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

Abstract

The application discloses a seat adjustment 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 following steps: performing fuzzy processing on environment perception data of the vehicle to obtain a fuzzy data set, wherein the environment perception data is obtained by a sensor array of the vehicle; predicting the fuzzy data set by using a preset random forest regression model to obtain a seat adjustment parameter corresponding to the environment perception data; and adjusting a seat state of the vehicle based on the seat adjustment parameter. Since the environment perception data is obtained in advance by the sensor array, and the fuzzy data set is predicted by using the random forest regression model after the fuzzy processing of the environment perception data, the seat adjustment parameter responding to the external environment change can be obtained. Therefore, the seat state of the vehicle can be automatically adjusted according to the environment perception data, unnecessary user operation is reduced, the distraction risk during driving is reduced, and the comfort and safety of the vehicle are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent cockpit, and particularly relates to a seat adjustment method and device, a storage medium, an intelligent seat and a vehicle. BACKGROUND

[0002] With the development of intelligent transportation systems, the comfort and safety of the vehicle interior environment are increasingly valued. Among them, the position of the car seat directly affects the driving posture of the driver, so reasonable adjustment of the car seat can ensure the comfort and safety of driving.

[0003] However, in the prior art, the intelligent cockpit integrated with multiple sensors and control units, although provides a certain degree of personalization and automation, but its adjustment mode is mostly dependent on user manual operation or preset fixed mode, and it is difficult to respond to external environment changes in real time. When the external environment changes, the comfort and functionality of the seat in the intelligent cockpit may be limited, resulting in poor driving and riding experience. SUMMARY

[0004] The main purpose of the present application is to provide a seat adjustment method, device, storage medium, intelligent seat and vehicle, which aims to solve the technical problems that the adjustment of the existing intelligent cockpit mostly depends on user manual operation or preset fixed mode, and it is difficult to respond to external environment changes in real time, resulting in poor driving and riding experience.

[0005] To achieve the above purpose, the present application provides a seat adjustment method, which comprises:

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

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

[0008] Adjusting the seat state of the vehicle based on the seat adjustment parameter.

[0009] In an embodiment, the step of fuzzy processing the environment perception data of the vehicle to obtain a fuzzy data set comprises:

[0010] Normalizing the environment perception data of the vehicle to obtain normalized data;

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

[0012] Discretely counting the normalized data to obtain a standard deviation;

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

[0014] In an embodiment, the step of normalizing the environment perception data of the vehicle to obtain normalized data comprises:

[0015] Obtaining historical perception data of each sensor in the sensor array;

[0016] Extracting a minimum value and a maximum value from the historical perception data;

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

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

[0019] Obtaining user preference data, the user preference data being obtained based on AI algorithm analysis of user behavior;

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

[0021] Based on the user preference data, the seat adjustment parameter and the adjustment weight, adjusting the seat state of the vehicle.

[0022] In an embodiment, the step of adjusting the seat state of the vehicle based on the seat adjustment parameter further comprises:

[0023] Collecting new perception data at a preset period through the sensor array;

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

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

[0026] According to the updated random forest regression model, returning to execute the step of fuzzy processing the environment perception data of the vehicle.

[0027] In an embodiment, the step of updating the model parameters of the random forest regression model based on the environment change data comprises:

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

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

[0030] when the output result does not reach the preset model performance, returning to performing the step of fine-tuning the model parameters of the random forest regression model based on the environment change data until the output result reaches the preset model performance, to obtain an updated random forest regression model.

[0031] In addition, to achieve the above object, the present application further provides a seat adjusting device, which comprises:

[0032] a preprocessing module, configured to perform fuzzy processing on environment perception data of a vehicle to obtain a fuzzy data set, the environment perception data being obtained by a sensor array of the vehicle;

[0033] a model prediction module, configured to predict the fuzzy data set by a preset random forest regression model to obtain a seat adjusting parameter corresponding to the environment perception data;

[0034] a seat adjusting module, configured to adjust a seat state of the vehicle based on the seat adjusting parameter.

[0035] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer readable storage medium, and a computer program is stored on the storage medium, and the computer program is executed by a processor to implement the steps of the seat adjusting method as described above.

[0036] In addition, to achieve the above object, the present application further provides an intelligent seat, which is applied to a vehicle, and the intelligent seat comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the seat adjusting method as described above.

[0037] In addition, to achieve the above object, the present application further provides a vehicle, which comprises the intelligent seat as described above.

[0038] The one or more technical solutions provided by the present application have at least the following technical effects: the environment perception data of a vehicle is first processed to obtain a fuzzy data set, the environment perception data being obtained by a sensor array of the vehicle; then the fuzzy data set is predicted by a preset random forest regression model to obtain a seat adjusting parameter corresponding to the environment perception data; finally, the seat state of the vehicle is adjusted based on the seat adjusting parameter.

[0039] Since the application 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, the seat adjustment parameters responding to the external environment change can be obtained. Thus, the seat state of the vehicle can be automatically adjusted according to the environmental perception data, unnecessary user operation is reduced, the distraction risk during driving is reduced, and the comfort and safety of the vehicle are improved. BRIEF DESCRIPTION OF DRAWINGS

[0040] The drawings incorporated in the specification and forming a part thereof illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the 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 to be used in the embodiment or prior art description will be briefly introduced as follows. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without creative labor.

[0042] Figure 1 An interactive schematic diagram of the seat adjustment process provided by the present application is provided.

[0043] Figure 2 A flowchart of the seat adjustment method embodiment one provided by the present application is provided.

[0044] Figure 3 A flowchart of the seat adjustment method embodiment two provided by the present application is provided.

[0045] Figure 4 A flowchart of the seat adjustment method embodiment three provided by the present application is provided.

[0046] Figure 5 A module structure schematic diagram of the seat adjustment device of the embodiment of the present application is provided.

[0047] Figure 6 A device structure schematic diagram of the hardware running environment involved in the seat adjustment method in the embodiment of the present application is provided.

[0048] The implementation of the object of the present application, functional features and advantages will be further described with reference to the 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 solutions of the present application, the following will be described in detail in combination with the drawings in the specification and specific embodiments.

[0051] It should be noted that in the field of intelligent cockpit, the existing seat adjustment technology mainly relies on manual operation or automatic adjustment based on fixed mode. In the automatic adjustment based on fixed mode, the seat adjustment system can save multiple seat configurations according to the settings of the driver, and automatically adjust to the preset configuration when the driver enters the vehicle. The working steps are as follows:

[0052] 1. User setting. The user inputs his / her seat position, angle, etc. preference settings through the vehicle central control screen, and saves it as a personal configuration file.

[0053] 2. Recognition and activation. The vehicle recognizes a specific user through vehicle-mounted sensors and systems (for example, through a mobile phone key or an in-vehicle camera), and automatically loads the seat configuration of the user when the user is recognized.

[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. The user can manually adjust the seat position at any time through the central control screen or the control panel beside the seat, and the system will ask whether to save the new settings.

[0056] The above method provides a certain degree of personalization and automation, but still relies on the fixed mode preset by the user, and cannot respond to environmental changes in real time (for example, sudden changes in weather such as temperature, humidity, and changes in light intensity). Therefore, when the external environment changes, the comfort and functionality of the seat may be limited, resulting in poor driving and riding experience.

[0057] To solve the above problems, the present application provides a seat adjustment method, which can be referred to Figure 1 , Figure 1 The interaction diagram of the seat adjustment process provided by the present application is shown in the figure. Through a series of sensors provided by the vehicle environment perception module, 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 the present 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) for executing the seat adjustment method of the present application, etc. The present embodiment does not limit this. The following will take the smart seat as an example to illustrate the present embodiment and each of the following embodiments.

[0059] Based on this, the present embodiment provides a seat adjustment method, which can be referred to Figure 2 , Figure 2A flowchart provided by the embodiment one of the seat adjustment method of the present application.

[0060] In this embodiment, the seat adjustment method comprises steps S10-S30:

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

[0062] It should be noted that the environment perception data can be various information about the environment around the vehicle collected by various sensors on the vehicle.

[0063] For example, the environment perception data can include temperature, humidity, illumination, air pressure, and internal parameters such as vehicle speed, etc., which are not limited in this embodiment.

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

[0065] It can be understood that the fuzzy data set can be obtained by fuzzy classification of the environment perception data of the vehicle through a specific fuzzy algorithm (for example, a fuzzy clustering algorithm, a fuzzy reasoning algorithm, etc.). In this way, the data can be simplified, the complexity and dimensionality of the data can be reduced, and the subsequent data prediction can be more easily performed.

[0066] In this embodiment, the sensor array of the vehicle monitors and collects the external environment of the vehicle in real time. After measuring the environment perception data, the environment perception data is transmitted to the vehicle computer. The vehicle computer transmits the received environment perception data to the intelligent seat on the vehicle. After receiving the environment perception data, the intelligent seat can first eliminate redundant data in the environment perception data, and then perform fuzzy classification on the environment perception data from which the redundant data is eliminated through a fuzzy clustering algorithm to obtain a fuzzy data set.

[0067] In a possible implementation, the step S10 of the present embodiment can comprise the steps of: normalizing the environment perception data of the vehicle to obtain normalized data; extracting a center value of the normalized data; performing discrete statistics on the normalized data to obtain a standard deviation; and performing fuzzy classification on the normalized data based on the center value and the standard deviation through a preset Gaussian membership function to obtain a fuzzy data set.

[0068] It should be noted that the normalized data can be data converted to a specific interval to eliminate the influence of the differences between the characteristics of different data due to the dimension, order of magnitude, etc.

[0069] For example, the normalization process can be achieved by minimum and maximum normalization, Z-score standardization, etc., which is not limited in the embodiment.

[0070] For ease of understanding, in a possible implementation, minimum and maximum normalization is taken as an example for illustration, but the present solution is not limited thereto. The step of normalizing the environment perception data of the vehicle to obtain normalized data in the embodiment includes: obtaining historical perception data of each sensor in the sensor array; extracting a minimum value and a maximum value from the historical perception data; and normalizing the environment perception data based on the minimum value and the maximum value to obtain normalized data corresponding to the environment perception data.

[0071] It should be noted that the historical perception data can be various information about the environment around the vehicle collected by various sensors on the vehicle in the past. The minimum value is the minimum value of all values in the historical perception data, and the maximum value is the maximum value of all values in the historical perception data.

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

[0073]

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

[0075] It can be understood that the central value can be the most central data of the normalized data, which is a typical value embodied by the normalized data. The standard deviation can be a data reflecting the dispersion degree 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; and a smaller standard deviation indicates that each data in the normalized data is more concentrated around the central value and the range is narrower.

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

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

[0078]

[0079] where c is the center value described above, and σ is the standard deviation.

[0080] In this embodiment, after receiving the environment perception data, the intelligent seat first normalizes the environment perception data to eliminate the influence of different dimensions and noise, and obtains normalized data. Then, the most central data is selected from the normalized data as the center value, and the standard deviation reflecting the dispersion degree of each data in the normalized data relative to the center value is obtained by discrete statistics on the normalized data. Finally, the membership degree of each data in the normalized data is calculated by the Gaussian membership function, and the normalized data is classified according to the membership degree to obtain multiple fuzzy data sets. Thus, the environment perception data is classified by the Gaussian membership function.

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

[0082] It should be noted that the random forest regression model can be a forest composed of multiple decision trees, and each decision tree can predict the fuzzy data set, and the final prediction value of the model can be obtained by synthesizing the prediction results of multiple decision trees.

[0083] It can be understood that the seat adjustment parameter can be an index for adjusting the intelligent seat on the vehicle.

[0084] For example, the seat adjustment parameter can include parameters in different aspects, such as the height, angle, and functional adjustment of heating or ventilation intensity of the intelligent seat, and the present embodiment does not limit this.

[0085] In this embodiment, after the fuzzy classification described above, the fuzzy data set is input into the pre-trained random forest regression model, and the seat adjustment parameter is predicted by the model. The general form of the random forest regression model is as follows:

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

[0087] where x1, x2,..., xn are characteristic variables affecting the seat adjustment parameter, 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 state can be various situations that the intelligent seat is currently presented. For example, the angle of the backrest, the front and rear position of the seat, the height level of the seat, the heating degree of the seat, and the like.

[0090] In this embodiment, for the convenience of understanding, as shown in Figure 1 , after the intelligent seat generates the seat adjustment parameters, the intelligent seat can input each data of the seat adjustment parameters into a series of execution units of the intelligent seat respectively, and each execution unit can complete the automatic adjustment of the seat state of the intelligent seat according to different seat adjustment parameters. Thus, the seat state of the vehicle can be automatically adjusted according to the change of the environment.

[0091] In the technical scheme provided in this embodiment, the sensor array of the vehicle monitors and collects the external environment of the vehicle in real time, transmits the environment perception data to the vehicle computer after measuring the environment perception data. The vehicle computer transmits the received environment perception data to the intelligent seat on the vehicle. After receiving the environment perception data, the intelligent seat can first eliminate the redundant data in the environment perception data, and then perform fuzzy classification on the environment perception data after eliminating the redundant data through a fuzzy clustering algorithm to obtain a fuzzy data set. Then, the fuzzy data set is input into a pre-trained random forest regression model to predict the seat adjustment parameters through the model. After generating the seat adjustment parameters, the intelligent seat can input each data of the seat adjustment parameters into a series of execution units of the intelligent seat respectively, and each execution unit can complete the automatic adjustment of the seat state of the intelligent seat according to different seat adjustment parameters. Since the environment perception data is obtained through the sensor array in advance in this embodiment, and the fuzzy data set is predicted through the random forest regression model after the fuzzy processing of the environment perception data, the seat adjustment parameters responding to the change of the external environment can be obtained. Thus, the seat state of the vehicle can be automatically adjusted according to the environment perception data, unnecessary user operation is reduced, the distraction risk during driving is reduced, and the comfort and safety of the vehicle are improved.

[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 the above embodiment one can refer to the above introduction, and the following will not be described in detail. On this basis, please refer to Figure 3 , Figure 3 for the flowchart of the second embodiment of the seat adjustment method of the present application.

[0093] The steps S30, S31-S33 of the present example are as follows:

[0094] Step S31: obtaining user preference data, the user preference data being obtained based on AI algorithm analysis on user behavior.

[0095] It should be noted that the user preference data can be data of personal preferences, tendencies, habits, etc. of the user for the intelligent seat adjustment. The AI algorithm can be an algorithm that allows a computer to simulate human intelligent behavior, such as machine learning algorithms (such as supervised learning, unsupervised learning algorithms, etc.), deep learning algorithms (such as neural network algorithms, etc.), etc. to learn, reason, make decisions, and identify user preference data.

[0096] Through the AI algorithm, the intelligent seat can automatically analyze the user behavior to obtain the user preference data to provide personalized seat setting recommendations.

[0097] Further, as shown in Figure 1 In addition to the above analysis of user behavior through the AI algorithm, the intelligent seat can also be connected to the display screen of the vehicle computer, and the user can make custom preference settings within a certain range on the user interface, or can manually intervene in special cases. In addition to touch operation, the user can also control the seat adjustment through voice commands to improve safety while driving. For critical operations such as changing important settings, the intelligent seat can also require the user to confirm twice to avoid misoperation.

[0098] Step S32: determining an adjustment weight between the user preference data and the seat adjustment parameter.

[0099] It should be noted that the adjustment weight can be a numerical value representing the relative importance when adjusting the user's various preferences (such as the specific preference degree for height, angle, etc.) and the actual seat adjustment parameter.

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

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

[0102] H = β (T - P);

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

[0104] In the technical solution provided in the embodiment, after generating the seat adjustment parameter, the intelligent seat can analyze user behavior by using an AI algorithm to obtain user preference data; or the user can make a self-defined preference setting within a certain range on a user interface to obtain the user preference data. Then, when the user preference data (such as a specific preference degree for height, angle, etc.) and the actual seat adjustment parameter are comprehensively considered, a relative importance degree adjustment weight is given. Finally, based on the user preference data, the seat adjustment parameter, and the adjustment weight, calculation is performed, and the seat state of the vehicle is adjusted according to the calculation result. In this way, the comfort of the vehicle ride is further improved.

[0105] Based on the above embodiments of the application, in the third embodiment of the application, the same or similar contents as the above embodiments can be referred to the above description, and will not be described in detail. On this basis, please refer to Figure 4 , Figure 4 The flowchart provided in the third embodiment of the seat adjustment method of the application is shown in the figure.

[0106] After the step S30 of the present example, steps S41-S43 are further included:

[0107] Step S41: Collect new perception data at a preset period by the sensor array.

[0108] It should be noted that the preset period can refer to a time interval for collecting various information about the environment around the vehicle that is set in advance. For example, half an hour or one hour, which is not limited in the present embodiment.

[0109] It can be understood that the new perception data can be various information about the environment around the vehicle that is monitored and collected again by various sensors on the vehicle.

[0110] Step S42: Compare the new perception data and the environment perception data to obtain environment change data.

[0111] It should be noted that the environment change data can be related data reflecting various changes in the state of the environment around the vehicle within a period of time. For example, it can include change data such as temperature, humidity, and light intensity.

[0112] Step S43: Update the model parameters of the random forest regression model based on the environment change data.

[0113] It should be noted that the model parameters can be key numerical values for constructing the random forest regression model, such as learning rate, regularization term, and other parameters, which are not limited in the present embodiment.

[0114] There are a series of parameters in the random forest regression model, such as the number of decision trees, the way of feature selection, the standard of splitting, etc. When there is new data or according to actual demand 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, the step S43 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 a preset model performance; and 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 when the output result does not reach the preset model performance.

[0116] It should be noted that the preset model performance can be an expected model prediction seat adjustment parameter performance level that is set in advance before the random forest regression model. For example, various performance indicators such as accuracy, precision, recall rate, F1 value, error rate, etc.

[0117] In the present embodiment, after the 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 learning rate, regularization term, etc. After successful fine-tuning, the seat adjustment parameter is input again to the random forest regression model after fine-tuning, and the adjustment result is output. At this time, the adjustment result is compared with the adjustment data input by the user to judge the accuracy. If the accuracy does not reach the preset model performance, it means that the model needs to be further trained. At this time, the model parameters of the random forest regression model need to be fine-tuned again based on the environmental change data. Until the accuracy reaches the preset model performance, the model with the accuracy reaching the preset model performance is taken as the updated random forest regression model. Thus, the prediction performance of the model is further improved.

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

[0119] In the technical scheme provided in the embodiment, to improve the accuracy and response speed of the model, a time interval for collecting various information about the environment around the vehicle can be set first. Then, the sensor array is used to monitor and collect various new perception data about the environment around the vehicle again at a preset period. Then, the new perception data and the environment perception data are compared to obtain environment change data reflecting various changes in the state of the environment around the vehicle in a period of time. Then, the model parameters of the random forest regression model are updated according to the environment change data, so that the random forest regression model predicts the seat adjustment parameters to reach the desired performance level. Finally, the step of performing fuzzy processing on the environment perception data of the vehicle is returned according to the updated random forest regression model. Thus, the model parameters can be automatically updated to absorb new data in real time, continuously optimize the performance of the model, further reduce user intervention, and realize automatic optimization of the seat parameters.

[0120] It should be noted that the above examples are only used to understand the present application and do not limit the seat adjustment method of the present application. More forms of simple changes based on the technical concept are within the protection scope of the present application.

[0121] The present application also provides a seat adjustment device, which will be described below Figure 5 , Figure 5 Fig. 1 is a schematic structural diagram of a seat adjustment device according to an embodiment of the present application; the seat adjustment device comprises:

[0122] The preprocessing module 501 is configured to perform fuzzy processing on the environment perception data of the vehicle to obtain a fuzzy data set, wherein the environment perception data is obtained by a sensor array of the vehicle.

[0123] The model prediction module 502 is configured to predict the fuzzy data set by a preset random forest regression model to obtain a seat adjustment parameter corresponding to the environment perception data.

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

[0125] As an implementation form, the preprocessing module 501 is further configured to perform normalization processing on the environment perception data of the vehicle to obtain normalized data, extract a central value of the normalized data, perform discrete statistics on the normalized data to obtain a standard deviation, and perform fuzzy classification on the normalized data by a preset Gaussian membership function based on the central value and the standard deviation to obtain the fuzzy data set.

[0126] As an implementation form, the preprocessing module 501 is further configured to acquire historical perception data of each sensor in the sensor array; extract minimum and maximum values from the historical perception data; and perform normalization processing on the environment perception data based on the minimum and maximum values to obtain normalized data corresponding to the environment perception data.

[0127] As an implementation form, the seat adjustment module 503 is further configured to acquire user preference data, the user preference data being obtained by analyzing user behavior based on an AI algorithm; determine an adjustment weight between the user preference data and the seat adjustment parameter; and adjust a seat state of the vehicle based on the user preference data, the seat adjustment parameter, and the adjustment weight.

[0128] As an implementation form, the model prediction module 502 is further configured to collect new perception data at a preset period through the sensor array; compare the new perception data with the environment perception data to obtain environment change data; update model parameters of the random forest regression model based on the environment change data; and return to perform the operation of performing fuzzy processing on the environment perception data of the vehicle according to the updated random forest regression model.

[0129] As an implementation form, the model prediction module 502 is further configured to fine-tune model parameters of the random forest regression model based on the environment change data to obtain an output result; determine whether the output result reaches a preset model performance; and return to perform the operation of fine-tuning the model parameters of the random forest regression model based on the environment change data until the output result reaches the preset model performance to obtain an updated random forest regression model, when the output result does not reach the preset model performance.

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

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

[0132] The application provides a smart seat applied to a vehicle, comprising: at least one processor; and a memory in communication connection with 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 to enable the at least one processor to perform the seat adjustment method in the embodiment one.

[0133] Reference will be made to the drawings Figure 6 , Figure 6 A device structure diagram of a hardware running environment involved in the seat adjustment method in the embodiments of the application is shown, which shows a structure diagram of a smart seat suitable for implementing the embodiments of the application. The smart seat in the embodiments of the application can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description), PMPs (Portable Media Player), vehicle-mounted terminals (for example, vehicle-mounted navigation terminals) and the like, and fixed terminals such as digital TVs, desktop computers and the like. Figure 6 The shown smart seat is only an example, and should not bring any limitation to the functions and use range of the embodiments of the application.

[0134] As Figure 6As shown, the intelligent seat can include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. Various programs and data required for operation of the intelligent seat are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the 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 intelligent seat to communicate wirelessly or wired with other devices to exchange data. Although the intelligent seat with various systems is shown in the figure, it should be understood that all the shown systems are not required to be implemented or possessed. More or less systems can be alternatively implemented or possessed.

[0135] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of embodiments of the present disclosure are performed.

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

[0137] It should be understood that various aspects of the disclosure can be implemented in 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 appropriate manner in any one or more embodiments or examples.

[0138] The above description is merely illustrative of the application and is not intended to limit the scope of the application. Any variations and modifications that can be made by any person skilled in the art within the spirit and scope of the application are intended to be encompassed by the application. The scope of the application is defined by the appended claims.

[0139] The application provides a computer readable storage medium having computer readable program instructions (i.e., computer programs) stored thereon, the computer readable program instructions being used to perform the seat adjustment method in the above embodiments.

[0140] The computer readable storage medium provided by the application may, for example, be a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared or semiconductor system, system or device, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electric connection with one or more conductive 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 the present embodiment, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer readable storage medium can be transmitted by any appropriate medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency) and the like, or any suitable combination of the above.

[0141] The above computer readable storage medium can be contained in the smart seat; or can exist separately without being assembled into the smart seat.

[0142] The computer readable storage medium described above carries one or more programs, when the one or more programs are executed by the intelligent seat, the intelligent seat: fuzzy processing of environment perception data of a vehicle, obtaining a fuzzy data set, the environment perception data is obtained by a sensor array of the vehicle; the fuzzy data set is predicted by a preset random forest regression model, and a seat adjustment parameter corresponding to the environment perception data is obtained; based on the seat adjustment parameter, the seat state of the vehicle is adjusted.

[0143] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can 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 the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0144] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may

[0145] The modules involved in the embodiments of the present application can be implemented in the form of software or in the form of hardware. Among them, the name of the module does not constitute a limitation to the unit itself in some cases.

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

[0147] The application also provides a vehicle, which comprises the intelligent seat as described above.

[0148] The vehicle provided in the application can solve the technical problem that the existing intelligent cabin adjustment is mostly dependent on manual operation or preset fixed mode of the user, and it is difficult to respond to external environment changes in real time, resulting in poor driving and riding experience. Compared with the prior art, the vehicle provided in the application has the same beneficial effects as the seat adjustment method provided in the above-mentioned embodiments, and will not be repeated here.

[0149] The above-mentioned is only part of the embodiments of the application, and does not limit the patent scope of the application, and any equivalent structural transformation, direct / indirect application in other related technical fields within the technical concept of the application, and the content of the specification and drawings of the application are included in the patent protection scope of the application.

Claims

1. A method for adjusting a seat, characterized in that, The method includes: The environmental perception data of the vehicle is subjected to fuzzy processing to obtain a fuzzy dataset, wherein the environmental perception data is acquired by the sensor array of the vehicle. The seat adjustment parameters corresponding to the environmental perception data are obtained by predicting the fuzzy dataset using a preset random forest regression model. Based on the seat adjustment parameters, adjust the seat state of the vehicle; The seat status includes seat heating intensity. The step of adjusting the seat status of the vehicle based on the seat adjustment parameters includes: analyzing user behavior and obtaining user-defined preferences set on the user interface through a neural network algorithm, and determining user preference data, wherein the user preference data is temperature preference. Determine the adjustment weight β between the temperature preference and the seat adjustment parameters; based on the temperature preference P, the seat adjustment parameters, and the adjustment weight, adjust the seat heating intensity of the vehicle, wherein the seat heating intensity is specifically calculated as follows: H=β(T P) ; Where H is the seat heating intensity; β is the adjustment weight used to adjust the heating rate; P is the temperature preference; and T is the external temperature.

2. The method as described in claim 1, characterized in that, The step of performing fuzzy processing on the vehicle's environmental perception data to obtain a fuzzy dataset includes: The environmental perception data of the vehicle is normalized to obtain normalized data; Extract the center value of the normalized data; Discrete statistics are performed on the normalized data to obtain the standard deviation; Based on the central value and the standard deviation, the normalized data is fuzzy classified using a preset Gaussian membership function to obtain a fuzzy dataset.

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

4. The method according to any one of claims 1 to 3, characterized in that, After the step of adjusting the vehicle seat state based on the seat adjustment parameters, the method further includes: New sensing data is collected at a preset period using the sensor array. The new sensing data and the environmental sensing data are compared to obtain environmental change data; Based on the environmental change data, update the model parameters of the random forest regression model; Based on the updated random forest regression model, return to the step of performing the fuzzing process on the vehicle's environmental perception data.

5. The method as described in claim 4, characterized in that, The step of updating the model parameters of the random forest regression model based on the environmental change data includes: Based on the environmental change data, the model parameters of the random forest regression model are fine-tuned to obtain the output results; Determine whether the output result meets the preset model performance; If the output result does not reach the preset model performance, return to 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, and obtain the updated random forest regression model.

6. A seat adjustment device, characterized in that, The seat adjustment device performs the seat adjustment method as described in claim 1, the device comprising: The preprocessing module is used to perform fuzzy processing on the vehicle's environmental perception data to obtain a fuzzy dataset. The environmental perception data is acquired by the vehicle's sensor array. The model prediction module is used to predict the fuzzy dataset using a preset random forest regression model to obtain the 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 parameters.

7. 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, it implements the steps of the seat adjustment method as described in any one of claims 1 to 5.

8. A smart seat, characterized in that, The smart seat is applied to a vehicle and includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the seat adjustment method as described in any one of claims 1 to 5.

9. A vehicle, characterized in that, The vehicle includes the intelligent seat as described in claim 8.

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