CN116188926A - Personnel behavior track management method based on digital twinning and AIoT technology - Google Patents

Personnel behavior track management method based on digital twinning and AIoT technology Download PDF

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CN116188926A
CN116188926A CN202211678369.9A CN202211678369A CN116188926A CN 116188926 A CN116188926 A CN 116188926A CN 202211678369 A CN202211678369 A CN 202211678369A CN 116188926 A CN116188926 A CN 116188926A
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李才磊
王伟
吴梅
罗俊
胡东平
王腾飞
高磊
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Zhongtong Hexin Technology Co ltd
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Abstract

The invention discloses a personnel behavior track management method based on digital twinning and AIoT technology, which belongs to the technical field of campus monitoring management, and specifically comprises the following steps: step one: through the multi-rail fusion of the relation graph, the behavior track information of the personnel is collected in all directions; step two: performing personnel track analysis, generating a 3D visual model, and supplementing monitoring equipment; step three: acquiring a target personnel list, and tracking target personnel; step four: performing personnel channel control; step five: configuring an alarm subsystem; the technology of the Internet of things, the technology of digital twinning, the technology of AI and the technology of 3D visualization are fully fused, the real-scene distribution and track tracking of the staff such as teachers, students, teaching staff, external staff and the like in the park are reproduced in the three-dimensional scene by taking the browser as a carrier, and the operations such as data viewing, service management, instruction issuing and the like are supported according to a plurality of scenes and dimensions such as personnel access, safety early warning, behavior monitoring and the like.

Description

Personnel behavior track management method based on digital twinning and AIoT technology
Technical Field
The invention belongs to the technical field of campus monitoring management, and particularly relates to a personnel behavior track management method based on digital twinning and AIoT technology.
Background
At present, management of staff in a park has the problems of difficult management and control of key staff, difficult accurate tracking and the like; the daily campus has large flow of people and frequent entrance and exit, the manager is easy to relax, the flowers are difficult to effectively manage, and whether high-risk people enter the management area cannot be known; the number of teachers, students and instructors and third party personnel is numerous, and the third party personnel has larger flow and cannot be effectively standardized; when a campus security event occurs, the video monitoring playback is used for verifying that the time is consumed and the trace of suspicious personnel is easy to miss, so that great difficulty exists in restoring the trace of suspicious personnel, and the key time is missed to further hinder the problem positioning; the video coverage degree of the existing campus is not enough as a whole, and particularly, the coverage of some remote and unusual places is less, and the video needs to be supplemented; aiming at the current campus security management, the management closed loop driven by 'people' at present has large manpower investment and low working efficiency, and needs to be converted into a management mode driven by 'data', and the management efficiency and effect are improved through the personnel behavior track management system; accordingly, the present invention provides a method of human behavior trace management based on digital twinning and AIoT technology.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides a personnel behavior track management method based on digital twinning and AIoT technology.
The aim of the invention can be achieved by the following technical scheme:
the personnel behavior track management method based on the digital twin and AIoT technology comprises the following specific steps:
step one: through the multi-rail fusion of the relation graph, the behavior track information of the personnel is collected in all directions;
step two: performing personnel track analysis, generating a 3D visual model, and supplementing monitoring equipment;
step three: acquiring a target personnel list, and tracking target personnel;
step four: performing personnel channel control;
step five: and (5) carrying out alarm subsystem configuration.
Further, the analysis content of the personnel trajectory analysis includes:
depicting a space-time activity track of a person based on the electronic map; for each track point, presenting the acquisition details of the person at the time; screening other people or entities which also appear at the periphery at the moment on each track point; an entity having a high degree of concomitance with the current overall trajectory; at each trace point of the overall trace line, analysis and batch derivation of entities that occur simultaneously.
Furthermore, personnel channel control is performed based on a personnel channel subsystem, and the personnel channel subsystem consists of a personnel channel gate, an access control host and a system workstation.
Further, the personnel channel subsystem further comprises configuration authentication comparison equipment.
Further, the alarm subsystem is based on audible and visual alarm equipment and a detector, and is used for carrying out a service function of uploading alarm information in real time according to alarm rule configuration and alarm defense area configuration.
Further, the supplementing method of the monitoring device comprises the following steps:
identifying non-monitored areas in the 3D visual model, identifying area information of each non-monitored area, determining corresponding areas needing to be monitored and supplemented, and marking the areas as supplementing areas; and supplementing the monitoring equipment to the supplementing area, and adjusting the 3D visual model.
Further, the method for determining the corresponding area needing monitoring supplement comprises the following steps:
and calculating a corresponding supplement value according to the area information corresponding to each non-monitored area, and regarding the non-monitored area with the supplement value larger than the threshold value X1 as an area needing to be monitored and supplemented, otherwise, not carrying out monitoring and supplementing.
Further, the method for calculating the corresponding supplementary value according to the area information corresponding to each non-monitored area comprises the following steps:
and setting corresponding category values according to the area information, identifying the position of the non-monitored area and the surrounding area information, setting corresponding boundary values, and calculating corresponding supplementary values according to the obtained category values and the boundary values.
Further, the method for calculating the corresponding supplementary value according to the obtained category value and the boundary value comprises the following steps:
the class value and the boundary value are respectively marked as ZL and BJ, and corresponding supplementary values are calculated according to the formula BC=b1×ZL+b2×BJ, wherein b1 and b2 are proportionality coefficients, and the value range is 0< b1 less than or equal to 1, and 0< b2 less than or equal to 1.
Compared with the prior art, the invention has the beneficial effects that:
the technology of the Internet of things, the technology of digital twinning, the technology of AI and the technology of 3D visualization are fully fused, the real-scene distribution and track tracking of the staff such as teachers, students, teaching staff, external staff and the like in the park are reproduced in the three-dimensional scene by taking the browser as a carrier, and the operations such as data viewing, service management, instruction issuing and the like are supported according to a plurality of scenes and dimensions such as personnel access, safety early warning, behavior monitoring and the like.
Realizing noninductive holographic collection of information of people coming and going: the acquisition and data recording of people entering and exiting are realized by deploying the equipment for acquiring the information of people without sense in areas with dense people and more mobility, and acquiring the information of faces, student cards, mobile phone MAC, license plates and the like; can in time early warning to high risk personnel and action: and according to the high-risk personnel information base, personnel and risk behaviors entering an area or a place are tracked in real time and early warned in time. The action and activity track of personnel in the campus can be traced back: the trace back is carried out through the activity track, so that the companion analysis of other entities is realized; establishing a personnel relationship map: based on the collected data such as the face, the mobile phone MAC, the vehicle and the like, associating the personnel, the mobile phone and the vehicle under the same time-space relationship to form a peer personnel relationship graph.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a topology of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1 to 2, the personnel behavior track management method based on digital twinning and AIoT technology comprises the following specific steps:
step one: through the multi-rail fusion of the relation graph, the behavior track information of the personnel is collected in all directions;
automatically collecting behavior data and positioning information of students through electronic student certificates worn by the students;
the method comprises the steps of collecting a personnel behavior track through an intelligent perception fusion probe: scanning a face, a license plate and a mobile phone MAC; the system is deployed at a position needing three-in-one collection, such as a gate, a classroom gate, a building entrance, a dormitory entrance and the like;
the personnel behavior track is collected through the mobile phone MAC independent scanning equipment and is deployed at a place where the MAC is required to be collected independently, such as an access gate.
And carrying out personnel identification and analysis to determine personnel information and related position information through the intelligent camera.
The multi-track fusion analysis is a real-time calculation analysis engine for big data, which constructs a real-time message component for real-time data distribution on a data layer by receiving perception data pushed by a data gateway in real time, establishes a cache library for distributed storage of temporary relationship data, carries out processing such as space, time sequence deduplication, clustering calculation, association analysis and the like on the data on a calculation layer by a unified scheduling component, finally generates a relationship calculation result to be respectively stored in a distributed fusion management library and a full-quantity relationship archive library, and provides personnel relationship analysis, vehicle relationship analysis and mobile phone relationship analysis functions for an application system.
Step two: performing personnel track analysis, generating a 3D visual model, and supplementing monitoring equipment; the 3D visualization model is generated based on campuses, existing monitoring conditions and 3D visualization technology;
the personnel trajectory analysis includes the following five aspects:
(1) the space-time activity track of the personnel is drawn based on the electronic map, and the space-time activity track can be dynamically deduced;
(2) specific presentation of the acquisition details of the person at the moment for each track point;
(3) at each locus point, it is possible to screen directly which other people/entities are present around this moment;
(4) an entity having a high degree of concomitance with the current overall trajectory;
(5) analysis (of possible contactors) and batch derivation of entities that occur simultaneously on each trace point of the entire trace line.
The supplementing method of the monitoring equipment comprises the following steps:
identifying non-monitored areas in the 3D visual model, and identifying area information of each non-monitored area, wherein the area information is the actual situation in the area, such as greening, vegetation types, obstacle shielding, lakes and the like; determining a corresponding area to be monitored and supplemented, and marking the area as a supplementing area; and supplementing monitoring equipment to the supplementing area, adjusting the 3D visual model, and perfecting a monitoring system.
The method for determining the corresponding area needing monitoring supplement comprises the following steps:
and calculating a corresponding supplement value according to the area information corresponding to each non-monitored area, and regarding the non-monitored area with the supplement value larger than the threshold value X1 as an area needing to be monitored and supplemented, otherwise, not carrying out monitoring and supplementing.
The method for calculating the corresponding supplementary value according to the area information corresponding to each non-monitored area comprises the following steps:
setting corresponding category values according to the regional information, namely, according to the reason why the regional information analysis is not monitored, such as large-area greening, setting the corresponding category values according to the analyzed reason, specifically, according to the possible occurrence condition in a campus, establishing a corresponding training set through manual simulation, establishing a corresponding information analysis model based on a CNN (computer numerical network) or a DNN (computer numerical network), training through the established training set, and analyzing through the information analysis model after the training success to obtain the corresponding category values, wherein the neural network is the prior art in the field, so that the specific establishment and training process is not described in detail in the invention; identifying the position of an unmonitored area and the surrounding area information, setting corresponding boundary values, namely judging whether personnel are likely to avoid monitoring through the unmonitored area according to the position of the unmonitored area and the surrounding area, turning out a campus, stealing into a building and the like, setting corresponding boundary values according to the corresponding possibility and importance, specifically establishing a corresponding boundary analysis model based on a CNN (computer network) or a DNN (computer network), establishing a corresponding training set in a manual mode for training, and analyzing by the boundary analysis model after successful training to obtain the corresponding boundary values; and calculating a corresponding supplementary value according to the obtained category value and the boundary value.
The method for calculating the corresponding supplementary value according to the obtained category value and the boundary value comprises the following steps:
the class value and the boundary value are respectively marked as ZL and BJ, and corresponding supplementary values are calculated according to the formula BC=b1×ZL+b2×BJ, wherein b1 and b2 are proportionality coefficients, and the value range is 0< b1 less than or equal to 1, and 0< b2 less than or equal to 1.
Step three: acquiring a target personnel list, namely key and suspicious (concerned) personnel, and tracking the target personnel;
the real-time tracking and early warning of suspicious personnel is to dynamically sense the important attention personnel in real time from the attention personnel in a designated area.
The multi-dimensional tracking is carried out on 3 features such as a campus card, a face picture, an MAC (media access control) and a license plate number of a person focused on, so that dynamic real-time sensing and early warning of the person focused on are realized. And the method provides timely information clue support for the treatment of hidden troubles such as the overload of the concerned person in the management area, the analysis of the contact object of the important person, and the like.
Information from personnel in the appointed area is mastered at the first time through real-time identification and early warning of MAC and vehicles from the appointed area, and information clues are provided for accurate and effective implementation prevention and control.
Step four: performing personnel channel control;
the personnel channel subsystem consists of personnel channel gate, access control host and system workstation, and can be configured with authentication comparison equipment for scenes with strict security requirements. According to the access channel management needs, a network type access control host is selected, the communication mode with an upper management layer is carried out through a TCP/IP communication mode, the online or offline independent operation is supported, the nearby video monitoring equipment can be linked to carry out snapshot storage, and the access control host is accessed to a comprehensive management platform to realize unified management of equipment resources, personnel rights and configuration.
Step five: and (5) carrying out alarm subsystem configuration.
The alarm subsystem is based on audible and visual alarm equipment and a detector, realizes service functions such as real-time uploading of alarm information according to alarm rule configuration and alarm defense area configuration, continues traditional alarm service functions, and increases more alarm customization demands (including subsystem, alarm elimination and keyboard operation host log reporting) on the basis of the traditional alarm service functions. The system combines B/S architecture configuration and C/S architecture control, and manages the alarm host device and uploads alarm information through an intrusion alarm device access server (IASDAG).
The system takes the collected operation and maintenance data as basic data, establishes an alarm monitoring mechanism around five major lines of alarm triggering, alarm grading, alarm displaying, alarm processing and alarm recovering, informs a system manager of the first time of an alarm event, and supports the exporting of an alarm list in an EXCEL mode.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.

Claims (9)

1. The personnel behavior track management method based on the digital twin and AIoT technology is characterized by comprising the following specific steps:
step one: through the multi-rail fusion of the relation graph, the behavior track information of the personnel is collected in all directions;
step two: performing personnel track analysis, generating a 3D visual model, and supplementing monitoring equipment;
step three: acquiring a target personnel list, and tracking target personnel;
step four: performing personnel channel control;
step five: and (5) carrying out alarm subsystem configuration.
2. The method for managing a human behavior trace based on digital twinning and AIoT technology according to claim 1, wherein the analysis contents of the human trace analysis include:
depicting a space-time activity track of a person based on the electronic map; for each track point, presenting the acquisition details of the person at the time; screening other people or entities which also appear at the periphery at the moment on each track point; an entity having a high degree of concomitance with the current overall trajectory; at each trace point of the overall trace line, analysis and batch derivation of entities that occur simultaneously.
3. The method for managing the human behavior track based on the digital twin and AIoT technology according to claim 1, wherein the human channel management is based on a human channel subsystem, and the human channel subsystem is composed of a human channel gate, an access control host and a system workstation.
4. The digital twinning and AIoT technology based personnel action trajectory management method of claim 2, wherein the personnel channel subsystem further comprises a configuration authentication comparison device.
5. The personnel behavior track management method based on the digital twin and AIoT technology according to claim 1, wherein the alarm subsystem is based on audible and visual alarm equipment and a detector, and performs a service function of uploading alarm information in real time according to alarm rule configuration and alarm defense area configuration.
6. The method for managing human behavior trace based on digital twinning and AIoT technology according to claim 1, wherein the complementary method for monitoring the device comprises:
identifying non-monitored areas in the 3D visual model, identifying area information of each non-monitored area, determining corresponding areas needing to be monitored and supplemented, and marking the areas as supplementing areas; and supplementing the monitoring equipment to the supplementing area, and adjusting the 3D visual model.
7. The method for managing human behavior trace based on digital twinning and AIoT technology according to claim 6, wherein the method for determining the corresponding area to be monitored and supplemented comprises:
and calculating a corresponding supplement value according to the area information corresponding to each non-monitored area, and regarding the non-monitored area with the supplement value larger than the threshold value X1 as an area needing to be monitored and supplemented, otherwise, not carrying out monitoring and supplementing.
8. The method for managing a human behavior trace based on digital twinning and AIoT technology according to claim 6, wherein the method for calculating a corresponding supplementary value according to the region information corresponding to each unmonitored region comprises:
and setting corresponding category values according to the area information, identifying the position of the non-monitored area and the surrounding area information, setting corresponding boundary values, and calculating corresponding supplementary values according to the obtained category values and the boundary values.
9. The method for managing a human behavior trace based on digital twinning and AIoT technology according to claim 8, wherein the method for calculating the corresponding supplementary value according to the obtained category value and the boundary value comprises:
the class value and the boundary value are respectively marked as ZL and BJ, and corresponding supplementary values are calculated according to the formula BC=b1×ZL+b2×BJ, wherein b1 and b2 are proportionality coefficients, and the value range is 0< b1 less than or equal to 1, and 0< b2 less than or equal to 1.
CN202211678369.9A 2022-12-26 2022-12-26 Personnel behavior track management method based on digital twinning and AIoT technology Pending CN116188926A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116823892A (en) * 2023-08-31 2023-09-29 戈尔电梯（天津）有限公司 Identity determination method, device, equipment and medium based on building management and control

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116823892A (en) * 2023-08-31 2023-09-29 戈尔电梯（天津）有限公司 Identity determination method, device, equipment and medium based on building management and control
CN116823892B (en) * 2023-08-31 2023-11-17 戈尔电梯（天津）有限公司 Identity determination method, device, equipment and medium based on building management and control

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