Campus conflict detection system

Abstract

The invention discloses a campus conflict detection system, and relates to the technical field of mode recognition. A campus conflict detection system comprises an intelligent watch, a camera is fixedly installed at the position, close to the top, of the left side of the front face of the intelligent watch, a microphone is arranged on the intelligent watch, watchband fixing frames are fixedly installed at the top and the bottom of the intelligent watch, and a control button is arranged on the right side of the intelligent watch. A central processing unit, a data comparison module, a data reading module, a database, a short video storage module, a monitoring module, a horizon sensor, a short message module, a positioning module, a voice recognition module and an action recognition module are arranged in the intelligent watch.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pattern recognition, in particular to a campus conflict detection system. BACKGROUND

[0002] Campus conflict is a global social problem that has a negative impact on students' physical and mental health and learning life. With the development of artificial intelligence technology, AI-based campus conflict detection systems have emerged to prevent, detect and intervene in campus conflict behavior. These systems usually integrate big data analysis, artificial intelligence recognition, Internet of Things devices and social media monitoring technologies to monitor behavior patterns in real time to detect and intervene in possible conflict behavior. However, the existing campus conflict detection system cannot save the conflict video alone when in use, which makes the conflict video easy to be overwritten, affecting the subsequent work efficiency. CONTENT OF THE UTILITY MODEL

[0003] The present application provides a campus conflict detection system to solve the problems in the background art.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a campus conflict detection system, comprising a smart watch, a camera is fixedly installed on the front left side of the smart watch near the top, a microphone is arranged on the smart watch, watchband fixing frames are fixedly installed on the top and the bottom of the smart watch, a control button is arranged on the right side of the smart watch, a central processing unit, a data comparison module, a data reading module, a database, a short video saving module, a monitoring module, a horizontal sensor, a short message module, a positioning module, a voice recognition module and a motion recognition module are arranged in the smart watch, and a display is arranged on the front of the smart watch.

[0005] Further, the watchband fixing frames are provided with watchbands.

[0006] Further, the data comparison module can identify conflict behavior by comparing the behavior data of students or the environment with preset conflict behavior patterns; these preset conflict behavior patterns may include specific actions, postures, language features, and through the analysis of the comparison module, the system can determine whether the current behavior matches these patterns; the data comparison module continuously optimizes algorithms and models to improve the accuracy of identifying conflict behavior; the system can continuously learn and adapt to different environments and situations, thereby more accurately identifying conflict behavior.

[0007] Furthermore, the action recognition module analyzes students' movements and postures in real time through video streams captured by cameras, identifying action patterns such as collisions, physical contact, physical confrontation, and provocative gestures. Utilizing advanced image processing and machine learning technologies, this module can recognize various complex action patterns, including but not limited to collisions resulting in physical contact, physical confrontation, and provocative gestures, which are often closely related to conflict behavior. Through continuous learning and algorithm optimization, the recognition module can gradually improve the accuracy of identifying conflict behavior. It can distinguish between normal student interactions and potential conflict behavior, reducing false alarms and missed alarms.

[0008] Furthermore, the speech recognition module can monitor the sound environment on campus in real time through a microphone, identify preset keywords or phrases, and use intelligent algorithms to identify preset keywords or phrases such as "conflict," "dispute," and "rescue." These keywords are usually related to conflict behavior or emergency requests for help. Once these keywords are identified, they are transmitted to the data comparison module, which determines whether it is a conflict behavior through data comparison. At the same time, the recognition efficiency and accuracy of the speech recognition module are constantly improving, and it can better adapt to the complex and ever-changing campus environment.

[0009] Furthermore, the horizontal sensor can detect changes in students' posture in real time, especially when the posture is abnormal; for example, in a conflict, the victim may be forced to maintain an unnatural posture, such as kneeling or bending over; by sensing these changes in posture, the horizontal sensor can make a preliminary judgment on whether there is potential conflict behavior.

[0010] Furthermore, the display and data reading module facilitate subsequent viewing of saved videos, thereby improving subsequent work efficiency.

[0011] Furthermore, the short video saving module can save conflict evidence videos in a separate storage space to avoid confusion with other daily monitoring videos or accidental deletion; for daily monitoring videos, a cyclic overwrite storage strategy is usually adopted to save storage space; however, for conflict evidence videos, it ensures that these key pieces of evidence will not be overwritten by new videos; so as to ensure that they can be accessed at any time when needed.

[0012] Compared with the prior art, the present invention provides a campus conflict detection system, which has the following features:

[0013] Beneficial effects:

[0014] 1. This campus conflict detection system, through the installation of cameras, monitoring modules, microphones, level sensors, data comparison modules, and short video storage modules, collects data from multiple sources, performs intelligent analysis, and preserves evidence to prevent conflict evidence from being covered up, enabling timely detection, accurate identification, and effective response to campus conflicts. This system not only provides timely assistance and protection to victims but also offers decision support to school management, promoting the continuous improvement of the campus safety environment.

[0015] 2. This campus conflict detection system utilizes smartwatches. Smartwatches, as everyday wearable devices, are highly portable; users can wear them anytime, anywhere without worrying about the added burden of carrying them. Furthermore, their similar appearance to ordinary watches provides good concealment, allowing monitoring to proceed unnoticed, thus protecting user privacy and preventing potential conflicts. Smartwatches typically have real-time communication capabilities; this means the system can collect, process, and transmit data in real time. Upon detecting anomalies or identifying conflict behavior, it can immediately trigger alarms and send notifications to relevant personnel. This immediacy is crucial for timely intervention and prevention of conflict. Attached Figure Description

[0016] Fig. 1 This is a schematic diagram of the overall structure of the present invention;

[0017] Fig. 2 This is a diagram of the internal structure of the smartwatch of the present invention;

[0018] Fig. 3 This is a schematic diagram of the present invention.

[0019] In the diagram: 1. Smartwatch; 2. Microphone; 3. Strap holder; 4. Camera; 5. Display; 6. Central processing unit; 7. Data comparison module; 8. Data reading module; 9. Database; 10. Short video storage module; 11. Monitoring module; 12. Horizontal sensor; 13. SMS module; 14. Positioning module; 15. Voice recognition module; 16. Motion recognition module; 17. Control button. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figs. 1-3This invention discloses a campus conflict detection system, including a smartwatch 1. A camera 4 is fixedly installed on the top left side of the front of the smartwatch 1. A microphone 2 is provided on the smartwatch 1. A watch strap holder 3 is fixedly installed on the top and bottom of the smartwatch 1. A control button 17 is provided on the right side of the smartwatch 1. The smartwatch 1 internally includes a central processing unit 6, a data comparison module 7, a data reading module 8, a database 9, a short video storage module 10, a monitoring module 11, a horizontal sensor 12, an SMS module 13, a positioning module 14, a voice recognition module 15, and a motion recognition module 16. A display 5 is provided on the front of the smartwatch 1.

[0022] Specifically, each of the watch strap holders 3 is equipped with a watch strap.

[0023] Specifically, the data comparison module 7 can identify conflict behaviors by comparing student or environmental behavioral data with preset conflict behavior patterns. These preset conflict behavior patterns may include specific actions, postures, and language features. Through the analysis of the comparison module, the system can determine whether the current behavior matches these patterns. The data comparison module 7 improves the accuracy of identifying conflict behaviors by continuously optimizing algorithms and models. The system can continuously learn and adapt to different environments and situations, thereby identifying conflict behaviors more accurately.

[0024] Specifically, the action recognition module 16 analyzes students' actions and postures in real time through the video stream captured by the camera 4, identifying action patterns such as collisions, physical contact, physical confrontation, and provocative gestures. Utilizing advanced image processing and machine learning technologies, this module can recognize various complex action patterns, including but not limited to collisions resulting in physical contact, physical confrontation, and provocative gestures, which are often closely related to conflict behaviors. Through continuous learning and algorithm optimization, the recognition module can gradually improve the accuracy of identifying conflict behaviors. It can distinguish between normal student interactions and potential conflict behaviors, reducing false alarms and missed alarms.

[0025] Specifically, the voice recognition module 15 can monitor the sound environment on campus in real time through the microphone 2, and identify preset keywords or phrases. It uses intelligent algorithms to identify preset keywords or phrases such as "conflict", "dispute", and "rescue". These keywords are usually related to conflict behavior or emergency help. Once these keywords are identified, they are transmitted to the data comparison module 7, which determines whether it is a conflict behavior through data comparison. At the same time, the recognition efficiency and accuracy of the voice recognition module 15 are constantly improving, and it can better adapt to the complex and ever-changing campus environment.

[0026] Specifically, the horizontal sensor 12 can detect changes in the student's posture in real time, especially when the posture is abnormal; for example, in a conflict, the victim may be forced to maintain an unnatural posture, such as kneeling or bending over; by sensing these changes in posture, the horizontal sensor 12 can make a preliminary judgment on whether there is potential conflict behavior.

[0027] Specifically, the display 5 and the data reading module 8 facilitate subsequent viewing of saved videos, thereby improving subsequent work efficiency.

[0028] Specifically, the short video storage module 10 can store conflict evidence videos in a separate storage space to avoid confusion with other daily monitoring videos or accidental deletion; for daily monitoring videos, a cyclic overwrite storage strategy is usually adopted to save storage space; however, for conflict evidence videos, it ensures that these key pieces of evidence will not be overwritten by new videos; so as to ensure that they can be accessed at any time when needed.

[0029] When in use, the smartwatch 1 is turned on, and the microphone 2 receives voice signals from the surrounding environment. The camera 4 and monitoring module 11 identify the surrounding environment and record events. The horizontal sensor 12 monitors the watch's movement. Based on the footage captured by the camera 4, it identifies whether there is any conflict on campus. Simultaneously, the voice recognition module 15 captures sound data and analyzes whether there is any conflict-related speech. The captured data is compared with pre-stored conflict behavior characteristics in the database 9 by the data comparison module 7. Data is read from the sensors and modules to provide raw data for analysis to the central processing unit 6. If the collected information differs from the data in the database 9, it is considered a non-conflict behavior; otherwise, it is considered a non-conflict behavior. Disagreements are identified as conflict behaviors, and the short video saving module 10 saves the videos recorded by the smartwatch 1 within a preset time period to the database 9, thereby obtaining short video data. This achieves automatic saving of short video data, preventing the overwriting of recorded videos and avoiding the loss of emergency videos. At the same time, the central processing unit 6 drives the SMS module 13 and the positioning module 14 to send alarms and location information. After receiving the message, when the guardian or other school leaders arrive, they can find and play back the newly saved video on the display 5 to observe it, making it easier to find the cause of the conflict. Guardians or school leaders can handle campus conflict incidents more effectively, protect the rights of victims, and take measures to prevent similar incidents from happening again. At the same time, this also helps to improve the school's overall management level of campus safety issues.

[0030] In summary, this campus conflict detection system, through the installation of camera 4, monitoring module 11, microphone 2, horizontal sensor 12, data comparison module 7, and short video storage module 10, collects data from multiple sources, performs intelligent analysis, and preserves evidence to prevent the cover-up of conflict evidence, enabling timely detection, accurate identification, and effective response to campus conflicts. This system not only provides timely assistance and protection for victims but also offers decision-making support to school management, promoting continuous improvement in the campus safety environment. The inclusion of a smartwatch 1, a highly portable everyday device, allows users to wear it anytime, anywhere without worrying about additional carrying burdens. Furthermore, its appearance, similar to a regular watch, provides good concealment, enabling monitoring without attracting attention, thus protecting user privacy and preventing potential conflicts. The smartwatch 1 typically has real-time communication capabilities, meaning the system can collect, process, and transmit data in real time. Upon detecting anomalies or identifying conflict behavior, it can immediately trigger alarms and send notifications to relevant personnel. This immediacy is crucial for timely intervention and prevention of conflict.

[0031] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A campus conflict detection system comprising a smart watch (1), characterized in that: The front left side of the smart watch (1) is fixedly installed with a camera (4) near the top, a microphone (2) is arranged on the smart watch (1), and watchband fixing frames (3) are fixedly installed on the top and the bottom of the smart watch (1), a control button (17) is arranged on the right side of the smart watch (1), and a central processing unit (6), a data comparison module (7), a data reading module (8), a database (9), a short video saving module (10), a monitoring module (11), a horizontal sensor (12), a short message module (13), a positioning module (14), a voice recognition module (15) and a motion recognition module (16) are arranged in the smart watch (1), and a display (5) is arranged on the front of the smart watch (1).

2. The campus conflict detection system of claim 1, wherein: The watchband fixing frames (3) are provided with watchbands.

3. The campus conflict detection system of claim 1, wherein: The data comparison module (7) can identify conflict behaviors by comparing the behavior data of students or the environment with preset conflict behavior patterns; These preset conflict behavior patterns may include specific actions, postures, and language characteristics. Through the analysis of the comparison module, the system can determine whether the current behavior matches these patterns; the data comparison module (7) continuously optimizes algorithms and models to improve the accuracy of identifying conflict behaviors; the system can continuously learn and adapt to different environments and situations, thereby more accurately identifying conflict behaviors.

4. The campus conflict detection system of claim 1, wherein: The motion recognition module (16) analyzes students' actions and postures in real time through video streams captured by the camera (4), identifies collision, body contact, body confrontation, and provocative gestures, and uses advanced image processing and machine learning techniques to identify various complex action patterns, including but not limited to collision, body contact, body confrontation, and gestures with provocation, which are often closely related to conflict behaviors; the recognition module can gradually improve the accuracy of identifying conflict behaviors through continuous learning and optimization of algorithms; it can distinguish between normal student interactions and potential conflict behaviors, reducing false positives and false negatives.

5. The campus conflict detection system of claim 1, wherein: The voice recognition module (15) can monitor the sound environment in the campus in real time through the microphone (2), identify preset keywords or phrases, and identify preset keywords or phrases such as "conflict", "dispute", and "rescue" through intelligent algorithms; these keywords are usually related to conflict behaviors or emergency assistance, and once these keywords are identified, they are transmitted to the data comparison module (7) for data comparison to determine whether it is a conflict behavior, and the recognition efficiency and accuracy of the voice recognition module (15) are continuously improved to better adapt to complex and variable campus environments.

6. The campus conflict detection system of claim 1, wherein: The horizontal sensor (12) can detect changes in students' postures in real time, especially when the posture is abnormal; for example, in a conflict event, the victim may be forced to maintain an unnatural posture, such as kneeling or bending; the horizontal sensor (12) can preliminarily determine whether there is a potential conflict behavior by sensing these changes in posture.

7. The campus conflict detection system of claim 1, wherein: The display (5) and the data reading module (8) can facilitate subsequent viewing of the saved video, thereby improving subsequent work efficiency.

8. The campus conflict detection system of claim 1, wherein: The short video saving module (10) can save the conflict evidence video in an independent storage space to avoid confusion with other daily monitoring videos or being mistakenly deleted; for daily monitoring videos, a cyclic coverage storage strategy is usually adopted to save storage space; However, for conflict evidence videos, it is ensured that these key evidences will not be covered by new videos; To ensure that it can be reviewed at any time when needed.

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