Vehicle seat self-adaptive adjusting method and system based on environment perception and medium

Abstract

The invention discloses a vehicle seat self-adaptive adjusting method and system based on environment perception and a medium. The method comprises the steps that physiological state data and human body image data of a target passenger and cabin environment data of a target cabin are obtained; determining body surface temperature and humidity, heart rate parameters and human body metabolic rate of the target passenger according to the physiological state data, and identifying clothing thermal resistance parameters of the target passenger according to the human body image data; determining a human body thermal comfort index of the target cabin according to the human body metabolic rate, the clothing thermal resistance parameter and the cabin environment data; inputting the body surface temperature and humidity, the heart rate parameter and the human body thermal comfort index into a pre-trained thermal comfort demand prediction model to obtain a target thermal comfort demand of the target passenger; and performing heating adjustment and / or ventilation adjustment on the seat of the target cabin according to the target thermal comfort demand. The riding comfort and riding experience of the user are improved, and the method can be widely applied to the technical field of vehicle control.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a vehicle seat adaptive adjustment method, system and medium based on environment perception. Background Art

[0002] Vehicle seat adjustment is a core function to ensure driving safety and riding comfort, and usually includes the following adjustment methods:

[0003] 1) Manual adjustment: that is, adjusting the seat position through a pull rod, knob or lever, including forward and backward movement, height adjustment and backrest angle adjustment, etc., with a simple structure, low cost and no need to rely on electricity;

[0004] 2) Electric adjustment: that is, multi-directional adjustment is achieved by controlling the motor through buttons, including front and rear / height adjustment, lumbar support adjustment, etc. It also has a memory function, making seat adjustment more accurate and convenient;

[0005] 3) Intelligent adjustment: This means using sensors to monitor the user's body shape and sitting posture, automatically adjusting the support strength (such as airbag inflation), and can also be linked to the navigation system or driving mode to dynamically optimize the seat angle.

[0006] However, most of the existing vehicle seat adjustment functions are aimed at adjusting the seat position and posture, and are unable to adaptively adjust the seat heating and ventilation conditions according to the physiological characteristics of the occupants and the environmental characteristics of the vehicle, affecting the user's riding comfort and riding experience. Summary of the invention

[0007] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0008] To this end, an object of an embodiment of the present invention is to provide a vehicle seat adaptive adjustment method based on environmental perception, which improves the user's riding comfort and riding experience.

[0009] Another object of an embodiment of the present invention is to provide a vehicle seat adaptive adjustment system based on environmental perception.

[0010] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include:

[0011] In a first aspect, an embodiment of the present invention provides a method for adaptively adjusting a vehicle seat based on environment perception, comprising the following steps:

[0012] Acquiring physiological status data, human body image data of the target occupant and cockpit environment data of the target cockpit;

[0013] Determining the body surface temperature and humidity, heart rate parameters, and human metabolic rate of the target occupant according to the physiological state data, and identifying clothing thermal resistance parameters of the target occupant according to the human body image data;

[0014] Determining a human thermal comfort index of the target cabin according to the human metabolic rate, the clothing thermal resistance parameter, and the cabin environment data;

[0015] Inputting the body surface temperature and humidity, the heart rate parameters, and the human thermal comfort index into a pre-trained thermal comfort demand prediction model to obtain the target thermal comfort demand of the target occupant;

[0016] The seats of the target cabin are heated and / or ventilated according to the target thermal comfort demand.

[0017] Furthermore, in one embodiment of the present invention, the step of acquiring physiological state data of the target occupant, human body image data, and cabin environment data of the target cabin specifically includes:

[0018] The body surface temperature and humidity, the heart rate parameters and the blood pressure parameters of the target occupant are obtained by using a human body temperature and humidity sensor, a heart rate sensor and a blood pressure sensor arranged on the seat to obtain the physiological state data;

[0019] Acquiring the human body image data through a vehicle-mounted camera;

[0020] The air temperature and humidity, average radiation temperature and air flow rate of the target cabin are obtained by an air temperature and humidity sensor, a thermal radiation sensor and an air flow rate sensor arranged in the target cabin, so as to obtain the cabin environment data.

[0021] Further, in one embodiment of the present invention, the human metabolic rate is obtained by the following steps:

[0022] determining a pulse rate of the target occupant according to the heart rate parameter;

[0023] determining a pulse pressure difference of the target occupant according to the blood pressure parameter;

[0024] The human metabolic rate is calculated according to the pulse rate and the pulse pressure difference.

[0025] Further, in one embodiment of the present invention, the identifying clothing thermal resistance parameters of the target occupant according to the human body image data specifically includes:

[0026] Obtaining the clothing type of the target occupant according to the human body image data;

[0027] The clothing thermal resistance parameter is determined according to a preset clothing thermal resistance estimation model and the clothing type.

[0028] Furthermore, in one embodiment of the present invention, determining the human thermal comfort index of the target cabin according to the human metabolic rate, the clothing thermal resistance parameter and the cabin environment data specifically includes:

[0029] determining air temperature, air humidity, mean radiant temperature and air velocity according to the cabin environment data;

[0030] The air temperature, the air humidity, the average radiation temperature, the air velocity, the human metabolic rate and the clothing thermal resistance parameter are substituted into the Fanger thermal comfort equation to obtain the human thermal comfort index.

[0031] Furthermore, in one embodiment of the present invention, the thermal comfort demand prediction model is trained by the following steps:

[0032] Obtaining sample body surface temperature and humidity, sample heart rate parameters, and sample indicators of thermal comfort of the test cabin environment of the test person, and recording seat adjustment information of the test person in the test cabin environment;

[0033] Determining a training sample according to the sample body surface temperature and humidity, the sample heart rate parameter, and the sample index of human thermal comfort, and determining a corresponding thermal comfort demand label according to the seat adjustment information;

[0034] Inputting the training samples into a pre-built deep learning neural network to obtain a thermal comfort demand prediction result;

[0035] determining a loss value according to the thermal comfort demand prediction result and the thermal comfort demand label;

[0036] Updating the parameters of the deep learning neural network according to the loss value to obtain the trained thermal comfort demand prediction model;

[0037] The seat adjustment information includes seat heating adjustment information and seat ventilation adjustment information, and the thermal comfort requirement label includes a heating requirement level label and a ventilation requirement level label.

[0038] Further, in one embodiment of the present invention, the heating and / or ventilation adjustment of the seats of the target cabin according to the target thermal comfort requirement specifically includes:

[0039] determining a target heating requirement level and a target ventilation requirement level for the target occupant according to the target thermal comfort requirement;

[0040] When the target heating requirement level is not 0, determining a target temperature and a heating gradient according to the target heating requirement level, and then performing heating adjustment on the seat of the target cabin according to the target temperature and the heating gradient;

[0041] When the target ventilation requirement level is not 0, a target ventilation intensity is determined according to the target ventilation requirement level, and then ventilation is adjusted for the seats in the target cabin according to the target ventilation intensity.

[0042] In a second aspect, an embodiment of the present invention provides a vehicle seat adaptive adjustment system based on environment perception, comprising:

[0043] A data acquisition module, used to acquire physiological state data of the target occupant, human body image data, and cabin environment data of the target cabin;

[0044] a parameter determination module, configured to determine the body surface temperature and humidity, heart rate parameters, and human metabolic rate of the target occupant according to the physiological state data, and to identify clothing thermal resistance parameters of the target occupant according to the human body image data;

[0045] A human thermal comfort index determination module, used to determine the human thermal comfort index of the target cabin according to the human metabolic rate, the clothing thermal resistance parameter and the cabin environment data;

[0046] A thermal comfort demand prediction module, used for inputting the body surface temperature and humidity, the heart rate parameters and the human thermal comfort index into a pre-trained thermal comfort demand prediction model to obtain the target thermal comfort demand of the target occupant;

[0047] The seat adjustment module is used to perform heating and / or ventilation adjustment on the seats of the target cabin according to the target thermal comfort demand.

[0048] In a third aspect, an embodiment of the present invention provides a vehicle seat adaptive adjustment device based on environment perception, comprising:

[0049] at least one processor;

[0050] at least one memory for storing at least one program;

[0051] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned method for adaptive adjustment of a vehicle seat based on environmental perception.

[0052] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to execute the above-mentioned method for adaptive adjustment of a vehicle seat based on environmental perception.

[0053] The advantages and beneficial effects of the present invention will be partly given in the following description, partly become apparent from the following description, or be understood through the practice of the present invention:

[0054] The embodiment of the present invention obtains the physiological state data, human image data and cabin environment data of the target occupant, determines the body surface temperature and humidity, heart rate parameters and human metabolic rate of the target occupant according to the physiological state data, identifies the clothing thermal resistance parameters of the target occupant according to the human image data, determines the human thermal comfort index of the target cabin according to the human metabolic rate, clothing thermal resistance parameters and cabin environment data, inputs the body surface temperature and humidity, heart rate parameters and human thermal comfort index into a pre-trained thermal comfort demand prediction model, obtains the target thermal comfort demand of the target occupant, and performs heating and / or ventilation adjustment on the seat of the target cabin according to the target thermal comfort demand. The embodiment of the present invention determines the human thermal comfort index based on the human metabolic rate, clothing thermal resistance parameters and cabin environment data of the occupant, predicts the target thermal comfort demand of the occupant according to the body surface temperature and humidity, heart rate parameters and the human thermal comfort index, so that the seat of the cabin can be heated and / or ventilated according to the target thermal comfort demand, realizes the adaptive adjustment of the heating and ventilation conditions of the vehicle seat, and improves the riding comfort and riding experience of the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solution in the embodiments of the present invention, the following introduction is made to the drawings required for use in the embodiments of the present invention. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solution of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0056] Figure 1 A flowchart of a method for adaptively adjusting a vehicle seat based on environmental perception provided by an embodiment of the present invention;

[0057] Figure 2 A structural block diagram of a vehicle seat adaptive adjustment system based on environment perception provided by an embodiment of the present invention;

[0058] Figure 3 A structural block diagram of a vehicle seat adaptive adjustment device based on environment perception provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0060] In the description of the present invention, the meaning of "a plurality" is two or more than two. If there is a description of "a first" or "a second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used in this document have the same meaning as those commonly understood by those skilled in the art.

[0061] Reference Figure 1 The embodiment of the present invention provides a vehicle seat adaptive adjustment method based on environment perception, which specifically includes the following steps:

[0062] S101, acquiring physiological state data, human body image data of a target occupant, and cockpit environment data of a target cockpit;

[0063] S102, determining the body surface temperature and humidity, heart rate parameters, and human metabolic rate of the target occupant based on the physiological state data, and identifying the clothing thermal resistance parameters of the target occupant based on the human body image data;

[0064] S103, determining a human thermal comfort index of a target cabin according to a human metabolic rate, clothing thermal resistance parameters, and cabin environment data;

[0065] S104, inputting body surface temperature and humidity, heart rate parameters, and human thermal comfort index into a pre-trained thermal comfort demand prediction model to obtain a target thermal comfort demand of the target occupant;

[0066] S105: Perform heating and / or ventilation adjustment on seats in the target cabin according to the target thermal comfort requirement.

[0067] Specifically, the embodiment of the present invention determines a human thermal comfort index based on the occupant's human metabolic rate, clothing thermal resistance parameters and cabin environment data, and predicts the occupant's target thermal comfort needs according to the occupant's body surface temperature and humidity, heart rate parameters and the human thermal comfort index. The cabin seats can then be heated and / or ventilated according to the target thermal comfort needs, thereby achieving adaptive adjustment of the heating and ventilation conditions of the vehicle seats and improving the user's riding comfort and riding experience.

[0068] As an optional implementation, the physiological state data of the target occupant, the human body image data and the cabin environment data of the target cabin are obtained, which specifically includes:

[0069] S1011, obtaining body surface temperature and humidity, heart rate parameters, and blood pressure parameters of the target occupant through a human body temperature and humidity sensor, a heart rate sensor, and a blood pressure sensor disposed on the seat to obtain physiological state data;

[0070] S1012, obtaining human body image data through a vehicle-mounted camera;

[0071] S1013, obtaining the air temperature and humidity, average radiation temperature and air flow rate of the target cabin through the air temperature and humidity sensor, thermal radiation sensor and air flow rate sensor installed in the target cabin, and obtaining cabin environment data.

[0072] Specifically, the body temperature, heart rate, surface humidity, blood pressure and other data of the occupants are collected in real time through the human body temperature and humidity sensors, piezoelectric film heart rate sensors (such as medical-grade ECG modules) and contact blood pressure sensors built into the seats; the body image data of the occupants is obtained through the on-board visual experience, and the body parts of the occupants need to be covered to facilitate the subsequent identification of the thermal resistance parameters of the clothing; the cabin environment data is collected in real time through the high-precision temperature and humidity sensors, thermal radiation sensors and air flow rate sensors distributed in the car. The sensors are usually installed near the cockpit or in the ventilation duct to ensure the comprehensiveness of the data; the sensor data is transmitted to the domain controller via the CAN bus or the on-board Ethernet, and the Kalman filter algorithm is used to eliminate environmental noise interference.

[0073] As an optional embodiment, the human metabolic rate is obtained by the following steps:

[0074] S1021, determining the pulse rate of the target occupant according to the heart rate parameter;

[0075] S1022, determining the pulse pressure difference of the target occupant according to the blood pressure parameter;

[0076] S1023. Calculate the human metabolic rate based on the pulse rate and pulse pressure difference.

[0077] Specifically, the human metabolic rate is an important parameter for calculating the thermal comfort index of the human body. In the embodiment of the present invention, the human metabolic rate is calculated according to the pulse rate and the pulse pressure difference. The specific formula is as follows:

[0078] Human metabolic rate% = (pulse rate + pulse pressure difference) - 111

[0079] Among them, since the pulse rate is consistent with the heart rate under normal circumstances, the pulse rate can be determined based on the heart rate parameters; the pulse pressure difference is equal to the difference between the systolic pressure and the diastolic pressure, and the systolic pressure and diastolic pressure can be determined based on the blood pressure parameters.

[0080] As an optional implementation, identifying clothing thermal resistance parameters of the target occupant according to the human body image data specifically includes:

[0081] S1024, obtaining the clothing type of the target occupant according to the human body image data recognition;

[0082] S1025. Determine clothing thermal resistance parameters according to a preset clothing thermal resistance estimation model and clothing type.

[0083] Specifically, according to the human body image data, the CNN algorithm is used to perform occupant clothing image recognition to obtain the corresponding clothing type; according to the clothing type, the corresponding clothing thermal resistance parameters are determined in combination with a preset clothing thermal resistance estimation model, for example, short sleeves are about 0.5 clo, and thick coats are about 1.5 clo. The clothing thermal resistance parameters corresponding to different clothing types can be calibrated in advance through experiments, which will not be elaborated here.

[0084] As an optional implementation, the human thermal comfort index of the target cabin is determined according to the human metabolic rate, clothing thermal resistance parameters and cabin environment data, which specifically includes:

[0085] S1031. Determine air temperature, air humidity, average radiation temperature, and air velocity according to cabin environment data;

[0086] S1032. Substitute air temperature, air humidity, mean radiation temperature, air velocity, human metabolic rate, and clothing thermal resistance parameters into the Fanger thermal comfort equation to obtain a human thermal comfort index.

[0087] Specifically, the human thermal comfort index (PMV, Predicted Mean Vote) is a comprehensive evaluation index based on the basic equation of human thermal balance and the level of psychological and physiological subjective thermal sensation, and takes into account many relevant factors of human thermal comfort. The human thermal comfort index can be calculated based on the Fanger thermal comfort equation, and the relevant formula is as follows:

[0088] PMV=[0.303exp(-0.036M)+0.0275]*{MW-3.05[5.733-0.007(MW)-Pa]-0.42(MW-58.2)-0. 0173M(5.867-Pa)-0.0014M(34-ta)-3.96*10fcl[(tcl+273)-(tr+273)]-fclhc(tcl-ta)}

[0089] Among them, PMV represents the thermal comfort index of human body, M represents the metabolic rate of human body (unit: W / m 2 ), W represents the mechanical work done by the human body (unit: W / m 2 , which can be regarded as 0 in the riding scenario), Pa represents the partial pressure of air water vapor (unit: kPa), which can be calculated by air humidity and saturated water vapor pressure (relative humidity × saturated water vapor pressure), ta represents air temperature (unit: ℃), fcl represents clothing area coefficient (fcl=1.0+0.2Icl, Icl represents clothing thermal resistance parameter), tcl represents clothing surface temperature (solved by iterative method, the initial value can be set to tcl=35.7-0.028(MW)), tr represents the average radiation temperature, hc represents the convection and radiation heat transfer coefficient (when air velocity ≤0.2m / s, hc=3.5, when air velocity>0.2m / s, hc=8.6v^{0.6}, v represents air velocity).

[0090] After calculating the human thermal comfort index, the body surface temperature and humidity, heart rate parameters and the human thermal comfort index are input into the pre-trained thermal comfort demand prediction model to predict the individual's thermal comfort demand.

[0091] As an optional implementation, the thermal comfort demand prediction model is trained by the following steps:

[0092] S201, obtaining sample body surface temperature and humidity, sample heart rate parameters, and sample indicators of thermal comfort of the test person in the test cabin environment, and recording seat adjustment information of the test person in the test cabin environment;

[0093] S202, determining a training sample according to the sample body surface temperature and humidity, the sample heart rate parameters, and the sample index of human thermal comfort, and determining a corresponding thermal comfort demand label according to the seat adjustment information;

[0094] S203, inputting the training samples into a pre-built deep learning neural network to obtain a thermal comfort demand prediction result;

[0095] S204, determining a loss value according to the thermal comfort demand prediction result and the thermal comfort demand label;

[0096] S205. Update the parameters of the deep learning neural network according to the loss value to obtain a trained thermal comfort demand prediction model;

[0097] Among them, the seat adjustment information includes seat heating adjustment information and seat ventilation adjustment information, and the thermal comfort demand label includes a heating demand level label and a ventilation demand level label.

[0098] Specifically, the input data of the thermal comfort demand prediction model include the human thermal comfort sample index of the test cabin environment and the sample surface temperature and humidity and sample heart rate parameters of the test personnel in the test cabin environment. The model architecture can adopt a deep learning neural network, and the model output is the thermal comfort demand. The sample surface temperature and humidity, sample heart rate parameters of the test personnel and the sample thermal comfort sample index of the test cabin environment are obtained, and the corresponding thermal comfort demand label is determined based on the seat adjustment information of the test personnel in the test cabin environment. For example, when the test personnel does not make any adjustments, the heating demand level label and the ventilation demand level label are determined to be 0. If the test personnel adjusts the seat heating state, the heating demand level label is determined according to the corresponding adjustment amplitude. If the test personnel adjusts the seat ventilation state, the ventilation demand level label is also determined according to the corresponding adjustment amplitude.

[0099] The direction sample surface temperature and humidity, sample heart rate parameters and human thermal comfort sample index are input as training samples into the deep learning neural network for training. After the training samples are input into the initialized deep learning neural network, the recognition result output by the model can be obtained, that is, the thermal comfort demand prediction result. The accuracy of the model recognition can be evaluated according to the thermal comfort demand prediction result and the aforementioned thermal comfort demand label, so as to update the parameters of the model. For the thermal comfort demand prediction model, the accuracy of the model recognition result can be measured by the loss function. The loss function is defined on a single training data and is used to measure the prediction error of a training data. Specifically, the loss value of the training data is determined by the label of a single training data and the prediction result of the model for the training data. In actual training, a training data set has a lot of training data, so the cost function is generally used to measure the overall error of the training data set. The cost function is defined on the entire training data set and is used to calculate the average value of the prediction error of all training data, which can better measure the prediction effect of the model. For general machine learning models, the aforementioned cost function, plus a regularization term that measures the complexity of the model, can be used as the objective function of training, and the loss value of the entire training data set can be calculated based on the objective function. There are many types of commonly used loss functions, such as 0-1 loss function, square loss function, absolute loss function, logarithmic loss function, cross entropy loss function, etc., which can all be used as loss functions of machine learning models, which will not be elaborated one by one here. In an embodiment of the present invention, any one of the loss functions can be selected to determine the loss value of training. Based on the loss value of training, the back propagation algorithm is used to update the parameters of the model, and a trained thermal comfort demand prediction model can be obtained after several rounds of iteration. The specific number of iterations can be set in advance, or the training is considered to be completed when the test set meets the accuracy requirements.

[0100] By inputting the target occupant's body surface temperature and humidity, heart rate parameters, and the human thermal comfort index of the target cabin into the trained thermal comfort demand prediction model, the real-time target thermal comfort demand of the target occupant inferred by the model can be obtained.

[0101] As an optional implementation, heating and / or ventilation adjustment is performed on the seats of the target cabin according to the target thermal comfort requirement, which specifically includes:

[0102] S1051. Determine a target heating requirement level and a target ventilation requirement level for a target occupant according to a target thermal comfort requirement;

[0103] S1052: when the target heating requirement level is not 0, determining a target temperature and a heating gradient according to the target heating requirement level, and then heating and adjusting the seats in the target cabin according to the target temperature and the heating gradient;

[0104] S1053: When the target ventilation requirement level is not 0, determine a target ventilation intensity according to the target ventilation requirement level, and then adjust the ventilation of the seats in the target cabin according to the target ventilation intensity.

[0105] Specifically, the target heating requirement level and target ventilation requirement level of the target occupant are determined according to the target thermal comfort requirement; when the target heating requirement level is not 0, the target temperature and heating gradient are determined according to the target heating requirement level, and then the seats in the target cabin are heated and adjusted according to the target temperature and heating gradient. For example, when the body surface temperature is <32°C, gradient heating is started, and the temperature is increased by 2°C every 5 minutes to the set threshold. When the passenger's heart rate is detected to be abnormally high (>120bpm) and the body surface temperature exceeds 37.5°C, the system determines it as a heat stress state and triggers the active cooling mode; when the target ventilation requirement level is not 0, the target ventilation intensity is determined according to the target ventilation requirement level, and then the seats in the target cabin are ventilated and adjusted according to the target ventilation intensity (3 adjustable levels, maximum wind speed 5m / s).

[0106] In some optional embodiments, a PTC heating film and a micro centrifugal fan array are embedded inside the seat, supporting 8-zone independent temperature control, stepless adjustment of heating power from 0 to 50W, precise control of ventilation volume from 0 to 15 CFM, and pulse width modulation (PWM) technology to achieve energy consumption optimization. Combined with the battery management system (BMS), power distribution is dynamically adjusted to avoid excessive power consumption, ensuring that while providing a comfortable riding experience, energy consumption is reduced and vehicle endurance is extended.

[0107] In some optional embodiments, the real-time comfort index is displayed through the vehicle display or mobile phone APP, allowing users to customize preferences (such as elderly mode / sports mode). In addition, the system has self-learning capabilities and can automatically memorize and apply personalized settings based on the passengers' historical preferences and real-time feedback, thereby achieving customized seat adjustment.

[0108] The method steps of the embodiment of the present invention are described above. It can be understood that the embodiment of the present invention determines the human thermal comfort index based on the occupant's human metabolic rate, clothing thermal resistance parameters and cabin environment data of the cabin, and predicts the occupant's target thermal comfort needs based on the occupant's body surface temperature and humidity, heart rate parameters and the human thermal comfort index, so that the cabin seat can be heated and / or ventilated according to the target thermal comfort needs, realizing adaptive adjustment of the heating and ventilation conditions of the vehicle seat, and improving the user's riding comfort and riding experience.

[0109] Reference Figure 2 , an embodiment of the present invention provides a vehicle seat adaptive adjustment system based on environment perception, comprising:

[0110] A data acquisition module, used to acquire physiological state data of the target occupant, human body image data, and cabin environment data of the target cabin;

[0111] A parameter determination module, used to determine the body surface temperature and humidity, heart rate parameters and human metabolic rate of the target occupant based on the physiological state data, and to identify the clothing thermal resistance parameters of the target occupant based on the human body image data;

[0112] A human thermal comfort index determination module is used to determine the human thermal comfort index of the target cabin based on the human metabolic rate, clothing thermal resistance parameters and cabin environment data;

[0113] The thermal comfort demand prediction module is used to input the body surface temperature and humidity, heart rate parameters and human thermal comfort index into the pre-trained thermal comfort demand prediction model to obtain the target thermal comfort demand of the target occupant;

[0114] The seat adjustment module is used to adjust the heating and / or ventilation of the seats in the target cabin according to the target thermal comfort requirements.

[0115] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0116] Reference Figure 3 The embodiment of the present invention provides a vehicle seat adaptive adjustment device based on environment perception, comprising:

[0117] at least one processor;

[0118] at least one memory for storing at least one program;

[0119] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned method for adaptively adjusting a vehicle seat based on environmental perception.

[0120] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0121] An embodiment of the present invention further provides a computer-readable storage medium, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to execute the above-mentioned method for adaptive adjustment of a vehicle seat based on environmental perception.

[0122] A computer-readable storage medium of an embodiment of the present invention can execute a vehicle seat adaptive adjustment method based on environmental perception provided by a method embodiment of the present invention, can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0123] The embodiment of the present invention also discloses a computer program product or a computer program, wherein the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes Figure 1 The method shown.

[0124] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0125] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified to the contrary, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can 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 the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0126] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0128] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the above-mentioned program is printed, since the above-mentioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in other suitable ways as necessary, and then stored in a computer memory.

[0129] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0130] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0131] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0132] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A vehicle seat adaptive adjustment method based on environment perception, characterized in that: The following steps are involved: Acquiring physiological status data, human body image data of the target occupant and cockpit environment data of the target cockpit; Determining the body surface temperature and humidity, heart rate parameters, and human metabolic rate of the target occupant according to the physiological state data, and identifying clothing thermal resistance parameters of the target occupant according to the human body image data; Determining a human thermal comfort index of the target cabin according to the human metabolic rate, the clothing thermal resistance parameter, and the cabin environment data; Inputting the body surface temperature and humidity, the heart rate parameters, and the human thermal comfort index into a pre-trained thermal comfort demand prediction model to obtain the target thermal comfort demand of the target occupant; The seats of the target cabin are heated and / or ventilated according to the target thermal comfort demand.

2. The method for adaptively adjusting a vehicle seat based on environment perception according to claim 1, characterized in that: The step of acquiring physiological state data of the target occupant, human body image data, and cabin environment data of the target cabin specifically includes: The body surface temperature and humidity, the heart rate parameters and the blood pressure parameters of the target occupant are obtained by using a human body temperature and humidity sensor, a heart rate sensor and a blood pressure sensor arranged on the seat to obtain the physiological state data; Acquiring the human body image data through a vehicle-mounted camera; The air temperature and humidity, average radiation temperature and air flow rate of the target cabin are obtained by an air temperature and humidity sensor, a thermal radiation sensor and an air flow rate sensor arranged in the target cabin, so as to obtain the cabin environment data.

3. The method for adaptively adjusting a vehicle seat based on environment perception according to claim 2, characterized in that: The human metabolic rate is obtained by the following steps: determining a pulse rate of the target occupant according to the heart rate parameter; determining a pulse pressure difference of the target occupant according to the blood pressure parameter; The human metabolic rate is calculated according to the pulse rate and the pulse pressure difference.

4. The method for adaptively adjusting a vehicle seat based on environment perception according to claim 1, characterized in that: The step of identifying the clothing thermal resistance parameter of the target occupant according to the human body image data specifically includes: Obtaining the clothing type of the target occupant according to the human body image data; The clothing thermal resistance parameter is determined according to a preset clothing thermal resistance estimation model and the clothing type.

5. The method for adaptively adjusting a vehicle seat based on environment perception according to claim 1, characterized in that: The step of determining the human thermal comfort index of the target cabin according to the human metabolic rate, the clothing thermal resistance parameter and the cabin environment data specifically includes: determining air temperature, air humidity, mean radiant temperature and air velocity according to the cabin environment data; The air temperature, the air humidity, the average radiation temperature, the air velocity, the human metabolic rate and the clothing thermal resistance parameter are substituted into the Fanger thermal comfort equation to obtain the human thermal comfort index.

6. The method for adaptively adjusting a vehicle seat based on environment perception according to claim 1, characterized in that: The thermal comfort demand prediction model is trained by the following steps: Obtaining sample body surface temperature and humidity, sample heart rate parameters, and sample indicators of thermal comfort of the test cabin environment of the test person, and recording seat adjustment information of the test person in the test cabin environment; Determining a training sample according to the sample body surface temperature and humidity, the sample heart rate parameter, and the sample index of human thermal comfort, and determining a corresponding thermal comfort demand label according to the seat adjustment information; Inputting the training samples into a pre-built deep learning neural network to obtain a thermal comfort demand prediction result; determining a loss value according to the thermal comfort demand prediction result and the thermal comfort demand label; Updating the parameters of the deep learning neural network according to the loss value to obtain the trained thermal comfort demand prediction model; The seat adjustment information includes seat heating adjustment information and seat ventilation adjustment information, and the thermal comfort requirement label includes a heating requirement level label and a ventilation requirement level label.

7. A vehicle seat adaptive adjustment method based on environment perception according to any one of claims 1 to 6, characterized in that: The step of heating and / or ventilating the seats of the target cabin according to the target thermal comfort requirement specifically includes: determining a target heating requirement level and a target ventilation requirement level for the target occupant according to the target thermal comfort requirement; When the target heating requirement level is not 0, determining a target temperature and a heating gradient according to the target heating requirement level, and then performing heating adjustment on the seat of the target cabin according to the target temperature and the heating gradient; When the target ventilation requirement level is not 0, a target ventilation intensity is determined according to the target ventilation requirement level, and then ventilation is adjusted for the seats in the target cabin according to the target ventilation intensity.

8. A vehicle seat adaptive adjustment system based on environmental perception, characterized in that: include: A data acquisition module, used to acquire physiological state data of the target occupant, human body image data, and cabin environment data of the target cabin; a parameter determination module, configured to determine the body surface temperature and humidity, heart rate parameters, and human metabolic rate of the target occupant according to the physiological state data, and to identify clothing thermal resistance parameters of the target occupant according to the human body image data; A human thermal comfort index determination module, used to determine the human thermal comfort index of the target cabin according to the human metabolic rate, the clothing thermal resistance parameter and the cabin environment data; A thermal comfort demand prediction module, used for inputting the body surface temperature and humidity, the heart rate parameters and the human thermal comfort index into a pre-trained thermal comfort demand prediction model to obtain the target thermal comfort demand of the target occupant; The seat adjustment module is used to perform heating and / or ventilation adjustment on the seats of the target cabin according to the target thermal comfort demand.

9. A vehicle seat adaptive adjustment device based on environmental perception, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the vehicle seat adaptive adjustment method based on environmental perception as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to execute a vehicle seat adaptive adjustment method based on environmental perception as claimed in any one of claims 1 to 7 when executed by the processor.

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