CN114067387A - Primary and secondary school student labor evaluation system based on face recognition - Google Patents
Primary and secondary school student labor evaluation system based on face recognition Download PDFInfo
- Publication number
- CN114067387A CN114067387A CN202111202281.5A CN202111202281A CN114067387A CN 114067387 A CN114067387 A CN 114067387A CN 202111202281 A CN202111202281 A CN 202111202281A CN 114067387 A CN114067387 A CN 114067387A
- Authority
- CN
- China
- Prior art keywords
- labor
- primary
- school students
- server
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000011156 evaluation Methods 0.000 title abstract description 10
- 239000000428 dust Substances 0.000 claims abstract description 50
- 230000001815 facial effect Effects 0.000 claims description 3
- 238000013528 artificial neural network Methods 0.000 claims description 2
- 210000001747 pupil Anatomy 0.000 claims 2
- 238000000034 method Methods 0.000 claims 1
- 238000004140 cleaning Methods 0.000 description 4
- 238000007664 blowing Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000005728 strengthening Methods 0.000 description 1
- 238000010408 sweeping Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
-
- 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
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Chemical & Material Sciences (AREA)
- Educational Administration (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Biochemistry (AREA)
- Game Theory and Decision Science (AREA)
- Immunology (AREA)
- General Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Pathology (AREA)
- Dispersion Chemistry (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a labor evaluation system for primary and secondary school students based on face recognition, which comprises an information acquisition subsystem and a server, wherein the information acquisition subsystem is responsible for acquiring dust concentration information of a labor site, face information of the primary and secondary school students and labor condition information of the primary and secondary school students and sending the information to the server, and the server is responsible for processing the information and evaluating and scoring the labor condition of each primary and secondary school student according to the information.
Description
Technical Field
The invention relates to the technical field of labor evaluation, in particular to a primary and secondary school student labor evaluation system based on face recognition.
Background
The labor education is the basic requirement of the education of the middle and primary school students in the present generation, and is a component of quality education. By taking part in labor exercise, the comprehensive development of primary and secondary school students can be promoted, and the education departments and other related national departments also propose suggestions for strengthening the labor education of primary and secondary schools.
In view of this, most of the middle and primary schools also arrange the middle and primary school students to perform cleaning work in the school range, and also perform various evaluation on the work of the students, and at the end of the period, the work evaluation also takes an important part for the students.
The current labor evaluation mode is that after the students finish cleaning in the specified time and place, the schools send specially-assigned persons to carry out labor inspection and score. The mode needs to send out special inspectors to check the sweeping condition of students, is low in efficiency, wastes manpower resources, can only check the cleaning condition of one area, and cannot evaluate the labor condition of individual students.
Disclosure of Invention
In order to solve the existing problems, the invention provides a labor evaluation system for primary and secondary school students based on face recognition.
The system consists of an information acquisition subsystem and a server; the information acquisition subsystem is responsible for acquiring dust concentration information of a labor site, facial information of primary and secondary school students and labor condition information of the primary and secondary school students and sending the information to the server, and the server is responsible for processing the information and evaluating and scoring the labor condition of each primary and secondary school student according to the information;
the information acquisition subsystem consists of one or more cameras, one or more dust sensors and one or more fans; a dust sensor is correspondingly arranged around each fan;
the server stores the labor score S of each student;
the one or more cameras collect facial information of primary and secondary school students in a labor site within a predetermined first time range;
the face information is sent to a server, and the server matches the received face information with face information of primary and secondary school students who participate in labor and are stored in the server in advance to obtain corresponding student information which does not participate in labor on time;
the server matches the received face information with face information of primary and secondary school students which are pre-stored in the server and are to participate in labor, specifically, the face information is matched by adopting a neural network;
according to the student information, obtaining a late arrival deduction M of the corresponding student, and updating the labor score of the corresponding student to be S-M;
in a preset first time range, the one or more fans blow air to the ground, the one or more dust sensors collect dust in the air, a first dust concentration D1 in the air is obtained, and the first dust concentration D1 is sent to a server;
collecting the labor video of the primary and secondary school students by the camera within a preset second time range, and sending the labor video to the server;
the server analyzes the labor video to obtain the labor bonus A of each student, and updates the labor score of each student to be S + A;
the labor bonus A is specifically that the labor time of each student is determined to be T1, the rest time is T2, and A is set to be T1/60-T2/60;
in a preset third time range, the one or more fans blow air to the ground, the one or more dust sensors collect dust in the air, a second dust concentration in the air is D2, the second dust concentration D2 is sent to a server, and a dust concentration difference D before and after labor is obtained, wherein D is D1-D2;
updating the labor score of each student to be S + D;
drawings
FIG. 1 is a flow chart of a system implementation of the present invention.
Detailed Description
In order to make the present invention clearer, the following explains the embodiments of the present invention with reference to the drawings.
Referring to fig. 1, an embodiment of the present invention provides a labor evaluation system for primary and secondary school students based on face recognition, including an information acquisition subsystem and a back-end server.
The information acquisition subsystem is responsible for acquiring dust concentration information of a labor site, face information of primary and secondary school students and labor condition information of the primary and secondary school students and sending the information to the server, and the server is responsible for processing the information and evaluating and scoring the labor condition of each primary and secondary school student according to the information.
The information acquisition subsystem consists of one or more cameras, one or more dust sensors and one or more fans; and a dust sensor is correspondingly arranged around each fan.
For example, there are 3 students S1, S2 and S3 to give 6: 00-6: 30, the cleaning work is carried out at a certain working site.
The server stores the historical labor scores 11, 17 and 16 of the three students.
Within a predetermined first time range, for example at 6 pm: in the time range of 00-6:03, the camera collects face information of primary and secondary school students in the labor field and sends the face information to the server.
The server matches the received face information with face information of primary and secondary school students S1, S2 and S3 which are pre-stored in the server and participate in the labor, and corresponding information of students who do not participate in the labor on time is obtained, for example, S3 is carried out in the following steps of 6: 04 minutes later, the vehicle arrives at the labor site, and it is judged that S3 does not participate in labor on time.
Obtaining a late-arrival deduction score of 0.5 in S3, and updating the labor score of 15.5(16-0.5) in S3;
within a preset first time range, namely within 6: 00-6: and blowing air to the ground by one or more fans within the time range of 03, collecting dust in the air by one or more dust sensors, obtaining a first dust concentration of 0.38 in the air, and sending the first dust concentration of 0.38 to a server.
Within a preset second time frame, 6 pm: 00-6: and collecting the labor videos of S1, S2 and S3 by the camera within 30 time range, and sending the labor videos to a server.
The server analyzes the labor video, and obtains that the labor time of S1 is 20 minutes, the rest time is 10 minutes, the labor time of S2 is 15 minutes, the rest time is 15 minutes, the labor time of S3 is 20 minutes, and the labor time is 10 minutes.
The labor bonus of S1 was obtained as 0.34, the labor bonus of S2 was obtained as 0, and the labor bonus of S3 was obtained as 0.34; the labor score of update S1 is 11+0.34 to 11.34, the labor score of S2 is 17+0 to 17, and the labor score of S3 is 15.5+0.34 to 15.84.
Within a preset third time frame, 6 pm: 28-6: and in the time range of 30, one or more fans blow air to the ground, one or more dust sensors collect dust in the air to obtain a second dust concentration of 0.07 in the air, and the second dust concentration D2 is sent to a server to obtain a dust concentration difference of 0.21 before and after labor.
The labor score of S1 was 11.34+0.21 — 11.55.
The labor score of S2 was 17+0.21 — 17.21.
The labor score of S3 was 15.84+0.21 ═ 16.05.
Claims (6)
1. A pupil labor assessment system based on face recognition, the system comprising:
the information acquisition subsystem is used for acquiring dust concentration information of a labor site, facial information of primary and secondary school students and labor condition information of the primary and secondary school students; the information acquisition subsystem consists of one or more cameras, one or more dust sensors and one or more fans; a dust sensor is correspondingly arranged around each fan;
a server: the server stores the labor score S of each student;
the server processes the dust concentration information of the labor field, the face information of the primary and secondary school students and the labor condition information of the primary and secondary school students which are acquired by the information acquisition subsystem;
the server updates the labor scores of the primary and middle school students according to the face information of the primary and middle school students;
the server updates the labor scores of the primary and middle school students according to the dust concentration information of the labor field;
and the server updates the labor scores of the primary and middle school students according to the labor condition information of the primary and middle school students.
2. The labor assessment system for primary and secondary school students based on face recognition according to claim 1, wherein:
the server updates the labor scores of the primary and secondary school students according to the face information of the primary and secondary school students, and particularly, the face information of the primary and secondary school students in the labor field is collected by the one or more cameras within a preset first time range;
sending the face information to a server;
the server matches the received face information with face information of primary and secondary school students who participate in labor, which is pre-stored in the server, to obtain corresponding student information which does not participate in labor on time;
and according to the student information, obtaining a late arrival deduction M of the corresponding student, and updating the labor score of the corresponding student to be S-M.
3. The labor assessment system for primary and secondary school students based on face recognition according to claim 1, wherein:
the server updates the labor scores of the primary and middle school students according to the dust concentration information of the labor sites, specifically, in a preset first time range, the one or more fans blow air to the ground, the one or more dust sensors collect dust in the air to obtain a first dust concentration D1 in the air, and the first dust concentration D1 is sent to the server;
in a preset third time range, the one or more fans blow air to the ground, the one or more dust sensors collect dust in the air to obtain a second dust concentration D2 in the air, and the second dust concentration D2 is sent to the server;
obtaining the dust concentration difference D before and after labor, namely D1-D2;
and updating the labor score of each student to be S + D.
4. The labor assessment system for primary and secondary school students based on face recognition according to claim 1, wherein:
the server updates the labor scores of the primary and secondary school students according to the labor condition information of the primary and secondary school students, specifically, the camera collects the labor videos of the primary and secondary school students in a preset second time range and sends the labor videos to the server;
the server analyzes the labor videos to obtain that the labor time of each student is T1, and the rest time is T2;
obtaining the labor bonus A of each student, wherein A is T1/60-T2/60;
and updating the labor score of each student to be S + A according to the obtained labor score A of each student.
5. The labor assessment system for elementary and secondary school students based on face recognition according to claim 2, wherein:
the server updates the labor scores of the primary and middle school students according to the dust concentration information of the labor sites, specifically, in a preset first time range, the one or more fans blow air to the ground, the one or more dust sensors collect dust in the air to obtain a first dust concentration D1 in the air, and the first dust concentration D1 is sent to the server;
in a preset third time range, the one or more fans blow air to the ground, the one or more dust sensors collect dust in the air to obtain a second dust concentration D2 in the air, and the second dust concentration D2 is sent to the server;
obtaining the dust concentration difference D before and after labor, namely D1-D2;
updating the labor score of each student to be S + D;
the server updates the labor scores of the primary and secondary school students according to the labor condition information of the primary and secondary school students, specifically, the camera collects the labor videos of the primary and secondary school students in a preset second time range and sends the labor videos to the server;
the server analyzes the labor videos to obtain that the labor time of each student is T1, and the rest time is T2;
obtaining the labor bonus A of each student, wherein A is T1/60-T2/60;
and updating the labor score of each student to be S + A according to the obtained labor score A of each student.
6. The face recognition-based labor assessment system for pupils and middle school students according to claim 5, wherein:
the server matches the received face information with face information of primary and secondary school students who participate in labor, which is stored in the server in advance, specifically, the face information is matched by adopting a neural network.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111202281.5A CN114067387A (en) | 2021-10-15 | 2021-10-15 | Primary and secondary school student labor evaluation system based on face recognition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111202281.5A CN114067387A (en) | 2021-10-15 | 2021-10-15 | Primary and secondary school student labor evaluation system based on face recognition |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114067387A true CN114067387A (en) | 2022-02-18 |
Family
ID=80234603
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111202281.5A Pending CN114067387A (en) | 2021-10-15 | 2021-10-15 | Primary and secondary school student labor evaluation system based on face recognition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114067387A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013039490A1 (en) * | 2011-09-14 | 2013-03-21 | Hewlett-Packard Development Company, L.P. | Determining risk associated with a determined labor type for candidate personnel |
| CN107807647A (en) * | 2017-11-21 | 2018-03-16 | 上海斐讯数据通信技术有限公司 | The cleaning method and sweeping robot of a kind of sweeping robot |
| CN111754065A (en) * | 2019-03-29 | 2020-10-09 | 本田技研工业株式会社 | Evaluation system, mobile object, computer-readable recording medium, and method |
| CN112950430A (en) * | 2021-04-23 | 2021-06-11 | 重庆多创云教育科技有限公司 | Intelligent happy farm management system and method |
-
2021
- 2021-10-15 CN CN202111202281.5A patent/CN114067387A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013039490A1 (en) * | 2011-09-14 | 2013-03-21 | Hewlett-Packard Development Company, L.P. | Determining risk associated with a determined labor type for candidate personnel |
| CN107807647A (en) * | 2017-11-21 | 2018-03-16 | 上海斐讯数据通信技术有限公司 | The cleaning method and sweeping robot of a kind of sweeping robot |
| CN111754065A (en) * | 2019-03-29 | 2020-10-09 | 本田技研工业株式会社 | Evaluation system, mobile object, computer-readable recording medium, and method |
| CN112950430A (en) * | 2021-04-23 | 2021-06-11 | 重庆多创云教育科技有限公司 | Intelligent happy farm management system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111144275A (en) | Intelligent running test system and method based on face recognition | |
| CN111353921A (en) | Examination management method and system and electronic equipment | |
| US20150294151A1 (en) | Education site improvement support system, education site improvement support method, information processing apparatus, communication terminal, and control methods and control programs of information processing apparatus and communication terminal | |
| CN104899556A (en) | System for counting persons in classroom based on image recognition | |
| CN112784740B (en) | Gait data acquisition and labeling method and application | |
| CN111275345A (en) | Classroom informatization evaluation and management system and method based on deep learning | |
| KR20190043513A (en) | System For Estimating Lecture Attention Level, Checking Course Attendance, Lecture Evaluation And Lecture Feedback | |
| CN112785205A (en) | Intelligent teaching comprehensive analysis system based on education big data | |
| CN106384316A (en) | Examination authority real-name verification system | |
| CN109271896B (en) | Student evaluation system and method based on image recognition | |
| CN113011835A (en) | Campus health data management method and system | |
| CN114067387A (en) | Primary and secondary school student labor evaluation system based on face recognition | |
| CN112766095B (en) | System and method for evaluating input degree of participants | |
| CN115937971B (en) | Method and device for identifying hand-lifting voting | |
| CN109711263B (en) | Examination system and processing method thereof | |
| CN112214651A (en) | Intelligent learning competition system and method | |
| CN110689275A (en) | Method and system for analyzing and quantitatively scoring wrong questions | |
| CN108921069A (en) | Children's interest likes recommended method and system | |
| CN110716920A (en) | Student behavior automatic analysis method and system based on face recognition | |
| CN110378261A (en) | A kind of student's recognition methods and device | |
| CN113379207B (en) | Control method of training platform, training platform and readable storage medium | |
| CN116188211A (en) | Online education learning assessment system | |
| CN114971425A (en) | Database information monitoring method, device, equipment and storage medium | |
| KR20160086618A (en) | System For Estimating Lecture Attention Level, Checking Course Attendance, Lecture Evaluation And Lecture Feedback | |
| CN110751062B (en) | Examinee attitude sequence generation method based on attitude voting |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |