LU504993B1 - System and method for managing personnel in power plant in production - Google Patents

System and method for managing personnel in power plant in production Download PDF

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Publication number
LU504993B1
LU504993B1 LU504993A LU504993A LU504993B1 LU 504993 B1 LU504993 B1 LU 504993B1 LU 504993 A LU504993 A LU 504993A LU 504993 A LU504993 A LU 504993A LU 504993 B1 LU504993 B1 LU 504993B1
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LU
Luxembourg
Prior art keywords
personnel
pixel
staff member
lbp
data
Prior art date
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LU504993A
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German (de)
Inventor
Yunzhe Gong
Yang Wang
Songtao Yu
Jiaqi Zou
Zuofu Gong
Ying Bi
Yuwei Li
Boran Yang
Zuojin Zhang
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Huaneng Power Int Inc Dandong Power Plant
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    • G—PHYSICS
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • 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
    • G06Q10/063118—Staff planning in a project environment
    • G—PHYSICS
    • 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
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G06V10/467—Encoded features or binary features, e.g. local binary patterns [LBP]
    • 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
    • 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
    • G06V40/40—Spoof detection, e.g. liveness detection

Abstract

A system and method for managing personnel in a power plant in production are provided, including: S1 recording positioning and check-in of each staff member correspondingly through a combination of software and hardware, by a personnel management module; S2 monitoring personnel online, identifying the staff member by an infrared camera arranged on top of a workshop, and performing online monitoring, by monitoring module; S3 working hour calculation module: calculating on-the-job time of the staff member based on a comparison of S1 and S2 as node data; S4 post manual adjustment: matching the corresponding staff member through a time model of each section to facilitate rotation of adjustment personnel; and S5 data processor terminal: returning data of S1, S2, S3 and S4 to the data processor terminal for processing, and displaying the obtained data for the staff member to perform a corresponding control operation.

Description

SYSTEM AND METHOD FOR MANAGING PERSONNEL IN POWER PLANT IN
PRODUCTION
FIELD
[0001] The present application relates to the technical field of power plants, and in particular to a system and a method for managing personnel in a power plant in production.
BACKGROUND
[0002] Power plants convert raw energy into electrical energy for use by fixed facilities or transportation, including thermal power plants, hydroelectric power plants, steam power plants, diesel power plants, nuclear power plants or the like.
[0003] Among the existing methods of managing the personnel of in a power plant, patent application No. CN201910636039.5 discloses an optical fiber preform intelligent production management system and method. The system includes: an optical fiber preform processing testing equipment, a storage equipment and a background server. The background server is connected to the optical fiber preform processing testing equipment and the storage equipment, to collect operation data of the optical fiber preform processing testing equipment and the storage equipment. The background server 1s provided with an input device and a display device. The input device and the display device are connected to the background server. The display device is configured to display information to an operator. The input device is configured for the operator to input instructions to view information or send control signals to the optical fiber preform processing testing equipment and the storage equipment.
Its benefit is that the provided hardware management system connects the optical fiber preform processing testing equipment and the storage equipment to the background server, uploads the operation data of the equipment in real time, thereby improving the intelligence of the entire optical fiber preform intelligent production management system.
[0004] However, the existing personnel management only controls the on-duty time and off- duty time of the personnel, failing to accurate to the technical problem of personnel deployment at the time of each post.
SUMMARY
[0005] A system and method for managing personnel in a power plant in production are provided in order to solve the technical problem proposed in the background part above.
[0006] The technical solution adopted by the present application to solve the above technical problem includes a system and method for managing personnel in a power plant in production, including:
S1, recording positioning and check-in of each staff member correspondingly through a combination of software and hardware, by a personnel management module;
S2, monitoring personnel online, identifying the staff member by an infrared camera arranged on top of a workshop, and performing online monitoring, by a monitoring module;
S3, calculating on-the-job time of the staff member based on a comparison of S1 and S2 as node data, by a working hour calculation module;
S4, matching the corresponding staff member through a time model of each section to facilitate rotation of adjustment personnel, for post manual adjustment; and
SS, returning data of S1, S2, S3 and S4 to the data processor terminal for processing, and displaying the obtained data for the staff member to perform a corresponding control operation, by a data processor terminal.
[0007] Further, in step S1, an intelligent device is connected to a 5G network using 5G and edge computing, Al processing and machine vision technologies, and a network monitoring video signal of a factory is transmitted through a 5G network link, to realize face recognition, boundary warning, personnel positioning, attendance management, and personnel statistics.
[0008] Further, in step S1, the infrared camera performs face recognition through liveness detection, the liveness detection adopts an optical flow, temporal variation and correlation of pixel intensity data in an image sequence are used to determine a "motion" of each pixel position, running information of each pixel is obtained from an image sequence, a Gaussian difference filter and an LBP feature and a support vector machine are used for statistical analysis of data, the optical flow is sensitive to object motion, eye motion and blinking are detected uniformly by using an optical flow field, and the liveness detection performs blind testing without cooperation of the staff member.
[0009] Further, an LBP algorithm is expressed in terms of (xc, yc) as a center pixel, ic as intensity of an adjacent pixel, and S as a function symbol; and steps of recognition through an
LBP feature vector include: (1) dividing a detection window of the infrared camera into small areas of 32*32; (2) for a pixel in each small area, comparing gray values of 8 neighboring pixels with a gray value of the pixel, marking a position of the pixel as 0 if a surrounding pixel value is smaller than a central pixel value, marking a position of the pixel as 1 if a surrounding pixel value is not smaller than the central pixel value, to generate an 8-bit binary number through comparison of 8 points in a 3*3 neighborhood, that is, to obtain a LBP value of the pixel in the center of the window; (3) calculating a histogram of each small area, that is, a frequency of occurrence of each number (assumed to be a decimal number LBP value), and normalizing the histogram; (4) connecting the obtained statistical histograms of the small areas into a feature vector, as an LBP texture feature vector of a whole image; and (5) matching the obtained LBP texture feature vector of the whole image with an LBP texture feature vector in a face recognition library to identity the staff member.
[0010] Further, a model of a time period for each post is established, to calculate a vacant post and a corresponding vacancy time period based on expected arrival and leave of the personnel, so as to facilitate corresponding post adjustment.
[0011] Compared with the prior art, the beneficial effects of the present application are as follows.
[0012] The present application is provided with a personnel management module, and uses the personnel matching mode in the personnel management module to realize the arrival of personnel. Through the corresponding auxiliary monitoring by the monitoring module, it is possible to clearly understand the corresponding leave time period and on-the-job time situation. Finally, through the manual post adjustment, the set working time is matched with the available time of the personnel. When there is a leave request, the corresponding matching adjustment is carried out to achieve the effect of accurate arrival.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0013] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to related materials. Several embodiments of the application are given, but the application can be embodied in different forms and are not limited to the embodiments described herein. Instead, these embodiments are provided to make the content disclosed in this application more thorough and comprehensive.
[0014] In an embodiment, a system and a method for managing personnel in a power plant in production, include the following steps.
S1, a personnel management module records positioning and check-in of each staff member correspondingly through a combination of software and hardware, connects an intelligent device to a 5G network using 5G and edge computing, AI processing and machine vision technologies, transmitting a network monitoring video signal of a factory through a 5G network link, to realize face recognition, boundary warning, personnel positioning, attendance management, and personnel statistics;
S2, a monitoring module performs online monitoring personnel, identifies each staff member by infrared cameras arranged on top of a workshop, and performs online monitoring;
S3, a working hour calculation module calculates on-the-job time of the staff member based on a comparison of S1 and S2, that is, node data;
S4, a post manual adjustment matches the corresponding staff member through a time model of each section to facilitate rotation of adjustment personnel; and
SS, a data processor terminal returns the data of S1, S2, S3 and S4 to the data processor terminal for processing, and displays the obtained data for the staff member to perform a corresponding control operation.
[0015] In step S1, the infrared camera performs face recognition through liveness detection.
The liveness detection adopts the optical flow method. The temporal variation and correlation of the pixel intensity data in an image sequence are used to determine the "motion" of the respective pixel positions. The running information of each pixel is obtained from the image sequence. A Gaussian difference filter, LBP feature and support vector machine are also used for statistical analysis of data. Since optical flow is sensitive to object motion, eye motion and blinking can be detected uniformly by using an optical flow field. This liveness detection can realize blind testing without the cooperation of a staff member.
[0016] The LBP algorithm may be expressed in terms of (xc, yc) as a center pixel, ic as intensity of an adjacent pixel, and S as a function symbol.
[0017] The steps of recognition through the LBP feature vector are as follows.
[0018] (1) Firstly, a detection window of the infrared camera is divided into small areas of 32*32.
[0019] (2) For a pixel in each small area, gray values of 8 neighboring pixels are compared with a gray value of the pixel. If a surrounding pixel value is smaller than a central pixel value,
the position of the pixel is marked as 0, otherwise marked as 1. In this way, an 8-bit binary number is generated through comparison of 8 points in the 3*3 neighborhood, that is, the LBP value of the pixel in the center of the window is obtained.
[0020] (3) Then a histogram of each small area, that is, a frequency of occurrence of each 5 number (assumed to be a decimal number LBP value) is calculated, and then the histogram is normalized.
[0021] (4) Finally, the obtained statistical histograms of the small areas are connected into a feature vector, that is, an LBP texture feature vector of the whole image.
[0022] (5) The obtained LBP texture feature vector of the whole image is matched with an
LBP texture feature vector in a face recognition library to identity the staff member.
[0023] A model of a time period for each post is established, to calculate a vacant post and a corresponding vacancy time period based on the expected arrival and leave of the personnel, so as to facilitate the corresponding post adjustment.
[0024] The above is an exemplary description of the present application. Apparently, the specific implementation of the present application is not limited by the above methods. All the insubstantial improvements made by adopting the method concept and technical solution of the present application, or directly application of the concept and technical solution of the present application to other occasions without improvement shall fall within the protection scope of the present application.

Claims (5)

  1. I. A system and method for managing personnel in a power plant in production, comprising the following steps: S1, recording positioning and check-in of each staff member correspondingly through a combination of software and hardware, by a personnel management module; S2, monitoring personnel online, identifying the staff member by an infrared camera arranged on top of a workshop, and performing online monitoring, by a monitoring module; S3, calculating on-the-job time of the staff member based on a comparison of S1 and S2 as node data, by a working hour calculation module; S4, matching the corresponding staff member through a time model of each section to facilitate rotation of adjustment personnel, by post manual adjustment; and SS, returning data of S1, S2, S3 and S4 to the data processor terminal for processing, and displaying the obtained data for the staff member to perform a corresponding control operation, by a data processor terminal.
  2. 2. The system and method for managing personnel in a power plant in production according to claim 1, wherein in step S1, an intelligent device is connected to a 5G network using 5G and edge computing, Al processing and machine vision technologies, and a network monitoring video signal of a factory is transmitted through a 5G network link, to realize face recognition, boundary warning, personnel positioning, attendance management, and personnel statistics.
  3. 3. The system and method for managing personnel in a power plant in production according to claim 1, wherein in step Sl, the infrared camera performs face recognition through liveness detection, the liveness detection adopts an optical flow, temporal variation and correlation of pixel intensity data in an image sequence are used to determine a "motion" of each pixel position, running information of each pixel is obtained from an image sequence, a Gaussian difference filter and an LBP feature and a support vector machine are used for statistical analysis of data, the optical flow is sensitive to object motion, eye motion and blinking are detected uniformly by using an optical flow field, and the liveness detection performs blind testing without cooperation of the staff member.
  4. 4. The system and method for managing personnel in a power plant in production according to claim 3, wherein an LBP algorithm is expressed in terms of (xc, yc) as a center pixel, ic as intensity of an adjacent pixel, and S as a function symbol; and steps of recognition through an LBP feature vector comprise: (1) dividing a detection window of the infrared camera into small areas of 32*32; (2) for a pixel in each small area, comparing gray values of 8 neighboring pixels with a gray value of the pixel, marking a position of the pixel as 0 if a surrounding pixel value is smaller than a central pixel value, marking a position of the pixel as 1 if a surrounding pixel value is not smaller than the central pixel value, to generate an 8-bit binary number through comparison of 8 points in a 3*3 neighborhood, to obtain a LBP value of the pixel in the center of the window; (3) calculating a histogram of each small area as a frequency of occurrence of each number (assumed to be a decimal number LBP value), and normalizing the histogram; (4) connecting the obtained statistical histograms of the small areas into a feature vector, as an LBP texture feature vector of a whole image; and (5) matching the obtained LBP texture feature vector of the whole image with an LBP texture feature vector in a face recognition library to identity the staff member.
  5. 5. The system and method for managing personnel in a power plant in production according to claim 1, wherein a model of a time period for each post is established, to calculate a vacant post and a corresponding vacancy time period based on expected arrival and leave of the personnel, so as to facilitate corresponding post adjustment.
LU504993A 2023-07-20 2023-08-24 System and method for managing personnel in power plant in production LU504993B1 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310892513.7A CN116957257A (en) 2023-07-20 2023-07-20 Power plant personnel production management system and management method thereof

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LU504993B1 true LU504993B1 (en) 2024-02-26

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Effective date: 20240226