Vehicle air conditioner automatic control method and system based on self-learning strategy

Abstract

The invention discloses a vehicle air conditioner automatic control method and system based on a self-learning strategy, and relates to the field of vehicle control. According to the method, after a vehicle is started, cabin environment data, passenger characteristic data and air conditioner initial operation parameters are collected, integrated and stored; a preset human body thermal comfort basic model is called, human body thermal physiological process calculation and cabin thermal comfort evaluation are completed in combination with collected data, and a passenger personalized thermal comfort database is established synchronously; based on the model, reversely outputting personalized air conditioner regulation and control parameters containing coordinated regulation parameters of the air conditioner and associated vehicle-mounted equipment; air conditioner manual adjustment operation of passengers is detected in real time, if the operation is detected, parameters after the operation are collected, the storage model is self-adaptively corrected and updated, and self-learning circulation is executed according to the corrected parameters; if not, air conditioner regulation and control are directly completed according to the personalized air conditioner regulation and control parameters. According to the method, self-learning intelligent control over the air conditioner is achieved, the personalized heat requirements of passengers are met, and the heat comfort experience of the cabin is improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle control, and in particular to a vehicle air conditioning automatic control method based on a self-learning strategy, a vehicle air conditioning automatic control system based on a self-learning strategy, electronic equipment, and storage medium. Background Technology

[0002] Intelligentization of automobiles has become a core trend in the automotive industry, and the intelligent cockpit, as the core carrier of this transformation, is a key component. Relying on advanced integrated software and hardware technologies across multiple fields, the intelligent cockpit drives the upgrade of automobiles from simple transportation tools to intelligent mobility partners with interactivity and personalization, continuously creating a more personalized and high-quality riding experience tailored to the needs of drivers and passengers. The automotive air conditioning system is a crucial component for ensuring cabin thermal comfort. However, traditional automotive air conditioning systems employ a control strategy that focuses on the average cabin temperature, achieving automated control through fixed thresholds. This model cannot adapt to the dynamically changing, non-uniform thermal environment within the cabin, nor can it match the personalized physiological thermal needs of different drivers and passengers. This not only fails to effectively meet the real-time dynamic thermal comfort needs of drivers and passengers but also easily leads to redundant operation problems such as ineffective cooling and heating, resulting in significant energy consumption and severely restricting the range of new energy vehicles. This has become a major pain point for the upgrading of the intelligent cockpit experience and the development of new energy vehicles. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a vehicle air conditioning automatic control method based on a self-learning strategy, a vehicle air conditioning automatic control system based on a self-learning strategy, an electronic device and a storage medium, aiming to solve the technical problem in the prior art that it is impossible to match the personalized physiological thermal needs of different drivers and passengers.

[0004] This invention provides the following solution:

[0005] According to one aspect of this application, a vehicle air conditioning automatic control method based on a self-learning strategy is provided, comprising the following steps:

[0006] In response to a vehicle start-up request, it collects cabin environment data, occupant characteristic data, and initial operating parameters of the vehicle's air conditioning system, and integrates and stores the data.

[0007] Obtain a pre-set basic model of human thermal comfort, complete the calculation of human thermal physiological processes and cabin thermal comfort evaluation based on the collected data, and establish a personalized thermal comfort database for occupants.

[0008] Based on the human thermal comfort model, personalized air conditioning control parameters are output in reverse. These control parameters include the air conditioning's own operating parameters and the coordinated adjustment parameters of related vehicle equipment.

[0009] Real-time detection of whether passengers manually adjust the air conditioning;

[0010] If manual adjustment is detected, the adjustment parameters after the operation are collected, the basic human thermal comfort model is adaptively corrected and updated and stored, and the process is executed according to the corrected parameters and enters the self-learning loop.

[0011] If no manual adjustment is detected, the air conditioning will be adjusted according to the personalized air conditioning control parameters.

[0012] Furthermore, the cabin environment data includes: cabin temperature distribution data, solar radiation intensity data, and ambient humidity data;

[0013] Occupant characteristic data includes: occupant facial feature data, limb movement data, clothing feature data, physiological feature data, and weight data.

[0014] Furthermore, including:

[0015] Occupant limb movement data is used to analyze the human metabolic rate corresponding to the occupant's activity level;

[0016] Clothing feature data is used to analyze the thermal resistance of the human body corresponding to the clothing worn by the occupants;

[0017] Physiological characteristic data is used to classify the types of occupant thermal comfort requirements;

[0018] Facial feature data is used to create a unique identity for each passenger.

[0019] Furthermore, the calculation of human thermal physiological processes includes: the calculation of human heat transfer processes and the calculation of human physiological thermoregulation processes;

[0020] Cabin thermal comfort evaluation includes evaluation of the comfort of the local thermal environment and evaluation of the overall thermal environment.

[0021] Furthermore, it includes: a personalized thermal comfort database for occupants that stores occupant unique identifiers, occupant characteristic data, cabin thermal comfort evaluation results, basic human thermal comfort model parameters, and air conditioning control parameters, and the database supports the addition, updating, and retrieval of data.

[0022] Furthermore, this includes: the basic human thermal comfort model presets the initial target comfort state as a neutral comfort state;

[0023] The initial target comfort state is dynamically adjusted based on real-time cabin environmental data and the types of thermal comfort needs of the occupants, and then personalized air conditioning control parameters are output in reverse.

[0024] Furthermore, the air conditioner's own operating parameters include: air outlet temperature, air outlet speed, air outlet direction, air outlet mode, and humidity adjustment parameters.

[0025] The associated in-vehicle equipment includes cabin sunshades and seat heating devices, and their coordinated adjustment parameters include sunshade light transmittance parameters and seat heating parameters.

[0026] Furthermore, including:

[0027] Once the self-learning loop begins, it continuously collects cabin environment data, occupant characteristic data, and air conditioning operating parameters. Based on real-time data, it iteratively corrects the basic human thermal comfort model until no manual adjustment operation by the occupant is detected.

[0028] According to two aspects of this application, a vehicle air conditioning automatic control system based on a self-learning strategy is provided, comprising:

[0029] The system includes a data acquisition module, a model analysis module, a parameter output module, an operation detection module, an air conditioning execution module, and a data storage module.

[0030] The data acquisition module is used to collect cabin environment data, occupant characteristic data and initial operating parameters of the vehicle air conditioning in response to the vehicle start request, and transmit the collected data to the data storage module.

[0031] The data storage module is used to integrate and store various types of received data, and it also pre-stores a basic model of human thermal comfort.

[0032] The model analysis module is used to retrieve the preset human thermal comfort basic model and various collected data from the data storage module, complete the calculation of human thermal physiological processes and cabin thermal comfort evaluation, and at the same time establish and store the occupant's personalized thermal comfort database to the data storage module.

[0033] When manual adjustment is detected, the system adaptively corrects the basic human thermal comfort model based on the adjusted parameters after the operation, updates and stores the corrected model in the data storage module, and transmits the corrected adjusted parameters to the air conditioning execution module, enabling the system to enter a self-learning loop. When no manual adjustment is detected, the system controls the air conditioning execution module to continuously adjust according to the personalized air conditioning control parameters.

[0034] The parameter output module is used to output personalized air conditioning control parameters to the air conditioning execution module based on the human thermal comfort basic model processed by the model analysis module. The control parameters include the air conditioning's own operating parameters and the coordinated adjustment parameters of the associated vehicle equipment.

[0035] The operation detection module is used to detect in real time whether the occupants manually adjust the air conditioning and feed the detection results back to the model analysis module.

[0036] The air conditioning execution module is used to receive personalized air conditioning control parameters from the parameter output module and execute control operations; it is also used to collect the control parameters after the operation and transmit them to the model analysis module when manual adjustment is detected, and to receive the control parameters corrected by the model analysis module and execute the control.

[0037] According to three aspects of this application, an electronic device is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0038] The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of a vehicle air conditioning automatic control method based on a self-learning strategy.

[0039] According to four aspects of this application, a computer-readable storage medium is provided that stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a vehicle air conditioning automatic control method based on a self-learning strategy.

[0040] Compared with the prior art, the present invention has the following advantages:

[0041] This application achieves comprehensive and detailed collection of cabin environmental parameters and occupant characteristic data through multiple types of sensing devices, breaking through the limitation of traditional air conditioning that only collects average temperature data. It can accurately perceive the dynamic non-uniform thermal environment of the cabin and explore the personalized physiological characteristics of occupants from dimensions such as metabolic rate and human thermal resistance, laying a reliable and comprehensive data foundation for subsequent precise control and effectively reducing control deviations caused by missing or incorrect data.

[0042] This application constructs a basic model of human thermal comfort based on collected data and establishes a unique identifier and personalized thermal comfort database for each passenger. This enables air conditioning control to shift from traditional average temperature control and fixed threshold control to personalized control based on the actual thermal comfort experience of passengers. At the same time, it enables separate evaluation of local and overall thermal comfort in the cabin, accurately adapts to the non-uniform thermal environment of the cabin and the personalized physiological thermal needs of different passengers, and improves the real-time dynamic thermal comfort experience of drivers and passengers.

[0043] This application uses a basic human thermal comfort model to output personalized control parameters and achieves coordinated control of the air conditioning system with related vehicle equipment such as sunshades and seat heating devices. This ensures that the control always targets the thermal comfort needs of the occupants, expanding from single temperature adjustment to integrated control of the overall cabin thermal environment. It realizes on-demand control of the air conditioning, avoids excessive cooling / heating of a single device, and effectively improves the accuracy and efficiency of cabin thermal environment control. Attached Figure Description

[0044] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a flowchart of a vehicle air conditioning automatic control method based on a self-learning strategy provided by one or more embodiments of the present invention.

[0046] Figure 2 This is a structural diagram of a vehicle air conditioning automatic control system based on a self-learning strategy provided by one or more embodiments of the present invention.

[0047] Figure 3 This is a schematic diagram of an intelligent automotive air conditioning control system according to a specific embodiment of the present invention.

[0048] Figure 4 This is a schematic diagram of an adaptive intelligent vehicle air conditioning control strategy according to a specific embodiment of the present invention.

[0049] Figure 5 This is an electronic device structural block diagram of a vehicle air conditioning automatic control method based on a self-learning strategy provided by one or more embodiments of the present invention. Detailed Implementation

[0050] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0052] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0053] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application for description, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0054] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0055] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0056] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0057] Figure 1 This is a flowchart of a vehicle air conditioning automatic control method based on a self-learning strategy provided by one or more embodiments of the present invention.

[0058] like Figure 1 As shown, it includes the following steps:

[0059] Step S1: In response to the vehicle start request, collect cabin environment data, occupant characteristic data, and initial operating parameters of the vehicle air conditioning, and integrate and store the data.

[0060] Specifically, the cabin environment data includes: cabin temperature distribution data, solar radiation intensity data, and ambient humidity data;

[0061] Occupant characteristic data includes: occupant facial feature data, limb movement data, clothing feature data, physiological feature data, and weight data.

[0062] Occupant limb movement data is used to analyze the human metabolic rate corresponding to the occupant's activity level; clothing feature data is used to analyze the human thermal resistance corresponding to the occupant's clothing; physiological feature data is used to classify the occupant's thermal comfort needs; facial feature data is used to establish a unique identity for the occupant.

[0063] By collecting data such as cabin temperature distribution, solar radiation intensity, and ambient humidity, it breaks through the limitations of traditional air conditioning that relies solely on average temperature. It can accurately capture the dynamic non-uniform thermal environment of the cabin, providing a comprehensive environmental basis for subsequent control.

[0064] By unifying and storing environmental data, occupant data, and initial air conditioning parameters, data silos were eliminated, data consistency and availability were improved, and a reliable data foundation was provided for subsequent model calculations.

[0065] Step S2: Obtain the preset basic model of human thermal comfort, complete the calculation of human thermal physiological processes and cabin thermal comfort evaluation based on the collected data, and establish a personalized thermal comfort database for occupants.

[0066] Specifically, calculations of human thermal physiological processes include: calculations of human heat transfer processes and calculations of human physiological thermoregulation processes;

[0067] Cabin thermal comfort evaluation includes evaluation of the comfort of the local thermal environment and evaluation of the overall thermal environment.

[0068] Calculations based on human body heat transfer and physiological thermoregulation processes are performed, and the local and overall thermal comfort of the cabin is evaluated. This shifts air conditioning control from simple temperature control to thermal comfort control centered on the actual physical sensation of the occupants, fundamentally improving the scientific nature and pertinence of the control.

[0069] Step S3: Based on the human thermal comfort basic model, output personalized air conditioning control parameters in reverse. The control parameters include the air conditioning's own operating parameters and the coordinated adjustment parameters of the associated vehicle equipment.

[0070] Specifically, the personalized thermal comfort database for occupants stores occupant unique identifiers, occupant characteristic data, cabin thermal comfort evaluation results, basic human thermal comfort model parameters, and air conditioning control parameters. The database also supports the addition, updating, and retrieval of data.

[0071] The basic model of human thermal comfort presupposes an initial target comfort state as a neutral comfort state.

[0072] Based on real-time cabin environmental data and the types of thermal comfort needs of passengers, the initial target comfort state is dynamically adjusted, and then personalized air conditioning control parameters are output in reverse.

[0073] The air conditioner's own operating parameters include: air outlet temperature, air outlet speed, air outlet direction, air outlet mode, and humidity adjustment parameters;

[0074] The associated in-vehicle equipment includes cabin sunshades and seat heating devices, and their coordinated adjustment parameters include sunshade light transmittance parameters and seat heating parameters.

[0075] With the goal of achieving a dynamically adjusted neutral comfort state, personalized control parameters are output in reverse to ensure that the air conditioning control always revolves around the real-time thermal comfort needs of the occupants, rather than mechanically pursuing a fixed temperature, thus significantly improving the thermal comfort experience of drivers and passengers.

[0076] By combining the operating parameters of the air conditioner itself with the coordinated adjustment parameters of related devices such as sunshades and seat heating, an upgrade has been achieved from single temperature adjustment to integrated control of the overall thermal environment of the cabin. Thermal comfort is optimized through multi-dimensional means (such as sunshades reducing heat radiation and seat heating improving the feeling of body) and the over-operation of the air conditioner as a single device is avoided.

[0077] Step S4: Real-time detection of whether passengers manually adjust the air conditioning.

[0078] If manual adjustment is detected, the adjustment parameters after the operation are collected, the basic human thermal comfort model is adaptively corrected and updated and stored, and the process is executed according to the corrected parameters and enters the self-learning loop.

[0079] If no manual adjustment is detected, the air conditioning will be adjusted according to the personalized air conditioning control parameters.

[0080] Once the self-learning loop begins, it continuously collects cabin environment data, occupant characteristic data, and air conditioning operating parameters. Based on real-time data, it iteratively corrects the basic human thermal comfort model until no manual adjustment operation by the occupant is detected.

[0081] Specifically, by detecting occupants' manual adjustments and using this information to adaptively correct the model, a self-learning closed loop of perception, control, feedback, and optimization is formed. This allows the system to dynamically adapt to changes in occupants' thermal comfort needs in different seasons and physical states, with the control parameters becoming increasingly tailored to the occupants' individual habits as the number of uses increases.

[0082] The revised model is updated and stored in the database, enabling the memorization of occupants' thermal comfort preferences. These preferences can be directly retrieved and used during subsequent driving or riding, significantly improving operational convenience and the intelligence level of the smart cockpit.

[0083] Precise on-demand control and self-learning optimization effectively avoid the energy waste caused by ineffective cooling and heating due to rigid control strategies in traditional air conditioners, significantly reducing the energy consumption of the air conditioning system and directly improving the range of new energy vehicles.

[0084] Figure 2 This is a structural diagram of a vehicle air conditioning automatic control system based on a self-learning strategy provided by one or more embodiments of the present invention.

[0085] like Figure 2 As shown, it includes:

[0086] The system includes a data acquisition module, a model analysis module, a parameter output module, an operation detection module, an air conditioning execution module, and a data storage module.

[0087] The data acquisition module is used to collect cabin environment data, occupant characteristic data and initial operating parameters of the vehicle air conditioning in response to the vehicle start request, and transmit the collected data to the data storage module.

[0088] The data storage module is used to integrate and store various types of received data, and it also pre-stores a basic model of human thermal comfort.

[0089] The model analysis module is used to retrieve the preset human thermal comfort basic model and various collected data from the data storage module, complete the calculation of human thermal physiological processes and cabin thermal comfort evaluation, and at the same time establish and store the occupant's personalized thermal comfort database to the data storage module.

[0090] When manual adjustment is detected, the system adaptively corrects the basic human thermal comfort model based on the adjusted parameters after the operation, updates and stores the corrected model in the data storage module, and transmits the corrected adjusted parameters to the air conditioning execution module, enabling the system to enter a self-learning loop. When no manual adjustment is detected, the system controls the air conditioning execution module to continuously adjust according to the personalized air conditioning control parameters.

[0091] The parameter output module is used to output personalized air conditioning control parameters to the air conditioning execution module based on the human thermal comfort basic model processed by the model analysis module. The control parameters include the air conditioning's own operating parameters and the coordinated adjustment parameters of the associated vehicle equipment.

[0092] The operation detection module is used to detect in real time whether the occupants manually adjust the air conditioning and feed the detection results back to the model analysis module.

[0093] The air conditioning execution module is used to receive personalized air conditioning control parameters from the parameter output module and execute control operations; it is also used to collect the control parameters after the operation and transmit them to the model analysis module when manual adjustment is detected, and to receive the control parameters corrected by the model analysis module and execute the control.

[0094] It is worth noting that although only some basic functional modules are disclosed in this embodiment, it does not mean that the composition of this system is limited to the above-mentioned basic functional modules. On the contrary, what this embodiment intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with existing technology to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. The fact that this embodiment only discloses a few basic functional modules does not mean that the scope of protection of the claims of this invention is limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above device is described separately according to its functions as various units and modules. Of course, in implementing this invention, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0095] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0096] Figure 3 This is a schematic diagram of an intelligent automotive air conditioning control system according to a specific embodiment of the present invention.

[0097] like Figure 3 As shown, it includes: a camera, an infrared temperature camera, a seat weight sensor, a light intensity sensor, a humidity sensor, a human thermal comfort controller, a data collection and analysis system, and an air conditioning system.

[0098] The system is divided into three layers from top to bottom: the perception layer, the control and analysis layer, and the execution layer. The modules at each layer are connected to each other in sequence. The specific connection relationships and functions are described below:

[0099] The sensing layer includes cameras, infrared temperature cameras, seat weight sensors, light intensity sensors, and humidity sensors. The outputs of all the above sensing devices are connected to the inputs of the human thermal comfort controller to collect cabin environmental parameters and occupant characteristic data, and transmit the collected raw data to the human thermal comfort controller.

[0100] The control and analysis layer includes a human thermal comfort controller and a data collection and analysis system connected in sequence. The human thermal comfort controller is used to receive the data collected by the perception layer and perform calculations on the basic human thermal comfort model. The data collection and analysis system is used to receive the calculation results, build a personalized database, and generate control instructions.

[0101] The execution layer is the hardware of the air conditioning system. Its input end is connected to the output end of the data collection and analysis system. It is used to receive personalized adjustment parameters issued by the control and analysis layer and execute specific air conditioning control operations.

[0102] Figure 4 This is a schematic diagram of an adaptive intelligent vehicle air conditioning control strategy according to a specific embodiment of the present invention.

[0103] like Figure 4 As shown, this includes: occupants entering the driver's cabin, vehicle startup, and acquisition of environmental parameters and occupant data;

[0104] Based on environmental parameters, driver and passenger data, and air conditioning system parameters, establish a basic model of human thermal comfort for different drivers and passengers and form a database.

[0105] The basic human thermal comfort model outputs personalized adjustment parameters for the air conditioning system corresponding to the comfort state of drivers and passengers.

[0106] The air conditioning system operates according to personalized parameters;

[0107] If the system detects whether the driver or passenger is operating or adjusting the air conditioning panel, it collects and analyzes the air conditioning system parameters and feeds them back to the human thermal comfort baseline model for adaptive adjustment; otherwise, the air conditioning system continues to operate according to personalized parameters.

[0108] In one embodiment, acquiring environmental parameters and occupant data includes: capturing facial photos of occupants via a camera for use in a big data acquisition and analysis system to establish an occupant database; capturing occupant movements to analyze the metabolic rate corresponding to the level of human activity; capturing images of clothing to analyze the thermal resistance of the human body corresponding to the clothing worn by the occupants; capturing physiological characteristics of the occupants to classify them into different types and determine their thermal comfort requirements; an infrared temperature camera located inside the driver's cab to capture images of the temperature distribution of the occupants and the area around the driver's cab; a seat weight sensor located at the bottom of the seat to collect occupant weight; a light intensity sensor located inside the driver's cab to collect sunlight radiation inside the driver's cab; and a humidity sensor located inside the driver's cab to collect humidity inside the driver's cab.

[0109] Further strategies include establishing basic models of human thermal comfort for different drivers and passengers and forming a database based on environmental parameters, driver and passenger data, and air conditioning system parameters. This includes: using parameters collected after drivers and passengers enter the cabin to establish basic models of human thermal comfort; calculating human heat transfer and physiological thermoregulation processes; evaluating local and overall thermal environments and corresponding thermal comfort levels; storing the initial model of the basic human thermal comfort model in a data collection and analysis system; and forming databases of different drivers and passengers based on facial images captured by cameras using image recognition technology.

[0110] Furthermore, the basic human thermal comfort model outputs personalized adjustment parameters for the air conditioning system corresponding to the comfort state of drivers and passengers. The initial target comfort state of the basic human thermal comfort model is a neutral comfort state. Different people have different comfort needs in different states. The thermal needs are used as the target to output key adjustment parameters, and further output personalized parameters for the air conditioning system.

[0111] Furthermore, the air conditioning system operates according to personalized parameters, including adjusting air temperature, air speed, air direction, air outlet mode, humidity, and linking the light transmittance of the sunshade and the seat heating.

[0112] Further, based on the judgment of the driver and passengers operating and adjusting the air conditioning panel, if so, the parameters of the air conditioning system, sunshade, and seat heating are collected and analyzed, fed back to the basic human thermal comfort model for further model correction, and the newly created model is stored in the database to realize the memory function, and enters the air conditioning system self-learning loop mode. Through the accumulation of big data, a personalized thermal comfort experience is achieved; if not, the air conditioning system continues to execute according to the personalized parameters, and the process ends.

[0113] Figure 5 This is an electronic device structural block diagram of a vehicle air conditioning automatic control method based on a self-learning strategy provided by one or more embodiments of the present invention.

[0114] like Figure 5 As shown, this application provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0115] The memory stores a computer program that, when executed by the processor, causes the processor to perform steps of a vehicle air conditioning automatic control method based on a self-learning strategy.

[0116] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a vehicle air conditioning automatic control method based on a self-learning strategy.

[0117] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0118] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle air conditioning automatic control method based on a self-learning strategy, characterized in that, Includes the following steps: In response to a vehicle start-up request, it collects cabin environment data, occupant characteristic data, and initial operating parameters of the vehicle's air conditioning system, and integrates and stores the data. Obtain a pre-set basic model of human thermal comfort, complete the calculation of human thermal physiological processes and cabin thermal comfort evaluation based on the collected data, and establish a personalized thermal comfort database for occupants. Based on the human thermal comfort model, personalized air conditioning control parameters are output in reverse. These control parameters include the air conditioning's own operating parameters and the coordinated adjustment parameters of related vehicle equipment. Real-time detection of whether passengers manually adjust the air conditioning; If manual adjustment is detected, the adjustment parameters after the operation are collected, the basic human thermal comfort model is adaptively corrected and updated and stored, and the process is executed according to the corrected parameters and enters the self-learning loop. If no manual adjustment is detected, the air conditioning will be adjusted according to the personalized air conditioning control parameters.

2. The vehicle air conditioning automatic control method based on a self-learning strategy according to claim 1, characterized in that, The cabin environment data includes: cabin temperature distribution data, solar radiation intensity data, and ambient humidity data; The occupant characteristic data includes: occupant facial feature data, limb movement data, clothing feature data, physiological feature data, and weight data.

3. The vehicle air conditioning automatic control method based on a self-learning strategy according to claim 2, characterized in that, The occupant limb movement data is used to analyze the human metabolic rate corresponding to the occupant's activity level. The clothing feature data is used to analyze the thermal resistance of the human body corresponding to the clothing worn by the occupants. The physiological characteristic data is used to classify the types of occupant thermal comfort needs; The facial feature data is used to establish a unique identity for each passenger.

4. The vehicle air conditioning automatic control method based on a self-learning strategy according to claim 1, characterized in that, The calculation of human thermophysiological processes includes the calculation of human heat transfer processes and the calculation of human physiological thermoregulation processes. The cabin thermal comfort evaluation includes both the comfort evaluation of the local thermal environment and the comfort evaluation of the overall thermal environment of the cabin.

5. The vehicle air conditioning automatic control method based on a self-learning strategy according to claim 1, characterized in that, The personalized thermal comfort database for occupants stores occupant unique identifiers, occupant characteristic data, cabin thermal comfort evaluation results, basic human thermal comfort model parameters, and air conditioning control parameters. The database also supports adding, updating, and retrieving data.

6. The automatic control method for vehicle air conditioning based on a self-learning strategy according to claim 1, characterized in that, The basic model for human thermal comfort presets the initial target comfort state as a neutral comfort state; After dynamically adjusting the initial target comfort state based on real-time cabin environmental data and the types of thermal comfort needs of the occupants, the personalized air conditioning control parameters are then output in reverse.

7. The vehicle air conditioning automatic control method based on a self-learning strategy according to claim 1, characterized in that, The air conditioner's own operating parameters include: air outlet temperature, air outlet speed, air outlet direction, air outlet mode, and humidity adjustment parameters. The associated vehicle-mounted equipment includes a cabin sunshade and a seat heating device, and their coordinated adjustment parameters include the sunshade light transmittance parameter and the seat heating parameter.

8. A vehicle air conditioning automatic control system based on a self-learning strategy, characterized in that, include: The system includes a data acquisition module, a model analysis module, a parameter output module, an operation detection module, an air conditioning execution module, and a data storage module. The data acquisition module is used to collect cabin environment data, occupant characteristic data and initial operating parameters of the vehicle air conditioning in response to the vehicle start request, and transmit the collected data to the data storage module. The data storage module is used to integrate and store various types of received data, and pre-stores a basic model of human thermal comfort. The model analysis module is used to retrieve the preset human thermal comfort basic model and various collected data from the data storage module, complete the calculation of human thermal physiological processes and cabin thermal comfort evaluation, and at the same time establish and store the occupant personalized thermal comfort database to the data storage module. When manual adjustment is detected, the system adaptively corrects the basic human thermal comfort model based on the adjustment parameters after the operation, updates and stores the corrected model in the data storage module, and transmits the corrected adjustment parameters to the air conditioning execution module, enabling the system to enter a self-learning loop; when no manual adjustment is detected, the system controls the air conditioning execution module to continuously adjust according to the personalized air conditioning control parameters. The parameter output module is used to output personalized air conditioning control parameters to the air conditioning execution module based on the human thermal comfort basic model processed by the model analysis module. The control parameters include the air conditioning's own operating parameters and the coordinated adjustment parameters of the associated vehicle equipment. The operation detection module is used to detect in real time whether the occupant manually adjusts the air conditioner and feeds the detection results back to the model analysis module. The air conditioning execution module is used to receive personalized air conditioning control parameters from the parameter output module and perform control operations. It is also used to collect the control parameters after the operation and transmit them to the model analysis module when manual adjustment is detected, and to receive the control parameters corrected by the model analysis module and perform the adjustment.

9. An electronic device, characterized in that, include: The processor, communication interface, memory, and communication bus are connected, with the processor, communication interface, and memory communicating with each other via the communication bus. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the automatic control method for vehicle air conditioning based on a self-learning strategy as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a vehicle air conditioning automatic control method based on a self-learning strategy as described in any one of claims 1-7.

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