CN112001284A - Labor service real-name system management system based on artificial intelligence - Google Patents
Labor service real-name system management system based on artificial intelligence Download PDFInfo
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Abstract
The invention relates to the technical field of labor management, and discloses a labor real-name management system based on artificial intelligence, which comprises the following steps: s1, collecting labor service personnel information, inputting the labor service personnel information into a system, and synchronizing the personnel information to a face recognition camera through a network by the system; s2, monitoring the moving state of an object in a range in real time by the face recognition camera; s3, when a labor worker passes through the access control channel, the face recognition camera captures facial features of the worker; and S4, extracting the features of the human face through an algorithm, converting the features into a feature numerical value string, comparing the feature numerical value string with the human face feature numerical value string in the database, and executing S5 if the matching similarity exceeds a threshold set by the system. The invention automatically identifies the illegal behaviors of workers on the spot, such as safety helmets, smoking and the like, through an artificial intelligence algorithm, and links and matches the illegal personnel information, sends out an alarm, links attendance and wage distribution.
Description
Technical Field
The invention relates to the technical field of labor management, in particular to a labor real-name management system based on artificial intelligence.
Background
Place such as some building sites generally all have access control system, and artifical mode access control management is access control management and control that the personnel of cominging in and going out carried out by security personnel completely promptly, and security personnel are not possible all personnel of management and control to come in and go out, can cause the problem of personnel's management leak of cominging in and going out promptly, and labour personnel's action safety also need monitor moreover, and it is too low to rely on the efficiency of the words of manpower entirely.
Through retrieval, a patent with an authorization publication number of CN106710043B discloses a time limit access control management system with visitor identity authentication and a method thereof, visitor information is established in a management server end through a client, and the visitor information converted into a two-dimensional bar code is provided to a visitor handheld device; the artificial intelligence of the patent is not enough, the illegal behaviors that workers do not wear safety helmets and smoke on site cannot be automatically identified, and the requirements of people cannot be met.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a labor service real-name management system based on artificial intelligence, and mainly aims to solve the problem of low intelligence degree of the prior art.
(II) technical scheme
In order to achieve the purpose, the invention provides the following technical scheme:
a labor service real-name management system based on artificial intelligence comprises the following steps:
s1, collecting labor service personnel information, inputting the labor service personnel information into a system, and synchronizing the personnel information to a face recognition camera through a network by the system;
s2, monitoring the moving state of an object in a range in real time by the face recognition camera;
s3, when a labor worker passes through the access control channel, the face recognition camera captures facial features of the worker;
s4, the system extracts the features of the human face through an algorithm, converts the features into a feature numerical value string, compares the feature numerical value string with the human face feature numerical value string in the database, and executes S5 if the matching similarity exceeds a threshold set by the system; if the similarity between the captured face characteristic value and all the face characteristic values in the database does not exceed the set threshold, executing S6;
s5, the face recognition camera transmits an instruction to the gate machine, the gate machine controller controls the electric control lock to be opened, and personnel entrance information is transmitted to the remote server through the network;
s6, feeding back comparison failure information to the user, and keeping the gate closed all the time;
s7, after receiving the message, the server updates the entering time of the person;
s8, after the person leaves the field, the system updates the departure time of the person, calculates the accumulated time of the field on the day, and updates the attendance data on the day according to the rule;
s9, monitoring the movement state of personnel in the range in real time by cameras at all positions of a construction site;
s10, when personnel movement information is captured, transmitting a video stream to a rear-end behavior analysis server in real time;
s11, the server calculates the face position of a person in the video by adopting a deep learning YOLOv3 algorithm, and estimates the area range of the safety helmet and the cigarette according to the relationship between the safety helmet and the face;
s12, enhancing the image of the region where the face is located, and extracting feature values of the face, the position of a safety helmet and the position of a mouth in the video;
s13, comparing the face characteristic value with a database face characteristic value to determine the identity of a person;
s14, matching the face characteristic values of the persons in the videos with a safety helmet wearing behavior model and a smoking behavior model in a database to obtain whether the faces of the persons in the videos have safety helmets and smoking behaviors or not, and if the faces of the persons in the videos have the safety helmets and the persons do not smoke, continuing to execute S9; if one of the safety helmet is not worn and the smoking behavior exists, executing S15;
s15, the system records the violation behaviors of the personnel in the behavior file and sends an alarm to the manager;
s16, the system generates an attendance report according to the attendance data, and calculates payroll to be sent according to the staff payroll standard;
and S17, deducting the penalty by combining the set rule of deducting the violation behaviors, and calculating the final payroll to be sent by each labor worker.
Further, the S1 includes a labor real-name management system, the labor real-name management system includes an information input module and an entrance guard control module, the information input module includes an identification card recognizer, the entrance guard control module is connected with a gate and a face recognition camera, the gate is composed of an entrance guard controller, an electric control lock and a network transmission unit, the entrance guard controller is responsible for the processing and storage control of the input and output information of the whole system, the electric control lock uses an electromagnetic lock as a lock, the network transmission unit is composed of an Ethernet interface and an RS-232 serial interface, the RS-232 serial interface is connected with the face recognition camera, the Ethernet interface is connected with the labor real-name management system, the face recognition camera is composed of an image acquisition and processing unit, an image comparison unit and a network transmission unit, the image acquisition and processing unit acquires the facial image of the user, the image recognition unit extracts the captured facial features of the personnel, the face information is converted into a numerical string, the numerical string is compared with the face photo feature value stored in the system to obtain a similar value, the comparison result is fed back, and the network transmission unit is internally provided with an Ethernet interface for connecting the gate machine and the face recognition camera.
On the basis of the scheme, the S3 includes a behavior analysis system, the behavior analysis system includes an artificial intelligence algorithm database, a video monitoring module, an unworn safety helmet analysis module and a smoking behavior analysis module, the behavior analysis system includes a hardware device and a software platform, the hardware device selects a rack server, a CPU, a hard disk, a memory and a plurality of 1000Mbps ethernet network interfaces are built in the behavior analysis system, the software platform uses Visual C + +, Python programming languages to transplant the YOLOv3 algorithm based on the Caffe framework into a development environment for processing and identifying video streams transmitted by the camera, the system further stores a wearing safety helmet behavior model and a smoking behavior model trained by image samples for matching and comparison, the video monitoring module is connected with the camera, the camera is composed of an image acquisition and processing unit and a network transmission unit, the image acquisition and processing unit includes an image sensor and a digital signal processing chip, the image sensor converts an optical image generated by a real picture through a lens into an electric signal, the electric signal is converted into a digital image signal through analog-to-digital conversion, the digital image signal is transmitted to a digital signal processing chip for video coding compression, an Ethernet interface is arranged in a network transmission unit and used for connecting a camera and a switch, the switch selects a 1000Mbps Ethernet switch and is used for connection between servers, between the servers and a router and between front-end equipment and an outlet router, a 4G network transmission unit and a plurality of Ethernet interfaces are arranged in the router, and the front-end equipment and the rear-end server can be connected through a 4G mode and a wired network mode.
As a further scheme of the present invention, the artificial intelligence algorithm database in S4 includes an update module, which is periodically updated every 1-3 months to prevent the update from affecting the normal database comparison work in time.
Further, the S16 and S17 include a payroll and payment module, and the payroll and payment module is connected to the real-time labor name management system.
On the basis of the scheme, the S7 and the S8 comprise attendance statistics modules, the entering time of the personnel is updated after entering the field, the leaving time of the personnel is updated after leaving the field, and the entering time and the leaving time are uploaded to the attendance statistics modules.
According to a further scheme of the invention, the S9 and S10 include violation recording modules, the violation recording modules are connected with the behavior analysis system, and video information collected by the behavior analysis system is analyzed and then recorded by the violation recording modules.
Further, the S15 includes a warning module, the warning module is connected to the violation recording module, and when a human violates the violation and is recorded, the warning module issues a warning to a manager.
(III) advantageous effects
Compared with the prior art, the invention provides a labor service real-name system management system based on artificial intelligence, which has the following beneficial effects:
1. the invention applies the artificial intelligence technology to various practical management scenes such as labor service real-name management, bad behavior detection of labor service personnel and the like, and can realize the management of the whole flow of information registration, field work, attendance statistics and wage distribution of the labor service personnel.
2. According to the invention, the non-contact type entrance identification mode is used through the video monitoring module, the information input module and the access control module of the behavior analysis system, and the opening and closing of the gate are controlled through face identification, so that the identification accuracy is high, the passing efficiency is high, and the system is more efficient compared with the traditional card swiping mode.
3. The invention automatically identifies the illegal behaviors of workers on the spot, such as safety helmets, smoking and the like, through an artificial intelligence algorithm, and links and matches the illegal personnel information, sends out an alarm, links attendance and wage distribution, compared with the traditional on-site supervision mode, the mode is full-automatic, manual operation is not needed, and the accuracy rate is high; meanwhile, the mode can avoid interference of human factors to the inspection and can practically standardize the behavior of the labor personnel.
4. In the invention, the server calculates the face position of the person in the video by adopting a deep learning YOLOv3 algorithm, estimates the range of the area where the safety helmet and the cigarette are located according to the relationship between the safety helmet and the face, enhances the image of the area where the face is located, extracts the face, the position of the safety helmet and the characteristic value of the mouth position in the video and improves the accuracy of identification.
Drawings
FIG. 1 is a schematic view of a flow structure of a labor real-name management system based on artificial intelligence according to the present invention;
fig. 2 is a schematic system structure diagram of the labor real-name management system based on artificial intelligence according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the labor real-name management system based on artificial intelligence comprises the following steps:
s1, collecting labor service personnel information, inputting the labor service personnel information into a system, and synchronizing the personnel information to a face recognition camera through a network by the system;
s2, monitoring the moving state of an object in a range in real time by the face recognition camera;
s3, when a labor worker passes through the access control channel, the face recognition camera captures facial features of the worker;
s4, the system extracts the features of the human face through an algorithm, converts the features into a feature numerical value string, compares the feature numerical value string with the human face feature numerical value string in the database, and executes S5 if the matching similarity exceeds a threshold set by the system; if the similarity between the captured face characteristic value and all the face characteristic values in the database does not exceed the set threshold, executing S6;
s5, the face recognition camera transmits an instruction to the gate machine, the gate machine controller controls the electric control lock to be opened, and personnel entrance information is transmitted to the remote server through the network;
s6, feeding back comparison failure information to the user, and keeping the gate closed all the time;
s7, after receiving the message, the server updates the entering time of the person;
s8, after the person leaves the field, the system updates the departure time of the person, calculates the accumulated time of the field on the day, and updates the attendance data on the day according to the rule;
s9, monitoring the movement state of personnel in the range in real time by cameras at all positions of a construction site;
s10, when personnel movement information is captured, transmitting a video stream to a rear-end behavior analysis server in real time;
s11, the server calculates the face position of a person in the video by adopting a deep learning YOLOv3 algorithm, and estimates the area range of the safety helmet and the cigarette according to the relationship between the safety helmet and the face;
s12, enhancing the image of the region where the face is located, and extracting feature values of the face, the position of a safety helmet and the position of a mouth in the video;
s13, comparing the face characteristic value with a database face characteristic value to determine the identity of a person;
s14, matching the face characteristic values of the persons in the videos with a safety helmet wearing behavior model and a smoking behavior model in a database to obtain whether the faces of the persons in the videos have safety helmets and smoking behaviors or not, and if the faces of the persons in the videos have the safety helmets and the persons do not smoke, continuing to execute S9; if one of the safety helmet is not worn and the smoking behavior exists, executing S15;
s15, the system records the violation behaviors of the personnel in the behavior file and sends an alarm to the manager;
s16, the system generates an attendance report according to the attendance data, and calculates payroll to be sent according to the staff payroll standard;
and S17, deducting the penalty by combining the set rule of deducting the violation behaviors, and calculating the final payroll to be sent by each labor worker.
The invention comprises a labor real-name management system in S1, wherein the labor real-name management system comprises an information input module and an entrance guard control module, the information input module comprises an identity card recognizer, the entrance guard control module is connected with a gate machine and a face recognition camera, the gate machine comprises an entrance guard controller, an electric control lock and a network transmission unit, the entrance guard controller is responsible for the processing and storage control of the input and output information of the whole system, the electric control lock takes an electromagnetic lock as a lock, the network transmission unit comprises an Ethernet interface and an RS-232 serial interface, the RS-232 serial interface realizes the connection with the face recognition camera, the Ethernet interface realizes the connection with the labor real-name management system, the face recognition camera comprises an image acquisition and processing unit, an image comparison unit and a network transmission unit, the image acquisition and processing unit acquires the facial photos of a user, the image recognition unit extracts the captured facial features of the personnel, the conversion of the face information into a numerical string is realized, the numerical string is compared with the characteristic value of a face photo stored in the system to obtain a similar value, and a comparison result is fed back, an Ethernet interface is arranged in the network transmission unit and is used for connecting a gate machine and a face recognition camera, S3 comprises a behavior analysis system, the behavior analysis system comprises an artificial intelligent algorithm database, a video monitoring module, an unworn safety cap analysis module and a smoking behavior analysis module, the behavior analysis system comprises hardware equipment and a software platform, the hardware equipment selects a rack server, a CPU, a hard disk, a memory and a plurality of 1000Mbps Ethernet interfaces are arranged in the behavior analysis system, the software platform uses Visual C + +, Python programming language to transplant the YOLOv3 algorithm based on Caffe framework into a development environment for processing and recognizing the video stream transmitted by the camera, the system also stores a wearing safety helmet behavior model and a smoking behavior model which are trained by image samples and are used for matching and comparison, the video monitoring module is connected with a camera, the camera consists of an image acquisition and processing unit and a network transmission unit, the image acquisition and processing unit comprises an image sensor and a digital signal processing chip, the image sensor converts an optical image generated by a real picture through a lens into an electric signal, the electric signal is converted into a digital image signal through analog-to-digital conversion and then transmitted into the digital signal processing chip for video coding and compression, the network transmission unit is internally provided with an Ethernet interface for connecting the camera and the switch, the switch selects a 1000Mbps Ethernet switch for connecting servers, the servers and the routers, the front-end equipment and the outlet router, and the router is internally provided with a 4G network transmission unit and a plurality of Ethernet interfaces, the connection between the front-end equipment and the back-end server can be realized through two modes of 4G and a wired network.
Particularly, the artificial intelligence algorithm database in the S4 includes an updating module, which is updated regularly every 1-3 months to prevent the update from affecting the normal database comparison work in time, S16 and S17 include payroll calculation and payment modules, which are connected to the labor and practice name management system, S7 and S8 include attendance statistics modules, which are used for updating the time of entering the field after entering the field and updating the time of leaving the field after leaving the field, and are uploaded to the attendance statistics modules, S9 and S10 include violation recording modules, which are connected to the behavior analysis system, and the video information collected by the behavior analysis system is analyzed and recorded by the violation recording module, S15 includes a warning module, which is connected to the violation recording module, and when someone is recorded, the warning module gives a warning to the manager.
In the description herein, it is noted that relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. Labor real-name management system based on artificial intelligence, which is characterized by comprising the following steps:
s1, collecting labor service personnel information, inputting the labor service personnel information into a system, and synchronizing the personnel information to a face recognition camera through a network by the system;
s2, monitoring the moving state of an object in a range in real time by the face recognition camera;
s3, when a labor worker passes through the access control channel, the face recognition camera captures facial features of the worker;
s4, the system extracts the features of the human face through an algorithm, converts the features into a feature numerical value string, compares the feature numerical value string with the human face feature numerical value string in the database, and executes S5 if the matching similarity exceeds a threshold set by the system; if the similarity between the captured face characteristic value and all the face characteristic values in the database does not exceed the set threshold, executing S6;
s5, the face recognition camera transmits an instruction to the gate machine, the gate machine controller controls the electric control lock to be opened, and personnel entrance information is transmitted to the remote server through the network;
s6, feeding back comparison failure information to the user, and keeping the gate closed all the time;
s7, after receiving the message, the server updates the entering time of the person;
s8, after the person leaves the field, the system updates the departure time of the person, calculates the accumulated time of the field on the day, and updates the attendance data on the day according to the rule;
s9, monitoring the movement state of personnel in the range in real time by cameras at all positions of a construction site;
s10, when personnel movement information is captured, transmitting a video stream to a rear-end behavior analysis server in real time;
s11, the server calculates the face position of a person in the video by adopting a deep learning YOLOv3 algorithm, and estimates the area range of the safety helmet and the cigarette according to the relationship between the safety helmet and the face;
s12, enhancing the image of the region where the face is located, and extracting feature values of the face, the position of a safety helmet and the position of a mouth in the video;
s13, comparing the face characteristic value with a database face characteristic value to determine the identity of a person;
s14, matching the face characteristic values of the persons in the videos with a safety helmet wearing behavior model and a smoking behavior model in a database to obtain whether the faces of the persons in the videos have safety helmets and smoking behaviors or not, and if the faces of the persons in the videos have the safety helmets and the persons do not smoke, continuing to execute S9; if one of the safety helmet is not worn and the smoking behavior exists, executing S15;
s15, the system records the violation behaviors of the personnel in the behavior file and sends an alarm to the manager;
s16, the system generates an attendance report according to the attendance data, and calculates payroll to be sent according to the staff payroll standard;
and S17, deducting the penalty by combining the set rule of deducting the violation behaviors, and calculating the final payroll to be sent by each labor worker.
2. The labor force real-name management system based on artificial intelligence of claim 1, wherein the S1 comprises the labor force real-name management system, the labor force real-name management system comprises an information input module and an entrance guard control module, the information input module comprises an identification card recognizer, the entrance guard control module is connected with a gate machine and a face recognition camera, the gate machine comprises an entrance guard controller, an electric control lock and a network transmission unit, the entrance guard controller is responsible for processing and storing control of input and output information of the whole system, the electric control lock takes the electric control lock as a lock, the network transmission unit comprises an Ethernet interface and an RS-232 serial interface, the RS-232 serial interface is connected with the face recognition camera, the Ethernet interface is connected with the labor force real-name management system, and the face recognition camera is connected with the image acquisition and processing unit, The image comparison unit and the network transmission unit are formed, and the image acquisition and processing unit acquires a user face photo.
3. The labor real-name management system based on artificial intelligence of claim 1, wherein the S3 comprises a behavior analysis system, the behavior analysis system comprises an artificial intelligence algorithm database, a video monitoring module, an unworn safety helmet analysis module and a smoking behavior analysis module, the behavior analysis system comprises a hardware device and a software platform, the hardware device is selected from a rack server, a built-in CPU, a hard disk, a memory and a plurality of 1000Mbps Ethernet network interfaces, the software platform uses Visual C + +, Python programming language to transplant the YOv 3 algorithm based on Caffe framework into a development environment for processing and identifying video stream transmitted by the camera, the system further stores a wearing safety helmet behavior model and a smoking behavior model trained by image samples for matching and comparison, the video monitoring module is connected with a camera, and the camera is composed of an image acquisition and processing unit and a network transmission unit, the image acquisition and processing unit comprises an image sensor and a digital signal processing chip.
4. The labor force real-name management system based on artificial intelligence of claim 1, wherein the artificial intelligence algorithm database in S4 comprises an updating module, which is periodically updated every 1-3 months to prevent the update from affecting the normal database comparison work in time.
5. The labor force system management system based on artificial intelligence as claimed in claim 2, wherein said S16 and S17 include payroll and payment module, and the payroll and payment module is connected with the labor force system management system.
6. The labor real-name management system based on artificial intelligence as claimed in claim 5, wherein the S7 and S8 include an attendance statistics module, the entering time of the personnel is updated after entering the field, the leaving time of the personnel is updated after leaving the field, and the entering time and the leaving time are uploaded to the attendance statistics module.
7. The labor force real-name management system based on artificial intelligence as claimed in claim 6, wherein the S9 and S10 include violation recording module, the violation recording module is connected with the behavior analysis system, and the video information collected by the behavior analysis system is analyzed and recorded by the violation recording module.
8. The labor force real-name management system based on artificial intelligence of claim 7, wherein the S15 comprises a warning module, the warning module is connected to the violation recording module, and the warning module gives a warning to the manager when a human violates the violation and is recorded.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202010818499.2A CN112001284A (en) | 2020-08-14 | 2020-08-14 | Labor service real-name system management system based on artificial intelligence |
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| CN202010818499.2A CN112001284A (en) | 2020-08-14 | 2020-08-14 | Labor service real-name system management system based on artificial intelligence |
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| CN114120462B (en) * | 2021-10-11 | 2024-03-01 | 合肥优尔电子科技有限公司 | Self-adaptive pushing system and method for labor service real-name system data based on supervision platform |
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| CN115410304A (en) * | 2022-08-26 | 2022-11-29 | 山东大佳机械有限公司 | Safety prevention and control method and system for poultry house breeding based on self-learning |
| CN115690932A (en) * | 2022-12-30 | 2023-02-03 | 中交第四航务工程勘察设计院有限公司 | Outdoor operation digital attendance checking method and system based on NFC technology |
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