New energy automobile seat long slide rail horizontal driver control method and system

Abstract

The invention discloses a new energy automobile seat long sliding rail horizontal driver control method and system, relates to the field of sliding rail automatic control, and aims to solve the problems that special passengers can get on the automobile only after manually adjusting seats preferentially, and the requirements of comfort and rapidness when getting on the automobile by users cannot be met. A seat pressure value applied by a user, a seat automatic adjustment difference and a driver motion rate are collected, a data analysis model is established, an adjustment evaluation coefficient is obtained and is compared with an adjustment threshold value for analysis, a comparison result is obtained, and comprehensive analysis is performed according to a comparison result label and an adjustment evaluation coefficient value. The current user feature similarity is obtained through the next seat adjustment amplitude, a set of fuzzy rules are formulated for fuzzy reasoning according to similar historical user set preference values, a current user seat preliminary adjustment scheme is determined, the comfortable angle and position of the user are met more quickly, and the riding experience is optimized.

Description

Technical Field

[0001] The present invention relates to the field of slide rail automation control, and more specifically, to a method and system for controlling a horizontal driver of a long slide rail of a new energy vehicle seat. Background Art

[0002] In new energy vehicles, the seat adjustment function is crucial to driving comfort and passenger experience. At present, most seat adjustment systems use electric drives for position adjustment, which have multiple adjustment methods, including front and rear, up and down, and tilt adjustment, allowing drivers and passengers to easily adjust the seat position according to personal preferences to meet personalized needs of riding or driving.

[0003] The prior art has the following deficiencies:

[0004] At present, with the development of the times, automatic memory seats have emerged. According to the adjustment position set by the user, when the user presses the automatic memory button, the system receives the memory data and controls the long slide rail horizontal driver to link the seat to adjust to the user's preset adjustment position. However, the initial position of the seat is usually unchanged, which requires some special passengers to manually adjust the seat before getting on the bus. However, manual adjustment is not accurate enough and wastes time, which cannot meet the user's needs for comfort and speed when getting on the bus. Therefore, a control method and system for the long slide rail horizontal driver of a new energy vehicle seat are proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for controlling a horizontal driver of a long slide rail of a new energy vehicle seat, which solves the problems raised in the above-mentioned background technology by using different product inspection methods.

[0007] To achieve the above object, the present invention provides the following technical solution, a method for controlling a horizontal driver of a long slide rail of a new energy vehicle seat, comprising:

[0008] S1: Collecting user applied information and seat characteristic information, and obtaining the user applied seat pressure value, seat automatic adjustment difference and driver movement rate through data processing;

[0009] S2: Obtain the seat pressure value applied by the user, the seat automatic adjustment difference and the driver movement rate, establish a data analysis model, and obtain the adjustment evaluation coefficient;

[0010] S3: Obtain the adjustment evaluation coefficient and compare it with the adjustment threshold to obtain a comparison result, and perform a comprehensive analysis based on the comparison result label and the adjustment evaluation coefficient value to determine the next seat adjustment range;

[0011] S4: Acquire the seat adjustment range, collect the seat pressure value applied by the current user and compare it with the maximum and minimum values ​​of the previous historical user, select and collect the current user features according to the comparison results, and calculate the similarity between the current user features and the user features of each historical segment to obtain the current user feature similarity, and then perform cluster analysis based on the historical user setting preferences in the user feature similarity to obtain the setting preference values ​​of similar historical users;

[0012] S5: Bring the current user feature similarity and similar historical user setting preference values ​​into fuzzy logic to determine the preliminary adjustment result of the current user's seat.

[0013] In a preferred embodiment, the user applied information includes the user applied seat pressure value; the seat characteristic information includes the seat automatic adjustment difference and the actuator movement rate;

[0014] The pressure sensor measures the pressure on the seat surface to obtain the seat pressure value Cp applied by the user. i ; Wherein, i represents the i-th collection time;

[0015] First define the coordinates of the original position and the memory position, calculate the difference of each coordinate axis, and then get the seat automatic adjustment difference De according to the Euclidean distance i ;

[0016] The drive motion rate Sv is calculated by calculating the ratio of the distance difference between the user's preset memory position and the current seat position to the time required for the drive to start moving to the target position, and multiplying it by the pressure function. i .

[0017] In a preferred embodiment, the seat pressure value Cp applied by the user is obtained. i , the seat automatically adjusts the difference i And the drive motion rate Sv i , generate the adjusted evaluation coefficient Ec i , according to the formula:

[0018]

[0019] In the formula, Ec i To adjust the evaluation coefficient, as well as Preset proportionality factors for the user applied seat pressure value, the seat automatic adjustment difference, and the actuator movement rate, respectively, and as well as Both are greater than 0.

[0020] In a preferred embodiment, after obtaining the adjustment evaluation coefficient, the adjustment evaluation coefficient is compared and analyzed with the continuously iterated adjustment threshold value;

[0021] If the adjustment evaluation coefficient is greater than or equal to the adjustment threshold, the next sliding distance of the current seat is marked as a larger adjustment distance, and an adjustment signal is generated;

[0022] If the adjustment evaluation coefficient is less than the adjustment threshold, the next sliding distance of the current seat is marked as the adjustment distance unchanged, and a holding signal is generated.

[0023] In a preferred embodiment, the comparison result label includes a larger adjustment distance and an unchanged adjustment distance;

[0024] The number of labels with larger adjustment distances and the total value of their adjustment evaluation coefficients, as well as the number of labels with unchanged adjustment distances and the total value of their adjustment evaluation coefficients are counted respectively; the number of labels with larger adjustment distances is marked as Q, and the number of labels with unchanged adjustment distances is marked as P, and the calculation is substituted into the formula:

[0025]

[0026] Where W is the seat adjustment range, Ec 较大 is the total value of the adjusted evaluation coefficient of the label with the larger adjustment distance, Ec 不变 The total value of the adjusted evaluation coefficient for the adjusted distance-invariant label;

[0027] The seat adjustment range is compared with a preset amplitude threshold. If the seat adjustment range is greater than or equal to the amplitude threshold, the seat position is adjusted backward to a first distance. If the seat adjustment range is less than the amplitude threshold, the seat position is adjusted backward to a second distance.

[0028] In a preferred embodiment, historical users correspond to various historical segments; the seat pressure value applied by the current user is collected and compared with the maximum and minimum values ​​of the previous historical user. If it is less than the minimum value and greater than the maximum value, the current user characteristics are collected and the similarity is calculated with the characteristics of each historical segment user. Otherwise, the current user is adjusted according to the memory position of the previous historical user.

[0029] Among them, the current user characteristics include the user's weight, height and waist circumference; the user characteristics of each historical segment correspond to the current user characteristics, and the current user characteristics similarity is obtained through cosine similarity;

[0030] Count the similarities of all current user features, use the memory locations of similar historical user settings as feature data, perform cluster analysis and calculation, construct the feature matrix X, set the number of clusters k, and the specific numerical calculation of the number of clusters k is obtained through the cost function;

[0031] When the cluster center no longer changes significantly or the user assignment no longer changes, the algorithm is considered to have converged, and the final cluster center and cluster label of each user feature are obtained. The weighted average is used to obtain the preference values ​​set by similar historical users.

[0032] In a preferred embodiment, the current user feature similarity and the similar historical user setting preference values ​​are defined as input variables and divided into different fuzzy sets respectively;

[0033] The preliminary adjustment result of the current user seat is defined as the output variable and divided into fuzzy sets;

[0034] Formulate fuzzy rules to describe the impact of the current user's feature similarity and the preference values ​​set by similar historical users on the initial adjustment results of the current user's seat;

[0035] Fuzzy reasoning is performed based on fuzzy rules to determine the preliminary adjustment plan for the current user's seat.

[0036] A new energy vehicle seat long slide rail horizontal drive control system includes a data acquisition module, a data processing module, an adjustment analysis module and a user optimization module;

[0037] The data acquisition module is used to collect user applied information and seat characteristic information, and obtain the user applied seat pressure value, seat automatic adjustment difference and driver movement rate through data processing and send them to the data processing module;

[0038] The data processing module is used to obtain the seat pressure value applied by the user, the seat automatic adjustment difference and the driver movement rate, establish a data analysis model, obtain the adjustment evaluation coefficient and send it to the adjustment analysis module;

[0039] The adjustment analysis module is used to obtain the adjustment evaluation coefficient and compare it with the adjustment threshold to obtain the comparison result. A comprehensive analysis is performed based on the comparison result label and its adjustment evaluation coefficient value to determine the next seat adjustment range and send it to the user optimization module.

[0040] The user optimization module is used to obtain the seat adjustment range, collect the seat pressure value applied by the current user and compare it with the maximum and minimum values ​​of the previous historical user, and select the current user characteristics to be collected based on the comparison results and calculate the similarity between the characteristics of each historical segment user to obtain the current user characteristic similarity, and then perform cluster analysis based on the historical user setting preferences in the user characteristic similarity to obtain similar historical user setting preference values ​​and bring them into fuzzy logic to determine the preliminary adjustment result of the current user seat.

[0041] Technical effects and advantages of the present invention:

[0042] 1. The present invention establishes a data analysis model by collecting the seat pressure value applied by the user, the seat automatic adjustment difference and the driver movement rate, obtains the adjustment evaluation coefficient and compares and analyzes it with the adjustment threshold to obtain the comparison result, and performs a comprehensive analysis based on the comparison result label and its adjustment evaluation coefficient value to determine the next seat adjustment range. The seat can meet the user's comfortable angle and position more quickly, optimize the riding experience, improve the seat adjustment efficiency and accuracy, and extend the service life of the long slide rail horizontal driver.

[0043] 2. The present invention obtains the next seat adjustment range, formulates a set of fuzzy rules for fuzzy reasoning according to the similarity of the current user's characteristics and the preference values ​​set by similar historical users, determines the preliminary adjustment plan of the current user's seat, reduces unnecessary position adjustments, meets the riding needs of new users, and improves the use accuracy and operational intelligence of the long slide rail horizontal drive. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The present invention is a method flow chart of a method for controlling a horizontal driver of a long slide rail of a new energy vehicle seat.

[0045] Figure 2 The present invention is a module schematic diagram of a new energy vehicle seat long slide rail horizontal drive control system. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The present invention specifically realizes the further refinement of the automatic memory seat, adjusts the seat according to the user characteristic information and the seat characteristic information, and further optimizes and realizes the automatic control of the horizontal drive of the long slide rail of the car seat;

[0048] Example 1

[0049] See also Figure 1 , a method for controlling the horizontal drive of a long slide rail of a new energy vehicle seat, the specific operation process is as follows:

[0050] S1: Collecting user applied information and seat characteristic information, and obtaining the user applied seat pressure value, seat automatic adjustment difference and driver movement rate through data processing;

[0051] The user applied information includes the seat pressure value applied by the user; the seat characteristic information includes the seat automatic adjustment difference and the actuator movement rate;

[0052] The user-applied seat pressure value refers to the vertical pressure generated by the user's weight and sitting posture when the user sits on the seat. The greater the user-applied seat pressure value, the greater the travel pressure of the long slide rail horizontal driver, which is more likely to cause slide rail fatigue, reduce energy efficiency and life, and the smaller the seat adjustment range is, so as to reduce the travel distance of the long slide rail horizontal driver;

[0053] The acquisition logic is to measure the pressure on the seat surface through the pressure sensor to obtain the seat pressure value Cp applied by the user i ; Wherein, i represents the i-th collection time;

[0054] The specific pressure calculation formula is:

[0055]

[0056] In the formula, Cp i is the seat pressure value applied by the user, F is the force applied by the user on the chair, and A is the area of ​​contact between the user and the chair;

[0057] It should be noted that the acquisition time is set by the experimenter according to the frequency of change of the seat pressure value applied by the user or the number of travels of the long slide rail horizontal driver. When the seat is moved, it is adjusted toward the user's preset memory position, and the seat pressure value applied by the user will change with the adjustment, etc., which will not be elaborated here;

[0058] The seat automatic adjustment difference refers to the difference between the user-preset memory position and the original position; its acquisition logic is to first define the coordinates of the original position and the memory position, calculate the difference of each coordinate axis, and then obtain the seat automatic adjustment difference De according to the Euclidean distance. i ;

[0059] Specifically, let the original position coordinates be (x1, y1, z1) and the memorized position coordinates be (x2, y2, z2); calculate the difference between each coordinate axis, specifically:

[0060] Δx=x2―x1, Δy=y2―y1, and Δz=z2―z1;

[0061] Where Δx, Δy, and Δz are the differences in the x-axis, y-axis, and z-axis, respectively;

[0062] The seat automatic adjustment difference is obtained according to the Euclidean distance. The specific formula is:

[0063]

[0064] In the formula, De i Automatically adjust differentials for seats;

[0065] It should be noted that the original position refers to the original position of the seat after the vehicle is turned off and the seat will be adjusted to the original position as the user leaves. Usually, this position is the unified standard of the current automobile manufacturing industry. The memory position refers to the memory position saved after the user manually adjusts it and clicks the memory button. The specific memory position is not limited, but is based on the result of the user's manual adjustment, which will not be repeated here.

[0066] The actuator movement rate refers to the movement speed of the actuator when adjusting the seat. Specifically, the movement speed is usually a uniform adjustment movement process, wherein the movement process is relatively slow to adapt to each user. The uniform adjustment movement process is set by the experimenter according to relevant production requirements and is not limited here.

[0067] The acquisition logic is to calculate the ratio of the distance difference between the user's preset memory position and the current seat position to the time required for the driver to start moving to the target position, and multiply it by the pressure function to obtain the driver movement rate Sv i ;

[0068] It should be noted that the current seat position does not refer to the original position, but the distance difference between the adjusted seat position and the memory position, then the user is sitting on the seat;

[0069] Specifically, the distance difference between the user's preset memory position and the current seat position is obtained through the Euclidean distance, and a pressure function is established. The pressure is set as the independent variable and the movement rate is set as the dependent variable. The best fitting curve is found through the least squares method, and then the parameters of the pressure function are calculated. The specific formula is expressed as follows:

[0070]

[0071] In the formula, Sv i is the actuator movement rate, D is the distance difference between the user's preset memory position and the current seat position, T is the time required for the actuator to start moving to reach the target position, and K(p) is the pressure function;

[0072] Among them, the expression of the pressure function is expressed by an exponential model, specifically:

[0073] K(p)=e ―dp

[0074] In the formula, e is the natural logarithm, p is the applied pressure, and -d is a positive parameter used to control the degree of influence of pressure on rate;

[0075] Furthermore, when the user sits on the seat for the first time, he manually adjusts it to a position that the user is satisfied with and then clicks the memory button. The system receives the current seat position data and saves it. When the user sits on the seat again, he clicks the memory button and the seat automatically adjusts to the position of the last seat adjustment based on the data information sent by the system.

[0076] Furthermore, when the user presses and holds the memory button when getting out of the vehicle, the system will calculate the adjustment range of the seat based on the user's characteristic information and the seat's characteristic information. After the user unlocks the vehicle, the seat in the vehicle will be adjusted to the appropriate position based on the adjustment range calculated by the system, saving the user's time in getting into the vehicle and meeting the user's needs.

[0077] It can be seen that the above parameters are all historical (data collected after the last passenger took a seat), and will not be described in detail here;

[0078] S2: Obtain the seat pressure value applied by the user, the seat automatic adjustment difference and the driver movement rate, establish a data analysis model, and obtain the adjustment evaluation coefficient;

[0079] Among them, the data analysis model refers to a weighted analysis model, which generates an adjustment evaluation coefficient through weighted calculation;

[0080] Get the seat pressure value Cp applied by the user i , the seat automatically adjusts the difference i And the drive motion rate Sv i , generate the adjusted evaluation coefficient Ec i , according to the formula:

[0081]

[0082] In the formula, Ec i To adjust the evaluation coefficient, as well as Preset proportionality factors for the user applied seat pressure value, the seat automatic adjustment difference, and the actuator movement rate, respectively, and as well as All are greater than 0;

[0083] Among them, the user-applied seat pressure value, the seat automatic adjustment difference and the actuator movement rate are all data-based manifestations that directly express the current seat adjustment range trend;

[0084] It can be seen from the above formula that when the difference between the seat pressure value applied by the user and the automatic adjustment of the seat is greater, it means that the difficulty of seat adjustment increases, indicating that the driver needs to overcome a larger load to achieve adjustment, and the adjustment evaluation coefficient is smaller. Conversely, the greater the driver movement rate, the greater the adjustment evaluation coefficient.

[0085] S3: Obtain the adjustment evaluation coefficient and compare it with the adjustment threshold to obtain a comparison result, and perform a comprehensive analysis based on the comparison result label and the adjustment evaluation coefficient value to determine the next seat adjustment range;

[0086] The logic for obtaining the adjustment threshold is to collect a set of historical seat adjustment positions, including manual adjustments by users and automatic memory adjustments of seats, and then divide the data set into a training set and a test set. The evaluation index and clustering algorithm are set. In each round of cross-validation, the model is trained on the training set and the model performance is evaluated on the test set. The threshold is then adjusted based on the performance of the validation set. Therefore, the adjustment threshold is continuously updated.

[0087] In the present invention, clustering algorithm is a kind of unsupervised learning algorithm, which is used to divide the data points in the data set into groups or clusters with similarity; a common one is K-means clustering, which divides the data points in the data set into K clusters, so that the distance between the data point of each curve and the center point (centroid) of the cluster to which it belongs is minimized, and finally the effect of the adjusted threshold is measured by the Euclidean distance, so as to set the adjustment threshold;

[0088] After obtaining the adjustment evaluation coefficient, compare and analyze the adjustment evaluation coefficient with the continuously iterated adjustment threshold;

[0089] If the adjustment evaluation coefficient is greater than or equal to the adjustment threshold, the next sliding distance of the current seat is marked as a larger adjustment distance, and an adjustment signal is generated;

[0090] If the adjustment evaluation coefficient is less than the adjustment threshold, the next sliding distance of the current seat is marked as unchanged adjustment distance, and a hold signal is generated;

[0091] Among them, the comparison result labels include a larger adjustment distance and an unchanged adjustment distance;

[0092] The number of labels with larger adjustment distances and the total value of their adjustment evaluation coefficients, as well as the number of labels with unchanged adjustment distances and the total value of their adjustment evaluation coefficients are counted respectively; the number of labels with larger adjustment distances is marked as Q, and the number of labels with unchanged adjustment distances is marked as P, and the calculation is substituted into the formula:

[0093]

[0094] Where W is the seat adjustment range, Ec 较大 is the total value of the adjusted evaluation coefficient of the label with the larger adjustment distance, Ec 不变 The total value of the adjusted evaluation coefficient for the adjusted distance-invariant label;

[0095] It should be noted that although the user is consistent, the state of each ride or drive may be different, and the next sliding distance of the seat needs to be adjusted continuously according to historical data (i.e., the i-th collection time). It is generally believed that the average value of the seat pressure applied by the user is the pressure index. When the seat pressure applied by the user is greater than the average value, it means that the user is in a relaxed state. The drive speed will decrease slightly as the pressure increases, and the adjustment distance needs to be reduced or unchanged to complete the automatic memory adjustment of the seat as soon as possible and reduce fatigue on the seat rails.

[0096] Compare the seat adjustment amplitude with a preset amplitude threshold, if the seat adjustment amplitude is greater than or equal to the amplitude threshold, adjust the seat position backward to a first distance, if the seat adjustment amplitude is less than the amplitude threshold, adjust the seat position backward to a second distance;

[0097] It should be noted that the first distance and the second distance are adjustment distances divided by the experimenter according to the height or weight of the specific user. For example, according to the height of the user, the seat position is adjusted 30% back to set the first distance, according to the weight of the user, the seat position is adjusted 10% back to set the second distance, and so on, which will not be elaborated here;

[0098] The amplitude threshold is calculated based on the current adjustment evaluation coefficient and the historical seat adjustment amplitude set, and is not limited here;

[0099] The present invention establishes a data analysis model by collecting the seat pressure value applied by the user, the seat automatic adjustment difference and the driver movement rate, obtains the adjustment evaluation coefficient and compares and analyzes it with the adjustment threshold to obtain the comparison result, and performs a comprehensive analysis based on the comparison result label and the adjustment evaluation coefficient value to determine the next seat adjustment range. The seat can meet the user's comfortable angle and position more quickly, optimize the riding experience, improve the seat adjustment efficiency and accuracy, and extend the service life of the long slide rail horizontal driver.

[0100] Example 2

[0101] In the first embodiment of the present invention, an example is given of collecting the seat pressure value applied by the user, the seat automatic adjustment difference and the driver movement rate, establishing a data analysis model, obtaining the adjustment evaluation coefficient and performing a comparison analysis with the adjustment threshold, obtaining the comparison result, and performing a comprehensive analysis based on the comparison result label and the adjustment evaluation coefficient value to determine the operation strategy for the next seat adjustment range; however, in the first embodiment, only the perspective of a single user's long-term riding is considered. Obviously, if the user does not delete the set seat automatic memory information, other users will make unnecessary position adjustments when riding, which not only fails to meet the needs of new users, but also affects the frequency of use of the long slide rail horizontal driver; in view of the above problems, the second embodiment of the present invention is further refined;

[0102] S4: Obtain the next seat adjustment range, collect the seat pressure value applied by the current user and compare it with the maximum and minimum values ​​of the previous historical user, select and collect the current user features according to the comparison results, and calculate the similarity between the current user features and the user features of each historical segment to obtain the current user feature similarity, and then perform cluster analysis based on the historical user setting preferences in the user feature similarity to obtain the setting preference values ​​of similar historical users;

[0103] The next seat adjustment range is based on the adjustment strategy described in the above embodiment 1, which will not be described in detail here;

[0104] Historical users correspond to various historical segments, which are specifically divided into: when a user presses and holds the memory button twice in a row during a ride, it is recorded as a historical segment. The historical segment is obtained based on the user's operation. The seat will remain in the original position the next time a passenger enters the vehicle, and the new user needs to manually adjust the seat to a suitable position and then press the memory button to lock the current memory position. Or, when the seat pressure applied by the current user is less than the minimum value or greater than the maximum value during a ride, the system automatically divides the seat into a historical segment, and the time from the end of the previous historical segment to the current time is recorded as a historical segment;

[0105] It should be noted that the historical segment is different from the above-mentioned collection time i. The historical segment includes multiple collection times. It can be understood by those skilled in the art that the historical segment is usually set by different body weights of different users or the reset of the memory button, etc., which is not limited here.

[0106] Collect the seat pressure value applied by the current user and compare it with the maximum and minimum values ​​of the previous historical user. If it is less than the minimum value and greater than the maximum value, collect the current user's characteristics and calculate the similarity with the characteristics of each historical segment user. Otherwise, adjust it according to the memory position of the previous historical user.

[0107] For example, the maximum seat pressure value applied by a user (100 kg) during the use of the seat is 3266 Pa (assuming that the seat area is 0.3 square meters and there is no backrest), and the minimum value is 1960 Pa (assuming that the seat plus the backrest area is 0.5 square meters and the backrest is fully backed). The seat pressure value applied by the current user is 1300 Pa, and the current user is completely inconsistent with the previous user, so the memory position needs to be readjusted. However, the current user has a blank (that is, it is impossible to know the seat position and angle that he feels comfortable, and it is impossible to know his other parameters except weight or height. Even if the weight or height is brought into the large model database to verify the memory position, there is still universality, and it is impossible to improve the possibility of satisfying users), and it is impossible to analyze the memory position required by the parameters in Example 1;

[0108] Among them, the current user characteristics include the user's weight, height and waist circumference; the user characteristics of each historical segment correspond to the current user characteristics;

[0109] Specifically, the user's weight, height and waist circumference are obtained by multiplying the seat area covered by the user and the pressure value obtained by the pressure sensor to obtain the user's weight. The height is measured by the laser sensor. The waist circumference is obtained by the pressure sensor. The average pressure value and the maximum pressure value of the waist area when the user sits down are substituted into the linear regression model to calculate the user's waist circumference. The relationship between pressure and waist circumference is established using historical data. The specific formula is as follows:

[0110] Wa=a·average pressure value+b·maximum pressure value+c

[0111] Among them, Wa is the user's waist circumference, a, b and c are coefficients trained based on historical data;

[0112] Furthermore, the current user feature similarity is obtained through cosine similarity. The specific formula is as follows:

[0113]

[0114] In the formula, Cs n is the feature similarity of the current user, A is the feature vector of the current user, B is the feature vector of the historical user, ‖A‖ is the modulus length of the feature vector of the current user, ‖B‖ is the modulus length of the feature vector of the historical user, and n is the nth comparison number;

[0115] Specifically, the expression of the feature vector of the current user is {We, He, Wa}, and the expression of the feature vector of the historical user is {hitoricalWe, hitoricalHe, hitoricalWa}; where We is the user's weight and He is the user's height;

[0116] Furthermore, the modulus of the feature vector of the current user is:

[0117]

[0118] The modulus length of the feature vector of historical users is:

[0119]

[0120] Count the similarity of all current user features, set the memory position of similar historical users as feature data, and construct the feature matrix X, set the number of clusters k, and calculate the value J(k) of the specific number of clusters k through the cost function. The specific formula is:

[0121]

[0122] In the formula, S j is the jth cluster, C j is the center of the jth cluster, x iThe sample points belonging to this cluster;

[0123] Plot the calculated k value and J(k) value, usually with k as the horizontal axis and J(k) as the vertical axis;

[0124] Observe the trend of J(k) changing with k in the figure. As k increases, J(k) gradually decreases; find the position in the curve where the decrease rate slows down significantly, which is the optimal cluster number k value;

[0125] Randomly select k user features as the initial cluster center C. For each user feature x i , calculate its relationship with each cluster center C j The Euclidean distance of , assigns the user features to the nearest cluster;

[0126] Update cluster center C j is the current cluster S j The mean of all user features in , the specific formula is:

[0127]

[0128] When the cluster center no longer changes significantly or the user assignment no longer changes, the algorithm converges and the final cluster center and cluster label of each user feature are obtained. The preference values ​​set by similar historical users are obtained by weighted average.

[0129] S5: Bringing the current user feature similarity and similar historical user setting preference values ​​into fuzzy logic to determine the preliminary adjustment result of the current user's seat;

[0130] For example, "High", "Low", and "Medium" are for the high, medium, and low judgments of the current user's feature similarity, and "Many", "Less", and "Average" are for the high, medium, and low judgments of similar historical user setting preference values;

[0131] Formulate a set of fuzzy rules to describe the impact of different input variables on output variables. The definition of rules can be based on professional knowledge or obtained through data analysis and experiments. For example:

[0132] The current user feature similarity is marked as X, the similar historical user setting preference value is marked as U, and the current user's preliminary seat adjustment result is marked as C_results;

[0133] Then we can define:

[0134] Rule 1:IF(X is High)AND(U is Many)THEN(C_results is Recommend)

[0135] Rule 2:IF(U is Low)AND(U is Less)THEN(C_results is Objection) ...

[0137] Perform fuzzy reasoning based on fuzzy rules to determine the initial adjustment plan for the current user's seat;

[0138] It should be noted that the division of fuzzy sets can be adjusted according to actual conditions. For example, although this embodiment takes three fuzzy sets as an example, it can actually be divided into more than three sets to facilitate better accurate adjustment according to different similarities.

[0139] Further, for the judgment of the similarity of the current user characteristics and the high, medium and low values ​​of the similar historical user setting preference values, a threshold value can be set according to the actual situation for judgment. For example, when the similarity of the current user characteristics exceeds 80%, it is marked as "High", and when the similar historical user setting preference value is higher than 74%, it is marked as "Many", etc., which will not be elaborated here;

[0140] The present invention obtains the next seat adjustment range, formulates a set of fuzzy rules for fuzzy reasoning according to the current user feature similarity and similar historical user setting preference values, determines the current user's preliminary seat adjustment plan, reduces unnecessary position adjustments, meets the riding needs of new users, and improves the use accuracy and operational intelligence of the long slide rail horizontal drive.

[0141] Example 3

[0142] See also Figure 2 , a new energy vehicle seat long slide rail horizontal drive control system, including a data acquisition module, a data processing module, an adjustment analysis module and a user optimization module;

[0143] The data acquisition module is used to collect user applied information and seat characteristic information, and obtain the user applied seat pressure value, seat automatic adjustment difference and driver movement rate through data processing and send them to the data processing module;

[0144] The data processing module is used to obtain the seat pressure value applied by the user, the seat automatic adjustment difference and the driver movement rate, establish a data analysis model, obtain the adjustment evaluation coefficient and send it to the adjustment analysis module;

[0145] The adjustment analysis module is used to obtain the adjustment evaluation coefficient and compare it with the adjustment threshold to obtain the comparison result. A comprehensive analysis is performed based on the comparison result label and its adjustment evaluation coefficient value to determine the next seat adjustment range and send it to the user optimization module.

[0146] The user optimization module is used to obtain the next seat adjustment range, collect the seat pressure value applied by the current user and compare it with the maximum and minimum values ​​of the previous historical user, and select the current user features to collect according to the comparison results and calculate the similarity between the current user features and the user features of each historical segment to obtain the current user feature similarity, and then perform cluster analysis based on the historical user setting preferences in the user feature similarity to obtain similar historical user setting preference values ​​and bring them into fuzzy logic to determine the preliminary adjustment result of the current user seat.

[0147] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0148] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0149] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0150] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0152] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0153] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0155] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0156] 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.

Claims

1. A method for controlling a horizontal drive of a long slide rail of a new energy vehicle seat, characterized in that: include: S1: Collecting user applied information and seat characteristic information, and obtaining the user applied seat pressure value, seat automatic adjustment difference and driver movement rate through data processing; S2: Obtain the seat pressure value applied by the user, the seat automatic adjustment difference and the driver movement rate, establish a data analysis model, and obtain the adjustment evaluation coefficient; S3: Obtain the adjustment evaluation coefficient and compare it with the adjustment threshold to obtain a comparison result, and perform a comprehensive analysis based on the comparison result label and the adjustment evaluation coefficient value to determine the next seat adjustment range; S4: Acquire the seat adjustment range, collect the seat pressure value applied by the current user and compare it with the maximum and minimum values ​​of the previous historical user, select and collect the current user features according to the comparison results, and calculate the similarity between the current user features and the user features of each historical segment to obtain the current user feature similarity, and then perform cluster analysis based on the historical user setting preferences in the user feature similarity to obtain the setting preference values ​​of similar historical users; S5: Bring the current user feature similarity and similar historical user setting preference values ​​into fuzzy logic to determine the preliminary adjustment result of the current user's seat.

2. The method for controlling the horizontal driver of the long slide rail of the new energy vehicle seat according to claim 1 is characterized in that: The user applied information includes the seat pressure value applied by the user; Seat characteristic information includes seat automatic adjustment differences and actuator movement rates; The pressure sensor measures the pressure on the seat surface to obtain the seat pressure value Cp applied by the user. i ; Wherein, i represents the i-th collection time; First define the coordinates of the original position and the memory position, calculate the difference of each coordinate axis, and then get the seat automatic adjustment difference De according to the Euclidean distance i ; The drive motion rate Sv is calculated by calculating the ratio of the distance difference between the user's preset memory position and the current seat position to the time required for the drive to start moving to the target position, and multiplying it by the pressure function. i .

3. The method for controlling the horizontal driver of the long slide rail of the new energy vehicle seat according to claim 2 is characterized in that: Get the seat pressure value Cp applied by the user i , the seat automatically adjusts the difference i And the drive motion rate Sv i , generate the adjusted evaluation coefficient Ec i , according to the formula: In the formula, Ec i To adjust the evaluation coefficient, as well as Preset proportionality factors for the user applied seat pressure value, the seat automatic adjustment difference, and the actuator movement rate, respectively, and as well as Both are greater than 0.

4. The method for controlling the horizontal driver of the long slide rail of the new energy vehicle seat according to claim 3 is characterized in that: After obtaining the adjustment evaluation coefficient, compare and analyze the adjustment evaluation coefficient with the continuously iterated adjustment threshold; If the adjustment evaluation coefficient is greater than or equal to the adjustment threshold, the next sliding distance of the current seat is marked as a larger adjustment distance, and an adjustment signal is generated; If the adjustment evaluation coefficient is less than the adjustment threshold, the next sliding distance of the current seat is marked as the adjustment distance unchanged, and a holding signal is generated.

5. The method for controlling the horizontal driver of the long slide rail of the new energy vehicle seat according to claim 4 is characterized in that: The labels of the comparison results include the larger adjustment distance and the unchanged adjustment distance; The number of labels with larger adjustment distances and the total value of their adjustment evaluation coefficients, as well as the number of labels with unchanged adjustment distances and the total value of their adjustment evaluation coefficients are counted respectively; the number of labels with larger adjustment distances is marked as Q, and the number of labels with unchanged adjustment distances is marked as P, and the calculation is substituted into the formula: Where W is the seat adjustment range, Ec 较大 is the total value of the adjusted evaluation coefficient of the label with the larger adjustment distance, Ec 不变 The total value of the adjusted evaluation coefficient for the adjusted distance-invariant label; The seat adjustment range is compared with a preset amplitude threshold. If the seat adjustment range is greater than or equal to the amplitude threshold, the seat position is adjusted backward to a first distance. If the seat adjustment range is less than the amplitude threshold, the seat position is adjusted backward to a second distance.

6. The method for controlling the horizontal driver of the long slide rail of the new energy vehicle seat according to claim 5 is characterized in that: Historical users correspond to each historical segment; collect the seat pressure value applied by the current user and compare it with the maximum and minimum values ​​of the previous historical user. If it is less than the minimum value and greater than the maximum value, collect the current user features and calculate the similarity with the user features of each historical segment. Otherwise, adjust according to the memory position of the previous historical user; Among them, the current user characteristics include the user's weight, height and waist circumference; the user characteristics of each historical segment correspond to the current user characteristics, and the current user characteristics similarity is obtained through cosine similarity; Count the similarities of all current user features, use the memory locations of similar historical user settings as feature data, perform cluster analysis and calculation, construct the feature matrix X, set the number of clusters k, and the specific numerical calculation of the number of clusters k is obtained through the cost function; When the cluster center no longer changes significantly or the user assignment no longer changes, the algorithm is considered to have converged, and the final cluster center and cluster label of each user feature are obtained. The weighted average is used to obtain the preference values ​​set by similar historical users.

7. The method for controlling the horizontal driver of the long slide rail of the new energy vehicle seat according to claim 6 is characterized in that: The current user feature similarity and similar historical user setting preference values ​​are defined as input variables and divided into different fuzzy sets respectively; The preliminary adjustment result of the current user seat is defined as the output variable and divided into fuzzy sets; Formulate fuzzy rules to describe the impact of the current user's feature similarity and the preference values ​​set by similar historical users on the initial adjustment results of the current user's seat; Fuzzy reasoning is performed based on fuzzy rules to determine the preliminary adjustment plan for the current user's seat.

8. A new energy vehicle seat long slide rail horizontal driver control system, used to implement the new energy vehicle seat long slide rail horizontal driver control method according to any one of claims 1 to 7, characterized in that: It includes data acquisition module, data processing module, adjustment analysis module and user optimization module; The data acquisition module is used to collect user applied information and seat characteristic information, and obtain the user applied seat pressure value, seat automatic adjustment difference and driver movement rate through data processing and send them to the data processing module; The data processing module is used to obtain the seat pressure value applied by the user, the seat automatic adjustment difference and the driver movement rate, establish a data analysis model, obtain the adjustment evaluation coefficient and send it to the adjustment analysis module; The adjustment analysis module is used to obtain the adjustment evaluation coefficient and compare it with the adjustment threshold to obtain the comparison result. A comprehensive analysis is performed based on the comparison result label and its adjustment evaluation coefficient value to determine the next seat adjustment range and send it to the user optimization module. The user optimization module is used to obtain the seat adjustment range, collect the seat pressure value applied by the current user and compare it with the maximum and minimum values ​​of the previous historical user, and select the current user characteristics to be collected based on the comparison results and calculate the similarity between the characteristics of each historical segment user to obtain the current user characteristic similarity, and then perform cluster analysis based on the historical user setting preferences in the user characteristic similarity to obtain similar historical user setting preference values ​​and bring them into fuzzy logic to determine the preliminary adjustment result of the current user seat.

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