CN110370996B - Intelligent electric seat for automobile - Google Patents

Intelligent electric seat for automobile Download PDF

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CN110370996B
CN110370996B CN201910633014.XA CN201910633014A CN110370996B CN 110370996 B CN110370996 B CN 110370996B CN 201910633014 A CN201910633014 A CN 201910633014A CN 110370996 B CN110370996 B CN 110370996B
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CN110370996A (en
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朱丹
牟春盛
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Zhejiang Hongli Zhixin Auto Parts Manufacturing Co ltd
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Zhejiang Hongli Zhixin Auto Parts Manufacturing Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B60N2/0224—Non-manual adjustments, e.g. with electrical operation
    • B60N2/0244—Non-manual adjustments, e.g. with electrical operation with logic circuits
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B60N2/0224—Non-manual adjustments, e.g. with electrical operation
    • B60N2/0244—Non-manual adjustments, e.g. with electrical operation with logic circuits
    • B60N2/0248—Non-manual adjustments, e.g. with electrical operation with logic circuits with memory of positions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B60N2/0224—Non-manual adjustments, e.g. with electrical operation
    • B60N2/0244—Non-manual adjustments, e.g. with electrical operation with logic circuits
    • B60N2/0268—Non-manual adjustments, e.g. with electrical operation with logic circuits using sensors or detectors for adapting the seat or seat part, e.g. to the position of an occupant
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B60N2/04—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable the whole seat being movable
    • B60N2/06—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable the whole seat being movable slidable
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B60N2/04—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable the whole seat being movable
    • B60N2/16—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable the whole seat being movable height-adjustable
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60N—SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
    • B60N2/00—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles
    • B60N2/02—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
    • B60N2/22—Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable the back-rest being adjustable
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30196—Human being; Person
    • G06T2207/30201—Face

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  • Engineering & Computer Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Mechanical Engineering (AREA)
  • Transportation (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Human Computer Interaction (AREA)
  • Multimedia (AREA)
  • General Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Image Processing (AREA)
  • Seats For Vehicles (AREA)

Abstract

Intelligent automobile electric seat, including electric seat, seat perception module, front end camera, intelligence control system, seat adjustment mechanism and information acquisition module. The invention has the beneficial effects that: the intelligent electric seat for the automobile is combined with a face recognition technology and an intelligent control technology, so that autonomous memory of the electric seat and automatic adjustment of the electric seat are achieved, and manual intervention is not needed.

Description

Intelligent electric seat for automobile
Technical Field
The invention relates to the field of electric seats, in particular to an intelligent automobile electric seat.
Background
Since the 80's of the 20 th century, automobiles have been rapidly developed in the Chinese market, and in people's daily lives, automobiles have become indispensable important vehicles. At present, the automobile market competition in China is more and more intense, the requirements of people on the safety and the stability of automobile driving are higher and higher, and the automobile seat is one of important parts of automobile accessories, so that the convenience and the comfort of the automobile seat are often related to the visual field, the experience and the mental state of a driver. The good driving sitting posture enables a driver to obtain the best vision, a steering wheel, a pedal, a gear lever and the like are easy to operate, the most comfortable and most customary sitting angle is obtained, and when users with different heights use the same vehicle, the seat needs to be readjusted, so that the vehicle seat capable of realizing the memory function is urgently on the market.
Disclosure of Invention
In view of the above problems, the present invention aims to provide an intelligent electric seat for an automobile.
The purpose of the invention is realized by the following technical scheme:
an intelligent automobile electric seat comprises an electric seat, a seat sensing module, a front-end camera, an intelligent control system, a seat adjusting mechanism and an information acquisition module, wherein when the seat sensing module detects that a person is on the electric seat, the front-end camera acquires a face image, the intelligent control system processes and identifies the acquired face image and matches the face image stored in a database, when the matching is successful, the corresponding adjusting parameter of the electric seat is sent to the seat control system, the seat control system controls the seat adjusting mechanism to adjust the electric seat according to the adjusting parameter, when the face matching is unsuccessful, the seat sensing module detects whether the electric seat moves, when the electric seat is detected to move, the information acquisition module acquires the adjusting parameter of the electric seat and sends the acquired adjusting parameter to the intelligent control system, and the intelligent control system stores the received adjusting parameters and the corresponding face images in a database.
The beneficial effects created by the invention are as follows: the intelligent electric seat for the automobile is combined with a face recognition technology and an intelligent control technology, so that autonomous memory of the electric seat and automatic adjustment of the electric seat are achieved, and manual intervention is not needed.
Drawings
The invention is further described with the aid of the accompanying drawings, in which, however, the embodiments do not constitute any limitation to the invention, and for a person skilled in the art, without inventive effort, further drawings may be derived from the following figures.
FIG. 1 is a schematic structural view of the present invention;
fig. 2 is a schematic view of the seat adjusting mechanism.
Reference numerals:
an electric seat 1; a seat sensing module 2; a front-end camera 3; an intelligent control system 4; a seat control system 5; a seat adjusting mechanism 6; an information acquisition module 7; a seat height adjusting mechanism 61; a seat front-rear adjusting mechanism 62; a seat back adjustment mechanism 63.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1 and 2, the intelligent electric seat for an automobile of the present embodiment includes an electric seat 1, a seat sensing module 2, a front-end camera 3, an intelligent control system 4, a seat control system 5, a seat adjusting mechanism 6, and an information obtaining module 7, when the seat sensing module 2 detects that there is a person on the electric seat 1, the front-end camera 3 is made to collect a face image, the intelligent control system 4 processes and identifies the collected face image, and matches the face image stored in a database, when matching is successful, the corresponding adjusting parameter of the electric seat 1 is sent to the seat control system 5, the seat control system 5 controls the seat adjusting mechanism 6 to adjust the electric seat 1 according to the adjusting parameter, when matching of the face is unsuccessful, the seat sensing module 2 detects whether the electric seat 1 moves, when it is detected that the electric seat 1 moves, the information acquisition module 7 acquires the adjustment parameters of the electric seat 1, and sends the acquired adjustment parameters to the intelligent control system 4, and the intelligent control system 4 stores the received adjustment parameters and the corresponding face images in the database.
Preferably, the seat adjusting mechanism 6 includes a seat height adjusting mechanism 61, a seat front-rear adjusting mechanism 62, and a seat back adjusting mechanism 63, the seat height adjusting mechanism 61 is used for adjusting the height of the power seat, the seat front-rear adjusting mechanism 62 is used for adjusting the front and rear of the power seat, and the seat back adjusting mechanism 63 is used for adjusting the backrest of the power seat.
This preferred embodiment provides an intelligent automobile electric seat, wants to combine with face identification technology and intelligent control technology, has realized the automatic regulation of the autonomic memory of electric seat and electric seat, need not artificial intervention.
Preferably, the intelligent control system 4 processes the acquired face image, including image denoising and image segmentation, where the image denoising is used to remove noise pollution in the acquired face image, and the image segmentation is used to perform target segmentation on the denoised face image.
The preferred embodiment is used for carrying out denoising processing and segmentation processing on the acquired face image, and lays a foundation for face image recognition in the later period.
Preferably, a particle swarm optimization-based multi-threshold image segmentation method is adopted to perform image segmentation processing on the denoised face image, the Otsu inter-class variance is used as a fitness function of the particle swarm optimization, and the optimal threshold in the multi-threshold segmentation algorithm is determined through optimization.
Preferably, in the optimization process of the particle swarm algorithm, the following method is adopted to update the position and the step length of the particle in the particle swarm algorithm, and specifically includes:
(1) defining the update probability corresponding to the particle i in the (k +1) th iteration as
Figure GDA0002197071170000031
Then
Figure GDA0002197071170000032
The calculation formula of (2) is as follows:
Figure GDA0002197071170000033
wherein O represents a search dimension of the particle swarm algorithm,
Figure GDA0002197071170000034
representing the fitness value corresponding to particle i at the kth iteration,
Figure GDA0002197071170000035
representing the fitness value of the particle L at the kth iteration, and L representing the number of particles in the population;
(2) particle i produces [0,1 ]]Uniformly distributed random numbers, if the generated random numbers are larger than the update probability corresponding to the particles i
Figure GDA0002197071170000036
The position update formula of the particle i is:
Figure GDA0002197071170000037
in the formula (I), the compound is shown in the specification,
Figure GDA0002197071170000038
indicating the updated position of the particle i,
Figure GDA0002197071170000039
represents the position of particle i at the kth iteration;
(3) particle i produces [0,1 ]]Random numbers distributed uniformly within, if generatedNumber less than update probability corresponding to particle i
Figure GDA00021970711700000310
The position and step size of particle i are updated using the following equation:
Figure GDA00021970711700000311
Figure GDA00021970711700000312
in the formula (I), the compound is shown in the specification,
Figure GDA00021970711700000313
indicating the updated position of the particle i,
Figure GDA00021970711700000314
denotes the position, V, of the particle i at the k-th iterationi k+1Denotes the step size, V, of the particle i at the (k +1) th iterationi kRepresenting the step size of particle i at the kth iteration,
Figure GDA00021970711700000315
represents the inertial weight of particle i at the kth iteration, and
Figure GDA00021970711700000316
ωstartrepresents the initial inertial weight value, ω, of the algorithmendRepresenting the value of the inertia weight when the algorithm is finished, k represents the current iteration times of the algorithm, and kmaxRepresents the maximum number of iterations of the algorithm,
Figure GDA00021970711700000317
representing the fitness value corresponding to particle i at the kth iteration,
Figure GDA00021970711700000318
represents the fitness value, c, corresponding to particle i at the (k-1) th iteration1And c2Is a normal learning coefficient, and c1And c2Is [1,2 ]]Constant between, rand1And rand2Is between [0,1]Any number of them and no dependency relationship between them, PkThe mean value of the particle positions in the population is expressed during the kth iteration, so that the particles can learn the mean value of the particle positions in the population in the updating process, and compared with the traditional mode of enabling the particles to learn the individual historical optimal value, the mean value of the particle positions in the population can more accurately reflect the local information of the algorithm, thereby greatly increasing the diversity of the particles in the algorithm and avoiding the algorithm from falling into the local optimal solution, wherein alpha is an adjusting parameter used for adjusting the weight of the particles learning the mean value of the particle positions in the population in the updating process, and alpha is an adjusting parameter
Figure GDA0002197071170000041
O represents the search dimension of the particle swarm algorithm, L represents the number of particles in the population, the adjusting parameter alpha adjusts the weight of the particles for learning the mean value of the particle positions in the population according to the search dimension of the particle swarm algorithm and the number of the particles in the population, when the search dimension of the particle swarm algorithm is larger or the population scale is smaller, the influence of local information on position adjustment is increased, the optimization precision of the algorithm is improved, and gkAnd expressing the global optimal value in the k iteration, wherein beta is an adjusting parameter and is used for adjusting the weight of the particles to learn the global optimal value, and the calculation formula of beta is as follows:
Figure GDA0002197071170000042
in the formula, gjRepresents the global optimum, g, at the jth iteration(j-1)Represents the global optimum at iteration (j-1), h (g)j) Representing a global optimum gjCorresponding fitness value, h (g)(j-1)) Representing a global optimum g(j-1)Corresponding fitness value, k representing the current number of iterations, kmaxExpressing the maximum iteration times, and judging the optimization condition of the algorithm according to the change condition of the fitness value of the global optimal value in the continuous iteration process in the adjusting parameter betaWhen the fitness value ratio of the global optimum value of 3 continuous iterations is less than or equal to 1, namely beta is made to be 0, the situation that the particles fall into the global optimum solution or the situation that the particles evolve towards the direction with poor fitness values is avoided, and when the mean value of the fitness value ratio of the global optimum value of 3 continuous iterations is greater than 1, the weight learned by the particles towards the global optimum solution is made to be increased according to the iteration times.
In the defined updating probability, the updating probability is adjusted in real time comprehensively according to the actual optimizing condition of the particles and basic parameters of the algorithm, the fitness value of the particles is introduced to measure the current optimizing condition of the particles, blind adjustment of the particles with better optimizing condition is avoided, the updating probability is adjusted according to the searching dimension of the particle swarm algorithm, when the searching dimension of the particle swarm is larger, the probability of particle adjustment is reduced, and therefore the running time of the particle swarm algorithm is prevented from being increased due to excessive particle position adjustment; in addition, the optimized embodiment adopts an improved inertia weight function, compared with the traditional inertia weight function, the inertia weight function of the optimized embodiment introduces the fitness value change before and after the update to measure the optimizing effect before and after the update of the algorithm, thereby avoiding the influence of adverse experience on the update of the particles; when the positions of the particles are updated, the updating mode of the step length of the particles is improved, and the influence degree of the local information of the algorithm is increased, so that the diversity of the particles is increased, and the algorithm is prevented from falling into the local optimal solution.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (2)

1. An intelligent electric seat of an automobile is characterized by comprising an electric seat, a seat sensing module, a front-end camera, an intelligent control system, a seat adjusting mechanism and an information acquisition module, wherein when the seat sensing module detects that a person is on the electric seat, the front-end camera acquires a face image, the intelligent control system processes and identifies the acquired face image and matches the face image with the face image stored in a database, when the matching is successful, the corresponding adjusting parameter of the electric seat is sent to the seat control system, the seat control system controls the seat adjusting mechanism to adjust the electric seat according to the adjusting parameter, when the face matching is unsuccessful, the seat sensing module detects whether the electric seat moves, when the movement of the electric seat is detected, the information acquisition module acquires the adjusting parameter of the electric seat, the acquired adjusting parameters are sent to an intelligent control system, and the intelligent control system stores the received adjusting parameters and the corresponding face images in a database;
the intelligent control system processes the acquired face image, and comprises image denoising processing and image segmentation processing, wherein the image denoising processing is used for removing noise pollution in the acquired face image, and the image segmentation processing is used for performing target segmentation on the denoised face image;
performing image segmentation processing on the denoised face image by adopting a particle swarm optimization-based multi-threshold image segmentation method, taking the Otsu inter-class variance as a fitness function of a particle swarm algorithm, and determining an optimal threshold in the multi-threshold segmentation algorithm through optimization;
in the optimization process of the particle swarm algorithm, the following modes are adopted to update the positions and the step lengths of particles in the particle swarm algorithm, and the method specifically comprises the following steps:
(1) defining the update probability corresponding to the particle i in the (k +1) th iteration as
Figure FDA0003103555530000011
Then
Figure FDA0003103555530000012
The calculation formula of (2) is as follows:
Figure FDA0003103555530000013
wherein O represents a search dimension of the particle swarm algorithm,
Figure FDA0003103555530000014
representing the fitness value corresponding to particle i at the kth iteration,
Figure FDA0003103555530000015
representing the fitness value of the particle L at the kth iteration, and L representing the number of particles in the population;
(2) particle i produces [0,1 ]]Uniformly distributed random numbers, if the generated random numbers are larger than the update probability corresponding to the particles i
Figure FDA0003103555530000016
The position update formula of the particle i is:
Figure FDA0003103555530000017
in the formula (I), the compound is shown in the specification,
Figure FDA0003103555530000021
indicating the updated position of the particle i,
Figure FDA0003103555530000022
represents the position of particle i at the kth iteration;
(3) particle i produces [0,1 ]]Uniformly distributed random numbers, if the generated random numbers are less than the update probability corresponding to the particles i
Figure FDA0003103555530000023
The position and step size of particle i are updated using the following equation:
Figure FDA0003103555530000024
Figure FDA0003103555530000025
in the formula (I), the compound is shown in the specification,
Figure FDA0003103555530000026
indicating the updated position of the particle i,
Figure FDA00031035555300000216
indicating the position of particle i at the kth iteration,
Figure FDA0003103555530000027
represents the step size of particle i at the (k +1) th iteration,
Figure FDA0003103555530000028
representing the step size of particle i at the kth iteration,
Figure FDA0003103555530000029
represents the inertial weight of particle i at the kth iteration, and
Figure FDA00031035555300000210
Figure FDA00031035555300000211
ωstartrepresents the initial inertial weight value, ω, of the algorithmendRepresenting the value of the inertia weight when the algorithm is finished, k represents the current iteration times of the algorithm, and kmaxRepresents the maximum number of iterations of the algorithm,
Figure FDA00031035555300000212
representing the fitness value corresponding to particle i at the kth iteration,
Figure FDA00031035555300000213
represents a particle iCorresponding fitness value at the (k-1) th iteration, c1And c2Is a normal learning coefficient, and c1And c2Is [1,2 ]]Constant between, rand1And rand2Is between [0,1]Arbitrary number of (A), (B) PkRepresents the mean of the positions of the particles in the population at the kth iteration, alpha is a regulatory parameter, and
Figure FDA00031035555300000214
o denotes the search dimension of the particle swarm algorithm, L denotes the number of particles in the population, gkAnd expressing a global optimal value at the k iteration, wherein beta is an adjusting parameter, and a calculation formula of beta is as follows:
Figure FDA00031035555300000215
in the formula, gjRepresents the global optimum, g, at the jth iteration(j-1)Represents the global optimum at iteration (j-1), h (g)j) Representing a global optimum gjCorresponding fitness value, h (g)(j-1)) Representing a global optimum g(j-1)Corresponding fitness value, k representing the current number of iterations, kmaxThe maximum number of iterations is indicated.
2. The intelligent electric seat of the automobile as claimed in claim 1, wherein the seat adjusting mechanism comprises a seat height adjusting mechanism, a seat front-back adjusting mechanism and a seat back adjusting mechanism, the seat height adjusting mechanism is used for adjusting the height of the electric seat, the seat front-back adjusting mechanism is used for adjusting the front and back of the electric seat, and the seat back adjusting mechanism is used for adjusting the back of the electric seat.
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