CN109697417A - A kind of production management system for pitch-controlled system cabinet - Google Patents
A kind of production management system for pitch-controlled system cabinet Download PDFInfo
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- CN109697417A CN109697417A CN201811532990.8A CN201811532990A CN109697417A CN 109697417 A CN109697417 A CN 109697417A CN 201811532990 A CN201811532990 A CN 201811532990A CN 109697417 A CN109697417 A CN 109697417A
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 24
- 238000001514 detection method Methods 0.000 claims abstract description 18
- 238000001727 in vivo Methods 0.000 claims abstract description 12
- 238000004364 calculation method Methods 0.000 claims abstract description 6
- 238000000034 method Methods 0.000 claims description 11
- 230000003287 optical effect Effects 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 3
- 210000005252 bulbus oculi Anatomy 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 210000003128 head Anatomy 0.000 claims 1
- 238000012544 monitoring process Methods 0.000 abstract description 6
- 238000009510 drug design Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
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- 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
- G06V40/161—Detection; Localisation; Normalisation
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
- G06Q10/063114—Status monitoring or status determination for a person or group
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/18—Eye characteristics, e.g. of the iris
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/40—Spoof detection, e.g. liveness detection
- G06V40/45—Detection of the body part being alive
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The invention discloses a kind of production management systems for pitch-controlled system cabinet, comprising the following steps: step 1: attendance；Step 2: personnel's on-line monitoring；Step 3: station personnel's on-line scheduling；Step 4: man-hour calculation；In step 1, in step 2 and step 4, it requires to carry out online recognition of face to staff, recognition of face is carried out by infrared camera, recognition of face is carried out by In vivo detection mode, this is in the production management system of pitch-controlled system cabinet, pass through the mode of In vivo detection mode, can to staff without cooperation in the case where realize Blind Test, substantially increase the ability of on-line monitoring, and in system, it joined the function of station personnel's on-line scheduling, improve production efficiency, improve the practicability of the production management system, the present invention has rational design, it is suitble to promote the use of.
Description
Technical field
The present invention relates to variable pitch production technical fields, and in particular to a kind of production management system for pitch-controlled system cabinet.
Background technique
In modern enterprise, for staff attendance all using swiping the card or the form of fingerprint, being easy to appear generation beats
Card phenomenon cannot achieve accurate attendance；And there is phenomenon of visiting others during work hours after slipping away from one's post in production process, the production order is upset, slow manufacturing schedule is dragged,
And cannot find and prevent in time, increase the management difficulty of production manager；Moreover, the vehicle of especially pitch-controlled system cabinet production
Between, since the type of staff is more, and everyone needs the process of work more, and these management systems are all only
For monitoring, scheduling feature is not had, the practicability of management system is thereby reduced.
Summary of the invention
The present invention is above-mentioned in order to overcome the shortcomings of, provides a kind of production management system for pitch-controlled system cabinet.
The present invention through the following technical solutions to achieve the above objectives:
A kind of production management system for pitch-controlled system cabinet, comprising the following steps:
Step 1: attendance carries out recognition of face to staff by attendance recorder, carrys out the opening of access control；
Step 2: personnel's on-line monitoring, each infrared camera by the way that workshop top is arranged in carry out each staff
Identification, and be monitored online；
Step 3: station personnel's on-line scheduling, according to the performance level of the staffing of different station and process, Lai Jinhang
Personal scheduling；
Step 4: man-hour calculation makees time and work process as personnel according to working artificial, to carry out real-time working hour meter
It calculates；
Step 1: requiring to carry out online recognition of face to staff, by infrared in step 2 and step 4
Camera carries out recognition of face, carries out recognition of face by In vivo detection mode；
In vivo detection mode uses optical flow method；Utilize the time domain variation of the pixel intensity data in image sequence and correlation
Property determine " movement " of respective location of pixels, the operation information of each pixel obtained from image sequence, also use Gauss
Difference filter, LBP feature and support vector machines carry out data statistic analysis；Meanwhile light stream is more sensitive to object of which movement, benefit
It can unify the movement of detection eyeball and blink with optical flow field, this In vivo detection mode can be the case where staff be without cooperation
Lower realization Blind Test；
LBP algorithm available expression is expressed as
WithFor center pixel, icFor the intensity of adjacent pixel, S is functional symbol, expression formula are as follows:
The step of being identified by LBP feature vector,
(1) detection window of infrared camera is divided into the zonule of 32*32 first；
(2) for a pixel in each zonule, the gray value of 8 adjacent pixels is compared with it, if
Surrounding pixel values are less than center pixel value, then otherwise it is 1 that the position of the pixel, which is marked as 0, in this way, 8 in 3*3 neighborhood
A point, which is compared, can produce 8 bits to get the LBP value for arriving the window center pixel；
(3) histogram of each zonule, i.e., the frequency that each digital (it is assumed that decimal number LBP value) occurs then are calculated
Then rate is normalized the histogram；
(4) finally the statistic histogram of obtained each zonule is attached as a feature vector, that is,
The LBP texture feature vector of whole picture figure；
(5) the LBP texture feature vector in the LBP texture feature vector of obtained whole picture figure and recognition of face library is carried out
Matching, to determine the identity of staff.
Preferably, in step 1, when attendance recorder recognition of face, when recognizing non-company personnel, can enter
Backstage application license, by being determined passage from the background.
Preferably, requiring to carry out voice prompting by loudspeaker in step 2 and step 3.
Preferably, the attendance recorder, infrared camera and loudspeaker be wirelessly connected in control backstage, middle control backstage is counts
Calculation machine.
Preferably, the recognition of face library is the recognition of face library based on Python.
The beneficial effects of the present invention are: this is used in the production management system of pitch-controlled system cabinet, pass through In vivo detection mode
Mode, can to staff without cooperation in the case where realize Blind Test, substantially increase the ability of on-line monitoring, and system
In, it joined the function of station personnel's on-line scheduling, improve production efficiency, improve the practicability of the production management system.
Detailed description of the invention
Examples of the present invention will be described by way of reference to the accompanying drawings, in which:
Fig. 1 is systematic schematic diagram of the invention.
Specific embodiment
In conjunction with the accompanying drawings, the present invention is further explained in detail.These attached drawings are simplified schematic diagram, only with
Illustration illustrates basic structure of the invention, therefore it only shows the composition relevant to the invention.
Embodiment 1:
Such as Fig. 1, a kind of production management system for pitch-controlled system cabinet, comprising the following steps:
Step 1: attendance carries out recognition of face to staff by attendance recorder, carrys out the opening of access control；
Step 2: personnel's on-line monitoring, each infrared camera by the way that workshop top is arranged in carry out each staff
Identification, and be monitored online；
Step 3: station personnel's on-line scheduling, according to the performance level of the staffing of different station and process, Lai Jinhang
Personal scheduling；
Step 4: man-hour calculation makees time and work process as personnel according to working artificial, to carry out real-time working hour meter
It calculates；
Step 1: requiring to carry out online recognition of face to staff, by infrared in step 2 and step 4
Camera carries out recognition of face, carries out recognition of face by In vivo detection mode；
In vivo detection mode uses optical flow method；Utilize the time domain variation of the pixel intensity data in image sequence and correlation
Property determine " movement " of respective location of pixels, the operation information of each pixel obtained from image sequence, also use Gauss
Difference filter, LBP feature and support vector machines carry out data statistic analysis；Meanwhile light stream is more sensitive to object of which movement, benefit
It can unify the movement of detection eyeball and blink with optical flow field, this In vivo detection mode can be the case where staff be without cooperation
Lower realization Blind Test；
LBP algorithm available expression is expressed as
With (xc, yc) it is center pixel, icFor the intensity of adjacent pixel, S is functional symbol, expression formula are as follows:
The step of being identified by LBP feature vector,
(1) detection window of infrared camera is divided into the zonule of 32*32 first；
(2) for a pixel in each zonule, the gray value of 8 adjacent pixels is compared with it, if
Surrounding pixel values are less than center pixel value, then otherwise it is 1 that the position of the pixel, which is marked as 0, in this way, 8 in 3*3 neighborhood
A point, which is compared, can produce 8 bits to get the LBP value for arriving the window center pixel；
(3) histogram of each zonule, i.e., the frequency that each digital (it is assumed that decimal number LBP value) occurs then are calculated
Then rate is normalized the histogram；
(4) finally the statistic histogram of obtained each zonule is attached as a feature vector, that is,
The LBP texture feature vector of whole picture figure；
(5) the LBP texture feature vector in the LBP texture feature vector of obtained whole picture figure and recognition of face library is carried out
Matching, to determine the identity of staff.
In the present embodiment:,, can be into when recognizing non-company personnel when attendance recorder recognition of face in step 1
Enter backstage application license, by being determined passage from the background.
In the present embodiment: in step 2 and step 3, requiring to carry out voice prompting by loudspeaker.
In the present embodiment: the attendance recorder, infrared camera and loudspeaker be wirelessly connected in control backstage, middle control is from the background
Computer.
In the present embodiment: the recognition of face library is the recognition of face library based on Python.
It is above-mentioned according to the present invention for enlightenment, through the above description, relevant staff completely can without departing from
In the range of this invention technical idea, various changes and amendments are carried out.The technical scope of this invention is not limited to
In the content on specification, it is necessary to which the technical scope thereof is determined according to the scope of the claim.
Claims (5)
1. a kind of production management system for pitch-controlled system cabinet, it is characterised in that: the following steps are included:
Step 1: attendance carries out recognition of face to staff by attendance recorder, carrys out the opening of access control；
Step 2: personnel are monitored online, and each infrared camera by the way that workshop top is arranged in identifies each staff,
And it is monitored online；
Step 3: station personnel's on-line scheduling, according to the performance level of the staffing of different station and process, Lai Jinhang personnel
Scheduling；
Step 4: man-hour calculation makees time and work process as personnel according to working artificial, to carry out real-time man-hour calculation；
Step 1: requiring to carry out online recognition of face to staff, passing through infrared photography in step 2 and step 4
Head carries out recognition of face, carries out recognition of face by In vivo detection mode；
In vivo detection mode uses optical flow method；Using the pixel intensity data in image sequence time domain variation and correlation come
" movement " for determining respective location of pixels, obtains the operation information of each pixel from image sequence, also uses difference of Gaussian
Filter, LBP feature and support vector machines carry out data statistic analysis；Meanwhile light stream is more sensitive to object of which movement, utilizes light
Flow field can unify the movement of detection eyeball and blink, and this In vivo detection mode can be real in the case where staff is without cooperation
Existing Blind Test；
LBP algorithm available expression is expressed as
With (xc, yc) it is center pixel, icFor the intensity of adjacent pixel, S is functional symbol, expression formula are as follows:
The step of being identified by LBP feature vector,
(1) detection window of infrared camera is divided into the zonule of 32*32 first；
(2) for a pixel in each zonule, the gray value of 8 adjacent pixels is compared with it, if surrounding
Pixel value is less than center pixel value, then otherwise it is 1 that the position of the pixel, which is marked as 0, in this way, 8 in 3*3 neighborhood point
Compared and can produce 8 bits to get the LBP value for arriving the window center pixel；
(3) then calculate the histogram of each zonule, i.e., the frequency that each digital (it is assumed that decimal number LBP value) occurs,
Then the histogram is normalized；
(4) finally the statistic histogram of obtained each zonule is attached as a feature vector, that is, whole picture
The LBP texture feature vector of figure；
(5) by the LBP texture feature vector progress in the LBP texture feature vector of obtained whole picture figure and recognition of face library
Match, to determine the identity of staff.
2. the production management system according to claim 1 for pitch-controlled system cabinet, it is characterised in that: in step 1, examine
When diligent machine recognition of face, when recognizing non-company personnel, backstage application license can be entered, by being determined from the background
It is current.
3. the production management system according to claim 1 for pitch-controlled system cabinet, it is characterised in that: step 2 and step
In three, require to carry out voice prompting by loudspeaker.
4. the production management system according to claim 3 for pitch-controlled system cabinet, it is characterised in that: the attendance recorder,
Infrared camera and loudspeaker control backstage in being wirelessly connected, and middle control backstage is computer.
5. the production management system according to claim 1 for pitch-controlled system cabinet, it is characterised in that: the recognition of face
Library is the recognition of face library based on Python.
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| CN201811532990.8A CN109697417A (en) | 2018-12-14 | 2018-12-14 | A kind of production management system for pitch-controlled system cabinet |
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| CN201811532990.8A CN109697417A (en) | 2018-12-14 | 2018-12-14 | A kind of production management system for pitch-controlled system cabinet |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111710085A (en) * | 2020-05-27 | 2020-09-25 | 南京金陵塑胶化工有限公司 | Production workshop safety management method and system based on facial recognition |
| CN111753660A (en) * | 2020-05-21 | 2020-10-09 | 江苏大学 | Terahertz millimeter wave-based human face bone identification method |
| CN113706757A (en) * | 2021-08-20 | 2021-11-26 | 上海云建网络科技有限公司 | Digital intelligent work reporting method for workshop |
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Cited By (3)
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