Seat adjusting method and device, storage medium, intelligent seat and vehicle

Abstract

The invention discloses a seat adjusting method and device, a storage medium, an intelligent seat and a vehicle, and relates to the technical field of intelligent cabins, the method comprises the steps that fuzzy processing is carried out on environment sensing data of the vehicle to obtain a fuzzy data set, and the environment sensing data is acquired by a sensor array of the vehicle; predicting the fuzzy data set through a preset random forest regression model to obtain seat adjustment parameters corresponding to the environment sensing data; and adjusting a seat state of the vehicle based on the seat adjustment parameter. According to the method, the environment sensing data is acquired through the sensor array in advance, and after the environment sensing data is subjected to fuzzy processing, the fuzzy data set is predicted through the random forest regression model, so that the seat adjusting parameters responding to the external environment change can be obtained. Therefore, the seat state of the vehicle can be automatically adjusted according to the environment sensing data, unnecessary user operation is reduced, the distraction risk during driving is reduced, and the comfort level and safety of vehicle riding are improved.

Description

Technical Field

[0001] The present application relates to the technical field of smart cockpits, and in particular to a seat adjustment method, device, storage medium, smart seat and vehicle. Background Art

[0002] With the development of intelligent transportation systems, the comfort and safety of the vehicle's internal environment have received more and more attention. Among them, the position of the car seat directly affects the driver's driving posture, so the reasonable adjustment of the car seat can ensure the comfort and safety of driving.

[0003] However, in the prior art, although the smart cockpit that integrates multiple sensors and control units provides a certain degree of personalization and automation, its adjustment mode mostly relies on manual operation by the user or preset fixed modes, and it is difficult to respond to changes in the external environment in real time. When the external environment changes, the comfort and functionality of the seats in the smart cockpit may be limited, resulting in a poor driving and riding experience. Summary of the invention

[0004] The main purpose of this application is to provide a seat adjustment method, device, storage medium, smart seat and vehicle, aiming to solve the technical problems that the adjustment of existing smart cockpits mostly relies on manual operation of the user or preset fixed modes, and it is difficult to respond to changes in the external environment in real time, resulting in poor driving and riding experience.

[0005] To achieve the above object, the present application proposes a seat adjustment method, the method comprising:

[0006] Performing fuzzy processing on the environment perception data of the vehicle to obtain a fuzzy data set, wherein the environment perception data is collected by a sensor array of the vehicle;

[0007] Predicting the fuzzy data set by using a preset random forest regression model to obtain seat adjustment parameters corresponding to the environmental perception data;

[0008] Based on the seat adjustment parameter, a seat state of the vehicle is adjusted.

[0009] In one embodiment, the step of performing fuzzy processing on the vehicle's environment perception data to obtain a fuzzy data set includes:

[0010] Normalizing the vehicle's environmental perception data to obtain normalized data;

[0011] Extracting the central value of the normalized data;

[0012] Performing discrete statistics on the normalized data to obtain a standard deviation;

[0013] Based on the central value and the standard deviation, the normalized data is fuzzily classified by a preset Gaussian membership function to obtain a fuzzy data set.

[0014] In one embodiment, the step of normalizing the vehicle's environmental perception data to obtain normalized data includes:

[0015] Acquiring historical sensing data of each sensor in the sensor array;

[0016] extracting minimum and maximum values ​​from the historical sensing data;

[0017] Based on the minimum value and the maximum value, the environmental perception data is normalized to obtain normalized data corresponding to the environmental perception data.

[0018] In one embodiment, the step of adjusting the seat state of the vehicle based on the seat adjustment parameter comprises:

[0019] Obtaining user preference data, wherein the user preference data is obtained by analyzing user behavior based on an AI algorithm;

[0020] determining an adjustment weight between the user preference data and the seat adjustment parameter;

[0021] A seat state of the vehicle is adjusted based on the user preference data, the seat adjustment parameters and the adjustment weights.

[0022] In one embodiment, after the step of adjusting the seat state of the vehicle based on the seat adjustment parameter, the method further includes:

[0023] By means of the sensor array, new sensing data is collected at a preset period;

[0024] Comparing the new perception data with the environmental perception data to obtain environmental change data;

[0025] Based on the environmental change data, updating the model parameters of the random forest regression model;

[0026] According to the updated random forest regression model, return to execute the step of fuzzy processing the vehicle's environmental perception data.

[0027] In one embodiment, the step of updating the model parameters of the random forest regression model based on the environmental change data includes:

[0028] Based on the environmental change data, fine-tuning the model parameters of the random forest regression model to obtain an output result;

[0029] Determining whether the output result reaches the preset model performance;

[0030] When the output result does not reach the preset model performance, return to execute the step of fine-tuning the model parameters of the random forest regression model based on the environmental change data until the output result reaches the preset model performance, thereby obtaining an updated random forest regression model.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes a seat adjustment device, the device comprising:

[0032] A preprocessing module, used for performing fuzzy processing on the environment perception data of the vehicle to obtain a fuzzy data set, wherein the environment perception data is collected by a sensor array of the vehicle;

[0033] A model prediction module, used to predict the fuzzy data set through a preset random forest regression model to obtain seat adjustment parameters corresponding to the environmental perception data;

[0034] A seat adjustment module is used to adjust the seat state of the vehicle based on the seat adjustment parameter.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the seat adjustment method described above are implemented.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a smart seat, which is applied to a vehicle. The smart seat includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the seat adjustment method described above.

[0037] In addition, to achieve the above objectives, the present application also proposes a vehicle, which includes the smart seat described above.

[0038] One or more technical solutions proposed in the present application have at least the following technical effects: the present application first performs fuzzy processing on the vehicle's environmental perception data to obtain a fuzzy data set, wherein the environmental perception data is collected by the sensor array of the vehicle; then the fuzzy data set is predicted through a preset random forest regression model to obtain seat adjustment parameters corresponding to the environmental perception data; finally, based on the seat adjustment parameters, the seat state of the vehicle is adjusted.

[0039] Since the present application obtains environmental perception data through the sensor array in advance, and after fuzzy processing of the environmental perception data, the fuzzy data set is predicted through the random forest regression model, the seat adjustment parameters that respond to changes in the external environment can be obtained. Therefore, the seat state of the vehicle can be automatically adjusted according to the environmental perception data, reducing unnecessary user operations, thereby reducing the risk of distraction during driving and improving the comfort and safety of vehicle riding. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0042] Figure 1 An interactive schematic diagram of the seat adjustment process provided for this application;

[0043] Figure 2 A schematic diagram of a process flow provided for the first embodiment of the seat adjustment method of the present application;

[0044] Figure 3 A schematic diagram of a flow chart provided for the second embodiment of the seat adjustment method of the present application;

[0045] Figure 4 A flowchart of the third embodiment of the seat adjustment method of the present application is provided;

[0046] Figure 5 This is a schematic diagram of the module structure of the seat adjustment device according to an embodiment of the present application;

[0047] Figure 6 Schematic diagram of the device structure of the hardware operating environment involved in the seat adjustment method in the embodiment of the present application.

[0048] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0049] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0050] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0051] It should be noted that in the field of smart cockpits, existing seat adjustment technologies are mainly based on manual operation or automatic adjustment based on fixed modes. In the automatic adjustment of fixed modes, the seat adjustment system can save multiple seat configurations according to the driver's settings and automatically adjust to the preset configuration when the driver enters the vehicle. Its working steps are as follows:

[0052] 1. User settings: Users input their own preferences such as seat position and angle through the vehicle's central control screen and save them as personal profiles.

[0053] 2. Identification and activation. The vehicle identifies a specific user through on-board sensors and systems (for example, through a mobile phone key or in-car camera), and automatically loads the seat configuration when the user is identified.

[0054] 3. Seat adjustment: The electric mechanical structure automatically adjusts the seat to the position set by the user according to the data in the configuration file.

[0055] 4. Manual override: Users can manually adjust the seat position at any time through the central control screen or the control panel next to the seat, and the system will ask whether to save the new settings.

[0056] Although the above methods provide a certain degree of personalization and automation, they still rely on fixed modes preset by users and are difficult to respond to environmental changes in real time (for example, sudden changes in weather such as temperature and humidity, and changes in light intensity). Therefore, when the external environment changes, the comfort and functionality of the seat may be limited, resulting in a poor driving and riding experience.

[0057] To solve the above problems, this application proposes a seat adjustment method, which can be referred to Figure 1 , Figure 1 The interactive schematic diagram of the seat adjustment process provided for this application. Through a series of sensors in the vehicle's environment perception module, the external environment data is monitored and collected in real time, so that the seat parameters can be automatically adjusted according to the collected environment perception data to meet the needs of different drivers and passengers.

[0058] It should be noted that the execution subject of this embodiment can be a computer device with data fuzzy processing, parameter prediction and parameter adjustment functions, such as a personal computer, a vehicle control center, etc., or an electronic device capable of realizing the above functions, a seat adjustment device (such as a smart seat) that executes the seat adjustment method of this application, etc., and this embodiment does not limit this. The following takes a smart seat as an example to illustrate this embodiment and the following embodiments.

[0059] Based on this, the embodiment of the present application provides a seat adjustment method, referring to Figure 2 , Figure 2A schematic diagram of the process flow provided for the first embodiment of the seat adjustment method of the present application.

[0060] In this embodiment, the seat adjustment method includes steps S10 to S30:

[0061] Step S10: fuzzy processing is performed on the environment perception data of the vehicle to obtain a fuzzy data set, wherein the environment perception data is collected by the sensor array of the vehicle.

[0062] It should be noted that environmental perception data can be various information about the vehicle's surrounding environment monitored and collected through various sensors on the vehicle.

[0063] Exemplarily, the environmental perception data may include: temperature, humidity, light, air pressure, and internal parameters such as vehicle speed, etc., which are not limited in this embodiment.

[0064] The above environmental perception data is collected by the sensor array. For ease of understanding, Figure 1 As shown, the environmental perception module on the vehicle is equipped with a variety of high-precision sensors to form a sensor array. The sensor array can monitor and collect the external environment of the vehicle in real time. After collecting the environmental perception data, the environmental perception data is transmitted to the smart seat for analysis by the central processing unit of the smart seat through AI or other algorithms.

[0065] It is understandable that the fuzzy data set can be obtained by fuzzily classifying the vehicle's environmental perception data through a specific fuzzy algorithm (for example, a fuzzy clustering algorithm, a fuzzy reasoning algorithm, etc.), which can simplify the data, reduce the complexity and dimension of the data, and make subsequent data prediction easier.

[0066] In this embodiment, the sensor array of the vehicle monitors and collects the external environment of the vehicle in real time, and after measuring the environmental perception data, transmits the environmental perception data to the on-board computer. The on-board computer transmits the received environmental perception data to the smart seat on the vehicle. After receiving the environmental perception data, the smart seat can first eliminate the redundant data in the environmental perception data, and then fuzzy classify the environmental perception data after eliminating the redundant data through a fuzzy clustering algorithm to obtain a fuzzy data set.

[0067] In a feasible implementation, step S10 of this embodiment may include the steps of: normalizing the vehicle's environmental perception data to obtain normalized data; extracting the central value of the normalized data; performing discrete statistics on the normalized data to obtain a standard deviation; and based on the central value and the standard deviation, fuzzy classifying the normalized data through a preset Gaussian membership function to obtain a fuzzy data set.

[0068] It should be noted that the normalized data may be data obtained by converting environmental perception data into a specific interval so as to eliminate the influence caused by differences in dimension, magnitude, etc. between features of different data.

[0069] Exemplarily, the normalization process may be implemented by minimum and maximum normalization, Z-score standardization, etc., which is not limited in this embodiment.

[0070] For ease of understanding, in a feasible implementation, minimum and maximum normalization is used as an example for illustration, but this solution is not limited. In this embodiment, the step of normalizing the vehicle's environmental perception data to obtain normalized data includes: obtaining historical perception data of each sensor in the sensor array; extracting the minimum value and the maximum value from the historical perception data; and normalizing the environmental perception data based on the minimum value and the maximum value to obtain normalized data corresponding to the environmental perception data.

[0071] It should be noted that the historical perception data can be various information about the vehicle's surrounding environment that has been monitored and collected by various sensors on the vehicle in the past. The minimum value is the smallest value among all the values ​​in the historical perception data, and the maximum value is the largest value among all the values ​​in the historical perception data.

[0072] All environmental perception data obtained from the environmental perception module can be normalized first to eliminate the influence of different dimensions and noise. The normalization formula is as follows:

[0073]

[0074] Where x is the raw reading of any sensor, x max and x min are respectively the minimum and maximum values ​​of the sensor in the historical perception data.

[0075] It is understandable that the central value can be the most central data of the normalized data, which is the typical value concentrated in the normalized data. The standard deviation can be data that reflects the degree of dispersion of each data in the normalized data relative to the central value, wherein a larger standard deviation indicates that the distribution of each data in the normalized data is more dispersed and the range is wider; a smaller standard deviation indicates that each data in the normalized data is more concentrated near the central value and the range is narrower.

[0076] It should be understood that the Gaussian membership function can be a function used in fuzzy logic to describe the degree of membership of data to a certain data set. Through the Gaussian membership function, the degree of membership of each data in the normalized data can be calculated, and the normalized data can be classified according to the degree of membership to obtain multiple fuzzy data sets, so as to realize the function of feature classification of environmental perception data.

[0077] Specifically, the specific formula for using the Gaussian membership function to calculate the degree of membership of each data point in the normalized data to a specific fuzzy set is as follows:

[0078]

[0079] Here, c is the central value mentioned above and σ is the standard deviation.

[0080] In this embodiment, after receiving the environmental perception data, the smart seat first normalizes the environmental perception data to eliminate the influence of different dimensions and noise to obtain normalized data. Then, the most central data is selected from the normalized data as the central value. At the same time, the normalized data is discretely counted to obtain the standard deviation reflecting the degree of dispersion of each data in the normalized data relative to the central value. Finally, the membership degree of each data in the normalized data is calculated through the Gaussian membership function, and the normalized data is classified according to the membership degree to obtain multiple fuzzy data sets. Thus, the environmental perception data is feature-classified through the Gaussian membership function.

[0081] Step S20: predicting the fuzzy data set through a preset random forest regression model to obtain seat adjustment parameters corresponding to the environmental perception data.

[0082] It should be noted that the random forest regression model can be composed of multiple decision trees. Each decision tree can predict the fuzzy data set. The final prediction model can be obtained by combining the prediction results of multiple decision trees.

[0083] It is understandable that the seat adjustment parameter may be an indicator for adjusting the smart seat on the vehicle.

[0084] Exemplarily, the seat adjustment parameters may include parameters of different aspects, such as the height and angle of the smart seat, and functional adjustments such as heating or ventilation intensity, which are not limited in this embodiment.

[0085] In this embodiment, after the fuzzy classification is performed and the fuzzy data set is obtained, the smart seat can input the fuzzy data set into a pre-trained random forest regression model to predict the seat adjustment parameters. The general form of the random forest regression model is as follows:

[0086] y=f(x1,x2,...,xn);

[0087] Among them, x1, x2, ..., xn are characteristic variables that affect the seat adjustment parameters, and y is the seat adjustment parameter output by the model.

[0088] Step S30: adjusting the seat state of the vehicle based on the seat adjustment parameter.

[0089] It should be noted that the seat status may refer to various situations currently presented by the smart seat, such as the angle of the backrest, the front and rear position of the seat, the height of the seat, the degree of heating of the seat, and so on.

[0090] In this implementation mode, for ease of understanding, Figure 1 As shown, after the smart seat generates the seat adjustment parameters, the seat adjustment parameters can be input into a series of execution units of the smart seat, and each execution unit can automatically adjust the seat state of the smart seat according to different seat adjustment parameters. Thus, it can automatically adjust according to environmental changes.

[0091] In the technical solution provided by this embodiment, the sensor array of the vehicle monitors and collects the external environment of the vehicle in real time, and after measuring the environmental perception data, transmits the environmental perception data to the on-board computer. The on-board computer transmits the received environmental perception data to the smart seat on the vehicle. After receiving the environmental perception data, the smart seat can first eliminate the redundant data in the environmental perception data, and then fuzzy classify the environmental perception data with redundant data eliminated through the fuzzy clustering algorithm to obtain a fuzzy data set. Then, the fuzzy data set is input into a pre-trained random forest regression model, and the seat adjustment parameters are predicted by the model. After the smart seat generates the seat adjustment parameters, the various data of the seat adjustment parameters can be input into a series of execution units of the smart seat respectively, and each execution unit completes the automatic adjustment of the seat state of the smart seat according to different seat adjustment parameters. Since this embodiment obtains environmental perception data in advance through the sensor array, and after fuzzy processing of the environmental perception data, the fuzzy data set is predicted through the random forest regression model, and the seat adjustment parameters that respond to changes in the external environment can be obtained. The vehicle's seat status can be automatically adjusted according to environmental perception data, reducing unnecessary user operations, thereby lowering the risk of distraction while driving and improving vehicle ride comfort and safety.

[0092] Based on the above first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above first embodiment can be referred to the above introduction, and will not be repeated in the following. Figure 3 , Figure 3 A schematic diagram of the flow chart of the second embodiment of the seat adjustment method of the present application.

[0093] In this example, steps S30, S31 to S33 are as follows:

[0094] Step S31: Obtain user preference data, where the user preference data is obtained by analyzing user behavior based on an AI algorithm.

[0095] It should be noted that user preference data may be data on the user's personal preferences, tendencies, habits, etc. for intelligent seat adjustment. AI algorithms may be algorithms that allow computers to simulate human intelligent behavior to learn, reason, make decisions, and identify user preference data, such as machine learning algorithms (such as supervised learning, unsupervised learning algorithms, etc.), deep learning algorithms (such as neural network algorithms, etc.), etc.

[0096] Through AI algorithms, smart seats can automatically analyze user behavior, obtain user preference data, and provide personalized seat setting recommendations.

[0097] Furthermore, if Figure 1 As shown in the figure, in addition to analyzing user behavior through AI algorithms, the smart seat can also connect to the display screen of the on-board computer. Users can customize their preferences within a certain range on the user interface, and can also intervene manually in special circumstances. In addition to touch operation, users can also control seat adjustment through voice commands to improve driving safety. For key operations, such as changing important settings, the smart seat can also require users to confirm twice to avoid misoperation.

[0098] Step S32: Determine the adjustment weight between the user preference data and the seat adjustment parameters.

[0099] It should be noted that the adjustment weight may be a numerical value of relative importance assigned when considering various preferences of the user (such as specific preferences for height, angle, etc.) and actual seat adjustment parameters.

[0100] Step S33: adjusting the seat state of the vehicle based on the user preference data, the seat adjustment parameters and the adjustment weights.

[0101] For example, the seat heating intensity is used as an example for explanation, but this solution is not limited. After obtaining the temperature preference P in the user preference data, the seat heating intensity H of the smart seat is calculated according to the external temperature T and the temperature preference P set by the user to automatically adjust various parameters of the seat. The specific calculation is as follows:

[0102] H = β(TP);

[0103] Among them, β is the adjustment weight, which can be used to adjust the heating speed.

[0104] In the technical solution provided in this embodiment, after generating the seat adjustment parameters, the smart seat can analyze the user behavior through the AI ​​algorithm to obtain the user preference data; or the user can customize the preference settings within a certain range on the user interface to obtain the user preference data. Then, when the user preference data (such as the specific preference for height, angle, etc.) and the actual seat adjustment parameters are combined, the adjustment weights of relative importance are assigned. Finally, calculations are performed based on the user preference data, the seat adjustment parameters and the adjustment weights, and the seat state of the vehicle is adjusted according to the calculation results. Thereby further improving the ride comfort of the vehicle.

[0105] Based on the above embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction, and will not be described in detail later. Figure 4 , Figure 4 This is a flow chart of the third embodiment of the seat adjustment method of the present application.

[0106] After step S30, this example further includes steps S41 to S43:

[0107] Step S41: collecting new sensing data through the sensor array according to a preset period.

[0108] It should be noted that the preset period may refer to a preset time interval for collecting various information about the vehicle's surrounding environment, such as half an hour or one hour, which is not limited in this embodiment.

[0109] It is understandable that the new perception data can be various information about the vehicle's surrounding environment that is monitored and collected again through various sensors on the vehicle.

[0110] Step S42: Compare the new perception data with the environmental perception data to obtain environmental change data.

[0111] It should be noted that the environmental change data may be relevant data reflecting various changes in the state of the vehicle's surrounding environment over a period of time, for example, including change data such as temperature, humidity, and light intensity.

[0112] Step S43: Based on the environmental change data, the model parameters of the random forest regression model are updated.

[0113] It should be noted that the model parameters may be key values ​​constituting the random forest regression model, such as parameters such as learning rate and regularization term, which are not limited in this embodiment.

[0114] There are a series of parameters in the random forest regression model, such as the number of decision trees, feature selection method, splitting criteria, etc. When there is new data or according to actual needs and effect evaluation, these model parameters can be modified to make the random forest regression model better fit the data and improve the prediction accuracy.

[0115] In a feasible implementation, step S43 of this embodiment includes the steps of: fine-tuning the model parameters of the random forest regression model based on the environmental change data to obtain an output result; judging whether the output result reaches the preset model performance; when the output result does not reach the preset model performance, returning to execute the step of fine-tuning the model parameters of the random forest regression model based on the environmental change data until the output result reaches the preset model performance to obtain an updated random forest regression model.

[0116] It should be noted that the preset model performance can be the performance level that the expected model should achieve in predicting the seat adjustment parameters before the random forest regression model is used, for example, various performance indicators such as accuracy, precision, recall rate, F1 value, error rate, etc.

[0117] In this embodiment, after the above-mentioned environmental change data is calculated, the model parameters of the random forest regression model can be fine-tuned according to the environmental change data, such as parameters such as the learning rate and the regularization term. After the fine-tuning is successful, the seat adjustment parameters are input into the fine-tuned random forest regression model again, and the adjustment result is output. At this time, the adjustment result is compared with the adjustment data input by the user to determine its accuracy. If the accuracy does not reach the preset model performance, it means that the model needs further training. At this time, it is necessary to fine-tune the model parameters of the random forest regression model based on the environmental change data again. Until the accuracy reaches the preset model performance, at this time, the model whose accuracy reaches the preset model performance is used as the updated random forest regression model. Thereby further improving the prediction performance of the model.

[0118] Step S44: According to the updated random forest regression model, return to execute the step of fuzzy processing the vehicle's environmental perception data.

[0119] In the technical solution provided in this embodiment, in order to improve the accuracy and response speed of the model, the time interval for collecting various information about the vehicle's surrounding environment can be set first. Through the sensor array, various new perception data about the vehicle's surrounding environment are monitored and collected again according to the preset period. Then, the new perception data and the environmental perception data are compared to obtain environmental change data reflecting the various changes in the state of the vehicle's surrounding environment over a period of time. Then, based on the environmental change data, the model parameters of the random forest regression model are updated so that the random forest regression model predicts the seat adjustment parameters to achieve the desired performance level. Finally, based on the updated random forest regression model, the step of fuzzy processing the vehicle's environmental perception data is returned. Therefore, by automatically updating the model parameters, new data can be absorbed in real time, the model performance can be continuously optimized, and user intervention can be further reduced to achieve automatic optimization of seat parameters.

[0120] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the seat adjustment method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0121] This application also provides a seat adjustment device, please refer to Figure 5 , Figure 5 This is a schematic diagram of the module structure of the seat adjustment device of the embodiment of the present application; the seat adjustment device comprises:

[0122] A preprocessing module 501 is used to perform fuzzy processing on the environment perception data of the vehicle to obtain a fuzzy data set, wherein the environment perception data is collected by the sensor array of the vehicle;

[0123] A model prediction module 502, configured to predict the fuzzy data set by using a preset random forest regression model to obtain seat adjustment parameters corresponding to the environmental perception data;

[0124] The seat adjustment module 503 is used to adjust the seat state of the vehicle based on the seat adjustment parameter.

[0125] As an implementation mode, the preprocessing module 501 is also used to normalize the environmental perception data of the vehicle to obtain normalized data; extract the central value of the normalized data; perform discrete statistics on the normalized data to obtain a standard deviation; based on the central value and the standard deviation, perform fuzzy classification on the normalized data through a preset Gaussian membership function to obtain a fuzzy data set.

[0126] As an implementation mode, the preprocessing module 501 is also used to obtain historical perception data of each sensor in the sensor array; extract the minimum value and the maximum value from the historical perception data; and normalize the environmental perception data based on the minimum value and the maximum value to obtain normalized data corresponding to the environmental perception data.

[0127] As an embodiment, the seat adjustment module 503 is also used to obtain user preference data, which is obtained by analyzing user behavior based on an AI algorithm; determine the adjustment weight between the user preference data and the seat adjustment parameters; and adjust the seat state of the vehicle based on the user preference data, the seat adjustment parameters and the adjustment weight.

[0128] As an implementation mode, the model prediction module 502 is also used to collect new perception data according to a preset period through the sensor array; compare the new perception data with the environmental perception data to obtain environmental change data; based on the environmental change data, update the model parameters of the random forest regression model; and according to the updated random forest regression model, return to execute the operation of fuzzy processing of the vehicle's environmental perception data.

[0129] As an implementation mode, the model prediction module 502 is also used to fine-tune the model parameters of the random forest regression model based on the environmental change data to obtain an output result; determine whether the output result reaches the preset model performance; when the output result does not reach the preset model performance, return to execute the operation of fine-tuning the model parameters of the random forest regression model based on the environmental change data until the output result reaches the preset model performance, thereby obtaining an updated random forest regression model.

[0130] Other embodiments or specific implementations of the seat adjustment device of the present application can refer to the above-mentioned method embodiments, which will not be described in detail here.

[0131] The seat adjustment device provided by the present application adopts the seat adjustment method in the above embodiment, which can solve the technical problem that the adjustment of the existing smart cockpit mostly relies on manual operation of the user or a preset fixed mode, and it is difficult to respond to changes in the external environment in real time, resulting in poor driving and riding experience. Compared with the prior art, the beneficial effects of the seat adjustment device provided by the present application are the same as the beneficial effects of the seat adjustment method provided by the above embodiment, and the other technical features of the seat adjustment device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0132] The present application provides a smart seat, which is applied to a vehicle, and the smart seat includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the seat adjustment method in the above-mentioned embodiment one.

[0133] Reference below Figure 6 , Figure 6 The device structure diagram of the hardware operating environment involved in the seat adjustment method in the embodiment of the present application shows a structure diagram of the smart seat suitable for implementing the embodiment of the present application. The smart seat in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The smart chair shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0134] like Figure 6As shown, the smart seat may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the smart seat are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the smart seat to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a smart seat with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0135] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0136] The smart seat provided by the present application adopts the seat adjustment method in the above embodiment, which can solve the technical problem that the adjustment of the existing smart cockpit mostly relies on manual operation of the user or a preset fixed mode, and it is difficult to respond to changes in the external environment in real time, resulting in poor driving and riding experience. Compared with the prior art, the beneficial effects of the smart seat provided by the present application are the same as the beneficial effects of the seat adjustment method provided by the above embodiment, and the other technical features of the smart seat are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0137] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0138] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0139] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the seat adjustment method in the above-mentioned embodiment.

[0140] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0141] The computer-readable storage medium may be included in the smart seat; or it may exist independently without being assembled into the smart seat.

[0142] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the smart seat, the smart seat: performs fuzzy processing on the vehicle's environmental perception data to obtain a fuzzy data set, wherein the environmental perception data is collected by the sensor array of the vehicle; predicts the fuzzy data set through a preset random forest regression model to obtain seat adjustment parameters corresponding to the environmental perception data; and adjusts the seat state of the vehicle based on the seat adjustment parameters.

[0143] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0144] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0145] The modules involved in the embodiments described in the present application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0146] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned seat adjustment method, and can solve the technical problem that the adjustment of the existing smart cockpit mostly relies on manual operation of the user or a preset fixed mode, and it is difficult to respond to changes in the external environment in real time, resulting in poor driving and riding experience. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the seat adjustment method provided by the above-mentioned embodiment, and will not be repeated here.

[0147] The present application also provides a vehicle, which includes the smart seat as described above.

[0148] The vehicle provided by the present application can solve the technical problem that the adjustment of the existing smart cockpit mostly relies on manual operation of the user or a preset fixed mode, and it is difficult to respond to changes in the external environment in real time, resulting in poor driving and riding experience. Compared with the prior art, the beneficial effects of the vehicle provided by the present application are the same as the beneficial effects of the seat adjustment method provided by the above embodiment, which will not be repeated here.

[0149] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A seat adjustment method, characterized in that: The method comprises: Performing fuzzy processing on the environment perception data of the vehicle to obtain a fuzzy data set, wherein the environment perception data is collected by a sensor array of the vehicle; Predicting the fuzzy data set by using a preset random forest regression model to obtain seat adjustment parameters corresponding to the environmental perception data; Based on the seat adjustment parameter, a seat state of the vehicle is adjusted.

2. The method according to claim 1, characterized in that The step of performing fuzzy processing on the vehicle's environmental perception data to obtain a fuzzy data set includes: Normalizing the vehicle's environmental perception data to obtain normalized data; Extracting the central value of the normalized data; Performing discrete statistics on the normalized data to obtain a standard deviation; Based on the central value and the standard deviation, the normalized data is fuzzily classified by a preset Gaussian membership function to obtain a fuzzy data set.

3. The method according to claim 2, characterized in that The step of normalizing the vehicle's environmental perception data to obtain normalized data includes: Acquiring historical sensing data of each sensor in the sensor array; extracting minimum and maximum values ​​from the historical sensing data; Based on the minimum value and the maximum value, the environmental perception data is normalized to obtain normalized data corresponding to the environmental perception data.

4. The method according to claim 1, characterized in that The step of adjusting the seat state of the vehicle based on the seat adjustment parameter comprises: Obtaining user preference data, wherein the user preference data is obtained by analyzing user behavior based on an AI algorithm; determining an adjustment weight between the user preference data and the seat adjustment parameter; A seat state of the vehicle is adjusted based on the user preference data, the seat adjustment parameters and the adjustment weights.

5. The method according to any one of claims 1 to 4, characterized in that After the step of adjusting the seat state of the vehicle based on the seat adjustment parameter, the method further includes: By means of the sensor array, new sensing data is collected at a preset period; Comparing the new perception data with the environmental perception data to obtain environmental change data; Based on the environmental change data, updating the model parameters of the random forest regression model; According to the updated random forest regression model, return to execute the step of fuzzy processing the vehicle's environmental perception data.

6. The method according to claim 5, characterized in that The step of updating the model parameters of the random forest regression model based on the environmental change data comprises: Based on the environmental change data, fine-tuning the model parameters of the random forest regression model to obtain an output result; Determining whether the output result reaches the preset model performance; When the output result does not reach the preset model performance, return to execute the step of fine-tuning the model parameters of the random forest regression model based on the environmental change data until the output result reaches the preset model performance, thereby obtaining an updated random forest regression model.

7. A seat adjustment device, characterized in that: The device comprises: A preprocessing module, used for performing fuzzy processing on the environment perception data of the vehicle to obtain a fuzzy data set, wherein the environment perception data is collected by a sensor array of the vehicle; A model prediction module, used to predict the fuzzy data set through a preset random forest regression model to obtain seat adjustment parameters corresponding to the environmental perception data; A seat adjustment module is used to adjust the seat state of the vehicle based on the seat adjustment parameter.

8. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the seat adjustment method according to any one of claims 1 to 6 are implemented.

9. A smart seat, characterized in that: The smart seat is applied to a vehicle, and comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the seat adjustment method as described in any one of claims 1 to 6.

10. A vehicle, characterized in that: The vehicle includes the smart seat as claimed in claim 9.

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