cabin; For each adjustment zone, the airflow direction, air volume and air temperature of the air conditioning vents, the start / stop and concentration of the fragrance release unit, and the brightness and color temperature of the ambient light are controlled separately. During the adjustment process, the multi-dimensional environmental parameters of each region are compared with the corresponding parameters in the target scenario mode in real time, and the execution strategy is dynamically adjusted so that the environmental state of each region approaches the comfort range defined by the target scenario mode.

[0008] Furthermore, the partitioned coordinated adjustment also includes: When the actual temperature of a certain adjustment zone deviates from the temperature set in the target scenario mode by more than a threshold, the air volume and direction of the corresponding air outlet in that zone are adjusted first, and the airflow is prevented from blowing directly towards the user. When the fragrance concentration is below the target value and there is no user command to turn it off, the local fragrance release will be automatically activated; Under strong sunlight, the color temperature of the ambient lighting in the corresponding area is dimmed simultaneously and the sunshade curtain is controlled in conjunction with the lighting.

[0009] Furthermore, the step of recording user feedback behavior on the adjustment results and adaptively optimizing the weight allocation strategy and the target scenario mode based on the feedback behavior includes the following steps: The system collects manual adjustments made by users to cabin environment parameters based on a combination of current solar term information and real-time weather information. These manual adjustments include modifications to temperature, humidity, wind speed, fragrance type, or light color temperature. The manual adjustment operation is associated with and stored in conjunction with the corresponding solar term information, real-time weather information, and the current target scenario mode; Statistics show the direction and frequency of user adjustments to the same environmental parameter under identical or similar environmental conditions; When the adjustment behavior of a certain parameter shows a stable trend within a preset time period, the initial weight ratio of solar term information and weather information under the combination of environmental conditions is automatically updated, and the default value of the corresponding parameter in the target scenario mode is corrected. The stable trend refers to the fact that multiple consecutive feedbacks all point to the same adjustment direction, and the fluctuation of the adjustment amplitude is less than the preset tolerance range. The preset time period and tolerance range can be dynamically adjusted according to the historical data of user feedback.

[0010] Furthermore, the target scenario mode supports user customization and can be added or updated with scenario modes corresponding to the solar term information through online upgrades.

[0011] On the other hand, this application provides an intelligent cockpit environment regulation system based on seasonal weather perception, comprising: The environmental information acquisition module is used to acquire the solar term information corresponding to the current date, real-time weather information outside the cockpit, and multi-dimensional environmental parameters inside the cockpit. The solar term information includes the temperature and humidity ranges associated with each solar term; the multi-dimensional environmental parameters include cabin temperature, humidity, air quality, light intensity, and fragrance concentration. The scenario decision module is used to dynamically allocate the weights of the solar term information and the real-time weather information in environmental regulation decisions based on the matching relationship between the two, and to generate a target scenario mode based on the fusion of the weights. The partition execution control module is used to coordinate and adjust the environment of different areas in the cockpit according to the target scenario mode and multi-dimensional environmental parameters. An adaptive learning module is used to record user feedback behavior on the adjustment results, and adaptively optimize the weight allocation strategy and the target scenario mode based on the feedback behavior.

[0012] Furthermore, the environmental information acquisition module includes an on-board positioning module, external cockpit sensors, and internal cockpit sensors; The real-time weather information outside the cockpit is obtained through the meteorological service interface associated with the vehicle positioning module, or directly collected through the external sensors of the cockpit. The cockpit interior sensors are distributed regionally, including: The first temperature, humidity and air quality sensor group is located near the head of the front passengers; The second temperature, humidity and fragrance concentration sensor is installed in the rear seat area; A light intensity sensor installed on the ceiling or dashboard; In addition, physiological auxiliary sensing units integrated into the seat or armrest are used to detect the passenger's heart rate, respiratory rate or skin temperature to help determine the user's comfort level.

[0013] On the other hand, this application provides a vehicle including a smart cockpit, the smart cockpit integrating the aforementioned smart cockpit environment regulation system based on throttle perception, and configured to execute the aforementioned smart cockpit environment regulation method based on throttle perception.

[0014] The beneficial effects of this invention are as follows: This application provides a method for intelligent cockpit environment adjustment based on solar term perception. By integrating traditional Chinese solar term culture with real-time external weather data and combining multi-dimensional environmental parameters inside the cockpit, it achieves more natural and humanized intelligent control of the cockpit environment. This method can dynamically adjust decision weights according to the degree of matching between solar terms and actual weather, generating target scenario modes that conform to natural rhythms while taking into account realistic conditions. On this basis, it can perform zoned and coordinated adjustment of multiple elements such as temperature, humidity, wind, light, and fragrance in different areas of the cockpit, effectively solving the problems of traditional systems having single adjustment, lack of context perception, and personalization. At the same time, by continuously recording and analyzing user feedback behavior, the system can adaptively optimize the weight allocation strategy and scenario mode parameters, continuously improving the accuracy of environmental adjustment and user satisfaction, thereby significantly enhancing the comfort, cultural affinity, and intelligence level of the intelligent cockpit. This application also provides a corresponding system and vehicle. The beneficial effects of the system and vehicle are the same as those of the above method, and will not be elaborated here.

[0015] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0016] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0017] Figure 1 This is a flowchart of the intelligent cockpit environment adjustment method based on seasonal perception provided in this application; Figure 2 This is a structural diagram of the intelligent cockpit environment regulation system based on seasonal sensing provided in this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] The present application will be further described below with reference to the accompanying drawings and specific embodiments. The described embodiments should not be considered as limitations on the present application, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present application.

[0020] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] With the rapid development of intelligent vehicle technology, users' demands for the cabin environment have gradually evolved from basic functional guarantees (such as cooling / heating) to a comprehensive pursuit of comfort, personalization, emotional connection, and cultural identity. The modern intelligent cabin is no longer just a driving space for a vehicle, but is regarded as a "third living space," and its environmental quality directly affects the physiological comfort, psychological pleasure, and even health of drivers and passengers.

[0023] Against this backdrop, cabin environment control systems urgently need to break through the single temperature control logic of traditional air conditioning systems and evolve towards multi-dimensional perception, multi-modal collaboration, contextualized response, and adaptive evolution. Especially in the Chinese market, the Twenty-Four Solar Terms, as an important part of traditional culture, profoundly influence people's daily lives, clothing, food, and emotional rhythms. Integrating the natural climate patterns and humanistic connotations of the solar terms into the intelligent cabin control logic not only aligns with the cultural understanding of local users but also provides an innovative path for differentiated user experiences.

[0024] Current in-vehicle environmental control technologies mainly include traditional fixed-frequency or variable-frequency air conditioning systems, basic zone temperature control, passive air purification functions with integrated air quality monitoring, and independently configured ambient lighting and fragrance devices. Although some high-end models can obtain external weather information through the Internet for preliminary environmental adaptation, the overall system still focuses on temperature control. The various subsystems (such as air conditioning, lighting, and fragrance) generally operate decoupled and lack unified coordination. The adjustment logic mostly relies on manual user settings or a simple "set-feedback" closed loop, and has not yet formed a comprehensive control capability based on multi-dimensional environmental parameter fusion perception and intelligent decision-making.

[0025] Existing technologies generally suffer from a lack of multi-parameter coordination and a single adjustment dimension, failing to achieve coordinated optimization of elements such as temperature, humidity, wind, light, and aroma. Furthermore, the systems lack the ability to perceive natural rhythms (such as the 24 solar terms) and user contexts, resulting in rigid adjustment strategies that fail to meet users' physiological and psychological expectations. Zoning control is often based on fixed area divisions, unable to dynamically adjust according to actual passenger distribution, easily causing direct drafts or localized discomfort. In addition, the systems generally lack user behavior learning and personalized evolution capabilities, leading to repetitive experiences and cumbersome operations. More importantly, existing solutions neglect the integration of traditional Chinese cultural elements, resulting in homogenized user experiences and weak emotional connections. Moreover, most systems are closed and rigid, making continuous upgrades and optimizations via OTA (Over-The-Air) updates difficult, limiting long-term usability and intelligent potential.

[0026] To address the shortcomings of existing technologies, this application proposes an intelligent cockpit environment adjustment method, system, and vehicle based on solar term perception. Its main technical features are: by integrating the natural climate patterns inherent in the twenty-four solar terms with real-time weather data outside the cockpit, and combining internal multi-dimensional environmental parameters (including temperature, humidity, air quality, light intensity, and fragrance concentration), the system dynamically evaluates the matching degree between the solar terms and the weather and adaptively allocates decision weights to generate a target scenario mode that combines cultural connotations and practical adaptability. Based on this, the system performs refined zoning of the cockpit according to passenger distribution, and coordinates the wind direction, air volume, temperature, humidity, fragrance release, and ambient lighting in each area to provide users with a comfortable experience. Simultaneously, by continuously recording user feedback behavior, an adaptive learning mechanism is constructed to continuously optimize the weight strategy and scenario parameters. The default mode can evolve with user habits and supports OTA online upgrades to add solar term scenarios, thereby realizing a leap in cockpit environment adjustment from function-driven to emotional, intelligent, cultural, and personalized.

[0027] First, the intelligent cockpit environment adjustment method based on seasonal perception provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0028] Reference Figure 1 The implementation process of the intelligent cockpit environment adjustment method based on seasonal sensing provided in this application includes, but is not limited to, the following steps.

[0029] Step S110: Obtain the solar term information corresponding to the current date, real-time weather information outside the cabin, and multi-dimensional environmental parameters inside the cabin.

[0030] The solar term information includes the temperature and humidity ranges associated with each solar term. Multi-dimensional environmental parameters include cabin temperature, humidity, air quality, light intensity, and fragrance concentration.

[0031] In step S110, comprehensive and accurate basic data input is provided for subsequent environmental adjustment decisions. Specifically, the solar term information is mapped to the traditional Chinese 24 solar terms system through the current date, and specifically reflected in the typical temperature and humidity ranges associated with each solar term, characterizing the general features of the natural climate under that solar term. Real-time weather information outside the cabin reflects the current actual atmospheric environment, including key indicators such as external temperature and humidity. Multi-dimensional environmental parameters inside the cabin cover temperature, humidity, air quality (such as PM2.5 and CO2), light intensity, and fragrance concentration, used to accurately depict the current comprehensive environmental conditions inside the vehicle. These three types of information together constitute the input basis of the system's perception layer, ensuring that the adjustment strategy respects natural rhythms while also conforming to real-world conditions and the actual situation inside the cabin.

[0032] Step S120: Based on the matching relationship between the solar term information and real-time weather information, dynamically allocate the weights of the two in environmental regulation decision-making, and generate a target scenario model based on weight fusion.

[0033] In step S120, the issue of potential discrepancies between the ideal state of a solar term and actual weather is addressed. This is achieved by assessing the degree of agreement between the temperature and humidity range defined by the solar term and real-time weather data, dynamically adjusting the relative importance of solar term information and real-time weather information in decision-making. When the two are highly matched, the system tends to adopt the cultural and contextualized adjustment logic embodied in the solar term; when the two deviate significantly, the weight of real-time weather is increased to ensure the practicality and comfort of environmental adjustments. Based on this, the scenario parameters corresponding to the two information sources are fused according to the calculated weights to generate a target scenario model that balances cultural connotations and practical adaptability, serving as a unified guiding principle for subsequent adjustments.

[0034] Step S130: Based on the target scenario mode and multi-dimensional environmental parameters, the environment of different areas in the cockpit is adjusted in a zoned and coordinated manner.

[0035] In step S130, the abstract target scenario mode is transformed into specific physical execution actions, and refined control is implemented to address the non-uniformity of the cabin space. The system combines the actual multi-dimensional environmental parameters of each area (such as local temperature, fragrance concentration, etc.) with the ideal parameters defined in the target scenario mode, and divides the cabin into multiple adjustment zones. It separately controls the airflow direction, air volume, and air temperature of the air conditioning vents, the start / stop and concentration of the fragrance release unit, and the brightness and color temperature of the ambient lights, etc., so as to achieve independent adjustment of different areas as needed, while maintaining the overall environmental style coordination and unity, thereby improving the individual comfort experience of each passenger.

[0036] Step S140: Record the user's feedback behavior on the adjustment results, and adaptively optimize the weight allocation strategy and target scenario mode based on the feedback behavior.

[0037] In step S140, the focus is on the system's continuous evolution capability. Feedback is collected by collecting user manual adjustments to environmental parameters (such as changing temperature settings or switching fragrance types) under specific solar terms and weather combinations. This feedback is then associated and stored with the current solar term information, weather data, and the applied target scenario mode. Based on historical feedback data, the system analyzes stable trends in user preferences and automatically corrects the initial weighting of solar term and weather information in decision-making. Simultaneously, it adjusts the default values ​​of relevant parameters in the target scenario mode, making the system's adjustment results increasingly closer to the user's personalized needs, achieving an adaptive optimization closed loop from "general adaptation" to "personalized fit."

[0038] In some embodiments of this application, step S120 involves dynamically allocating the weights of the two in environmental regulation decisions based on the matching relationship between the solar term information and real-time weather information, and generating a target scenario model based on weight fusion, including the following steps.

[0039] Step S210: Compare the ambient temperature and humidity in the real-time weather information with the temperature and humidity range associated with the current solar term.

[0040] In step S210, a quantitative comparison basis is established between the ideal climate characteristics of the solar term and the current actual external environment. By extracting specific environmental temperature and humidity values ​​from real-time weather information and comparing them item by item with the typical temperature and humidity ranges predefined in the knowledge base for the solar term corresponding to the current date, the system can objectively determine whether the actual weather conforms to the general climate pattern of that solar term. This comparison process is a prerequisite for subsequently determining whether the solar term and the weather match, and thus determining their relative importance in decision-making, ensuring that environmental regulation strategies respect the traditional cultural context while remaining grounded in physical reality.

[0041] Step S220: If the ambient temperature and humidity in the real-time weather information both fall within the range corresponding to the current solar term, then the solar term and weather are determined to match, the weight ratio of the solar term information in the fusion calculation is increased, and the weight ratio of the real-time weather information is reduced accordingly.

[0042] In step S220, under the premise of confirming a high degree of consistency between the external climate conditions and the characteristics of the solar term, the dominant role of the cultural connotations and contextualized adjustment logic carried by the solar term in decision-making is strengthened. When both temperature and humidity, the two key indicators, are within the reasonable range defined by the solar term, it indicates that the current natural environment is exhibiting the typical comfortable or expected state of that solar term. At this time, the system is more inclined to adopt the scenario parameter template associated with the solar term (such as "clear and bright weather is suitable for Qingming" and "cool and moist weather is suitable for Bailu") to create a cabin atmosphere with cultural resonance. By increasing the weight of solar term information, the system can output adjustment schemes with more emotional value and seasonal ritual, without overly relying on redundant real-time data corrections.

[0043] Step S230: If either the ambient temperature or humidity in the real-time weather information exceeds the range corresponding to the current solar term and the deviation exceeds the preset threshold, it is determined that the solar term and the weather do not match, the weight ratio of the solar term information in the fusion calculation is reduced, and the weight ratio of the real-time weather information is increased accordingly.

[0044] In step S230, the practicality and physiological comfort of environmental regulation take precedence over cultural expression. When real-time temperature or humidity is detected to deviate significantly from the normal range defined by the solar terms (e.g., abnormally high temperatures during the "Great Cold" solar term, or continuous low temperatures and rain during the "Lesser Heat" solar term), the system determines that the current weather is abnormal or extreme. Mechanically adhering to the solar term template in this situation could lead to cabin discomfort or even health risks. Therefore, by proactively reducing the decision-making weight of solar term information and giving real-time weather information greater influence, the system can quickly shift to an adjustment strategy oriented towards real-world environmental needs, ensuring that the user's basic comfort and safety are not interfered with by cultural logic.

[0045] Step S240: Based on the adjusted weights, the scenario parameter templates corresponding to the solar term information and the environmental parameters recommended based on real-time weather information are fused to generate a target scenario mode for guiding cabin environment adjustment.

[0046] Step S240 achieves the crucial transformation from multi-source information to unified execution instructions. After completing the dynamic weight allocation, the system weights and fuses or interpolates the standardized scenario parameter templates associated with the solar term information (including ideal settings for dimensions such as temperature, humidity, light, and aroma) with adaptive environmental parameters derived from real-time weather, according to the calculated weight ratios. This ultimately generates a "target scenario model" that comprehensively considers cultural context and real-world conditions. This model serves as the benchmark for subsequent regional adjustments, preserving the differentiated experiential characteristics brought by the solar terms while ensuring an effective response to actual climate, thus achieving a dynamic balance between cultural expression and functional practicality.

[0047] In some embodiments of this application, in step S240, recommending environmental parameters based on real-time weather information is achieved in the following ways.

[0048] Step S310: Based on the real-time ambient temperature and humidity outside the cabin, and in conjunction with a preset human thermal comfort model, determine the appropriate target cabin temperature and target humidity.

[0049] In step S310, external meteorological data is transformed into an internal environmental setting benchmark that meets human physiological comfort requirements. By introducing a validated human thermal comfort model (such as the PMV-PPD model or a simplified version), the system can comprehensively consider the impact of external temperature and humidity on occupant comfort and scientifically calculate the target cabin temperature and humidity that are most conducive to maintaining thermal balance under current conditions. This process avoids subjective biases caused by relying solely on experience or fixed threshold settings, ensuring that basic comfort based on ergonomic principles can be provided under different climatic environments, laying the core parameter foundation for subsequent multi-dimensional environmental regulation.

[0050] Step S320: Determine whether the current situation is under strong sunlight or cloudy based on the external light intensity, and recommend the corresponding cabin ambient lighting color temperature and sunshade strategy accordingly.

[0051] In step S320, the visual environment inside the cabin is optimized to enhance comfort and emotional experience by sensing external natural lighting conditions. When high external light intensity is detected (such as strong midday sunlight), the system recommends using a lower color temperature (warmer yellow) or dimming the ambient lighting, and automatically closes or dims the motorized sunshade to reduce glare and visual fatigue. In cloudy or low-light environments, a higher color temperature (cooler white) lighting may be recommended to refresh the mind, and the sunshade device will remain open to introduce natural light. This strategy not only improves visual comfort but also enhances the cabin environment's adaptability to changes in external light.

[0052] In step S330, under hot and humid weather conditions, it is recommended to prioritize enhancing the cooling and dehumidification capabilities of the air conditioner and activate the air purification function in conjunction with it.

[0053] Step S330 focuses on comprehensive environmental intervention to address extreme and uncomfortable weather conditions such as hot and humid conditions. High temperature and humidity not only cause stuffiness but also easily breed bacteria and mold and exacerbate VOC emissions inside the vehicle, affecting health. Therefore, in such weather conditions, the system proactively increases the power of the air conditioning compressor and the opening of the dehumidifying damper to accelerate the reduction of cabin temperature and humidity; simultaneously, it automatically triggers air purification modules (such as HEPA filters and negative ion generators) to suppress the rise in pollutant concentrations. This multi-system collaborative response mechanism effectively alleviates the multiple discomforts caused by hot and humid environments, reflecting a proactive protection logic centered on health and comfort.

[0054] Step S340: In cold and dry weather, it is recommended to increase the cabin temperature, activate the humidification function, and select a warm fragrance type.

[0055] In step S340, an integrated soothing and regulating solution is proposed to address issues such as dry, cracked skin, respiratory discomfort, and low mood caused by cold, dry climates. While increasing the output of warm air for rapid temperature rise, the system activates the in-vehicle humidifier (such as an ultrasonic or evaporative humidifier) ​​to maintain the cabin humidity within a comfortable range (e.g., 40%–60%). Simultaneously, it recommends releasing warm, woody, or sweet fragrances (such as sandalwood, amber, and vanilla) to create a warm and calming psychological atmosphere through olfactory stimulation. This combined strategy alleviates the negative feelings caused by winter dryness from both physiological and psychological perspectives, enhancing the user's experience of feeling "cared for" in the cabin.

[0056] Step S350: The above recommended results are used as weather-dominated environmental parameter suggestions for weighted fusion with the solar term scenario parameters.

[0057] In step S350, the crucial integration from specific weather response strategies to unified decision input is completed. The parameter suggestions for temperature, humidity, lighting, air purification, and fragrance generated in the preceding steps are aggregated to construct a complete "weather-driven environmental parameter set," representing the optimal functional adjustment scheme under current external meteorological conditions. This parameter set is then used as one input for weighted fusion calculations, and is weighted and synthesized with the contextualized parameter template defined by the solar term information (the other input) according to the dynamic weights determined in steps S220 or S230, ultimately forming a target scenario model that balances practical adaptability and cultural context. This step ensures the integrity and operability of weather-related adjustment logic and provides a structured data foundation for multi-source information fusion.

[0058] In some embodiments of this application, the human thermal comfort model includes an intelligent model constructed based on the coupling relationship between physiological thermoregulation mechanisms and environmental parameters, such as the PMV (Predicted Mean Vote)-PPD (Predicted Percentage of Dissatisfied) model, the Adaptive Thermal Comfort Model, or a data-driven comfort prediction model trained using machine learning algorithms (such as support vector machines, neural networks, etc.). These models can comprehensively consider factors such as external and internal cabin temperature, humidity, wind speed, radiant temperature, as well as occupant clothing and activity levels, dynamically assess human thermal sensation, and output optimal cabin target temperature and humidity setpoints, thereby providing a scientific, personalized, and human-perceptual-compliant decision-making basis for environmental regulation.

[0059] In some embodiments of this application, step S130 involves zoning and coordinating the environment of different areas within the cockpit based on the target scenario mode and multi-dimensional environmental parameters, specifically including the following steps.

[0060] Step S410: Divide the cabin into multiple adjustment zones based on the distribution of passengers within the cabin.

[0061] In step S410, the foundation for refined and personalized environmental adjustment is established. The system uses seat occupancy sensors, cameras, or millimeter-wave radar to perceive the specific positions of passengers in the cabin in real time (e.g., driver, front passenger, left rear, right rear, etc.), and dynamically divides the cabin into several independent adjustment zones accordingly. This zone division method, guided by the actual occupant distribution, avoids the resource waste or adjustment blind spots caused by traditional fixed partitions (such as simply dividing into front / rear or left / right), ensuring that each occupied seat receives targeted environmental services and providing spatial logic support for subsequent differentiated control.

[0062] Step S420: For each adjustment zone, control the airflow direction, air volume and air temperature of the air conditioning vent, the start / stop and concentration of the fragrance release unit, and the brightness and color temperature of the ambient light.

[0063] In step S420, the independent and coordinated execution of multi-dimensional environmental elements in the spatial dimension is achieved. Based on the parameter requirements set for each area according to the target scenario mode, the system drives the corresponding actuators in each area: for example, adjusting the angle of the guide vanes of the dedicated air outlet to avoid direct airflow onto passengers' faces, adjusting local airflow and temperature to match individual comfort needs; activating fragrance diffusers near the area as needed and controlling the release concentration; and independently adjusting the brightness and color temperature of ambient lights in corresponding locations to create a unified yet personalized lighting environment. This "one zone, one policy" control strategy truly decouples and precisely targets the five dimensions of temperature, humidity, wind, light, and fragrance in the physical space, significantly enhancing the personalized experience for each passenger.

[0064] Step S430: During the adjustment process, the multi-dimensional environmental parameters of each region are compared with the corresponding parameters in the target scenario mode in real time, and the execution strategy is dynamically adjusted so that the environmental state of each region approaches the comfort range defined by the target scenario mode.

[0065] In step S430, a closed-loop feedback adjustment mechanism is constructed to ensure the continuous accuracy and adaptability of zone control. The system continuously collects multi-dimensional environmental parameters such as actual temperature, humidity, fragrance concentration, and light intensity in each adjustment zone through distributed sensors, and compares them in real time with the ideal parameters set for that zone in the target scenario mode. Once a deviation is found to exceed the allowable range, the working state of the corresponding execution unit is dynamically adjusted (such as increasing airflow, adding fragrance, adjusting brightness, etc.) until the measured values ​​stably fall into the target comfort range. This process not only ensures the stability and convergence of the adjustment effect, but also enables the system to have robust response capabilities to external disturbances (such as door opening, sunlight movement) or internal changes (such as passenger additions or subtractions), truly realizing intelligent cabin environment management of "on-demand adjustment and dynamic balance".

[0066] In some embodiments of this application, step S130, the partitioned collaborative adjustment further includes the following.

[0067] (1) When the actual temperature of a certain adjustment area is detected to deviate from the set temperature in the target scenario mode by more than a threshold, the air volume and direction of the corresponding air outlet in that area shall be adjusted first, and the airflow shall be prevented from blowing directly to the user.

[0068] This measure aims to balance passenger comfort and health safety while ensuring efficient temperature control. The system monitors the local thermal environment in real time using zone temperature sensors. If the actual temperature deviates significantly from the target value (such as being too hot or too cold), it immediately increases the response priority of the air conditioning actuator in that area, dynamically increasing the airflow to accelerate adjustment. At the same time, it intelligently adjusts the angle of the air outlet guide vanes, directing airflow along non-direct blowing paths such as the ceiling, footwell, or side walls. This effectively avoids the discomfort and even health risks caused by traditional air conditioning's "cold air blowing directly on the head" or "hot air blowing on the face," achieving a balance between "rapid temperature adjustment" and "imperceptible air delivery."

[0069] (2) When the fragrance concentration is lower than the target value and there is no user instruction to turn it off, the local fragrance release will be automatically started.

[0070] This strategy demonstrates the system's proactive ability to maintain the olfactory environment. Fragrance, as a crucial dimension in creating cabin ambiance, is susceptible to decreases in concentration due to factors such as ventilation and time decay. The system continuously monitors the actual level in each area using fragrance concentration sensors. If the current concentration is determined to be below the target scene mode requirements, and the user has not manually turned off the fragrance function, the system automatically triggers the corresponding fragrance release unit (such as a micro-atomizer or diffusion module) for precise fragrance replenishment. This mechanism ensures the continuity and consistency of the fragrance experience, preventing interruptions in the ambiance due to excessively low concentrations, while also respecting user wishes and preventing forced release when the user explicitly refuses.

[0071] (3) Under strong sunlight conditions, the color temperature of the ambient light in the corresponding area is dimmed simultaneously and the sunshade curtain is controlled in conjunction with the control.

[0072] This control logic focuses on the coordinated optimization of the lighting environment to address the interference of strong external light on visual comfort. When the light intensity sensor detects that an area (such as the passenger side or rear window area) is under strong sunlight, the system will not only automatically dim the ambient lighting in that area or adjust the color temperature to a softer, warmer tone to reduce screen glare and eye strain, but will also simultaneously send a command to the electric sunshade actuator to lower the corresponding window's sunshade or darken the dimming glass. This coordinated control of the lighting and sunshade systems creates a multi-layered glare protection system, significantly improving driving comfort and safety in strong light environments.

[0073] In some embodiments of this application, step S140 involves recording the user's feedback behavior on the adjustment results and adaptively optimizing the weight allocation strategy and target scenario mode based on the feedback behavior, including the following steps.

[0074] Step S510: Collect the user's manual adjustments to cabin environment parameters based on the combination of current solar term information and real-time weather information. These manual adjustments include modifications to temperature, humidity, wind speed, fragrance type, or light color temperature.

[0075] In step S510, firsthand evidence of the user's actual comfort needs is obtained by real-time monitoring and recording the user's proactive intervention behaviors in the cabin environment under specific external environmental backgrounds (i.e., the combination of the current solar term and real-time weather). The system identifies and captures the user's specific operations on key environmental dimensions (such as increasing the temperature, switching to woody fragrance, and decreasing the brightness of ambient lights). These manual adjustments are considered "implicit feedback" to the current automatic adjustment results, reflecting the deviation between the target scenario mode and the user's individual expectations. This step ensures that subsequent optimizations have objective and quantifiable input, rather than relying on assumptions or general standards.

[0076] Step S520: The manual adjustment operation is associated with and stored with the corresponding solar term information, real-time weather information and the current target scenario mode.

[0077] In step S520, a structured user feedback knowledge base is established. The system not only records what adjustments the user made, but also strongly associates and binds this operation with its complete context—including the current solar term, external temperature and humidity, the system's target scenario mode at the time, and its specific parameter configuration—and stores this information in a local or cloud database. This multi-dimensional, tagged storage method allows subsequent data analysis to accurately trace "how users tended to modify their environment under what climatic and cultural contexts," providing a reliable data foundation for identifying personalized patterns and constructing conditional optimization strategies, and preventing feedback information from losing its guiding value when detached from the context.

[0078] Step S530: Statistically analyze the direction and frequency of user adjustments to the same environmental parameter under the same or similar environmental conditions.

[0079] In step S530, the repetitiveness and tendency of user behavior are mined from historical feedback. Based on the data stored in step S520, the system clusters multiple usage scenarios with similar solar term types and similar external weather ranges (such as "Minor Heat + high temperature and humidity"). For each environmental condition, the system statistically analyzes the direction (increase / decrease) and frequency of user adjustments to specific parameters (such as fragrance concentration). Through consistency analysis of frequency and direction, the system can determine whether a certain type of adjustment belongs to the user's stable preference, rather than an accidental operation. This statistical process is a key step in identifying "personalized patterns" and provides quantitative criteria for whether to trigger model updates.

[0080] Step S540: When the adjustment behavior of a certain parameter shows a stable trend within a preset time period, automatically update the initial weight ratio of the solar term information and weather information under the combination of environmental conditions, and correct the default value of the corresponding parameter in the target scenario mode.

[0081] Among them, a stable trend means that multiple consecutive feedbacks all point to the same adjustment direction, and the fluctuation of the adjustment amplitude is less than the preset tolerance range. The preset time period and tolerance range can be dynamically adjusted based on historical data from user feedback.

[0082] In step S540, the core closed-loop mechanism of system adaptive evolution is implemented. When statistical analysis confirms that users' adjustments to a certain parameter under specific environmental combinations are highly consistent (i.e., satisfying a stable trend of "continuous same direction + stable amplitude"), the system will determine that there is a systematic deviation in the existing target scenario mode or weight allocation strategy, and automatically perform two optimizations: First, adjust the initial weight ratio of solar term information and real-time weather information in the fusion calculation under this environmental condition (for example, if users always lower the temperature when it is "autumn begins but it is hot", then reduce the solar term weight and increase the weather weight); second, directly correct the default setting value of the parameter in the target scenario mode (such as increasing the default fragrance concentration from level 3 to level 4). In addition, the preset time period and tolerance range used to judge the "stable trend" can also be dynamically optimized based on long-term user behavior data, making the system learning mechanism more flexible and robust, and ultimately achieving a personalized intelligent experience that "understands you better the more you use it".

[0083] In some embodiments of this application, the target scenario mode can be customized by the user, and scenario modes corresponding to the solar term information can be added or updated through online upgrades.

[0084] Specifically, the target scenario mode supports user customization, empowering users with proactive control and personalized shaping of the cabin environment experience. The system allows users to manually adjust parameters such as temperature, humidity, fragrance type, and light color temperature based on their preferences, building upon the automatically recommended target scenario mode, and saving the adjusted configuration as a new personalized scenario mode. This mechanism not only respects individual differences (such as the elderly preferring warmth, children being sensitive to wind, and users preferring specific fragrances), but also enables the system to accommodate users' subjective aesthetic and emotional needs. This transforms the originally standardized seasonal scenarios into a unique experience that combines cultural commonality with personal characteristics, significantly enhancing user satisfaction and a sense of belonging.

[0085] Meanwhile, target scenario modes can be added or updated online to correspond to solar term information. This breaks the limitations of traditional fixed and uniterable in-vehicle functions, building a continuously evolving and content-rich intelligent cockpit ecosystem. Through vehicle OTA (Over-The-Air) technology, manufacturers or third-party service providers can regularly push optimized scenario templates (such as adding a "moisturizing and refreshing" mode for "Grain Rain"), adjustment strategies adapted to new vehicle hardware, or highly satisfactory modes extracted from user big data. This cloud-based collaborative mechanism gives the cockpit environment system long-term vitality, not only responding to dynamic needs such as seasonal changes and regional differences, but also incorporating innovative content such as festival themes and brand collaborations, continuously providing users with a sense of freshness and value, achieving a leap from "factory-defined" to "constantly fresh."

[0086] Secondly, refer to Figure 2 This application provides an intelligent cockpit environment regulation system based on seasonal weather perception, which includes the following modules.

[0087] The environmental information acquisition module is used to acquire the solar term information corresponding to the current date, real-time weather information outside the cabin, and multi-dimensional environmental parameters inside the cabin. The solar term information includes the temperature and humidity ranges associated with each solar term. The multi-dimensional environmental parameters include cabin temperature, humidity, air quality, light intensity, and fragrance concentration.

[0088] The environmental information acquisition module serves as the foundation for the system's perception layer, comprehensively collecting three key types of information that influence cabin environmental regulation: first, information on the 24 solar terms mapped from the current date, specifically the typical temperature and humidity ranges associated with each solar term, used to characterize natural climate features within the context of traditional culture; second, real-time weather information outside the cabin, reflecting the current atmospheric environmental conditions; and third, multi-dimensional environmental parameters inside the cabin, including temperature, humidity, air quality (such as PM2.5 and CO2), light intensity, and fragrance concentration, used to accurately depict the current microenvironment within the vehicle. This module provides a complete, real-time, and structured environmental context for subsequent decision-making through multi-source sensing and data interfaces.

[0089] The scenario-based decision-making module dynamically allocates the weights of solar term information and real-time weather information in environmental regulation decisions based on their matching relationship, and generates a target scenario model based on weighted fusion. The core function of this module is to achieve intelligent fusion decision-making between cultural logic and real-world conditions. It dynamically adjusts the relative weights of solar terms and weather in regulation strategies by analyzing the degree of matching between the ideal temperature and humidity range defined by the solar term information and external real-time weather data—strengthening the contextual expression of the solar term when they are consistent, and prioritizing real-world comfort when there is a significant deviation. Based on this, the module weightedly fuses the scenario parameter templates corresponding to the solar term with weather-driven environmental suggestions to generate a target scenario model that combines cultural connotations, seasonal rituals, and practical adaptability, serving as a unified guide for the entire system's regulatory behavior.

[0090] The zoned execution control module is used to coordinate and adjust the environment of different areas within the cabin according to the target scenario mode and multi-dimensional environmental parameters. This module is responsible for translating the abstract target scenario mode into precise execution actions in the physical space, and realizing personalized zoned control of the cabin environment. It divides multiple independent adjustment zones according to the actual distribution of passengers, and controls the airflow direction, air volume and temperature of the air conditioning vents, the start / stop and concentration of the fragrance release unit, and the brightness and color temperature of the ambient lights for each zone. At the same time, it continuously compares the measured environmental parameters of each zone with the target values ​​during the adjustment process, and dynamically corrects the output strategy to ensure that the five dimensions of temperature, humidity, airflow, light and fragrance are coordinated and optimized in space and approach the ideal state, thereby providing a personalized comfort experience for passengers in different positions.

[0091] The adaptive learning module records user feedback on adjustment results and adaptively optimizes weight allocation strategies and target scenario modes based on this feedback. This module endows the system with the ability to continuously evolve and personalize. It continuously records users' manual adjustments to environmental parameters (such as changing temperature or switching scents) under specific solar terms and weather combinations, and associates and stores these feedbacks with the environmental context and the scenario mode used. By statistically analyzing the direction and frequency of user adjustments under the same conditions, it identifies stable preference trends and automatically optimizes the initial weight allocation strategy for solar terms and weather information accordingly, while correcting the default values ​​of relevant parameters in the target scenario mode. This mechanism enables the system to continuously adapt to individual user habits over time, achieving an intelligent leap from "general recommendations" to "personalized fit."

[0092] In some embodiments of this application, the environmental information acquisition module includes an on-board positioning module, external cockpit sensors, and internal cockpit sensors.

[0093] Real-time weather information outside the cockpit is obtained through the meteorological service interface associated with the vehicle positioning module, or directly collected through external sensors.

[0094] The vehicle positioning module provides the system with precise geographical location and time information, supporting accurate identification of solar terms and acquisition of external weather data. By locating the vehicle's latitude and longitude and the current date in real time, the system can accurately map the corresponding 24 solar terms and call the meteorological service interface (such as a cloud-based weather API) matching the location to obtain high-precision real-time external weather information (such as temperature, humidity, and light intensity). This module ensures effective alignment between the cultural logic of solar terms and the reality of regional climate, which is a prerequisite for achieving "adapting to local conditions and adjusting according to the seasons."

[0095] External sensors are used to directly collect physical parameters of the vehicle's surrounding environment, serving as a supplementary or backup data source for the meteorological service interface. In the absence of network connectivity or cloud data latency, external sensors (such as external temperature and humidity probes and light sensors) can measure actual weather conditions outside the vehicle in real time, ensuring the system retains basic environmental awareness even offline. The collected data can be cross-validated with meteorological service information, improving the reliability and robustness of external environmental inputs.

[0096] The cockpit's internal sensors employ a regionally distributed layout strategy to comprehensively and meticulously perceive the multi-dimensional environmental conditions at key locations within the cockpit, providing high-resolution data support for zoned coordinated adjustments. Different types of sensors are optimized for specific areas and parameters, ensuring an accurate depiction of the actual microenvironment in which the occupants are located, avoiding adjustment blind spots or misjudgments caused by traditional single-point sampling.

[0097] The cockpit's internal sensors are distributed regionally, including: (1) A first temperature, humidity and air quality sensor group is installed near the head of the front passenger. This sensor group is arranged near the breathing zone height of the front passenger and focuses on monitoring the thermal and humidity environment and air cleanliness (such as PM2.5, CO2, VOC, etc.) in the most sensitive areas of the driver and front passenger. Since the head area is extremely sensitive to temperature fluctuations and changes in air quality, this layout can promptly capture environmental deterioration signals that may cause discomfort, providing higher priority comfort and health protection for the front passengers.

[0098] (2) A second temperature, humidity and fragrance concentration sensor is installed in the rear seat area. This sensor is specifically deployed in the rear space to independently monitor the temperature and humidity levels and fragrance diffusion concentration in the area where the rear passengers are located. Since the rear seats are far from the main air conditioning vents and the fragrance is easily attenuated, this sensor can ensure that the rear environment is not neglected, support differentiated temperature control and precise fragrance replenishment for the rear seats, and achieve a balanced experience between the front and rear seats.

[0099] (3) A light intensity sensor installed on the ceiling or dashboard. This sensor is used to detect the illuminance levels of natural light and ambient light in the cabin in real time, with particular attention to the risk of glare caused by direct sunlight or strong reflections. Its installation location (such as the center of the ceiling or above the dashboard) can effectively capture the intensity of incident light from the windshield or side windows, providing key input for light environment optimization strategies such as ambient light color temperature adjustment and sunshade linkage control.

[0100] (4) A physiological auxiliary sensing unit integrated into the seat or armrest is used to detect the passenger's heart rate, respiratory rate, or skin temperature to help determine the user's comfort state. This unit monitors the passenger's physiological indicators such as heart rate, respiratory rate, or skin temperature in a non-invasive manner (such as capacitive or photoelectric sensors) to indirectly assess their thermal comfort state or emotional changes. When the environmental parameters are within the theoretical comfort range but the user's physiological signals indicate tension, heat, or cold, the system can trigger deeper regulatory interventions, enabling environmental control to move from "parameter compliance" to "realistic physical comfort," significantly improving the level of humanization.

[0101] Furthermore, embodiments of this application provide a vehicle including a smart cockpit, the smart cockpit integrating the aforementioned smart cockpit environment regulation system based on throttle perception, and configured to execute the aforementioned smart cockpit environment regulation method based on throttle perception.

[0102] In summary, the intelligent cockpit environment adjustment method, system, and vehicle based on thrift perception provided in this application have the following technical effects.

[0103] This application's embodiments achieve collaborative perception and precise control of multi-dimensional environmental parameters. The system integrates the natural climate patterns contained in the 24 solar terms with real-time weather information outside the cabin, and achieves a balance between cultural contextual expression and practical comfort through a dynamic weight allocation mechanism. At the same time, by combining multi-dimensional sensor data such as cabin temperature, humidity, air quality, light intensity, and fragrance concentration, it performs independent and collaborative zoned adjustments for different passenger areas, effectively avoiding direct airflow, achieving precise fragrance replenishment, and linking sunshade and lighting control, significantly improving the overall comfort, health, and personalized experience of the cabin.

[0104] Furthermore, this solution possesses powerful adaptive learning and continuous evolution capabilities. By recording users' manual adjustment behaviors under specific solar terms and weather combinations, the system correlates these behaviors with context and identifies stable preference trends, automatically optimizing decision weights and default parameters for scenario modes. This ensures that the adjustment strategy becomes increasingly tailored to individual habits with repeated use. Simultaneously, the target scenario modes support user customization and can be continuously updated or optimized via OTA (Over-The-Air) upgrades, ensuring constantly updated functionality and a continuously improved user experience. Therefore, this application not only overcomes the shortcomings of existing technologies in terms of adjustment dimensions, context awareness, and personalization, but also deeply integrates traditional Chinese culture with the intelligent cockpit, constructing a new generation of cockpit environment adjustment system that combines technological sophistication, humanistic care, and high intelligence.

[0105] It should be noted that in all specific embodiments of this application, all data processing activities related to user identity or personal characteristics, such as user information, user behavior data, historical data, and location information, will be conducted in accordance with the principles of legality, legitimacy, and necessity. All data collection, use, storage, and processing will be subject to compliance with applicable national and regional laws, regulations, and industry standards, and informed consent from users will be obtained in a clear and explicit manner before processing. For the processing of sensitive personal information, separate consent from users will be obtained through prominent means such as pop-up prompts and independent confirmation pages. If any processing conflicts with laws and regulations, the laws and regulations will prevail, and necessary data processing will only be carried out within the scope permitted by laws and regulations, ensuring that all data-based applications, analyses, and technical implementations are conducted within the scope permitted by laws and regulations.

[0106] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0107] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of ordinary skill of an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary skill. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0108] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a 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 programs 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 of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable programs for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can retrieve and execute a program from or in conjunction with such a program execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with a program execution system, apparatus, or device.

[0110] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Additionally, computer-readable media can even be paper or other suitable media on which programs can be printed, for example, by optically scanning the paper or other media, then editing, interpreting, or, if necessary, processing it in a suitable manner to obtain the program electronically, and then storing it in computer memory.

[0111] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0112] In the foregoing description of this specification, the reference to terms such as "one embodiment / implementation," "another embodiment / implementation," or "certain embodiments / implementations," etc., indicates that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in an embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0113] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0114] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for intelligent cockpit environment adjustment based on solar term perception, characterized in that, Includes the following steps: It can obtain the solar term information corresponding to the current date, real-time weather information outside the cabin, and multi-dimensional environmental parameters inside the cabin. The solar term information includes the temperature and humidity ranges associated with each solar term; the multi-dimensional environmental parameters include cabin temperature, humidity, air quality, light intensity, and fragrance concentration. Based on the matching relationship between the solar term information and the real-time weather information, the weights of the two in environmental regulation decisions are dynamically allocated, and a target scenario model is generated by fusing the weights. Based on the target scenario mode and multi-dimensional environmental parameters, the environment of different areas in the cockpit is adjusted in a zoned and coordinated manner. Record user feedback on the adjustment results, and adaptively optimize the weight allocation strategy and the target scenario mode based on the feedback.

2. The intelligent cockpit environment adjustment method based on solar term perception according to claim 1, characterized in that, The step of dynamically assigning weights to the solar term information and the real-time weather information in environmental regulation decisions based on their matching relationship, and generating a target scenario model based on the weighted fusion, includes the following steps: The ambient temperature and humidity in the real-time weather information are compared with the temperature and humidity range associated with the current solar term; If the ambient temperature and humidity in the real-time weather information both fall within the range corresponding to the current solar term, then the solar term and weather are determined to match, the weight of the solar term information in the fusion calculation is increased, and the weight of the real-time weather information is reduced accordingly. If either the ambient temperature or humidity in the real-time weather information exceeds the range corresponding to the current solar term and the deviation exceeds a preset threshold, it is determined that the solar term and the weather do not match, the weight ratio of the solar term information in the fusion calculation is reduced, and the weight ratio of the real-time weather information is increased accordingly. Based on the adjusted weights, the scenario parameter templates corresponding to the solar term information and the environmental parameters recommended based on real-time weather information are fused to generate a target scenario model for guiding cabin environment adjustment.

3. The intelligent cockpit environment adjustment method based on solar term perception according to claim 2, characterized in that, The recommendation of environmental parameters based on real-time weather information is achieved in the following way: Based on the real-time ambient temperature and humidity outside the cabin, and combined with a preset human thermal comfort model, the appropriate target cabin temperature and humidity are determined. Determine whether the current situation is one of strong sunlight or cloudy based on the intensity of external light, and recommend corresponding cabin ambient lighting color temperature and sunshade strategies accordingly; In hot and humid weather, it is recommended to prioritize enhancing the cooling and dehumidification capabilities of the air conditioner and activate the air purification function in conjunction with it. In cold and dry weather, it is recommended to increase the cabin temperature, turn on the humidification function, and select a warm fragrance type; The above recommendations are used as weather-driven environmental parameter suggestions for weighted fusion with seasonal scenario parameters.

4. The intelligent cockpit environment adjustment method based on solar term perception according to claim 1, characterized in that, The step of performing zoned and coordinated adjustment of the environment in different areas of the cockpit based on the target scenario mode and multi-dimensional environmental parameters specifically includes: The cabin is divided into multiple adjustment zones based on the distribution of passengers within the cabin; For each adjustment zone, the airflow direction, air volume and air temperature of the air conditioning vents, the start / stop and concentration of the fragrance release unit, and the brightness and color temperature of the ambient light are controlled separately. During the adjustment process, the multi-dimensional environmental parameters of each region are compared with the corresponding parameters in the target scenario mode in real time, and the execution strategy is dynamically adjusted so that the environmental state of each region approaches the comfort range defined by the target scenario mode.

5. The intelligent cockpit environment adjustment method based on solar term perception according to claim 1, characterized in that, The partitioned coordinated adjustment also includes: When the actual temperature of a certain adjustment zone deviates from the temperature set in the target scenario mode by more than a threshold, the air volume and direction of the corresponding air outlet in that zone are adjusted first, and the airflow is prevented from blowing directly towards the user. When the fragrance concentration is below the target value and there is no user command to turn it off, the local fragrance release will be automatically activated; Under strong sunlight, the color temperature of the ambient lighting in the corresponding area is dimmed simultaneously and the sunshade curtain is controlled in conjunction with the lighting.

6. The intelligent cockpit environment adjustment method based on solar term perception according to claim 1, characterized in that, The process of recording user feedback on the adjustment results and adaptively optimizing the weight allocation strategy and the target scenario mode based on the feedback includes the following steps: The system collects manual adjustments made by users to cabin environment parameters based on a combination of current solar term information and real-time weather information. These manual adjustments include modifications to temperature, humidity, wind speed, fragrance type, or light color temperature. The manual adjustment operation is associated with and stored in conjunction with the corresponding solar term information, real-time weather information, and the current target scenario mode; Statistics show the direction and frequency of user adjustments to the same environmental parameter under identical or similar environmental conditions; When the adjustment behavior of a certain parameter shows a stable trend within a preset time period, the initial weight ratio of solar term information and weather information under the combination of environmental conditions is automatically updated, and the default value of the corresponding parameter in the target scenario mode is corrected. The stable trend refers to the fact that multiple consecutive feedbacks all point to the same adjustment direction, and the fluctuation of the adjustment amplitude is less than the preset tolerance range. The preset time period and tolerance range can be dynamically adjusted according to the historical data of user feedback.

7. The intelligent cockpit environment adjustment method based on solar term perception according to claim 1, characterized in that, The target scenario mode can be customized by the user, and scenario modes corresponding to the solar term information can be added or updated through online upgrades.

8. An intelligent cockpit environment regulation system based on solar term sensing, characterized in that, include: The environmental information acquisition module is used to acquire the solar term information corresponding to the current date, real-time weather information outside the cockpit, and multi-dimensional environmental parameters inside the cockpit. The solar term information includes the temperature and humidity ranges associated with each solar term; the multi-dimensional environmental parameters include cabin temperature, humidity, air quality, light intensity, and fragrance concentration. The scenario decision module is used to dynamically allocate the weights of the solar term information and the real-time weather information in environmental regulation decisions based on the matching relationship between the two, and to generate a target scenario mode based on the fusion of the weights. The partition execution control module is used to coordinate and adjust the environment of different areas in the cockpit according to the target scenario mode and multi-dimensional environmental parameters. An adaptive learning module is used to record user feedback behavior on the adjustment results, and adaptively optimize the weight allocation strategy and the target scenario mode based on the feedback behavior.

9. The intelligent cockpit environment control system based on seasonal weather perception according to claim 8, characterized in that, The environmental information acquisition module includes an on-board positioning module, external cockpit sensors, and internal cockpit sensors. The real-time weather information outside the cockpit is obtained through the meteorological service interface associated with the vehicle positioning module, or directly collected through the external sensors of the cockpit. The cockpit interior sensors are distributed regionally, including: The first temperature, humidity and air quality sensor group is located near the head of the front passengers; The second temperature, humidity and fragrance concentration sensor is installed in the rear seat area; A light intensity sensor installed on the ceiling or dashboard; In addition, physiological auxiliary sensing units integrated into the seat or armrest are used to detect the passenger's heart rate, respiratory rate or skin temperature to help determine the user's comfort level.

10. A vehicle, characterized in that, The system includes an intelligent cockpit that integrates a solar-sensing-based intelligent cockpit environmental control system as described in claim 8 or 9, and is configured to perform a solar-sensing-based intelligent cockpit environmental control method as described in any one of claims 1 to 7.

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