CN112609765A - Excavator safety control method and system based on facial recognition - Google Patents
Excavator safety control method and system based on facial recognition Download PDFInfo
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- CN112609765A CN112609765A CN202011292961.6A CN202011292961A CN112609765A CN 112609765 A CN112609765 A CN 112609765A CN 202011292961 A CN202011292961 A CN 202011292961A CN 112609765 A CN112609765 A CN 112609765A
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- 238000000034 method Methods 0.000 title claims abstract description 23
- 230000001815 facial effect Effects 0.000 title claims abstract description 17
- 238000012544 monitoring process Methods 0.000 claims abstract description 15
- 206010000117 Abnormal behaviour Diseases 0.000 claims abstract description 10
- 230000002159 abnormal effect Effects 0.000 claims abstract description 10
- 210000000887 face Anatomy 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 230000006399 behavior Effects 0.000 description 20
- 230000000391 smoking effect Effects 0.000 description 5
- 238000009430 construction management Methods 0.000 description 3
- 238000011217 control strategy Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
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- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02F—DREDGING; SOIL-SHIFTING
- E02F9/00—Component parts of dredgers or soil-shifting machines, not restricted to one of the kinds covered by groups E02F3/00 - E02F7/00
- E02F9/24—Safety devices, e.g. for preventing overload
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- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02F—DREDGING; SOIL-SHIFTING
- E02F9/00—Component parts of dredgers or soil-shifting machines, not restricted to one of the kinds covered by groups E02F3/00 - E02F7/00
- E02F9/20—Drives; Control devices
- E02F9/2025—Particular purposes of control systems not otherwise provided for
- E02F9/205—Remotely operated machines, e.g. unmanned vehicles
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- 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
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
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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
- G06V40/168—Feature extraction; Face representation
- G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
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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
- G06V40/172—Classification, e.g. identification
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Abstract
The invention discloses an excavator safety control method based on facial recognition, which comprises the following steps: acquiring driver face information and video, and matching and identifying the driver face information and video with preset driver information; recording and storing work statistics and driving behavior analysis results of drivers with different identities; judging whether abnormal operation exists in the excavator or not according to the identified monitoring information, the acquired video information and the driving behavior analysis result; and feeding back the judgment result to the client terminal through wireless. According to the invention, the identity and the abnormal behavior of the driver are identified through the face identification of the photosensitive camera, the strong storage and calculation capabilities of the whole machine network and the cloud platform are combined, the safety and the anti-theft performance of the excavator are improved, meanwhile, the abnormal behavior is monitored, and the operation safety of the driver is improved.
Description
Technical Field
The invention relates to a face recognition-based excavator safety control method and system, and belongs to the technical field of excavator control.
Background
The intelligent level of the current excavator is generally not high, the anti-theft performance of an excavator key starting system is insufficient, and the existing protection measures are insufficient in protecting the excavator; the excavator has bad working condition, long working time of a driver and easy fatigue driving, and meanwhile, potential safety hazards are easily caused by bad driving habits, so that loss is brought to construction.
At present, the anti-theft protection of the excavator is mainly traditional key protection, no corresponding supervision means is provided for the operation normative of a driver, fatigue driving is a monitoring blind area, and no monitoring measures are provided for potential safety hazards caused by fatigue driving.
Disclosure of Invention
The invention aims to provide an excavator safety control method and system based on facial recognition, and aims to overcome the defects that in the prior art, no corresponding monitoring means exists for the operation normalization of a driver, fatigue driving is a monitoring blind area, and no monitoring measures exist for potential safety hazards caused by fatigue driving.
An excavator safety control method based on facial recognition, the method comprising the steps of:
acquiring driver face information and video, and matching and identifying the driver face information and video with preset driver information;
recording and storing work statistics and driving behavior analysis results of drivers with different identities;
judging whether abnormal operation exists in the excavator or not according to the identified monitoring information, the acquired video information and the driving behavior analysis result;
and feeding back the judgment result to the client terminal through wireless.
Further, the driver information matching method includes:
carrying out multi-group and multi-angle facial information input on a driver;
identifying a driver who is starting up through a camera; if the engine starting authority is matched with the preset driver information, opening the engine starting authority and the excavator control authority and recording the driver operation information; if the driver information is not matched with the preset driver information, the illegal driver is notified to the user side wirelessly.
Further, the driver information matching method further includes:
when the driver changes in the midway, identifying the driver information;
if the driver information is matched with the driver information, recording the updated driver information, and updating the new driver in a timing manner; if not, the illegal driver notifies the user end wirelessly.
Further, the driving behavior analysis includes:
identifying the facial information of a driver through the collected video information, and managing and identifying the characteristics of eyes, mouths and faces through dotted lines so as to judge whether a human body is in illegal operation; and controlling the state of the excavator according to the judgment result.
Further, the illegal operation includes fatigue driving; the fatigue driving recognition method comprises the following steps:
comparing the driving time of the driver with preset time; if the preset time is exceeded, the functions of the excavator are controlled by the main controller to limit; and if the time is shorter than the preset time, controlling the excavator to normally operate through the main controller.
A facial recognition based excavator safety control system, the system comprising:
DMS host computer: the system is used for identifying the items collected by the camera and transmitting the identified monitoring information and video to the main controller;
a main controller: the system is used for receiving identity recognition monitoring information, videos and abnormal behavior alarm information sent by the DMS host computer so as to control the excavator not to be operated abnormally;
cloud platform: and the GPS terminal is used for receiving and storing data and videos transmitted by the GPS terminal, simultaneously carrying out work statistics and driving behavior analysis on drivers with different identities, and feeding back the work statistics and driving behavior analysis to the client terminal.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, the identity and the abnormal behavior of the driver are recognized through the face recognition of the photosensitive camera, the strong storage and calculation capabilities of the whole machine network and the cloud platform are combined, the safety anti-theft performance of the excavator is improved, the abnormal behavior is monitored, the operation safety of the driver is improved, the cloud platform can directly push the driver behavior report and the related abnormal alarm to the terminal client, so that the excavator is protected from being illegally stolen, meanwhile, the identity recognition of different drivers is aimed at, and the construction management of different drivers can be completed through the cloud computing.
Drawings
FIG. 1 is a flow chart of the excavator safety control strategy of the present invention;
FIG. 2 is a diagram of the safety control system architecture of the present invention;
FIG. 3 is a flow chart of the DMS system calibration control of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
The invention provides an excavator safety control method based on facial recognition, which is characterized in that the control of an excavator is subjected to authority management through identity authentication, so that the excavator is protected from being started or controlled by illegal personnel, and the safety is improved; according to the driver information recorded by multiple identities, working hour statistics is carried out on different drivers respectively by combining cloud platform data, and the construction management level is improved; meanwhile, according to abundant facial parameters, the behavior of the driver is monitored and analyzed, abnormal information such as fatigue driving, smoking, telephone and the like of the driver is alarmed and recorded, and the driver is scored by combining cloud platform data, so that the management level is improved, dangerous operation is avoided, and the safety is improved.
The method has the following specific technical scheme: FIG. 2 is a DMS system calibration control flow chart, which includes the following steps:
step S101, entering a DMS calibration interface of the instrument 60, wherein the interface comprises a driver identity calibration and setting functional area;
step S102, a manager inputs a manager password for verification, driver information is input, and a human face is aligned with the camera 40;
step S103, operating on a DMS calibration interface of the instrument 60, inputting face information of a driver, performing multi-group and multi-angle face information input on the driver in the operation, performing data interaction between the instrument 60 and the DMS host 30 through a CAN (controller area network) and a video line in the input process, displaying the input face information by the instrument 60, sending a control instruction to the DMS host 30 by the instrument 60, receiving the information of the DMS host 30, performing face information input judgment in a reverse 30 rows, and entering step S105 if the driver input is judged to be successful, and entering S103 to perform new input if the driver input is failed;
step S105, the driver information can be continuously input through the instrument 60, and when the input requirement does not exist, the driver information can directly enter a driver behavior setting interface;
step S106, the meter 60 provides a driver behavior setting interface;
step S107, the driver behavior setting interface comprises the monitoring of abnormal driver behaviors such as left expectation, fatigue doze, smoking, calling and the like, the detection enabling of related behaviors can be selected through the instrument 60, meanwhile, the interface provides alarm setting, the enabling setting of abnormal driver behaviors such as fatigue driving, smoking, non-authorized identity and the like can be carried out, and the instrument 60 sends the setting information to the DMS host 30 for execution.
Fig. 3 is a flowchart of a safety control strategy of an excavator, which includes the following specific steps:
step S201, electrifying the key, electrifying all control parts of the system to operate, and identifying the identity of the positive camera 40 by a driver;
step S202, DMS host 30 judges whether it is a registered driver, if yes, then enter S203, otherwise, it is an illegal driver, then enter S212;
step S203, the DMS host 30 sends identification information to notify the master controller 70 of the registered driver, uploads the driver information to the cloud platform 10 via the GPS20, records information on the cloud, records driver operation information and working hours of different drivers, and performs construction management;
in an information transmission network, all parts on the whole excavator are communicated by using a Controller Area Network (CAN), video information is transmitted by a video line, the communication between a Global Position System (GPS) 20 and the cloud platform 10 is a 3G, 4G or 5G network, and meanwhile, a direct memory access (DMS) host 30 supports local storage of driver operation and abnormal information;
step S204, the master controller 70 confirms the identity of the driver and develops the engine starting authority and the excavator control authority;
step S205, the camera 40 monitors the driver information in real time, judges whether to replace the driver, if so, the step S206 is executed, otherwise, the step S207 is executed;
step S206, transmitting the driver replacement information to the cloud platform 10 through the GPS20, recording the driver update information, and timing and updating the new driver;
step S207, the DMS host 30 judges whether the driver has behaviors such as fatigue driving, yawning, smoking, calling and the like, if so, the step S206 is executed, and if not, the step S209 is executed;
in this step, the DMS host 30 performs human face information recognition through the camera 40, manages and recognizes other features of eyes, mouth, and face through a dotted line, and determines whether the human body is in abnormal information such as fatigue driving, making a call, smoking, and the like;
step S208, the instrument 60 receives the abnormal instruction and gives an alarm, and meanwhile, the GPS20 uploads the abnormal behavior and alarm information to the cloud platform 10 and the cloud platform 10 records the abnormal behavior and alarm information;
step S209, the DMS host 30 performs risk determination on the fatigue driving behavior, performs time determination on continuous fatigue driving, and if the time exceeds time T1, the process proceeds to step S211, and if the time does not exceed time T1, the process proceeds to step S210;
step S210, when the DMS host 30 judges that abnormal behaviors such as fatigue driving do not exist or the fatigue driving time is less than T1, sending instruction information to the main controller 70, and controlling the excavator to normally operate;
step S211, after judging the dangerous behavior, the main controller 70 receives the instruction of the DMS host 30, the safety protection control strategy is started, the engine is forced to enter an idling state, and meanwhile, the hydraulic control system is cut off, so that the man-machine safety is protected;
step S212, the meter 60 gives an alarm for an illegal driver, and meanwhile, the GPS20 uploads the alarm to the cloud platform 10 and sends the alarm to the client terminal 50 through the cloud platform 10 to remind the client of safety and theft prevention of the excavator;
step S213, the main controller 70 receives the information of the illegal driver, starts the engine start authority and operation authority protection, and prohibits the engine start and the digging operation until the driver is legal and opens the authority.
As shown in fig. 1, is an architecture diagram of the safety control system:
the cloud platform 10 is an internet of things platform server, receives and stores data and videos transmitted by the GPS terminal 20, and meanwhile, through strong computing power of the cloud, work statistics and driving behavior analysis of drivers with different identities can be performed and fed back to the client terminal 50;
the GPS terminal 20 is a communication terminal, and mainly transmits information and video fed back by the DMS host 30 to the cloud platform 10, and receives instruction information of the cloud platform 10;
the DMS host 30 is a system core arithmetic unit, and recognizes facial information, performs identity recognition and behavior analysis, and transmits monitoring information and video;
the camera 40 is a system sensing element, and is used for completing identity recognition and behavior monitoring by recognizing human faces;
the client terminal 50 is an excavator owner client, and can receive driver data transmitted by the cloud platform 10 and a driver behavior report analyzed through cloud computing, so that construction progress is better managed;
the instrument 60 is a system man-machine interaction unit, and can perform functions of identity calibration, fault alarm setting and the like on a system through the instrument;
the main controller 70 is an excavator core controller and is responsible for receiving the identity identification information, the abnormal behavior alarm information and the like sent by the DMS host, so as to control the excavator not to be abnormally operated and prevent potential safety hazards.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (6)
1. An excavator safety control method based on facial recognition is characterized by comprising the following steps:
acquiring driver face information and video, and matching and identifying the driver face information and video with preset driver information;
recording and storing work statistics and driving behavior analysis results of drivers with different identities;
judging whether abnormal operation exists in the excavator or not according to the identified monitoring information, the acquired video information and the driving behavior analysis result;
and feeding back the judgment result to the client terminal through wireless.
2. The excavator safety control method based on facial recognition of claim 1, wherein the driver information matching method comprises:
carrying out multi-group and multi-angle facial information input on a driver;
identifying a driver who is starting up through a camera; if the engine starting authority is matched with the preset driver information, opening the engine starting authority and the excavator control authority and recording the driver operation information; if the driver information is not matched with the preset driver information, the illegal driver is notified to the user side wirelessly.
3. The excavator safety control method based on face recognition as set forth in claim 2, wherein the driver information matching method further includes:
when the driver changes in the midway, identifying the driver information;
if the driver information is matched with the driver information, recording the updated driver information, and updating the new driver in a timing manner; if not, the illegal driver notifies the user end wirelessly.
4. The excavator safety control method based on facial recognition of claim 1, wherein the driving behavior analysis includes:
identifying the facial information of a driver through the collected video information, and managing and identifying the characteristics of eyes, mouths and faces through dotted lines so as to judge whether a human body is in illegal operation; and controlling the state of the excavator according to the judgment result.
5. The face recognition-based excavator security control method of claim 4, wherein the illegal operation includes fatigue driving; the fatigue driving recognition method comprises the following steps:
comparing the driving time of the driver with preset time; if the preset time is exceeded, the functions of the excavator are controlled by the main controller to limit; and if the time is shorter than the preset time, controlling the excavator to normally operate through the main controller.
6. An excavator safety control system based on facial recognition, the system comprising:
DMS host computer: the system is used for identifying the items collected by the camera and transmitting the identified monitoring information and video to the main controller;
a main controller: the system is used for receiving identity recognition monitoring information, videos and abnormal behavior alarm information sent by the DMS host computer so as to control the excavator not to be operated abnormally;
cloud platform: and the GPS terminal is used for receiving and storing data and videos transmitted by the GPS terminal, simultaneously carrying out work statistics and driving behavior analysis on drivers with different identities, and feeding back the work statistics and driving behavior analysis to the client terminal.
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| CN202011292961.6A CN112609765A (en) | 2020-11-18 | 2020-11-18 | Excavator safety control method and system based on facial recognition |
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| CN202011292961.6A CN112609765A (en) | 2020-11-18 | 2020-11-18 | Excavator safety control method and system based on facial recognition |
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| CN115262687A (en) * | 2022-08-17 | 2022-11-01 | 山重建机有限公司 | Excavator monitor with face recognition login function and use method |
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