Vehicle cabin preconditioning method, system, apparatus, and storage medium

Abstract

The application discloses a vehicle cabin pre-adjustment method, system, device and medium, comprising: obtaining the in-vehicle environment information and the out-of-vehicle environment information of a target vehicle, and obtaining the driving and riding personnel distribution information input by a target occupant; performing feature fusion on the in-vehicle environment information, the out-of-vehicle environment information and the driving and riding personnel distribution information to obtain multi-dimensional cabin driving and riding state information; inputting the multi-dimensional cabin driving and riding state information into a pre-trained cabin adjustment parameter prediction model to obtain a target cabin adjustment parameter; pre-adjusting the vehicle cabin of the target vehicle according to the target cabin adjustment parameter, and feeding back the pre-adjustment result to the target occupant. The application realizes pre-adjustment of the vehicle cabin before the driving and riding personnel get on the vehicle, improves the travel efficiency and driving and riding experience of the driving and riding personnel, and can be widely applied to the technical field of vehicle control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, and in particular to a vehicle cabin pre-adjustment method, system, device and storage medium. BACKGROUND

[0002] With the development of intelligent networking of automobiles, vehicle monitoring and control technology is becoming more and more intelligent, bringing more and more rich driving experience to drivers and passengers. At present, the adjustment of the vehicle cabin is mostly completed after the drivers and passengers get on the vehicle. In hot summer or cold winter, the drivers and passengers often need to wait for a period of time to reach a comfortable indoor temperature after getting on the vehicle, and the indoor light and odor also need to be manually adjusted by the drivers and passengers after getting on the vehicle, which brings inconvenience to the drivers and passengers and affects the travel efficiency and driving experience of the drivers and passengers. SUMMARY

[0003] The present application aims to at least partly solve one of the problems in the prior art.

[0004] To this end, one object of the present application is to provide a vehicle cabin pre-adjustment method, which realizes the pre-adjustment of the vehicle cabin before the drivers and passengers get on the vehicle, and improves the travel efficiency and driving experience of the drivers and passengers.

[0005] Another object of the present application is to provide a vehicle cabin pre-adjustment system.

[0006] In order to achieve the above technical purpose, the technical solution adopted by the present application comprises:

[0007] In a first aspect, the present application provides a vehicle cabin pre-adjustment method, comprising the following steps:

[0008] Obtaining the indoor environment information and the outdoor environment information of a target vehicle, and obtaining the driver and passenger distribution information input by a target passenger;

[0009] Performing feature fusion on the indoor environment information, the outdoor environment information and the driver and passenger distribution information to obtain multi-dimensional cabin driving state information;

[0010] Inputting the multi-dimensional cabin driving state information into a pre-trained cabin adjustment parameter prediction model to obtain a target cabin adjustment parameter;

[0011] Pre-adjusting the vehicle cabin of the target vehicle according to the target cabin adjustment parameter, and feeding back the pre-adjustment result to the target passenger.

[0012] Furthermore, in one embodiment of the present invention, the in-vehicle environment information includes temperature distribution information, brightness distribution information, and odor distribution information inside the target vehicle; the out-of-vehicle environment information includes temperature conditions and lighting conditions outside the target vehicle; and the occupant distribution information includes the number of occupants and their seating positions.

[0013] Furthermore, in one embodiment of the present invention, the step of acquiring the in-vehicle environment information and the external environment information of the target vehicle, and acquiring the driver and passenger distribution information input by the target occupants, specifically includes:

[0014] The temperature distribution information is obtained by installing multiple first temperature sensors inside the target vehicle.

[0015] The brightness distribution information is obtained by multiple first light sensors installed inside the target vehicle;

[0016] The odor distribution information is obtained by installing multiple odor sensors inside the target vehicle.

[0017] The air temperature information is obtained by a second temperature sensor located on the outside of the target vehicle.

[0018] The illumination condition information is obtained by a second light sensor installed on the exterior of the target vehicle.

[0019] The distribution information of the driver and passengers is obtained through the user terminal of the target occupant;

[0020] The user terminal is used to display a cabin area distribution map of the target vehicle, and to determine several target cabin areas selected by the target occupant based on the selection operation of the target occupant, thereby using the target cabin areas as the seating positions of the driver and passengers, and obtaining the distribution information of the driver and passengers.

[0021] Furthermore, in one embodiment of the present invention, the step of fusing features of the in-vehicle environment information, the external environment information, and the passenger distribution information to obtain multi-dimensional cockpit driving status information specifically includes:

[0022] A temperature distribution map is generated based on the temperature distribution information, and a temperature distribution feature vector is extracted based on the temperature distribution map. Then, the temperature distribution feature vector is subjected to dimensionality-upgrading processing based on the air temperature condition information to obtain a first feature vector.

[0023] A brightness distribution map is generated based on the brightness distribution information, and a brightness distribution feature vector is extracted based on the brightness distribution map. Then, the brightness distribution feature vector is upgraded based on the illumination condition information to obtain a second feature vector.

[0024] An odor distribution map is generated based on the odor distribution information, and a third feature vector is extracted based on the odor distribution map;

[0025] A driver and passenger distribution map is generated based on the driver and passenger distribution information, and a fourth feature vector is extracted based on the driver and passenger distribution map;

[0026] The first feature vector, the second feature vector, the third feature vector, and the fourth feature vector are concatenated to obtain the multi-dimensional cockpit driving status information.

[0027] Furthermore, in one embodiment of the present invention, the vehicle cabin pre-adjustment method further includes the step of pre-training the cabin adjustment parameter prediction model, which specifically includes:

[0028] Acquire multiple preset cockpit driving state sample data, and determine the cockpit parameter adjustment labels corresponding to each cockpit driving state sample data through manual annotation;

[0029] A training dataset is constructed based on the cockpit ride status sample data and the corresponding cockpit parameter adjustment labels;

[0030] The training dataset is input into a pre-built convolutional neural network for training to obtain a trained cabin adjustment parameter prediction model.

[0031] The cabin parameter adjustment labels include air conditioning temperature adjustment labels, air conditioning air volume adjustment labels, seat temperature adjustment labels, light brightness adjustment labels, and air purification adjustment labels for each cabin area.

[0032] Furthermore, in one embodiment of the present invention, the step of inputting the training dataset into a pre-constructed convolutional neural network for training to obtain the trained cabin adjustment parameter prediction model specifically includes:

[0033] The training dataset is input into the convolutional neural network to obtain the cabin adjustment parameter prediction results;

[0034] The loss value of the convolutional neural network is determined based on the predicted results of the cabin adjustment parameters and the cabin parameter adjustment labels.

[0035] The model parameters of the convolutional neural network are updated using the backpropagation algorithm based on the loss value, and the process of inputting the training dataset into the convolutional neural network is returned.

[0036] When the loss value reaches a preset first threshold, training stops, and the trained cockpit adjustment parameter prediction model is obtained.

[0037] Furthermore, in one embodiment of the present invention, the step of pre-adjusting the vehicle cabin of the target vehicle according to the target cabin adjustment parameters specifically includes:

[0038] Based on the target cabin adjustment parameters, determine the target air conditioning temperature adjustment parameters, target air conditioning air volume adjustment parameters, target seat temperature adjustment parameters, target lighting brightness adjustment parameters, and target air purification adjustment parameters for each cabin area;

[0039] The air conditioning devices in each cabin area are controlled and adjusted according to the target air conditioning temperature adjustment parameters and the target air conditioning air volume adjustment parameters. The seat heating devices in each cabin area are controlled and adjusted according to the target seat temperature adjustment parameters. The lighting devices in each cabin area are controlled and adjusted according to the target light brightness adjustment parameters. The air purification devices in each cabin area are controlled and adjusted according to the target air purification adjustment parameters.

[0040] Secondly, embodiments of the present invention provide a vehicle cabin pre-adjustment system, comprising:

[0041] The information acquisition module is used to acquire the in-vehicle environment information and external environment information of the target vehicle, and to acquire the distribution information of the driver and passengers input by the target occupants;

[0042] The feature fusion module is used to perform feature fusion on the in-vehicle environment information, the out-of-vehicle environment information, and the driver and passenger distribution information to obtain multi-dimensional cockpit driving and passenger status information.

[0043] The cockpit adjustment parameter prediction module is used to input the multi-dimensional cockpit driving state information into the pre-trained cockpit adjustment parameter prediction model to obtain the target cockpit adjustment parameters.

[0044] The cabin adjustment module is used to pre-adjust the vehicle cabin of the target vehicle according to the target cabin adjustment parameters, and to feed back the pre-adjustment result to the target occupant.

[0045] Thirdly, embodiments of the present invention provide a vehicle cabin pre-adjustment device, comprising:

[0046] At least one processor;

[0047] At least one memory for storing at least one program;

[0048] When the at least one program is executed by the at least one processor, the at least one processor implements the above-described vehicle cabin pre-adjustment method.

[0049] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to perform the above-described vehicle cabin pre-adjustment method.

[0050] The advantages and beneficial effects of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention:

[0051] This invention acquires in-vehicle and external environmental information of the target vehicle, and obtains passenger distribution information input by the target occupants. It then performs feature fusion on the in-vehicle, external, and passenger distribution information to obtain multi-dimensional cabin driving state information. This multi-dimensional cabin driving state information is input into a pre-trained cabin adjustment parameter prediction model to obtain target cabin adjustment parameters. Based on these target cabin adjustment parameters, the vehicle cabin is pre-adjusted, and the pre-adjustment results are fed back to the target occupants. This invention achieves pre-adjustment of the vehicle cabin before occupants board, providing a comfortable cabin environment and improving travel efficiency and driving experience. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments of the present invention are described below. It should be understood that the drawings described below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart illustrating the steps of a vehicle cabin pre-adjustment method provided in an embodiment of the present invention;

[0054] Figure 2 A structural block diagram of a vehicle cabin pre-adjustment system provided in an embodiment of the present invention;

[0055] Figure 3 This is a structural block diagram of a vehicle cabin pre-adjustment device provided in an embodiment of the present invention. Detailed Implementation

[0056] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0057] In the description of this invention, "multiple" means two or more. The use of "first" and "second" is for distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0058] Reference Figure 1 This invention provides a method for pre-adjusting a vehicle cabin, specifically including the following steps:

[0059] S101. Obtain the in-vehicle environment information and external environment information of the target vehicle, and obtain the distribution information of the driver and passengers input by the target occupants.

[0060] Specifically, the system collects information about the in-vehicle environment and the external environment through onboard sensors, and obtains the distribution information of drivers and passengers input by the target occupants through the user terminal.

[0061] As an optional implementation, the in-vehicle environment information includes temperature distribution information, brightness distribution information, and odor distribution information inside the target vehicle; the out-of-vehicle environment information includes temperature conditions and lighting conditions outside the target vehicle; and the occupant distribution information includes the number of occupants and their seating positions.

[0062] As a further optional implementation, the step of acquiring the in-vehicle environment information and external environment information of the target vehicle, and acquiring the occupant distribution information input by the target occupants, specifically includes:

[0063] S1011. Temperature distribution information is obtained by using multiple first temperature sensors installed inside the target vehicle;

[0064] S1012. Obtain brightness distribution information by using multiple first light sensors installed inside the target vehicle;

[0065] S1013. Odor distribution information is obtained by using multiple odor sensors installed inside the target vehicle;

[0066] S1014. Obtain temperature condition information through a second temperature sensor installed on the outside of the target vehicle;

[0067] S1015. Obtain illumination condition information by using a second illumination sensor installed on the outside of the target vehicle;

[0068] S1016. Obtain the distribution information of drivers and passengers through the user terminal of the target occupant;

[0069] The user terminal is used to display the cabin area distribution map of the target vehicle, and to determine several target cabin areas selected by the target occupants based on their selection operations, thereby using the target cabin areas as the seating positions of the drivers and passengers and obtaining the distribution information of the drivers and passengers.

[0070] Specifically, the first temperature sensor, the first light sensor, and the odor sensor are all distributed in multiple locations inside the vehicle. By combining the data collected by the sensors at different locations with their location information, temperature distribution information, brightness distribution information, and odor distribution information can be formed. The second temperature sensor and the second light sensor can be installed on the outside of the vehicle to directly obtain the temperature and light conditions of the external environment. The user terminal can display a map of the cabin area distribution of the target vehicle, and the target occupants can select to confirm whether there are people sitting in each cabin area. The cabin area where people are sitting is the target cabin area. Based on the number and location distribution of the target cabin areas, the distribution information of the driver and passengers can be obtained, and then transmitted to the vehicle system via the user terminal.

[0071] S102. Feature fusion is performed on in-vehicle environmental information, external environmental information, and passenger distribution information to obtain multi-dimensional cockpit driving status information.

[0072] As an optional implementation, the step of fusing features from in-vehicle environmental information, external environmental information, and passenger distribution information to obtain multi-dimensional cockpit driving status information specifically includes:

[0073] S1021. Generate a temperature distribution map based on the temperature distribution information, extract the temperature distribution feature vector based on the temperature distribution map, and then perform dimensionality-upgrading processing on the temperature distribution feature vector based on the air temperature condition information to obtain the first feature vector.

[0074] S1022. Generate a brightness distribution map based on the brightness distribution information, extract the brightness distribution feature vector based on the brightness distribution map, and then perform dimensionality-upgrading processing on the brightness distribution feature vector based on the illumination condition information to obtain the second feature vector.

[0075] S1023. Generate an odor distribution map based on the odor distribution information, and extract the third feature vector based on the odor distribution map;

[0076] S1024. Generate a driver and passenger distribution map based on the driver and passenger distribution information, and extract the fourth feature vector based on the driver and passenger distribution map;

[0077] S1025. Perform vector concatenation on the first feature vector, the second feature vector, the third feature vector, and the fourth feature vector to obtain multi-dimensional cockpit driving status information.

[0078] Specifically, a temperature distribution map is generated based on temperature distribution information. This temperature distribution map can be in the form of a heatmap. The temperature distribution feature vector can be extracted using an image feature extraction algorithm. Then, a one-dimensional temperature condition feature vector is generated based on the temperature condition information. The temperature distribution feature vector is then upgraded (i.e., an external temperature condition dimension is added) to obtain the first feature vector. A brightness distribution map is generated based on brightness distribution information. The brightness distribution feature vector can be extracted using an image feature extraction algorithm. Then, a one-dimensional light condition feature vector is generated based on the light condition information. The brightness distribution feature vector is then upgraded (i.e., an external light condition dimension is added) to obtain the second feature vector. An odor distribution map is generated based on odor distribution information. The third feature vector can be extracted using an image feature extraction algorithm. A occupant distribution map is generated based on occupant distribution information. The fourth feature vector can be extracted using an image feature extraction algorithm. The first, second, third, and fourth feature vectors are then concatenated to obtain multi-dimensional cabin occupant status information.

[0079] S103. Input the multi-dimensional cockpit driving state information into the pre-trained cockpit adjustment parameter prediction model to obtain the target cockpit adjustment parameters.

[0080] As a further optional implementation, the vehicle cabin pre-adjustment method also includes a step of pre-training a cabin adjustment parameter prediction model, which specifically includes:

[0081] S201. Obtain multiple preset cockpit driving status sample data, and determine the cockpit parameter adjustment labels corresponding to each cockpit driving status sample data through manual annotation;

[0082] S202. Construct a training dataset based on cockpit passenger status sample data and corresponding cockpit parameter adjustment labels;

[0083] S203. Input the training dataset into the pre-built convolutional neural network for training to obtain a trained cockpit adjustment parameter prediction model.

[0084] The cabin parameter adjustment labels include labels for adjusting the air conditioning temperature, air conditioning air volume, seat temperature, lighting brightness, and air purification in each cabin area.

[0085] Specifically, when constructing the training dataset, sample data of the cabin driving and riding status of the test vehicle is obtained. This sample data is also multidimensional data obtained by extracting feature vectors and concatenating vectors based on the internal environment, external environment, and distribution of test personnel in the test vehicle. The cabin parameter adjustment labels corresponding to each sample data of the cabin driving and riding status are determined based on manual annotation. These are the cabin adjustment parameters that make the passengers most comfortable under the corresponding sample data of the cabin driving and riding status, including air conditioning temperature adjustment labels, air conditioning air volume adjustment labels, seat temperature adjustment labels, headlight brightness adjustment labels, and air purification adjustment labels for each cabin area. The training dataset can be generated based on the sample data of the cabin driving and riding status and the corresponding cabin parameter adjustment labels.

[0086] As an optional implementation, the step of inputting the training dataset into a pre-built convolutional neural network for training to obtain a trained cockpit adjustment parameter prediction model specifically includes:

[0087] S2031. Input the training dataset into the convolutional neural network to obtain the prediction results of the cockpit adjustment parameters;

[0088] S2032. Determine the loss value of the convolutional neural network based on the prediction results of the cockpit adjustment parameters and the cockpit parameter adjustment labels;

[0089] S2033. Update the model parameters of the convolutional neural network based on the loss value using the backpropagation algorithm, and return to the step of inputting the training dataset into the convolutional neural network;

[0090] S2034. When the loss value reaches the preset first threshold, stop training and obtain the trained cockpit adjustment parameter prediction model.

[0091] Specifically, after inputting the data from the training dataset into the initialized convolutional neural network, the model outputs the recognition result, i.e., the cabin adjustment parameter prediction result. The accuracy of the model's prediction can be evaluated based on the cabin adjustment parameter prediction result and the aforementioned cabin parameter adjustment labels, thereby updating the model's parameters. For the cabin adjustment parameter prediction model, the accuracy of the model's prediction result can be measured by a loss function. The loss function is defined on a single training data point and is used to measure the prediction error of that training data point. Specifically, the loss value of that training data point is determined by the label of that individual training data point and the model's prediction result for that training data point. In actual training, a training dataset contains many training data points. Therefore, a cost function is generally used to measure the overall error of the training dataset. The cost function is defined on the entire training dataset and is used to calculate the average prediction error of all training data points, providing a better measure of the model's prediction performance. For general machine learning models, the aforementioned cost function, plus a regularization term to measure model complexity, can be used as the training objective function. Based on this objective function, the loss value of the entire training dataset can be calculated. There are many commonly used loss functions, such as 0-1 loss, squared loss, absolute loss, logarithmic loss, and cross-entropy loss, all of which can be used as loss functions for machine learning models. These will not be elaborated upon here. In this embodiment of the invention, any one of these loss functions can be selected to determine the training loss value. Based on the training loss value, the backpropagation algorithm is used to update the model parameters. After several iterations, a well-trained cockpit adjustment parameter prediction model can be obtained. The specific number of iterations can be preset, or training can be considered complete when the accuracy requirement on the test set is met.

[0092] S104. Pre-adjust the vehicle cabin of the target vehicle according to the target cabin adjustment parameters, and feed back the pre-adjustment results to the target occupants.

[0093] Specifically, after obtaining the target cabin adjustment parameters output by the model, the target air conditioning air volume adjustment parameters, target seat temperature adjustment parameters, target headlight brightness adjustment parameters, and target air purification adjustment parameters for each cabin area are determined based on the target cabin adjustment parameters. Then, the relevant vehicle devices in each cabin area can be controlled and adjusted through the vehicle body controller, thereby enabling more precise adjustment of the cabin environment in each area and ensuring that every passenger can enjoy a comfortable riding experience after getting into the vehicle.

[0094] As a further optional implementation, the step of pre-adjusting the vehicle cabin of the target vehicle according to the target cabin adjustment parameters specifically includes:

[0095] S1041. Determine the target air conditioning temperature adjustment parameters, target air conditioning air volume adjustment parameters, target seat temperature adjustment parameters, target lighting brightness adjustment parameters, and target air purification adjustment parameters for each cabin area based on the target cabin adjustment parameters.

[0096] S1042. Control and adjust the air conditioning devices in each cabin area according to the target air conditioning temperature adjustment parameters and the target air conditioning air volume adjustment parameters; control and adjust the seat heating devices in each cabin area according to the target seat temperature adjustment parameters; control and adjust the lighting devices in each cabin area according to the target light brightness adjustment parameters; and control and adjust the air purification devices in each cabin area according to the target air purification adjustment parameters.

[0097] The method steps of the embodiments of the present invention have been described above. It can be understood that the embodiments of the present invention perform feature fusion of in-vehicle environmental information, external environmental information, and passenger distribution information to obtain multi-dimensional cabin driving state information. Using a pre-trained cabin adjustment parameter prediction model, target cabin adjustment parameters with high comfort can be predicted. This allows for accurate control of air conditioning temperature, air conditioning airflow, seat temperature, in-vehicle lighting brightness, and air purification status in each cabin area. This achieves pre-adjustment of the vehicle cabin before passengers board, providing a comfortable cabin environment and improving travel efficiency and driving experience.

[0098] Reference Figure 2 This invention provides a vehicle cabin pre-adjustment system, comprising:

[0099] The information acquisition module is used to acquire the in-vehicle environment information and external environment information of the target vehicle, and to acquire the distribution information of the driver and passengers input by the target occupants;

[0100] The feature fusion module is used to fuse in-vehicle environmental information, out-of-vehicle environmental information, and passenger distribution information to obtain multi-dimensional cockpit driving status information.

[0101] The cockpit adjustment parameter prediction module is used to input multi-dimensional cockpit driving state information into a pre-trained cockpit adjustment parameter prediction model to obtain the target cockpit adjustment parameters.

[0102] The cabin adjustment module is used to pre-adjust the vehicle cabin of the target vehicle according to the target cabin adjustment parameters and feed back the pre-adjustment results to the target occupants.

[0103] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0104] Reference Figure 3 This invention provides a vehicle cabin pre-adjustment device, comprising:

[0105] At least one processor;

[0106] At least one memory for storing at least one program;

[0107] When the above-mentioned at least one program is executed by the above-mentioned at least one processor, the above-mentioned at least one processor implements the above-mentioned vehicle cabin pre-adjustment method.

[0108] The content of the above method embodiments is applicable to the device embodiments. The specific functions implemented by the 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.

[0109] This invention also provides a computer-readable storage medium storing a processor-executable program that, when executed by a processor, performs the aforementioned vehicle cabin pre-adjustment method.

[0110] This invention provides a computer-readable storage medium that can execute a vehicle cabin pre-adjustment method provided in the method embodiments of this invention. It can execute any combination of the implementation steps of the method embodiments and has the corresponding functions and beneficial effects of the method.

[0111] This invention also discloses a computer program product or computer program, which includes computer instructions 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, causing the computer device to perform... Figure 1 The method shown.

[0112] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the aforementioned blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0113] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the aforementioned functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0114] If the aforementioned functions are implemented as 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 this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0115] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0116] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or, if necessary, processing in other suitable ways, and then stored in computer memory.

[0117] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0118] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0119] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0120] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for pre-adjusting a vehicle cabin, characterized in that, Includes the following steps: Obtain information about the interior and exterior environment of the target vehicle, and obtain information about the distribution of drivers and passengers input by the target occupants; The in-vehicle environment information, the out-of-vehicle environment information, and the passenger distribution information are fused to obtain multi-dimensional cockpit driving status information; The multi-dimensional cockpit driving state information is input into a pre-trained cockpit adjustment parameter prediction model to obtain the target cockpit adjustment parameters. The vehicle cabin of the target vehicle is pre-adjusted according to the target cabin adjustment parameters, and the pre-adjustment result is fed back to the target occupant. The in-vehicle environment information includes temperature distribution information, brightness distribution information, and odor distribution information inside the target vehicle; the out-of-vehicle environment information includes temperature conditions and lighting conditions outside the target vehicle; and the passenger distribution information includes the number of passengers and their seating positions. The step of fusing features from the in-vehicle environment information, the external environment information, and the passenger distribution information to obtain multi-dimensional cockpit passenger status information specifically includes: A temperature distribution map is generated based on the temperature distribution information, and a temperature distribution feature vector is extracted based on the temperature distribution map. Then, the temperature distribution feature vector is subjected to dimensionality-upgrading processing based on the air temperature condition information to obtain a first feature vector. A brightness distribution map is generated based on the brightness distribution information, and a brightness distribution feature vector is extracted based on the brightness distribution map. Then, the brightness distribution feature vector is upgraded based on the illumination condition information to obtain a second feature vector. An odor distribution map is generated based on the odor distribution information, and a third feature vector is extracted based on the odor distribution map; A driver and passenger distribution map is generated based on the driver and passenger distribution information, and a fourth feature vector is extracted based on the driver and passenger distribution map; The first feature vector, the second feature vector, the third feature vector, and the fourth feature vector are concatenated to obtain the multi-dimensional cockpit driving status information. The vehicle cabin pre-adjustment method further includes the step of pre-training the cabin adjustment parameter prediction model, which specifically includes: Acquire multiple preset cockpit driving state sample data, and determine the cockpit parameter adjustment labels corresponding to each cockpit driving state sample data through manual annotation; A training dataset is constructed based on the cockpit ride status sample data and the corresponding cockpit parameter adjustment labels; The training dataset is input into a pre-built convolutional neural network for training to obtain a trained cabin adjustment parameter prediction model. The cabin parameter adjustment labels include air conditioning temperature adjustment labels, air conditioning air volume adjustment labels, seat temperature adjustment labels, light brightness adjustment labels, and air purification adjustment labels for each cabin area; The step of pre-adjusting the vehicle cabin of the target vehicle according to the target cabin adjustment parameters specifically includes: Based on the target cabin adjustment parameters, determine the target air conditioning temperature adjustment parameters, target air conditioning air volume adjustment parameters, target seat temperature adjustment parameters, target lighting brightness adjustment parameters, and target air purification adjustment parameters for each cabin area; The air conditioning devices in each cabin area are controlled and adjusted according to the target air conditioning temperature adjustment parameters and the target air conditioning air volume adjustment parameters. The seat heating devices in each cabin area are controlled and adjusted according to the target seat temperature adjustment parameters. The lighting devices in each cabin area are controlled and adjusted according to the target light brightness adjustment parameters. The air purification devices in each cabin area are controlled and adjusted according to the target air purification adjustment parameters.

2. The vehicle cabin pre-adjustment method according to claim 1, characterized in that, The step of obtaining the in-vehicle environment information and external environment information of the target vehicle, and obtaining the distribution information of the driver and passengers input by the target occupants, specifically includes: The temperature distribution information is obtained by installing multiple first temperature sensors inside the target vehicle. The brightness distribution information is obtained by multiple first light sensors installed inside the target vehicle; The odor distribution information is obtained by installing multiple odor sensors inside the target vehicle. The air temperature information is obtained by a second temperature sensor located on the outside of the target vehicle. The illumination condition information is obtained by a second light sensor installed on the exterior of the target vehicle. The distribution information of the driver and passengers is obtained through the user terminal of the target occupant; The user terminal is used to display a cabin area distribution map of the target vehicle, and to determine several target cabin areas selected by the target occupant based on the selection operation of the target occupant, thereby using the target cabin areas as the seating positions of the driver and passengers, and obtaining the distribution information of the driver and passengers.

3. The vehicle cabin pre-adjustment method according to claim 1, characterized in that, The step of inputting the training dataset into a pre-built convolutional neural network for training to obtain the trained cabin adjustment parameter prediction model specifically includes: The training dataset is input into the convolutional neural network to obtain the cabin adjustment parameter prediction results; The loss value of the convolutional neural network is determined based on the predicted results of the cabin adjustment parameters and the cabin parameter adjustment labels. The model parameters of the convolutional neural network are updated using the backpropagation algorithm based on the loss value, and the process of inputting the training dataset into the convolutional neural network is returned. When the loss value reaches a preset first threshold, training stops, and the trained cockpit adjustment parameter prediction model is obtained.

4. A vehicle cabin pre-adjustment system, characterized in that, A method for implementing a vehicle cabin pre-adjustment as described in any one of claims 1 to 3 includes: The information acquisition module is used to acquire the in-vehicle environment information and external environment information of the target vehicle, and to acquire the distribution information of the driver and passengers input by the target occupants; The feature fusion module is used to perform feature fusion on the in-vehicle environment information, the out-of-vehicle environment information, and the driver and passenger distribution information to obtain multi-dimensional cockpit driving and passenger status information. The cockpit adjustment parameter prediction module is used to input the multi-dimensional cockpit driving state information into the pre-trained cockpit adjustment parameter prediction model to obtain the target cockpit adjustment parameters. The cabin adjustment module is used to pre-adjust the vehicle cabin of the target vehicle according to the target cabin adjustment parameters, and to feed back the pre-adjustment result to the target occupant.

5. A vehicle cabin pre-adjustment device, 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 a vehicle cabin pre-adjustment method as described in any one of claims 1 to 3.

6. A computer-readable storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to perform a vehicle cabin pre-adjustment method as described in any one of claims 1 to 3.

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