Automobile air conditioner self-adaptive adjusting method and system based on multi-source data fusion

Abstract

The invention provides an automobile air conditioner self-adaptive adjusting method and system based on multi-source data fusion, and belongs to the field of automobile heat management control systems. The method comprises the steps that the setting state of a user for the air conditioner, environment data and user state information are obtained, and the user state information comprises user characteristics, mental states and health indexes; on the basis of the setting state, the environment data and the user state information, personalized target air conditioner parameters are generated through statistical analysis and a fitting algorithm; according to the real-time environment change, the user state and the health data, the target air conditioner parameters are dynamically compensated, and optimized air conditioner control parameters are generated; and according to the optimized air conditioner control parameters, the air conditioner operation state is adjusted. The purpose of automatically adjusting the air conditioner to the expected state of the user under different states of different users can be achieved, and precise control over the climate in the cabin is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobile thermal management control systems, and in particular relates to an automobile air-conditioning adaptive adjustment method and system based on multi-source data fusion. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of intelligent automotive technology, vehicle air conditioning systems have gradually evolved from simple environmental regulation devices to key systems that affect the driving experience. Currently, vehicle air conditioning systems on the market are mainly divided into two control modes: Manual control mode: Users need to manually adjust parameters such as temperature, air volume, and airflow pattern based on their personal preferences. This mode has obvious drawbacks: First, different users have different perceptions of comfortable temperature, and novice drivers often need to try multiple times to find the right setting. Second, during long drives, as ambient temperature and user status change, frequent adjustments to the air conditioning settings are required, which not only distracts the driver but also increases the operator's workload.

[0004] Automatic control mode: Existing automatic air conditioning systems primarily maintain a set temperature through pre-set temperature sensors and simple control algorithms. Although some high-end models have introduced automatic adjustment functions based on environmental parameters such as light intensity and outside temperature, the following technical limitations still exist: It is unable to identify the personalized needs of different users, and all users share the same control logic; it lacks the ability to perceive the user's physiological state and cannot actively adjust to conditions such as fatigue and discomfort; it cannot predict environmental changes along the driving route and can only passively respond to the current environment; the control algorithm is rigid and lacks the ability to continuously learn and optimize. Summary of the Invention

[0005] To overcome the deficiencies of the above-mentioned prior art, the present invention provides a method and system for adaptive adjustment of automobile air conditioning based on multi-source data fusion. By obtaining the setting status of the air conditioning, user status and environmental data of the user each time the car is used over a period of time, statistics and control algorithm optimization are performed to predict the state variables of the air conditioning at future moments. By calculating the optimal control quantity through the continuous increase in the obtained data, the rolling optimization of the air conditioning system is realized, so that the air conditioning is automatically adjusted to the user's expected state based on the user's current state each time the user gets in the car.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of the present invention provides an automobile air-conditioning adaptive adjustment method based on multi-source data fusion; A method for adaptively adjusting an automobile air conditioner based on multi-source data fusion, comprising: Obtaining the user's air conditioning settings, environmental data, and user status information, including user characteristics, mental state, and health indicators; Based on the setting status, environmental data and user status information, generating personalized target air conditioning parameters through statistical analysis and fitting algorithms; Dynamically compensate the target air conditioning parameters based on real-time environmental changes, user status, and health data to generate optimized air conditioning control parameters; Adjust the air conditioning operation status according to the optimized air conditioning control parameters.

[0007] As a further technical solution, the environmental data includes at least one of in-vehicle temperature and humidity data, out-vehicle temperature and humidity data, and glass temperature data.

[0008] As a further technical solution, the user characteristics include user number and clothing; the mental state includes at least one of fatigue state and distraction state; the health indicators include at least one of heart rate, blood pressure, blood oxygen, and body temperature.

[0009] As a further technical solution, the fitting algorithm includes statistical analysis of the user's historical air-conditioning setting data, generating target air-conditioning parameters, and applying compensation when different environmental factors change to obtain optimized air-conditioning settings.

[0010] As a further technical solution, the dynamic compensation includes: Environmental prediction compensation, used to adjust air conditioning parameters in advance based on weather and road condition information provided by the vehicle system; Mental state compensation, which is used to adjust air conditioning parameters according to the user's fatigue or distraction state to alleviate abnormal conditions; Health data compensation is used to reversely adjust air conditioning parameters based on user health indicators to avoid worsening physical conditions.

[0011] A second aspect of the present invention provides an automobile air-conditioning adaptive adjustment system based on multi-source data fusion.

[0012] An automobile air conditioning adaptive adjustment system based on multi-source data fusion, comprising: The sensor acquisition module is configured to collect and process the temperature and humidity data inside the vehicle, the temperature and humidity data outside the vehicle, and the glass temperature in real time, and feed the processed results back to the vehicle air conditioning control module for analysis; The health management module is configured to monitor the user's physical condition in real time and provide optimization solutions to the vehicle's air conditioning control module when abnormalities occur, ensuring that the vehicle's air conditioning responds promptly to in-cabin climate control. The vehicle computer module is configured to provide navigation information and feed back information about the route, vehicle speed, and weather conditions to the vehicle air conditioning control module; The occupant detection module is configured to: identify user characteristics and mental state and feed the data back to the vehicle air conditioning control module; The vehicle air conditioning control module is configured to: store in real time the information uploaded by the sensor acquisition module, occupant detection module, and air conditioning setting module at the same time, perform statistical analysis on the stored information, and fit the user's air conditioning needs and preferences in different environments; the vehicle air conditioning control module is connected to the vehicle computer module and the health management module to monitor anomalies during vehicle use, predict and dynamically compensate for air conditioning conditions that require adjustment, and control the air conditioning setting module to enter a specified operating state; The air conditioning setting module is configured to adjust the air conditioning operating status according to the instructions issued by the automobile air conditioning control module to achieve precise control of the climate in the cabin.

[0013] As a further technical solution, the automobile air-conditioning control module calculates the user's air-conditioning setting preferences in different environments, fits the target air-conditioning settings, and makes compensatory adjustments when the environment or user status changes.

[0014] As a further technical solution, the automobile air-conditioning control module predicts the impact of the climate in the cabin based on the navigation route information and weather data provided by the vehicle computer module, and adjusts the air-conditioning settings in advance.

[0015] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the automobile air-conditioning adaptive adjustment method based on multi-source data fusion as described in the first aspect of the present invention.

[0016] The fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for adaptively adjusting an automobile air conditioner based on multi-source data fusion as described in the first aspect of the present invention are implemented.

[0017] One or more of the above technical solutions have the following beneficial effects: This invention analyzes different users' air conditioning usage habits and creates a personalized comfort model, enabling precise, personalized adjustments for each user, significantly reducing the need for manual adjustments. It also recognizes and memorizes the preferences of multiple users, automatically switching to the corresponding mode when different users enter the vehicle, addressing the difficulty of catering to diverse tastes with traditional air conditioners.

[0018] The present invention can also obtain the user's mental state and health status, and can compensate the air-conditioning settings in real time during the use of the car in combination with user information, and alleviate the current user status through changes in the cabin environment; by accessing the vehicle system, it can predict the environment and weather information ahead, and avoid it in advance by adjusting the air-conditioning settings to ensure that the vehicle environment is not affected, and ensure that the control of the self-learning car air-conditioning can better adapt to different users, external environments and changes in user status. Not only is the control method more flexible, but it can also be one step ahead of the user and provide nanny-level cabin climate control.

[0019] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0021] Figure 1 This is a flow chart of the method of the first embodiment.

[0022] Figure 2 This is a system structure diagram of the second embodiment. DETAILED DESCRIPTION

[0023] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0024] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0025] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0026] Example 1 This embodiment discloses a method for adaptively adjusting automobile air conditioners based on multi-source data fusion; like Figure 1 As shown, a method for adaptively adjusting automobile air conditioner based on multi-source data fusion includes: Step S101, obtaining the user's air conditioning setting status, environmental data and user status information, wherein the user status information includes user characteristics, mental state and health indicators; Step S102, generating personalized target air-conditioning parameters through statistical analysis and fitting algorithm based on the setting status, environmental data and user status information; Step S103, dynamically compensating the target air-conditioning parameters according to real-time environmental changes, user status, and health data to generate optimized air-conditioning control parameters; Step S104: adjusting the air conditioning operation state according to the optimized air conditioning control parameters.

[0027] Specifically, it also includes the following: In this embodiment, when a user begins using the air conditioning system, they will operate according to the air conditioning settings T, which are based on a pre-calibrated automatic air conditioning algorithm based on different seasons. Specifically, the automatic air conditioning algorithm determines the current season based on the external ambient temperature. For example, if the outside temperature is >25°C, it is considered summer. The user's desired interior temperature, air flow pattern, and circulation state, determined by the calibration and acceptance personnel based on parameters such as the interior temperature setting and varying sunlight intensities, are then set as the base air conditioning settings T (which includes the target interior temperature, target air volume, target circulation state, target air flow pattern, and target evaporator temperature). The automatic air conditioning algorithm then guides the user in adjusting the air conditioning settings to their needs, providing a data foundation for the air conditioning's self-learning. Furthermore, in the process of obtaining the user's setting status, environmental data and user status information of the air conditioner in step S101, the environmental information of the current user is synchronously recorded in order to associate the user's setting of the air conditioner with the current environment, and provide environmental factor judgment for subsequent statistical analysis; by collecting the user characteristics and clothing information reported by the occupant detection module, and binding it with the collected user's setting status information and environmental data information of the air conditioner, a user-specific database is formed to provide a basis for personalized air conditioning for subsequent statistical analysis; in addition, the different users collected in the above steps are stored, and only the data stored within a period of time will be self-learning accessed to avoid large learning deviations due to too small data samples, which affects the user experience.

[0028] In step S102, the user data stored in step S101 are counted and processed to fit the target air conditioning settings under the ideal state of the corresponding user. When the user runs the basic air conditioning setting T, the user's adjustment of the car's temperature, mode, etc. will be recorded to confirm whether the temperature T meets the user's needs. For example, if the user manually lowers the car's temperature setting when the car is running at T, it is considered that T is too hot for the user, and the basic temperature T is adjusted to After fitting the target air conditioning settings for different users, each time a user gets on the bus, the occupant detection system will identify the current user and clothing, and call the user's target air conditioning settings. At the same time, the environmental changes are detected by sensors, and the changes of different environmental factors are Make corresponding compensation based on (Only means there is a compensation mark), and finally get the air conditioning setting that can meet the user's cabin climate comfort needs .

[0029] Furthermore, in step S103, dynamic compensation includes environmental prediction compensation, mental state compensation and health data compensation.

[0030] Among them, environmental prediction compensation is used to adjust the air conditioning parameters in advance according to the weather and road conditions information provided by the vehicle system. By obtaining information from the vehicle module, it is possible to understand the weather, vehicle speed, route and other information in the current user's environment. The import of this information will be predicted in advance during the air conditioning self-learning. When it is judged that there is an abnormal situation, the route time can be judged in combination with the vehicle speed, and the air conditioning settings can be adjusted in advance to ensure that the comfortable climate in the cabin is not affected; the abnormal situation mainly refers to passing through tunnels, passing through industrial areas with poor air quality, or passing through thunderstorm areas, etc., which can affect the cabin environment, but are not limited to the listed situations. On the contrary, factors that do not affect the cabin environment are considered to be normal; when an abnormal situation occurs, in order to ensure that the cabin climate is not affected by the outside world, and at the same time ensure the comfort and driving safety in the car, the relevant control parameters in the air conditioning settings such as air purification and defogger will be adjusted in advance before entering the corresponding area to compensate for each parameter. (Indicates there is a compensation sign) to get the adjusted air conditioning settings ; Mental state compensation is used to adjust the air conditioning parameters according to the user's fatigue or distraction state to alleviate abnormal conditions. By obtaining information from the occupant detection system, it is possible to understand the current user's mental state and fatigue state and other information. The air conditioning self-learning can combine the user status information to adjust the cabin climate in time, and alleviate the user's abnormal state through climate changes; among them, the abnormal conditions referred to here mainly refer to factors that affect driving such as distraction and fatigue, as well as factors such as sweating and adding clothes that require adjustment of the cabin environment, but are not limited to the listed situations. On the contrary, factors that require adjustment of the cabin environment are considered to be normal; when an abnormal situation occurs, in order to ensure that the current abnormal state can be alleviated, the various parameters are compensated by changing the cabin climate and changing the relevant control parameters in the air conditioning settings, such as adjusting the temperature and fragrance concentration. (Indicates there is a compensation sign) to get the adjusted air conditioning settings ; Health data compensation is used to reversely adjust air conditioning parameters according to the user's health indicators to avoid worsening physical conditions. By obtaining information from the health management system, it is possible to understand the current user's physical condition such as blood pressure, body temperature, heart rate and other information, so as to optimize the results of self-learning, adaptively adjust the cabin climate, and avoid the cabin environment having additional effects on the user. The abnormal conditions referred to here mainly refer to scenarios such as abnormal physical indicators such as increased blood pressure, abnormal body temperature, and increased heart rate, but are not limited to the listed situations. Otherwise, they are considered to be normal. When an abnormal situation occurs, in order to ensure that the current cabin environment does not worsen the user's abnormal indicators, the relevant control parameters such as adjusting temperature and fragrance concentration will be used to reversely compensate for each parameter. (Indicates there is a compensation sign) to get the adjusted air conditioning settings ; In step S104, based on the optimized air conditioning control parameters, an air conditioning setting T suitable for the user in the current state is predicted, and this setting meets the user's various requirements for cabin environmental comfort.

[0031] Example 2 This embodiment discloses an automobile air conditioning adaptive adjustment system based on multi-source data fusion; like Figure 2 As shown, an automobile air conditioning adaptive adjustment system based on multi-source data fusion includes: The sensor acquisition module 201 integrates multiple sensors to collect and process the temperature and humidity data inside the vehicle, the temperature and humidity data outside the vehicle, and the glass temperature in real time, and feeds the processed results back to the automobile air conditioning control module 205 for analysis; The health management module 202 is the user's health data storage center. This module monitors the user's body temperature, blood oxygen, blood pressure, heart rate and other health data in real time and generates reports. When abnormal indicators appear, it can promptly alert the user and provide reasonable suggestions. At the same time, it can feed back the cabin climate optimization plan to the car air conditioning control module 205 to ensure that the car air conditioning responds to the cabin climate adjustment in a timely manner. The vehicle computer module 203 is used to provide navigation information and feed back information about the route, vehicle speed, and weather conditions to the vehicle air conditioning control module 205; The occupant detection module 204 is configured to: identify user characteristics and mental state, and feed the data back to the automobile air conditioning control module 205; specifically, the occupant detection module 204 identifies and labels different drivers and their clothing conditions, and can monitor the user's status in real time while the user is using the vehicle. If any abnormal conditions such as fatigue, distraction, sweating, etc. occur, the module can promptly remind the user and feed back the status to the automobile air conditioning control module 205 so that the automobile air conditioning can take timely action to alleviate the user's current abnormal state.

[0032] The vehicle air conditioning control module 205 is configured to store in real time the information uploaded by the sensor acquisition module 201, the occupant detection module 204, and the air conditioning setting module 206 simultaneously, perform statistical analysis on the stored information, and approximate the user's air conditioning preferences under different environments. The vehicle air conditioning control module 205 is connected to the vehicle computer module 203 and the health management module 202 to monitor for abnormalities during vehicle use, predict and dynamically compensate for air conditioning conditions that require adjustment, and control the air conditioning setting module 206 to enter a specified operating state. Dynamic compensation includes environmental prediction compensation, mental state compensation, and health data compensation. Environmental prediction compensation is used to preemptively adjust air conditioning parameters based on weather and road condition information provided by the vehicle computer system; mental state compensation is used to adjust air conditioning parameters based on the user's fatigue or distraction to alleviate abnormal conditions; and health data compensation is used to reversely adjust air conditioning parameters based on the user's health indicators to avoid worsening physical conditions.

[0033] The air conditioning setting module 206 is used to adjust the air conditioning circulation status, air volume, and air outlet mode according to the instructions issued by the automobile air conditioning control module 205 to adjust the comfort in the cabin. It can also adjust the air purification system and the fragrance system to adjust the freshness of the air in the car. In addition, it also takes into account the adjustment of the ventilation, heating and massage of the seat system to further improve user comfort and experience. This module combines the instructions issued by the automobile air conditioning control module to achieve precise control of the climate in the cabin.

[0034] After the car's air conditioning system is started, it is initialized to automatic adjustment mode, including default temperature, air volume, and airflow pattern. The system prompts the user to manually adjust the air conditioning settings based on their personal comfort needs and records each adjustment (such as temperature, air volume, and airflow direction), thereby capturing the user's air conditioning settings. By continuously collecting user data on air conditioning adjustments, the system can identify the user's preferences for air conditioning in different environments. Through continuous data learning, the system can provide personalized predictions and proactively adjust the air conditioning settings in response to environmental changes. Specifically, in this embodiment, the car's air conditioning system incorporates a built-in automatic air conditioning algorithm as the basis for the air conditioning settings T. When using this algorithm, different users adjust the relevant air conditioning settings T based on their own comfort needs. For example, if the airflow in the cabin is too high, the user may manually adjust the air volume, or if the temperature is too cold, the user may adjust the temperature. Each user adjustment is an effort to find a comfortable cabin climate, and each adjustment provides the data foundation for the car's self-learning system.

[0035] In the process of acquiring environmental data, the sensor acquisition module 201 uses multiple sensors to acquire the temperature and humidity data inside the vehicle, the temperature and humidity data outside the vehicle, and the glass temperature data. By analyzing the above environmental data acquired, it ensures that the air conditioner is adjusted in time according to the external environmental information.

[0036] The user information includes user characteristics, mental state, and health indicators. User characteristics include user ID and clothing. By identifying different users and collecting air conditioning usage habits, the system can proactively set the air conditioning to an ideal setting that meets the user's preferences after the user gets in the vehicle, taking into account the user's characteristics. This provides the user with the fastest cooling or heating. Furthermore, the system detects changes in the environment and user status in real time while the user is using the vehicle, making timely adjustments to the air conditioning settings to reduce cabin climate fluctuations and ensure real-time monitoring. Specifically, the mental state includes at least one of fatigue and distraction; the health indicators include at least one of heart rate, blood pressure, blood oxygen, and body temperature.

[0037] The car air conditioning control module 205 obtains the air conditioning setting information that the user has manually changed to meet the comfort requirements within a period of time, and combines the current sensor acquisition module 201 and the occupant detection module 204 to match and store the acquired information with the user, and at the same time, statistics and analyzes the user's air conditioning usage habits in the current environment, and optimizes the preset air conditioning settings in a rolling manner. Specifically, by analyzing and calculating the data obtained by the sensor acquisition module 101, the air conditioning settings that meet the current user's requirements are obtained. Over a period of time, the target air conditioning settings under the ideal state of the corresponding user are calculated and fitted by collecting statistics on the clothing conditions of different users in the occupant monitoring module, the air conditioning usage information uploaded in the air conditioning setting module, and the environmental information uploaded by the sensor acquisition module. .

[0038] After obtaining the target air conditioning settings for different users, each time a user gets on the bus, the occupant detection module 204 will identify the current user and clothing, and call the target air conditioning settings for the user. At the same time, the environmental changes are detected by sensors, and the changes of different environmental factors are Make corresponding compensation based on (Only means there is a compensation mark), and finally get the air conditioning setting that can meet the user's cabin climate comfort needs For example, taking the air volume in the air conditioner setting as an example, the air volume requirements of users in different environments over a period of time are collected, and the target air volume requirements of the user in the ideal environment and ideal clothing conditions are calculated by combining statistics and fitting. Based on the target air volume, the air volume is compensated every time the user's clothing changes or any environmental factors change. , thus obtaining the final air conditioning setting volume that can meet the user's cabin climate comfort needs Other air conditioning settings such as air flow mode and circulation control can be executed according to this logic.

[0039] Dynamic compensation includes environmental prediction compensation, mental state compensation, and health data compensation. Based on the user's current driving environment, the vehicle's navigation route and weather information are obtained through the vehicle's computer system to predict the impact on the cabin climate and promptly adjust the current air conditioning settings. Simultaneously, the health management system 202 and occupant detection system 204 monitor the user's state. If abnormal conditions such as fatigue are present, the air conditioning settings are adjusted to change the cabin climate.

[0040] In this embodiment, the air conditioner is set By collecting information from the vehicle module 203, the user's current environment and weather conditions, as well as the next route and vehicle speed information, can be understood. When it is determined that there are abnormal areas on the route, such as tunnels, industrial areas or thunderstorm areas, the time of passing through the above areas is predicted in combination with the current vehicle speed, and the air conditioning settings are adjusted in advance. At the same time, the information collected by the occupant detection module 204 and the health management module 202 can be used to know the current user's mental and physical state. When abnormalities occur, such as fatigue, sweating, irritability, etc., the air conditioning settings can be adjusted in time. Make rolling optimizations, adjust the cabin climate, and improve user abnormal conditions.

[0041] Through the self-learning function of the air conditioner, after the user gets in the car, the air conditioner can combine the user's characteristics and actively set the air conditioner to an ideal state that meets the user's preferences. It can provide cooling or heating to the user at the fastest speed. At the same time, it will detect environmental changes, user status changes and other factors in real time during the user's use of the vehicle, and make timely adjustments to the air conditioning settings to reduce climate fluctuations in the cabin and real-time follow-up. Not only is the control method more flexible, but it can also take a step ahead to provide users with an intelligent and comfortable car environment and provide nanny-level cabin climate control.

[0042] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0043] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the automobile air-conditioning adaptive adjustment method based on multi-source data fusion as described in Example 1.

[0044] Example 4 The purpose of this embodiment is to provide an electronic device.

[0045] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for adaptively adjusting an automobile air conditioner based on multi-source data fusion as described in Example 1 are implemented.

[0046] The steps involved in the apparatuses of Examples 2, 3, and 4 above correspond to those of Method Example 1. For detailed implementations, please refer to the relevant description of Example 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0047] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0048] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A method for adaptively adjusting automobile air conditioner based on multi-source data fusion, characterized in that: include: Obtaining the user's air conditioning settings, environmental data, and user status information, including user characteristics, mental state, and health indicators; Based on the setting status, environmental data and user status information, generating personalized target air conditioning parameters through statistical analysis and fitting algorithms; Dynamically compensate the target air conditioning parameters based on real-time environmental changes, user status, and health data to generate optimized air conditioning control parameters; Adjust the air conditioning operation status according to the optimized air conditioning control parameters.

2. The method for adaptively adjusting automobile air conditioner based on multi-source data fusion according to claim 1, characterized in that: The environmental data includes at least one of vehicle interior temperature and humidity data, vehicle exterior temperature and humidity data, and glass temperature data.

3. The method for adaptively adjusting automobile air conditioner based on multi-source data fusion according to claim 1, characterized in that: The user characteristics include user number and clothing; the mental state includes at least one of fatigue state and distraction state; the health indicators include at least one of heart rate, blood pressure, blood oxygen, and body temperature.

4. The method for adaptively adjusting automobile air conditioner based on multi-source data fusion according to claim 1, characterized in that: The fitting algorithm includes statistical analysis of the user's historical air conditioning setting data to generate target air conditioning parameters, and applying compensation when different environmental factors change to obtain optimized air conditioning settings.

5. The method for adaptively adjusting automobile air conditioner based on multi-source data fusion according to claim 1, characterized in that: The dynamic compensation includes: Environmental prediction compensation, used to adjust air conditioning parameters in advance based on weather and road condition information provided by the vehicle system; Mental state compensation, which is used to adjust air conditioning parameters according to the user's fatigue or distraction state to alleviate abnormal conditions; Health data compensation is used to reversely adjust air conditioning parameters based on user health indicators to avoid worsening physical conditions.

6. An automobile air conditioning adaptive adjustment system based on multi-source data fusion, characterized in that: include: The sensor acquisition module is configured to collect and process the temperature and humidity data inside the vehicle, the temperature and humidity data outside the vehicle, and the glass temperature in real time, and feed the processed results back to the vehicle air conditioning control module for analysis; The health management module is configured to monitor the user's physical condition in real time and provide optimization solutions to the vehicle's air conditioning control module when abnormalities occur, ensuring that the vehicle's air conditioning responds promptly to in-cabin climate control. The vehicle computer module is configured to provide navigation information and feed back information about the route, vehicle speed, and weather conditions to the vehicle air conditioning control module; The occupant detection module is configured to: identify user characteristics and mental state and feed the data back to the vehicle air conditioning control module; The vehicle air conditioning control module is configured to: store in real time the information uploaded by the sensor acquisition module, occupant detection module, and air conditioning setting module at the same time, perform statistical analysis on the stored information, and fit the user's air conditioning needs and preferences in different environments; the vehicle air conditioning control module is connected to the vehicle computer module and the health management module to monitor anomalies during vehicle use, predict and dynamically compensate for air conditioning conditions that require adjustment, and control the air conditioning setting module to enter a specified operating state; The air conditioning setting module is configured to adjust the air conditioning operating status according to the instructions issued by the automobile air conditioning control module to achieve precise control of the climate in the cabin.

7. The automobile air conditioning adaptive adjustment system based on multi-source data fusion according to claim 6, characterized in that: The automobile air conditioning control module calculates target air conditioning settings by collecting statistics on the user's air conditioning setting preferences in different environments, and makes compensatory adjustments when the environment or user status changes.

8. The automobile air-conditioning adaptive adjustment system based on multi-source data fusion according to claim 6, characterized in that: The automobile air conditioning control module predicts the impact of the climate in the cabin based on the navigation route information and weather data provided by the vehicle computer module and adjusts the air conditioning settings in advance.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the automobile air-conditioning adaptive adjustment method based on multi-source data fusion as described in any one of claims 1 to 5 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the automobile air-conditioning adaptive adjustment method based on multi-source data fusion as described in any one of claims 1 to 5 are implemented.

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