AI campus intelligent defense system based on intelligent perception and automatic response early warning

Abstract

The invention relates to the field of campus safety prevention and control, and discloses an AI campus intelligent prevention system based on intelligent perception and automatic response early warning, and the system comprises a data collection and perception module which is used for collecting multi-dimensional data in a campus in real time through a collection device; the event perception and behavior identification module and the multi-modal data fusion module are used for carrying out fusion analysis on data from different perception devices; the AI analysis and event research and judgment module, the event early warning and notification module, the emergency response and disposal module and the resource scheduling and management module are used for reasonable allocation and efficient use of resources; the event recording and data archiving module is used for carrying out detailed recording and archiving on each processed event; and a disaster emergency response and auxiliary system connection module. By constructing a video, audio and environment data three-in-one multi-source sensing system, comprehensive real-time monitoring of abnormal behaviors, sound and environment changes in a campus is realized, and the effects of improving event recognition accuracy and sensing coverage are achieved.

Description

Technical Field

[0001] The present invention relates to the field of campus security prevention and control, and specifically to an AI campus intelligent prevention system based on intelligent perception and automatic response warning. Background Art

[0002] With the continuous advancement of the construction of smart campuses, the campus security prevention and control system has gradually developed towards informatization and intelligence. Traditional campus security measures mostly rely on manual patrols and post-event playback of video surveillance. In the face of emergencies, violent acts, or disaster warnings, there are problems such as lagging response, insufficient coverage, and inaccurate identification.

[0003] In recent years, some systems have begun to introduce intelligent analysis functions for video surveillance, but most of them mainly rely on single video data and lack the comprehensive perception and analysis capabilities of environmental audio and environmental parameters, resulting in the risk of event omission still existing in complex or non-visible visual scenarios (such as enclosed classrooms, stairwells, and nighttime areas). Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides an AI campus intelligent prevention system based on intelligent perception and automatic response warning, which solves the problems in the existing technology that mainly rely on a single video surveillance method and cannot cover all campus areas, especially in non-typical monitoring locations such as stairwells, remote corners, and nighttime dark areas, where there are obvious perception blind spots, resulting in abnormal behaviors not being discovered in a timely manner.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An AI campus intelligent prevention system based on intelligent perception and automatic response warning, comprising: A data collection and perception module, used to collect multi-dimensional data in the campus in real time through collection devices and provide raw data for subsequent analysis; An event perception and behavior recognition module, used to perform real-time analysis on the collected raw data by using AI technology, and identify abnormal behaviors and judge potential security threats according to the analyzed data; A multi-modal data fusion module, used to perform fusion analysis on the analyzed data and provide the ability to identify security events through the fused data; An AI analysis and event research and judgment module, used to perform comprehensive analysis on the fused data, determine the nature of security events, classify them according to the risk level, and generate the level of security events; An event warning and notification module, used to generate real-time warnings according to the determined level of security events and notify relevant personnel to enable timely response to security events; An emergency response and handling module, connected to the AI analysis and event research and judgment module, used to initiate automatic trigger emergency response measures and dispatch security personnel to initiate emergency plans; The resource scheduling and management module is used to automatically allocate and manage security resources on campus according to the determined security event level; Event recording and data archiving module, used to record and archive each processed event in detail; The disaster emergency response and auxiliary system connection module is used to connect with the emergency system to provide local notification and intelligent response when a disaster occurs, and to assist in dealing with the disaster.

[0006] Preferably, the acquisition device includes a camera, an audio pickup, and an environmental sensor; the data acquisition and perception module includes a video perception unit, an audio perception unit, and an environmental perception unit; the video perception unit is used to deploy a camera to collect image and video data; the audio perception unit is used to collect environmental sounds through a pickup; the environmental sounds include screaming and abnormal noise; the environmental perception unit is used to collect temperature, humidity, and smoke environmental data; and the multidimensional data includes video, audio, temperature, humidity, and smoke.

[0007] Preferably, the event perception and behavior recognition module includes a behavior detection unit, a crowd analysis unit and a sound anomaly recognition unit. The behavior detection unit is used to detect abnormal behaviors, including running, fighting and gathering. The crowd analysis unit is used to count the flow of people and analyze the crowd density. The sound anomaly recognition unit is used to identify abnormal audio, including screaming, smashing and cries for help.

[0008] Preferably, the data includes video, audio, and environmental data, and the multimodal data fusion module includes a data synchronization unit, a feature fusion unit, and a data standardization unit. The data synchronization unit is used to align multi-source data in time and space, the feature fusion unit is used to fuse the features of video, audio, and environmental data, and the data standardization unit is used to unify various data formats for subsequent AI analysis.

[0009] Preferably, the AI ​​analysis and event assessment module includes an event classification unit, a risk assessment unit and a decision support unit. The event classification unit is used to preliminarily classify events, including minor events and major events. The risk assessment unit is used to intelligently assess the risk level and urgency of events. The decision support unit is used to provide disposal suggestions for the emergency response module.

[0010] Preferably, the event warning and notification module includes a warning generation unit, a multi-channel notification unit and a feedback confirmation unit. The warning generation unit is used to generate warning information for the corresponding event. The multi-channel notification unit is used to notify relevant personnel through multiple channels such as APP, SMS, and broadcast. The relevant personnel include managers, security personnel, or teachers and students. The feedback confirmation unit is used to collect alarm feedback from security personnel or managers.

[0011] Preferably, the emergency response and handling module includes a response strategy unit, an operation scheduling unit, and an emergency linkage unit. The response strategy unit is used to formulate emergency handling strategies for different events. The operation scheduling unit is used to command and dispatch security and medical emergency personnel. The emergency linkage unit is used to link emergency resources inside and outside the school. The emergency resources include off-campus ambulance and fire protection.

[0012] Preferably, the security resources include security personnel, monitoring equipment, and emergency equipment. The resource scheduling and management module includes a resource list unit, a task assignment unit, and a resource tracking unit. The resource list unit is used to manage the security personnel and the equipment emergency resource library. The task assignment unit is used to assign security tasks or equipment scheduling according to events. The resource tracking unit is used to track the resource status and execution progress in real time.

[0013] Preferably, the recording and archiving are used to trace event data and provide a basis for subsequent analysis and review. The event recording and data archiving module includes an event log unit, an evidence preservation unit, and a data audit unit. The event log unit is used to record the whole process of the event, alarm information, and handling process. The evidence preservation unit is used to save key evidence data such as videos and audios. The data audit unit is used to provide support for post-event audit, analysis, and review.

[0014] Preferably, the emergency system includes a fire protection and earthquake detection system. The disaster emergency response and auxiliary system connection module includes a system docking unit, a local notification unit, and an evacuation guidance unit. The system docking unit is used to dock external systems such as fire protection, earthquake, and meteorology. The local notification unit is used to accurately push local personnel warning information according to the disaster impact range. The evacuation guidance unit is used to provide emergency evacuation instructions and routes to assist the crowd in evacuating.

[0015] The present invention provides an AI campus intelligent security prevention system based on intelligent perception and automatic response warning. It has the following beneficial effects: 1. By constructing a multi-source perception system integrating video, audio, and environmental data, the present invention realizes the comprehensive and real-time monitoring of abnormal behaviors, sounds, and environmental changes on campus, and obtains the effects of improving the accuracy of event recognition and the perception coverage.

[0016] 2. By integrating multi-modal data fusion and AI intelligent analysis technology, the present invention realizes the accurate classification and risk assessment of complex campus security events, and obtains the effects of significantly improving the scientificity of warning judgment and the efficiency of response decision-making.

[0017] 3. The present invention realizes fast and accurate information transmission to relevant groups such as teachers, students, and administrators by establishing a multi-channel early warning notification mechanism and a feedback confirmation closed-loop, achieving the effects of faster early warning response speed and more timely personnel scheduling.

[0018] 4. The present invention realizes regional emergency notification and scientific evacuation in disaster situations by linking external emergency systems such as fire and earthquake and combining with local evacuation guidance functions, achieving the effects of enhancing the ability to respond to sudden disasters and reducing the risk of casualties. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a system architecture diagram of the AI campus intelligent security prevention system based on intelligent perception and automatic response early warning of the present invention; Figure 2 It is a data collection and perception module architecture diagram of the AI campus intelligent security prevention system based on intelligent perception and automatic response early warning of the present invention; Figure 3 It is an event perception and behavior recognition module architecture diagram of the AI campus intelligent security prevention system based on intelligent perception and automatic response early warning of the present invention; Figure 4 It is a multi-modal data fusion module architecture diagram of the AI campus intelligent security prevention system based on intelligent perception and automatic response early warning of the present invention; Figure 5 It is an AI analysis and event judgment module architecture diagram of the AI campus intelligent security prevention system based on intelligent perception and automatic response early warning of the present invention; Figure 6 It is a relationship architecture diagram of the AI analysis and event judgment module of the AI campus intelligent security prevention system based on intelligent perception and automatic response early warning of the present invention; Figure 7 It is an event early warning and notification module architecture diagram of the AI campus intelligent security prevention system based on intelligent perception and automatic response early warning of the present invention; Figure 8 It is an emergency response and disposal module architecture diagram of the AI campus intelligent security prevention system based on intelligent perception and automatic response early warning of the present invention; Figure 9 It is a resource scheduling and management module architecture diagram of the AI campus intelligent security prevention system based on intelligent perception and automatic response early warning of the present invention; Figure 10 It is an event record and data archiving module architecture diagram of the AI campus intelligent security prevention system based on intelligent perception and automatic response early warning of the present invention; Figure 11 It is a disaster emergency response and auxiliary system connection module architecture diagram of the AI campus intelligent security prevention system based on intelligent perception and automatic response early warning of the present invention; Figure 12This is the architecture diagram of the connection module between the disaster emergency response and auxiliary system of the AI campus intelligent security prevention system based on intelligent perception and automatic response warning of the present invention. Detailed implementation manners

[0020] Next, the technical solutions of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0021] Please refer to the attached Figure 1 - attached Figure 12 , the embodiment of the present invention provides an AI campus intelligent security prevention system based on intelligent perception and automatic response warning, including: A data collection and perception module, which is used to collect multi-dimensional data on campus in real time through collection devices to provide raw data for subsequent analysis; An event perception and behavior recognition module, which is used to perform real-time analysis on the collected raw data by using AI technology, and identify abnormal behaviors and judge potential security threats according to the analyzed data; A multi-modal data fusion module, which is used to perform fusion analysis on the analyzed data and provide the ability to identify security events through the fused data; An AI analysis and event research and judgment module, which is used to comprehensively analyze the fused data, determine the nature of security events and classify them according to the risk level; An event warning and notification module, which is used to generate real-time warnings according to the determined security event levels and notify relevant personnel to enable timely response to security events; An emergency response and handling module, which is connected to the AI analysis and event research and judgment module, and is used to automatically trigger emergency response measures and dispatch security personnel to start emergency plans; A resource scheduling and management module is used to automatically allocate and manage security resources on campus according to the determined security event levels; An event record and data archiving module, which is used to record and archive each processed event in detail; A disaster emergency response and auxiliary system connection module, which is used to connect with the emergency system to provide local notifications and intelligent responses during disasters and assist in coping with the occurring disasters.

[0022] The collection equipment includes cameras, audio pickups, and environmental sensors. The data collection and perception module includes video perception units, audio perception units, and environmental perception units. The video perception unit is used to deploy cameras to collect image and video data. The audio perception unit is used to collect environmental sounds through pickups. Environmental sounds include screaming and abnormal noises. The environmental perception unit is used to collect temperature, humidity, and smoke environmental data. Multi-dimensional data includes video, audio, temperature and humidity, and smoke.

[0023] Specifically, the video perception unit, audio perception unit and environmental perception unit are deployed with cameras, audio pickups and environmental sensors respectively to achieve multi-dimensional data collection. Among them, the video perception unit deploys network cameras in key areas of the campus (such as teaching buildings, playgrounds, dormitory entrances and exits, etc.) to achieve real-time image and video collection of personnel activities, and basic visual data collection of crowd gathering, abnormal behavior, etc.; the audio perception unit deploys environmental pickups to monitor the surrounding sound field in real time, especially to capture abnormal sounds such as screaming, smashing, crying, and arguing, to ensure rapid perception of potential abnormal events; the environmental perception unit is based on the deployed temperature and humidity sensors and smoke sensors to continuously monitor the temperature and humidity changes of the environment and the smoke concentration in the air, which is used to identify early signs of sudden disasters such as fires. The above units work together to form a multi-dimensional data collection covering video, audio, temperature and humidity, smoke, etc., which constitutes a complete perception layer data input source and provides basic data support for subsequent AI analysis and intelligent early warning. The system aggregates multi-dimensional data through standardized data interfaces, and manages it in a unified manner by combining timestamps and spatial positioning information to ensure the real-time and accuracy of the data. This technical solution has the advantages of flexible deployment, comprehensive perception, and reliable data. It provides a basic guarantee for the intelligent identification and efficient response of campus security incidents, and can effectively improve the overall security prevention and control capabilities of the campus. It has good practical value and promotion prospects.

[0024] The event perception and behavior recognition module includes a behavior detection unit, a crowd analysis unit and a sound anomaly recognition unit. The behavior detection unit is used to detect abnormal behaviors, including running, fighting and gathering. The crowd analysis unit is used to count the flow of people and analyze the crowd density. The sound anomaly recognition unit is used to identify abnormal audio, including screaming, smashing and cries for help.

[0025] Specifically, the behavior detection unit uses target detection and action recognition algorithms to judge the behavior of individuals and groups based on the video data collected by the front end. It can effectively detect abnormal behaviors with potential risks such as running, fighting, crowding and gathering, extract and classify action features through deep learning models, and judge the degree of abnormal behavior in combination with scene information; the crowd analysis unit dynamically identifies the number and distribution of people in video images, combines density estimation algorithms and trajectory tracking technology, conducts real-time statistics of human flow, and analyzes the gathering trend of people in specific areas, identifies possible dangerous clusters or abnormal flow patterns, and is particularly suitable for risk assessment in scenarios such as peak hours between classes and concentrated activity areas; the sound anomaly recognition unit processes real-time sound data collected from the audio perception module, extracts sound features through spectrum analysis and voiceprint recognition technology, and then identifies abnormal audio signals with emergency or violent tendencies, including screaming, smashing, and cries for help, which can effectively make up for video surveillance blind spots and realize perception and alarm of abnormal events in non-visual areas. The above-mentioned units work together to build an event perception system with wide coverage, fast response and accurate identification, which can realize early detection and early warning response to sudden safety incidents on campus, greatly enhancing the practicality and intelligence level of the system, and meeting the efficient and accurate needs of safety prevention and control in actual campus scenarios.

[0026] The data includes video, audio, and environmental data. The multimodal data fusion module includes a data synchronization unit, a feature fusion unit, and a data standardization unit. The data synchronization unit is used to align multi-source data in time and space. The feature fusion unit is used to fuse the features of video, audio, and environmental data. The data standardization unit is used to unify various data formats for subsequent AI analysis.

[0027] Specifically, firstly, the data synchronization unit is used to align the time and space dimensions of video, audio and environmental data. In order to solve the problems of inconsistent sampling frequency and signal delay of different acquisition devices, the synchronous processing of multi-source data is realized through timestamp calibration and location information mapping technology, ensuring the consistency of data correspondence and complete information association in the subsequent analysis process. Secondly, the feature fusion unit is responsible for deep feature extraction and fusion processing of synchronized multi-category data. The video data is processed through convolutional neural networks to extract image features, the audio data is processed through spectral analysis to obtain audio signal features, and the environmental data is encoded with numerical or state features. The fusion process adopts multimodal fusion strategies, such as attention mechanism or feature splicing, to achieve semantic layer integration of multi-source information, so as to enhance the model's ability to understand complex events. Finally, the data normalization unit is used to perform unified format conversion and standard processing on various types of structured and unstructured data, including steps such as data unit normalization, encoding unification, and missing value filling, to ensure that all data meets the system input standards and can be efficiently processed and learned by subsequent AI algorithms. The setting of this multi-modal data fusion module not only solves key problems such as inconsistent formats, out-of-sync time, and large differences in feature dimensions among multiple types of sensor data, but also provides a complete, accurate, and highly complementary data input source for the AI model, effectively improving the comprehensive performance of abnormal event recognition and early warning. It is one of the core links to ensure the efficient operation of the system.

[0028] The AI analysis and event judgment module includes an event classification unit, a risk assessment unit, and a decision support unit. The event classification unit is used to perform preliminary classification on events, including minor events and major events. The risk assessment unit is used to intelligently evaluate the risk level and urgency of events, and the decision support unit is used to provide disposal suggestions for the emergency response module.

[0029] Specifically, it consists of an event classification unit, a risk assessment unit, and a decision support unit, and the three form a complete processing flow from data analysis to response suggestions. The event classification unit first performs preliminary classification on the identified event information. Relying on the trained deep neural network model, combined with the source data type, behavior characteristics, sound characteristics, and environmental parameters of the event, it comprehensively judges the nature of the event and divides it into minor events (such as general gatherings, brief noises) and major events (such as physical conflicts, fire omens, continuous screaming, etc.). The classification basis is dynamically optimized in combination with historical data labels and the rule library. The risk assessment unit further intelligently evaluates the risk level and urgency of the event on the basis of the preliminary event classification. This unit comprehensively considers various context factors such as the time period, location, crowd density, and duration of the event; Through quantitative analysis using a multi-dimensional weight model, it outputs a risk level score and marks the event as general, warning, or high-risk level, which is used to guide the priority and resource allocation of subsequent emergency responses. The decision support unit, based on the classification and risk assessment results, combines the built-in emergency disposal knowledge base and rule engine of the system to provide actionable disposal suggestions for the emergency response module, such as which type of plan to activate, which security forces to link, and whether external support is needed. As the core bridge from "data understanding" to "action suggestions" in the system, this module ensures the scientificity and timeliness of the system response. Its application greatly improves the accuracy and automation of campus abnormal event disposal, is applicable to intelligent campus security management systems in various scenarios, and has good practical effects and promotion prospects.

[0030] The event warning and notification module includes a warning generation unit, a multi-channel notification unit, and a feedback confirmation unit. The warning generation unit is used to generate warning information corresponding to events. The multi-channel notification unit is used to notify relevant personnel through multiple channels such as APP, SMS, and broadcast. The relevant personnel include management personnel, security personnel, or teachers and students. The feedback confirmation unit is used to collect the alarm feedback from security personnel or management personnel.

[0031] Specifically, the warning generation unit, the multi-channel notification unit, and the feedback confirmation unit work together to achieve closed-loop management from event identification to personnel response. Specifically, the warning generation unit automatically constructs corresponding warning content based on the event type and risk level information output by the AI analysis and event research and judgment module, including event category, occurrence time, location, risk level, recommended measures, etc., and assigns a unique event number for subsequent tracking and recording. The multi-channel notification unit is responsible for synchronously pushing the generated warning information to relevant personnel through multiple channels. The notification methods include, but are not limited to, APP message push on the campus security management platform, SMS sending system, and campus broadcast system, etc. The relevant personnel include campus management personnel, security guards, teaching building responsible persons, and on-campus teachers and students in specific scenarios, etc. The notification mechanism supports hierarchical push and regional precise notification functions to ensure that the warning information is covered in a timely and effective manner without causing unnecessary panic. The feedback confirmation unit is used to receive and record the receipt and response of relevant personnel to the warning information, including whether the security personnel have arrived at the scene, whether the management personnel have confirmed the event, the disposal progress, etc. The feedback data will be synchronously transmitted back to the system database and used for subsequent emergency response evaluation and system operation optimization. Through the implementation of this module, a real-time connection channel can be effectively established between events and responders, improving the speed and coordination of event handling. At the same time, a traceable and quantifiable response process recording mechanism is built for the system, further enhancing the scientific nature and closed-loop execution power of campus security management.

[0032] The emergency response and disposal module includes a response strategy unit, an action scheduling unit, and an emergency linkage unit. The response strategy unit is used to formulate emergency disposal strategies for different events. The action scheduling unit is used to command and dispatch security and medical emergency personnel. The emergency linkage unit is used to link internal and external emergency resources. The emergency resources include off-campus ambulance and fire fighting.

[0033] Specifically, it consists of a response strategy unit, an action scheduling unit, and an emergency linkage unit, constituting a complete emergency response system from policy formulation to personnel scheduling and then to resource linkage. Specifically, the response strategy unit is used to automatically match a preset emergency response plan according to the type, risk level, and occurrence location of the event. The content of the plan includes the response process, key control points, disposal principles, and required resource types. The system supports the policy deduction function based on the knowledge base, can generate standardized disposal strategies for common events (such as fights, fire omens, overcrowding, sudden illnesses, etc.), and also supports rule combination and dynamic decision-making for new or combined events; After the strategy is clear, the action scheduling unit automatically dispatches on-campus security forces, school doctors, or full-time first-aid personnel to the scene for handling according to the area where the event is located and the resources required by the plan. At the same time, combined with the real-time location data and task status management function, it realizes the rapid assembly and refined scheduling of security forces. The emergency linkage unit, in the case of a higher event level or insufficient on-campus disposal resources, activates the off-campus resource linkage mechanism, including interface connection with units such as local public security, fire, and hospitals, sends standardized emergency linkage request information, and receives the response status in real time to ensure that external forces can intervene in the disposal in a timely manner. The overall module is characterized by high automation, fast response speed, and strong resource coordination ability, which can significantly improve the response efficiency and disposal effect of campus security incidents, especially suitable for handling multi-source and multi-type complex emergencies, and has important practical application value for building a campus intelligent security management system.

[0034] Security resources include security personnel, monitoring equipment, and emergency equipment. The resource scheduling and management module includes a resource list unit, a task assignment unit, and a resource tracking unit. The resource list unit is used to manage the security personnel and equipment emergency resource library. The task assignment unit is used to assign security tasks or equipment scheduling according to the event. The resource tracking unit is used to track the resource status and execution progress in real time.

[0035] Specifically, the resource inventory unit is used to build and maintain the campus security resource database. The management content includes security personnel information (such as positions, duty hours, and responsible areas), monitoring equipment information (such as camera numbers, coverage areas, and working status), and emergency equipment information (such as the locations and available status of first aid kits, fire extinguishers, emergency broadcasts, etc.). This unit supports classified management of resources and real-time status updates to ensure that resource information is comprehensive, accurate, and available. The task assignment unit is used to automatically match the required resources according to the event type, risk level, and geographical location after an event occurs, and generate specific dispatching tasks, such as assigning designated security personnel to the event site, enabling monitoring equipment in specific areas for real-time tracking, and deploying first aid equipment to key positions. The system supports multi-task concurrent dispatching and task priority sorting to meet the resource coordination requirements in complex event scenarios. The resource tracking unit is used to monitor and feedback the real-time status of the dispatched resources. The tracking content includes whether the resources have responded to the tasks, their current locations, task completion progress, and on-site disposal feedback, etc., and presents the resource operation status through a visual interface to facilitate managers to grasp the progress of emergency response and make necessary adjustments. Through the implementation of this module, the system can achieve refined management and efficient dispatching of campus security resources, significantly improve the organizational coordination ability for handling security incidents, and provide key support for building an intelligent campus security system with timely response, reasonable dispatching, and clear feedback.

[0036] Recording and archiving are used to trace event data and provide a basis for subsequent analysis and review. The event recording and data archiving module includes an event log unit, an evidence preservation unit, and a data audit unit. The event log unit is used to record the whole process of the event, alarm information, and disposal process. The evidence preservation unit is used to save key evidence data such as videos and audios. The data audit unit is used to provide post-event audit, analysis, and review support.

[0037] Specifically, it consists of an event log unit, an evidence preservation unit, and a data audit unit, which together build a complete data closed-loop management mechanism from information recording to evidence retention and then to post-event review. Among them, the event log unit is used to record the whole process data of security events from discovery, alarm to disposal, specifically including the event occurrence time, location, event type, system identification results, response action execution status, disposal completion time, and operation logs of relevant personnel, etc. All information is automatically archived in a timeline manner, supporting event number indexing and keyword field retrieval; the evidence preservation unit screens and solidifies key evidence materials such as video clips, audio data, and environmental change records collected during the event, and uses encrypted storage and digital signature methods to ensure the originality and immutability of the evidence data, which is applicable for subsequent legal liability determination, accident investigation, or internal accountability; the data audit unit combines log and evidence data to conduct systematic review and retrospective analysis of the event handling process, supports comprehensive evaluation of indicators such as event response efficiency, resource scheduling rationality, and personnel operation compliance, and can output an audit report as a basis for management improvement, and can also provide structured historical data for AI model training. The setting of this module ensures full traceability, accountability, and data availability in the process of handling campus security events, improves the standardized and transparent management level of the system, is an important part of realizing the closed-loop management of campus security intelligent prevention and control, and has significant practicality and engineering promotion value.

[0038] The emergency system includes a fire protection and earthquake detection system. The disaster emergency response and auxiliary system connection module includes a system docking unit, a local notification unit, and an evacuation guidance unit. The system docking unit is used to dock external systems such as fire protection, earthquake, and meteorology. The local notification unit is used to accurately push local personnel warning information according to the disaster impact range. The evacuation guidance unit is used to provide emergency evacuation instructions and routes to assist the crowd in evacuating.

[0039] Specifically, it maximally guarantees the safety of personnel and reduces the losses caused by disasters. This module includes a system docking unit, a local notification unit, and an evacuation guidance unit, which operate in coordination to form a closed-loop linkage mechanism from external data access to internal response execution. The system docking unit is used to dock external emergency systems including fire alarm systems, earthquake monitoring systems, and meteorological warning platforms, and receives real-time disaster data and warning information pushed by external platforms through standardized communication protocols, such as the estimated time of arrival of seismic waves, the location of fire alarm triggers, red rainstorm warnings, etc. The system can immediately activate the internal response mechanism after receiving the signal; After receiving the disaster signal, the local notification unit conducts targeted notifications to the personnel in the affected areas based on the scope of the incident and the campus spatial layout information, combined with the real-time personnel distribution data. The push channels include the mobile terminal APP, classroom / dormitory broadcast systems, emergency information screens, etc. The notification content covers information such as the type of incident, danger level, and evacuation suggestions, ensuring the accurate and efficient dissemination of early warnings and avoiding the spread of unnecessary panic. The evacuation guidance unit dynamically generates a reasonable evacuation route map based on the campus building structure information, personnel distribution, and disaster characteristics, and issues evacuation instructions to the personnel in the affected areas through graphical prompts, voice broadcasts, or guiding lighting control systems, etc., guiding the crowd to evacuate orderly to safe areas; this unit can also be linked with the security resource scheduling system to arrange tasks such as personnel guidance, order control, and assisting special groups to evacuate. Through the implementation of this module, the campus emergency system realizes the real-time access and response to external disaster data, and through internal intelligent analysis, it achieves accurate notification of disaster information and effective evacuation, greatly improving the organizational ability and handling efficiency of the campus in dealing with sudden disaster events, and is of great significance for building a resilient campus and improving the public safety management system.

[0040] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The AI ​​campus intelligent defense system based on intelligent perception and automatic response warning is characterized by: include: Data collection and perception module, used to collect multi-dimensional data on campus in real time through collection equipment, and provide raw data for subsequent analysis; The event perception and behavior recognition module is used to analyze the collected raw data in real time by using AI technology, and to identify abnormal behaviors and potential security threats based on the analyzed data; Multimodal data fusion module, used to perform fusion analysis on the analyzed data and provide security incident identification capability through the fused data; The AI ​​analysis and incident assessment module is used to conduct a comprehensive analysis of the fused data, determine the nature of security incidents, classify them according to risk levels, and generate security incident levels; The event warning and notification module is used to generate real-time warnings based on the determined security event level and notify relevant personnel so that security events can be responded to in a timely manner; The emergency response and disposal module is connected to the AI ​​analysis and event assessment module to automatically trigger emergency response measures and dispatch security personnel to initiate emergency plans; The resource scheduling and management module is used to automatically allocate and manage security resources on campus according to the determined security event level; Event recording and data archiving module, used to record and archive each processed event in detail; The disaster emergency response and auxiliary system connection module is used to connect with the emergency system to provide local notification and intelligent response when a disaster occurs, and to assist in dealing with the disaster.

2. The AI ​​campus intelligent defense system based on intelligent perception and automatic response warning according to claim 1 is characterized by: The acquisition equipment includes a camera, an audio pickup, and an environmental sensor. The data acquisition and perception module includes a video perception unit, an audio perception unit, and an environmental perception unit. The video perception unit is used to deploy a camera to collect image and video data. The audio perception unit is used to collect environmental sounds through a pickup. The environmental sounds include screaming and abnormal noise. The environmental perception unit is used to collect temperature, humidity, and smoke environmental data. The multi-dimensional data includes video, audio, temperature and humidity, and smoke.

3. The AI ​​campus intelligent defense system based on intelligent perception and automatic response warning according to claim 1 is characterized by: The event perception and behavior recognition module includes a behavior detection unit, a crowd analysis unit and a sound abnormality recognition unit. The behavior detection unit is used to detect abnormal behaviors, including running, fighting, and gathering. The crowd analysis unit is used to count the flow of people and analyze the crowd density. The sound abnormality recognition unit is used to identify abnormal audio, including screaming, smashing, and cries for help.

4. The AI ​​campus intelligent defense system based on intelligent perception and automatic response warning according to claim 1 is characterized by: The data includes video, audio, and environmental data. The multimodal data fusion module includes a data synchronization unit, a feature fusion unit, and a data standardization unit. The data synchronization unit is used to align multi-source data in time and space. The feature fusion unit is used to fuse the features of video, audio, and environmental data. The data standardization unit is used to unify various data formats for subsequent AI analysis.

5. The AI ​​campus intelligent defense system based on intelligent perception and automatic response warning according to claim 1 is characterized by: The AI ​​analysis and event assessment module includes an event classification unit, a risk assessment unit and a decision support unit. The event classification unit is used to preliminarily classify events, including minor events and major events. The risk assessment unit is used to intelligently assess the risk level and urgency of events. The decision support unit is used to provide disposal suggestions for the emergency response module.

6. The AI ​​campus intelligent defense system based on intelligent perception and automatic response warning according to claim 1 is characterized by: The event warning and notification module includes a warning generation unit, a multi-channel notification unit and a feedback confirmation unit. The warning generation unit is used to generate warning information of the corresponding event. The multi-channel notification unit is used to notify relevant personnel through multiple channels such as APP, SMS, and broadcast. The relevant personnel include managers, security personnel, or teachers and students. The feedback confirmation unit is used to collect alarm feedback from security personnel or managers.

7. The AI ​​campus intelligent defense system based on intelligent perception and automatic response warning according to claim 1 is characterized by: The emergency response and disposal module includes a response strategy unit, an action scheduling unit and an emergency linkage unit. The response strategy unit is used to formulate emergency disposal strategies for different events, the action scheduling unit is used to command and dispatch security and medical emergency personnel, and the emergency linkage unit is used to link emergency resources inside and outside the school. The emergency resources include off-campus rescue and fire fighting.

8. The AI ​​campus intelligent defense system based on intelligent perception and automatic response warning according to claim 1 is characterized by: The security resources include security personnel, monitoring equipment, and emergency equipment. The resource scheduling and management module includes a resource inventory unit, a task allocation unit, and a resource tracking unit. The resource inventory unit is used to manage security personnel and equipment emergency resource libraries. The task allocation unit is used to allocate security tasks or equipment scheduling according to events. The resource tracking unit is used to track resource status and execution progress in real time.

9. The AI ​​campus intelligent defense system based on intelligent perception and automatic response warning according to claim 1 is characterized by: The recording and archiving are used to trace event data and provide a basis for subsequent analysis and review. The event recording and data archiving module includes an event log unit, an evidence preservation unit and a data audit unit. The event log unit is used to record the entire event process, alarm information, and disposal process. The evidence preservation unit is used to save key video and audio evidence data. The data audit unit is used to provide post-audit, analysis, and review support.

10. The AI ​​campus intelligent defense system based on intelligent perception and automatic response warning according to claim 1 is characterized by: The emergency system includes fire protection and earthquake detection systems, and the disaster emergency response and auxiliary system connection module includes a system docking unit, a local notification unit and an evacuation guidance unit. The system docking unit is used to dock with fire protection, earthquake and meteorological external systems, and the local notification unit is used to accurately push local personnel warning information according to the scope of disaster impact. The evacuation guidance unit is used to provide emergency evacuation instructions and routes to assist people in evacuating.

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