CN118038589A - Face recognition system for transformer substation personnel - Google Patents
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Abstract
The invention discloses a face recognition system for transformer substation personnel, which comprises a face recognition module and a video recording component, wherein the imaging range is the same; the behavior analysis module is used for analyzing the path of the personnel travelling in the transformer substation; a database module for storing temporary and permanent information; a communication and warning module for notifying personnel entering the substation; the whole device monitors the moving path of each person entering the transformer substation by means of the face recognition module, does not act if the person advances according to a preset path, alerts the person if the person does not advance according to the preset path, has high safety coefficient, and can be widely applied to the system for entering and exiting the transformer substation personnel.
Description
Technical Field
The invention relates to the technical field of face recognition, in particular to a face recognition system for transformer substation personnel.
Background
In the operation management of modern substations, it is crucial to ensure the safety and the operating efficiency of the sites. Conventional security management typically relies on a fixed monitoring system, manual authentication and a key or password access system. These methods have limitations in handling temporary access personnel, such as difficulty in quickly verifying identity, controlling access rights, and monitoring the behavior trace of the visitor in real time. Along with the development of technology, the face recognition technology is widely applied to a plurality of fields as an efficient and non-contact identity verification mode to improve safety and convenience.
Although face recognition technology has matured quite well, there are still some special needs and challenges in particular applications in key infrastructure such as substations. For example, a substation may need special access management to temporarily entered individuals or groups to ensure that these guests can only access pre-authorized areas for a limited period of time and can move along a predetermined route. Conventional access control and monitoring systems are unable to meet such flexibility and real-time response requirements.
Most of the current face recognition systems are designed for normal access management, such as daily attendance and identity authentication of staff, rather than dynamic access control for temporary visitors or groups in complex scenarios. Therefore, there is a need to develop a face recognition system capable of realizing highly customized management, which can recognize not only the identity of temporary visitors but also set specific access rights for them and guide them to move along a predetermined route.
In addition, how to provide feedback and guide the correction of the visitor in time when the visitor deviates from the preset route, not just simply prohibiting access or triggering an alarm, is also a problem to be solved in the prior art. This not only can improve the security management level of the substation, but also can increase the flexibility and efficiency of operation.
In summary, the main problems faced by the face recognition and access control systems of the current transformer substation include how to implement personalized authority setting for temporary visitors, real-time dynamic monitoring, and intelligent intervention and correction when the visitors deviate from a preset route. Techniques to address these issues would greatly improve the security and management efficiency of the substation.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a face recognition system for transformer substation personnel for better and effectively solving the problems.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
The face recognition system for the transformer substation personnel comprises,
The face recognition module and the video recording component have the same imaging range;
the behavior analysis module is used for analyzing the path of the personnel travelling in the transformer substation;
A database module for storing temporary and permanent information;
a communication and warning module for notifying personnel entering the substation;
The processor is used for processing information of the face recognition module, the video recording component, the behavior analysis module, the database module and the communication and warning module;
The method comprises the following specific steps:
Step A, a face recognition module is arranged in different areas of a transformer substation, the face recognition module is numbered, and a transformer substation access control corresponds to the corresponding number;
B, selecting a buffer area in a frame mode, and recording the buffer area into a behavior analysis module;
Step C, face information of staff or temporary staff is input: staff inputs face information by means of a face recognition module and stores the face information in a permanent information area of the database module, and temporary staff inputs face information by means of the face recognition module and stores the face information in a temporary information area of the database module;
step D, the processor corresponds each face information with the face recognition module with the corresponding number and forms a secret key, the staff secret key is stored in a permanent information area of the database module, and the temporary staff information is stored in a temporary information area;
step E, staff or temporary staff walk in the transformer substation by means of the secret key, when walking according to the specified route, corresponding access control can be opened, and when walking not according to the specified route and entering the buffer area, a behavior analysis module is started to analyze the behavior of the staff or temporary staff, and whether the staff or temporary staff has a problem of traveling not according to the specified route is judged;
Step F, when judging that the staff or the temporary staff does not travel according to the prescribed route, starting a communication and warning module to warn the staff or the temporary staff, storing warning information into a database module through a processor, and when judging that the staff or the temporary staff is only mistaken and correcting in time, not prompting;
And G, deleting information in the database module after the temporary personnel go out of the transformer substation, wherein the information comprises face information and a secret key, and still retaining the warning information of the temporary personnel.
Preferably, the step of selecting the buffer area in the step B specifically includes:
Step B1, drawing a specified path: drawing a correct path according to the actual condition of the transformer substation, and matching the correct path with a face recognition module;
Step B2, risk area identification: setting a risk area according to the requirement, and adding the risk area to a correct path;
Step B3, designing a buffer area: the overlapping area of the adjacent face recognition module detection and video recording components on the correct path takes the center as a round dot, takes a circle with the radius of 2-4 meters as a buffer area, and the buffer area is close to the risk area but is not overlapped with the risk area.
Preferably, the behavior analysis module in the step E analyzes the behavior as follows,
E1, building a behavior model: learning and establishing a normal behavior mode by using a machine learning algorithm, and establishing an identification model for abnormal behaviors;
E2, judging the direction and the distance: judging the trend of staff or temporary staff in the buffer area, and setting an early warning threshold distance;
and E3, starting a communication and warning module when the recognition model detects abnormal behaviors or staff or temporary staff reaches a threshold distance of a buffer zone.
Preferably, the specific step of determining the direction and the distance in the step E2 includes,
E2a, determining the circle center of the buffer area: taking the geometric center of each buffer area as a circle center, and establishing a circle center coordinate;
E2b, a video stream captured by a video recording component identifies the position of a pedestrian through a target detection algorithm and calculates the coordinates of the pedestrian in each frame;
e2c, calculating the distance between the position of each detected employee or temporary person and the circle center of the buffer area, wherein the specific calculation formula is as follows:
；
Wherein D is distance, (-) ，/>) Is the coordinates of the pedestrian, (/ >，/>) Is the coordinates of the center of the buffer area;
Step E2d, distance change analysis: continuously calculating the position change of staff or temporary staff, analyzing the change condition of the distance from the center of a circle along with time, recording the position of the staff or temporary staff at each time point, and calculating the change rate of the distance from the staff or temporary staff to the center of the circle;
E2E, judging behaviors:
1) Judging the behavior mode of staff or temporary personnel according to the change of the distance, continuously reducing the distance to move the staff or temporary personnel to the center of the buffer area, and continuously increasing the distance to move the staff or temporary personnel away from the center of the buffer area;
2) Setting a threshold value, and judging whether the distance change of staff or temporary staff exceeds the threshold value.
Preferably, the step F communication and warning module comprises the following specific steps,
Step F1, triggering a warning signal: the behavior analysis module sends a signal to the warning module to request to send a warning, and the warning module determines the type of warning according to the signal sent by the behavior analysis module, including but not limited to visual, audio or text types;
step F2, behavior tracking and confirmation: after issuing the alert, the behavior analysis module still tracks staff or temporary personnel marked as abnormal to confirm whether they corrected the direction of travel;
Step F3, warning of revocation or escalation: if the employee or temporary personnel correct the direction of travel in time back to the prescribed path, the system will deactivate the alert state and stop all alert signals, and if the employee or temporary personnel fail to correct the direction, the alert module upgrades the alert level, including but not limited to notifying security personnel to intervene.
Preferably, the communication and warning module comprises emergency light, an audio signal and an automatic short message sending or application pusher.
Preferably, the database module includes a local memory and a cloud memory.
The beneficial effects of the invention are as follows:
1. the security is improved, and by using an advanced face recognition technology, the system can accurately identify personnel entering the transformer substation, and effectively prevent unauthorized access, so that the security level of the transformer substation is remarkably improved.
2. Dynamic monitoring and real-time alerting: the integrated behavior analysis module can monitor the behaviors of personnel in real time, and for behaviors deviating from a predefined path or entering a dangerous area, the system can immediately give out a warning and take measures, so that accidents are effectively prevented.
3. Intelligent data management: the design of the database module not only considers the instant storage and deletion of temporary information, but also keeps the long-term storage of permanent information and warning records, thus protecting personal privacy and facilitating the security audit and subsequent analysis.
4. The manual intervention is reduced: the automated recognition and warning mechanism reduces reliance on manual safety monitoring, reduces labor costs, and reduces safety risks that may be caused by human error.
Drawings
FIG. 1 is an overall flow chart of a face recognition system for substation personnel of the present invention;
FIG. 2 is a flow chart of the face information entry system of the present invention;
FIG. 3 is a flow chart of the key forming system of the present invention.
Detailed Description
The invention will be further described with reference to the drawings.
As shown in fig. 1-3, the face recognition system for transformer substation personnel of the present invention includes,
The face recognition module and the video recording component have the same imaging range;
the behavior analysis module is used for analyzing the path of the personnel travelling in the transformer substation;
A database module for storing temporary and permanent information;
a communication and warning module for notifying personnel entering the substation;
The processor is used for processing information of the face recognition module, the video recording component, the behavior analysis module, the database module and the communication and warning module;
The method comprises the following specific steps:
Step A, a face recognition module is arranged in different areas of a transformer substation, the face recognition module is numbered, and a transformer substation access control corresponds to the corresponding number;
B, selecting a buffer area in a frame mode, and recording the buffer area into a behavior analysis module;
Step C, face information of staff or temporary staff is input: staff inputs face information by means of a face recognition module and stores the face information in a permanent information area of the database module, and temporary staff inputs face information by means of the face recognition module and stores the face information in a temporary information area of the database module;
step D, the processor corresponds each face information with the face recognition module with the corresponding number and forms a secret key, the staff secret key is stored in a permanent information area of the database module, and the temporary staff information is stored in a temporary information area;
step E, staff or temporary staff walk in the transformer substation by means of the secret key, when walking according to the specified route, corresponding access control can be opened, and when walking not according to the specified route and entering the buffer area, a behavior analysis module is started to analyze the behavior of the staff or temporary staff, and whether the staff or temporary staff has a problem of traveling not according to the specified route is judged;
Step F, when judging that the staff or the temporary staff does not travel according to the prescribed route, starting a communication and warning module to warn the staff or the temporary staff, storing warning information into a database module through a processor, and when judging that the staff or the temporary staff is only mistaken and correcting in time, not prompting;
And G, deleting information in the database module after the temporary personnel go out of the transformer substation, wherein the information comprises face information and a secret key, and still retaining the warning information of the temporary personnel.
Specifically, the step of selecting the buffer area in the step B specifically includes:
Step B1, drawing a specified path: drawing a correct path according to the actual condition of the transformer substation, and matching the correct path with a face recognition module;
Step B2, risk area identification: setting a risk area according to the requirement, and adding the risk area to a correct path;
Step B3, designing a buffer area: the overlapping area of the adjacent face recognition module detection and video recording components on the correct path takes the center as a round dot, takes a circle with the radius of 2-4 meters as a buffer area, and the buffer area is close to the risk area but is not overlapped with the risk area.
Specifically, the behavior analysis module in the step E analyzes the steps as follows,
E1, building a behavior model: learning and establishing a normal behavior mode by using a machine learning algorithm, and establishing an identification model for abnormal behaviors;
E2, judging the direction and the distance: judging the trend of staff or temporary staff in the buffer area, and setting an early warning threshold distance;
and E3, starting a communication and warning module when the recognition model detects abnormal behaviors or staff or temporary staff reaches a threshold distance of a buffer zone.
Specifically, the specific step of determining the direction and the distance in the step E2 includes,
E2a, determining the circle center of the buffer area: taking the geometric center of each buffer area as a circle center, and establishing a circle center coordinate;
E2b, a video stream captured by a video recording component identifies the position of a pedestrian through a target detection algorithm and calculates the coordinates of the pedestrian in each frame;
e2c, calculating the distance between the position of each detected employee or temporary person and the circle center of the buffer area, wherein the specific calculation formula is as follows:
；
Wherein D is distance, (-) ，/>) Is the coordinates of the pedestrian, (/ >，/>) Is the coordinates of the center of the buffer area;
Step E2d, distance change analysis: continuously calculating the position change of staff or temporary staff, analyzing the change condition of the distance from the center of a circle along with time, recording the position of the staff or temporary staff at each time point, and calculating the change rate of the distance from the staff or temporary staff to the center of the circle;
E2E, judging behaviors:
1) Judging the behavior mode of staff or temporary personnel according to the change of the distance, continuously reducing the distance to move the staff or temporary personnel to the center of the buffer area, and continuously increasing the distance to move the staff or temporary personnel away from the center of the buffer area;
2) Setting a threshold value, and judging whether the distance change of staff or temporary staff exceeds the threshold value.
In particular, the communication and warning module in the step F comprises the following specific steps of,
Step F1, triggering a warning signal: the behavior analysis module sends a signal to the warning module to request to send a warning, and the warning module determines the type of warning according to the signal sent by the behavior analysis module, including but not limited to visual, audio or text types;
step F2, behavior tracking and confirmation: after issuing the alert, the behavior analysis module still tracks staff or temporary personnel marked as abnormal to confirm whether they corrected the direction of travel;
Step F3, warning of revocation or escalation: if the employee or temporary personnel correct the direction of travel in time back to the prescribed path, the system will deactivate the alert state and stop all alert signals, and if the employee or temporary personnel fail to correct the direction, the alert module upgrades the alert level, including but not limited to notifying security personnel to intervene.
Specifically, the communication and warning module comprises emergency light, an audio signal and an automatic short message sending or application pusher.
Specifically, the database module comprises a local memory and a cloud memory.
In order to better illustrate the beneficial effects of the present invention, four specific embodiments of the present invention are described below:
Embodiment 1, employee registration and identification procedure:
staff initial registration: when all staff enter the transformer substation for the first time, face information is input through the face recognition module, and the information is stored in a permanent information area of the database module after being encrypted.
And (3) key generation: the system generates a unique key for each employee and associates with their face information.
Daily identification and entrance guard: when staff enter the transformer substation every day, the face recognition module can recognize the staff and check the secret key, and if the face recognition module is matched with the secret key, the access control system is unlocked, and the staff is allowed to enter.
Behavioral analysis: if the employee enters an unauthorized area, the behavioral analysis module will alert the employee and record the event.
Embodiment 2, temporary personnel access control procedure:
Temporary person registration: before entering the transformer substation, temporary personnel enter face information through a face recognition module, and the information is stored in a temporary information area of a database module.
Temporary key: the system generates a temporary key and associates the temporary key with face information of the temporary person.
Access rights: temporary personnel can act on a prescribed route within the substation using the temporary key. If the route is deviated, the behavior analysis module will alert and record.
And (3) deleting information: after the temporary personnel leave the transformer substation, the system automatically deletes the face information and the secret key of the temporary personnel, but keeps a warning record.
Embodiment 3, error buffer entry process flow:
False entry detection: when an employee or temporary person inadvertently enters the buffer area, the behavior analysis module will detect and issue a primary warning signal.
Behavior confirmation: if the person exits the buffer area immediately after receiving the alert, the system will not record the event or escalate the alert further.
Alert revocation: once the person is confirmed to have returned to the prescribed route, the system will cancel the warning.
Embodiment 4, force-entry buffer response procedure:
abnormal behavior detection: if a person is forced into or remains in the buffer area, the system will detect and calculate the distance through the behavior analysis module.
Emergency alert: when the pre-warning threshold distance is exceeded, the system will initiate an emergency alert, including an audio signal, emergency lights, and automatically send a short message or application push to security personnel.
Alert level escalation: if the person does not respond to the primary alert, the system will escalate the alert and notify the security personnel to intervene.
Event recording and follow-up: all alert events and personnel responses are recorded in the database module for future review and analysis.
The foregoing has outlined and described the basic principles, features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. Substation personnel is with face identification system, its characterized in that: comprising the steps of (a) a step of,
The face recognition module and the video recording component have the same imaging range;
the behavior analysis module is used for analyzing the path of the personnel travelling in the transformer substation;
A database module for storing temporary and permanent information;
a communication and warning module for notifying personnel entering the substation;
The processor is used for processing information of the face recognition module, the video recording component, the behavior analysis module, the database module and the communication and warning module;
The method comprises the following specific steps:
setting face recognition modules in different areas of a transformer substation, numbering the face recognition modules, and corresponding the transformer substation access control with corresponding numbers;
step (B), selecting a buffer area in a frame, and inputting the buffer area into a behavior analysis module;
Step (C), face information of staff or temporary personnel is input: staff inputs face information by means of a face recognition module and stores the face information in a permanent information area of the database module, and temporary staff inputs face information by means of the face recognition module and stores the face information in a temporary information area of the database module;
Step (D), the processor corresponds each face information with the face recognition module with the corresponding number and forms a secret key, the staff secret key is stored in a permanent information area of the database module, and the temporary staff information is stored in a temporary information area;
Step (E), staff or temporary staff walk in the transformer substation by means of the secret key, when walking according to the specified route, corresponding access control can be opened, and when walking not according to the specified route and entering the buffer area, a behavior analysis module is started to analyze the behavior of the staff or temporary staff, and whether the staff or temporary staff has a problem of traveling not according to the specified route is judged;
Step (F), when judging that the staff or the temporary staff does not travel according to the prescribed route, starting a communication and warning module to warn the staff or the temporary staff, storing warning information into a database module through a processor, and when judging that the staff or the temporary staff is only mistaken and correcting in time, not prompting;
And (G) deleting information in the database module after the temporary personnel go out of the transformer substation, wherein the information comprises face information and a secret key, and still retaining the warning information of the temporary personnel.
2. The face recognition system for transformer substation personnel according to claim 1, wherein: the block selection specific steps of the buffer area in the step (B) are as follows:
Step (B1), drawing a prescribed path: drawing a correct path according to the actual condition of the transformer substation, and matching the correct path with a face recognition module;
Step (B2), risk area identification: setting a risk area according to the requirement, and adding the risk area to a correct path;
Step (B3), buffer area design: the overlapping area of the adjacent face recognition module detection and video recording components on the correct path takes the center as a round dot, takes a circle with the radius of 2-4 meters as a buffer area, and the buffer area is close to the risk area but is not overlapped with the risk area.
3. The face recognition system for transformer substation personnel according to claim 2, wherein: the behavior analysis module in the step (E) analyzes the steps specifically as follows,
Step (E1), behavior model establishment: learning and establishing a normal behavior mode by using a machine learning algorithm, and establishing an identification model for abnormal behaviors;
Step (E2), direction and distance judgment: judging the trend of staff or temporary staff in the buffer area, and setting an early warning threshold distance;
And (E3) starting a communication and warning module when the recognition model detects abnormal behaviors or staff or temporary personnel reach a threshold distance of a buffer zone.
4. A face recognition system for transformer substation personnel according to claim 3, wherein: the specific step of judging the direction and the distance in the step (E2) comprises the following steps,
Step (E2 a), determining the center of a buffer area: taking the geometric center of each buffer area as a circle center, and establishing a circle center coordinate;
E2b, the video stream captured by the video recording component recognizes the position of the pedestrian through a target detection algorithm and calculates the coordinates of the pedestrian in each frame;
Step (E2 c), for each detected employee or temporary person, calculating the distance between the position of the employee or temporary person and the center of the buffer area, wherein the specific calculation formula is as follows:
；
Wherein D is distance, (-) ，/>) Is the coordinates of the pedestrian, (/ >，/>) Is the coordinates of the center of the buffer area;
Step (E2 d), distance change analysis: continuously calculating the position change of staff or temporary staff, analyzing the change condition of the distance from the center of a circle along with time, recording the position of the staff or temporary staff at each time point, and calculating the change rate of the distance from the staff or temporary staff to the center of the circle;
step (E2E), behavior judgment:
1) Judging the behavior mode of staff or temporary personnel according to the change of the distance, continuously reducing the distance to move the staff or temporary personnel to the center of the buffer area, and continuously increasing the distance to move the staff or temporary personnel away from the center of the buffer area;
2) Setting a threshold value, and judging whether the distance change of staff or temporary staff exceeds the threshold value.
5. The face recognition system for transformer substation personnel according to claim 1, wherein: the step (F) of the communication and warning module comprises the following specific steps of,
Step (F1), the warning signal triggers: the behavior analysis module sends a signal to the warning module to request to send a warning, and the warning module determines the type of warning according to the signal sent by the behavior analysis module, including but not limited to visual, audio or text types;
step (F2), behavior tracking and confirmation: after issuing the alert, the behavior analysis module still tracks staff or temporary personnel marked as abnormal to confirm whether they corrected the direction of travel;
Step (F3), warning of withdrawal or escalation: if the employee or temporary personnel correct the direction of travel in time back to the prescribed path, the system will deactivate the alert state and stop all alert signals, and if the employee or temporary personnel fail to correct the direction, the alert module upgrades the alert level, including but not limited to notifying security personnel to intervene.
6. The face recognition system for transformer substation personnel according to claim 5, wherein: the communication and warning module comprises emergency light, an audio signal and an automatic short message sending or application pusher.
7. The face recognition system for transformer substation personnel according to claim 1, wherein: the database module comprises a local memory and a cloud memory.
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| CN114360136A (en) * | 2022-01-14 | 2022-04-15 | 云南电网有限责任公司电力科学研究院 | Intelligent safety access control management method and system for power grid operation |
| CN114708679A (en) * | 2022-02-15 | 2022-07-05 | 国网浙江省电力有限公司开化县供电公司 | Unattended management method for transformer substation |
| CN116778621A (en) * | 2023-06-16 | 2023-09-19 | 三峡金沙江云川水电开发有限公司 | Access control management method and device for large-scale power production |
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