AI intelligent alarm event analysis method and system

Abstract

The invention discloses an AI intelligent alarm event analysis method and system, and the method comprises the following steps: receiving an alarm message uploaded by a monitoring device, and calling an AI model to carry out the intelligent analysis of an alarm picture in response to the determination that the monitoring device opens an AI alarm service, generating an AI analysis result containing the scenarized description text and at least one event tag; and generating and storing a recombined alarm message based on the AI analysis result. According to the AI intelligent alarm event analysis method and system, a whole-process AI alarm event analysis system of alarm collection, intelligent analysis, accurate pushing, intelligent retrieval and feedback optimization is constructed, deep analysis and efficient utilization of alarm information are achieved, and the problems that a traditional alarm system is low in information value and low in retrieval efficiency are solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent security technology, and in particular to an AI-powered intelligent alarm event analysis method and system. Background Technology

[0002] In the field of security monitoring, traditional alarm systems generally have many pain points, making it difficult to meet users' needs for efficient and accurate alarm analysis. On the one hand, the number of alarm events triggered by monitoring devices is huge and the types are complex. Users need to manually filter key content from massive amounts of alarm information, which is not only time-consuming and labor-intensive, but also very easy to miss important events. On the other hand, traditional alarms can only provide basic alarm prompts and lack detailed descriptions and accurate classifications of alarm scenarios. Users cannot quickly understand the core information of alarm events, which greatly reduces the practicality of alarm analysis.

[0003] Meanwhile, existing alarm systems have obvious functional limitations: they lack intelligent search and filtering mechanisms, making it difficult for users to quickly locate alarm events of specific types or scenarios; the presentation of alarm information is monotonous, and it does not combine AI technology to achieve in-depth analysis of alarm images; the service configuration is not flexible enough, and it cannot adjust service parameters according to users' language preferences, usage habits and other personalized needs; and there is a lack of clear and intelligent adaptation mechanisms for the usage permissions and management logic of alarm services for different devices and accounts, which affects the user experience.

[0004] Therefore, there is an urgent need for an intelligent alarm event analysis solution that integrates AI technology to solve the problems of low efficiency, insufficient accuracy, and low personalization of existing alarm systems, and to improve the user's alarm analysis experience. Summary of the Invention

[0005] To address the aforementioned technical challenges, this invention provides an AI-powered intelligent alarm event analysis method and system. This system enables intelligent recognition and deep analysis of alarm images, automatically generating contextualized descriptions and precise tags, allowing users to quickly grasp the core information of alarm events. It offers efficient alarm event retrieval capabilities, supporting keyword search and tag filtering to help users quickly locate target events within massive amounts of alarm data. A flexible and configurable service system is built, allowing users to personalize service parameters based on language preferences, usage needs, and other factors, adapting to different scenarios. An intelligent permission management and service adaptation mechanism is established, clearly defining service usage and management permissions for different devices (own devices / shared devices) and accounts, ensuring the security and rationality of service use. A user feedback mechanism is established to collect user evaluations of alarm recognition accuracy, providing data support for model optimization and continuously improving the accuracy of alarm recognition.

[0006] This invention provides an AI-powered intelligent alarm event analysis method, comprising the following steps: S1: Receive alarm messages uploaded by monitoring equipment, wherein the alarm messages include at least an alarm image; S2: In response to determining that the monitoring device has enabled the AI ​​alarm service, the AI ​​model is invoked to perform intelligent analysis on the alarm image, and an AI analysis result containing contextual description text and at least one event tag is generated; S3: Based on the AI ​​analysis results, generate and store the reorganization alarm message; S4: Provide an alarm event interaction interface on the client side. The interface dynamically adapts to and displays the corresponding functional modules and information according to the service status and permissions of the monitoring device. For alarm events for which services have been enabled, at least the contextual description text and / or event tags shall be displayed.

[0007] Preferably, the step of "providing an alarm event interaction interface on the client" specifically includes: The interface provides an intelligent search module that, in response to user-input keywords or selected event tags, performs matching searches from the stored recombined alarm messages and returns a list of alarm events that match the search criteria.

[0008] Preferably, the intelligent search module supports switching the query date after selecting search conditions, and re-initiating the search based on the current search conditions and the switched date to update the results.

[0009] Preferably, the method further includes a personalization configuration step: A configuration interface is provided for users to set the output language of the scenario-based description text, as well as the AI ​​alarm service switch for the monitoring device; When displaying the alarm image, the user controls whether to display the recognition frame drawn based on the target coordinate information in the AI ​​analysis results, according to the switch configuration stored locally on the client.

[0010] Preferably, the "dynamic adaptation" specifically includes: Based on whether the monitoring device is the current user's own device and whether the AI ​​alarm service is enabled, the display of the AI ​​function bar in the interactive interface is controlled, as well as the interactive logic for guiding activation or disabling the function when the AI ​​function bar is clicked.

[0011] Preferably, the method further includes user feedback and optimization steps: The interactive interface provides a feedback entry point for each alarm event; Collect and store feedback data submitted by users through the feedback portal that is associated with alarm event identifiers, and use it for the optimization training of AI models.

[0012] Preferably, after generating the AI ​​analysis results, the method further includes: using the contextualized descriptive text as push content to send an alarm notification to the user.

[0013] An AI-powered intelligent alarm event analysis system, the system comprising a backend service unit and a client unit; The backend service unit includes: The message processing module is used to receive alarm messages and determine the AI ​​service status of the monitoring equipment; The AI ​​analysis module is used to intelligently analyze alarm images and generate AI analysis results that include contextualized descriptive text and event tags. The data management module is used to store reorganization alarm messages generated based on the AI ​​analysis results and to provide retrieval services; The client unit includes: The adaptation and display module is used to render and display dynamically adapted alarm event interaction interfaces and corresponding information based on device service status and user permissions.

[0014] Preferably, the client unit further includes: The intelligent search module is used to receive the user's search or filtering operation and call the search service of the background service unit to obtain the results; The configuration and feedback module provides a personalized service configuration interface and a user feedback entry point.

[0015] Compared with related technologies, the AI ​​intelligent alarm event analysis method and system provided by this invention have the following beneficial effects: A full-process AI alarm event analysis system has been built, which includes "alarm collection - intelligent analysis - precise push - intelligent retrieval - feedback optimization". This system enables in-depth analysis and efficient utilization of alarm information, and solves the problems of low information value and low retrieval efficiency in traditional alarm systems.

[0016] A dynamic function adaptation mechanism based on device status (service enabled / service not enabled, owned device / shared device) is proposed to intelligently adjust function display and interaction logic, taking into account both service availability and access control security.

[0017] It enables multi-dimensional personalized configuration, supports custom settings for outputs such as description language, service switches, and image recognition frame parameters, adapts to the usage habits and needs of different users, and improves the flexibility of services and user experience.

[0018] An intelligent retrieval solution combining AI recognition results was designed, supporting keyword search and tag filtering. The date switching linkage mechanism in the search / filter state ensures the real-time and accuracy of the retrieval results, greatly improving the efficiency of alarm event location.

[0019] A linkage mechanism between user feedback and model optimization was established. By collecting user feedback data on the accuracy of alarm recognition, real and effective data support was provided for the iterative optimization of the AI ​​model, thereby achieving continuous improvement in service performance. Attached Figure Description

[0020] Figure 1 The flowchart of the AI ​​intelligent alarm event analysis method provided by the present invention. Detailed Implementation

[0021] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0022] In the specific implementation process, such as Figure 1 As shown, an AI-powered intelligent alarm event analysis method includes the following steps: Step S1: Receive alarm message After a monitoring device (such as an IP camera) captures an abnormal event (such as intrusion or equipment failure), it generates an alarm message and uploads it to the backend service unit. The alarm message includes an alarm image (such as an infrared image or video frame), a timestamp, a device ID, and a preliminary alarm type (such as "motion detection"). For example, when a perimeter camera in a factory detects an unauthorized intrusion, it uploads an alarm message containing the intruder's image and coordinate data.

[0023] Step S2: AI Intelligent Analysis The message processing module of the backend service unit determines the status of the device's AI service (e.g., "Intrusion Detection" service is enabled), and the AI ​​analysis module calls a pre-trained model (e.g., YOLOv5 object detection model) to analyze the alarm images: Contextualized description generation: The model identifies key elements in an image (such as human actions and object positions) and generates natural language descriptions (e.g., "A person was detected loitering in a restricted area"). Event tag extraction: Based on the analysis results, multiple tags (such as "illegal intrusion", "high risk", "nighttime event") are added. The tags are associated with historical alarm patterns through semantic encoding.

[0024] Model output: Contextualized text "Unauthorized personnel found at warehouse entrance", tag set {"Intrusion", "Warning Zone"}.

[0025] Step S3: Reassemble alarm message The data management module integrates AI analysis results (contextualized text, tags, and original images) into reconstructed alarm messages, stores them in a time-series database, and associates them with device IDs and timestamps. Example message structure: {“Device ID”: “000000”, “Time”: “2000-00-00T00:00:00”, “Scene Description”: “Personnel detected loitering at the entrance of the power distribution room”, “Tags”: [“Illegal intrusion”, “High risk”], “Image URL”: “D: / / AAA / BBB_image.jpg”} The reconstructed message supports subsequent retrieval and display.

[0026] Step S4: Client Interaction Interface The client unit (App or Web) dynamically renders the interactive interface based on the device service status (AI service enabled) and user permissions (administrator or operator): Functional module adaptation: If the device is owned by the user and the AI ​​service is enabled, the interface will display the "AI Analysis Results", "Real-time Video" and "Alarm History" modules; if not enabled, the AI ​​function will be disabled and only the basic alarm list will be displayed.

[0027] Information display logic: For alarm events for which services have been enabled, prioritize displaying contextual description text and event tags (such as "Scenario: People loitering at the warehouse entrance | Tag: Illegal intrusion"), and support clicking to expand details (such as images and coordinate information).

[0028] Intelligent search module: Users can input keywords (such as "intrusion") or select tags (such as "high risk"), and the system will match the event list from the recombined alarm messages. For example, searching for the tag "nighttime events" will return the alarm records that match in the past 24 hours. Users can switch the date range (such as "past 7 days"), and the system will re-search and update the results based on the new conditions.

[0029] Personalized configuration: Users can set the contextual text output language (such as Chinese or English) through the configuration interface and control the AI ​​alarm service switch. For example, if the user selects "Chinese", all alarm descriptions will be generated in Chinese; if the service is turned off, the AI ​​analysis module will be hidden in the interface.

[0030] Recognition frame control: Based on the user's locally stored switch configuration (such as "show frame"), recognition frames (such as rectangles marking the location of intruders) drawn based on the target coordinate information analyzed by AI are superimposed when displaying alarm images to help users quickly locate abnormal areas.

[0031] User feedback and optimization Feedback entry: The interface provides an "Alarm Feedback" button, where users can submit feedback for a single event (such as "false alarm" or "requires additional information"). Feedback data is associated with alarm event identifiers (such as ID and timestamp) and stored in the database.

[0032] Model optimization: The backend service unit collects feedback data for incremental training of the AI ​​model (such as optimizing object detection accuracy through transfer learning) to improve the accuracy of subsequent analysis.

[0033] Alarm notification push: After generating AI analysis results, the system sends alarm notifications to users via App push or SMS with contextual description text (such as "personnel detected loitering in restricted areas"), and supports customized receiving strategies (such as only pushing high-risk events).

[0034] The AI-powered intelligent alarm event analysis system architecture consists of a backend service unit and a client unit, achieving high scalability through modular design.

[0035] Backend service unit The message processing module receives alarm messages, parses the device ID and AI service status (e.g., determined through the device registry), and routes them to the AI ​​analysis module. For example, in a smart park system, after a camera uploads an alarm, the module automatically identifies the device type (e.g., "perimeter camera") and service activation status. AI Analysis Module: Integrates deep learning models (such as TensorFlow / PyTorch) to perform intelligent analysis on alarm images. Input processing: Image preprocessing (normalization, noise reduction) to adapt to model input requirements.

[0036] Model inference: The pre-trained model is called to generate scenario-based descriptions (such as "person fall detection") and event labels (such as "emergency events"). The model uses RAG technology to combine with the enterprise knowledge base (such as safety procedures) to improve the accuracy of the descriptions.

[0037] Output generation: The analysis results (text, tags) are encapsulated into structured data for use by the data management module.

[0038] Data management module: Stores recombined alarm messages to a distributed database (such as MongoDB) and provides retrieval services. For example, when a user searches for the "fire risk" tag, the module matches relevant events from the database and supports multi-dimensional filtering by time, device, or tag.

[0039] Client Unit Adaptive display module: Dynamically renders the interactive interface based on device service status (e.g., "AI service enabled") and user permissions (e.g., "administrator"). Interface elements include: an alarm list, a contextual description display area, a tag filter bar, and an AI function button. Adaptation logic: If the device is owned by the user and the AI ​​service is enabled, the interface will display advanced functions such as "AI Analysis" and "Real-time Video"; if not enabled, only basic alarm information (such as time and device ID) will be displayed.

[0040] Intelligent search module: Receives user input (such as keywords "fight" or tags "violent incidents"), calls the background search service to obtain a list of matching alarm events, and supports date switching (such as "past 1 hour" or "custom range"), and the system automatically updates the results.

[0041] Configuration and Feedback Module: Configuration interface: Provides a web form or app settings page, where users can set contextual text language (such as "Chinese"), AI service on / off (such as "on / off"), and notification preferences (such as "only push high-risk notifications").

[0042] Feedback entry point: Add a "Feedback" button to the alarm details page. User-submitted feedback data (such as "false alarm" or "information needed") is associated with event identifiers and stored for model optimization.

[0043] This invention significantly improves alarm processing efficiency (such as reducing false alarm rate) by using AI to intelligently analyze alarm images, and supports personalized configuration and user feedback, making it suitable for multiple scenarios. Industrial safety: In factory equipment monitoring, AI identifies early signs of mechanical failure (such as abnormal vibration), generates alarms, and pushes them to maintenance personnel to prevent accidents from escalating.

[0044] Smart cities: Traffic intersection cameras detect violations (such as driving against traffic), and contextual descriptions assist traffic police in quick response.

[0045] Home security: Users receive alarm notifications via the app (such as "person detected loitering in restricted areas") and report false alarms to optimize model accuracy.

[0046] The system adopts a modular design, with the back-end service unit and the client unit communicating through an API interface. It supports high-concurrency alarm processing (such as thousands of alarms per second) and is compatible with various monitoring devices (such as IP cameras and sensors).

[0047] The AI ​​intelligent alarm event analysis method and system of the present invention constructs a full-process AI alarm event analysis system of "alarm collection - intelligent analysis - precise push - intelligent retrieval - feedback optimization", realizing in-depth analysis and efficient utilization of alarm information, and solving the problems of low information value and low retrieval efficiency of traditional alarm systems.

[0048] A dynamic function adaptation mechanism based on device status (service enabled / service not enabled, owned device / shared device) is proposed to intelligently adjust function display and interaction logic, taking into account both service availability and access control security.

[0049] It enables multi-dimensional personalized configuration, supports custom settings for outputs such as description language, service switches, and image recognition frame parameters, adapts to the usage habits and needs of different users, and improves the flexibility of services and user experience.

[0050] An intelligent retrieval solution combining AI recognition results was designed, supporting keyword search and tag filtering. The date switching linkage mechanism in the search / filter state ensures the real-time and accuracy of the retrieval results, greatly improving the efficiency of alarm event location.

[0051] A linkage mechanism between user feedback and model optimization was established. By collecting user feedback data on the accuracy of alarm recognition, real and effective data support was provided for the iterative optimization of the AI ​​model, thereby achieving continuous improvement in service performance.

[0052] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An AI-powered intelligent alarm event analysis method, characterized in that, Includes the following steps: S1: Receive alarm messages uploaded by monitoring equipment, wherein the alarm messages include at least an alarm image; S2: In response to determining that the monitoring device has enabled the AI ​​alarm service, the AI ​​model is invoked to perform intelligent analysis on the alarm image, and an AI analysis result containing contextual description text and at least one event tag is generated; S3: Based on the AI ​​analysis results, generate and store the reorganization alarm message; S4: Provide an alarm event interaction interface on the client side. The interface dynamically adapts to and displays the corresponding functional modules and information based on the service status and permissions of the monitoring device. For alarm events for services that have been enabled, at least the contextual description text and / or event label should be displayed.

2. The AI ​​intelligent alarm event analysis method according to claim 1, characterized in that, The step of "providing an alarm event interaction interface on the client" specifically includes: The interface provides an intelligent search module that, in response to user-input keywords or selected event tags, performs matching searches from the stored recombined alarm messages and returns a list of alarm events that match the search criteria.

3. The AI ​​intelligent alarm event analysis method according to claim 2, characterized in that, The intelligent search module supports switching the query date after selecting search criteria, and re-initiating the search based on the current search criteria and the switched date to update the results.

4. The AI ​​intelligent alarm event analysis method according to claim 1, characterized in that, The method also includes a personalization configuration step: A configuration interface is provided for users to set the output language of the scenario-based description text, as well as the AI ​​alarm service switch for the monitoring device; When displaying the alarm image, the user controls whether to display the recognition frame drawn based on the target coordinate information in the AI ​​analysis results, according to the switch configuration stored locally on the client.

5. The AI ​​intelligent alarm event analysis method according to claim 1, characterized in that, The "dynamic adaptation" specifically includes: Based on whether the monitoring device is the current user's own device and whether the AI ​​alarm service is enabled, the display of the AI ​​function bar in the interactive interface is controlled, as well as the interactive logic for guiding activation or disabling the function when the AI ​​function bar is clicked.

6. The AI ​​intelligent alarm event analysis method according to claim 1, characterized in that, The method also includes user feedback and optimization steps: The interactive interface provides a feedback entry point for each alarm event; Collect and store feedback data submitted by users through the feedback portal that is associated with alarm event identifiers, and use it for the optimization training of AI models.

7. The AI ​​intelligent alarm event analysis method according to claim 1, characterized in that, After generating the AI ​​analysis results, the method further includes: using the contextualized descriptive text as push content to send an alert notification to the user.

8. A system for implementing the AI ​​intelligent alarm event analysis method according to any one of claims 1 to 7, characterized in that, The system includes a backend service unit and a client unit; The backend service unit includes: The message processing module is used to receive alarm messages and determine the AI ​​service status of the monitoring equipment; The AI ​​analysis module is used to intelligently analyze alarm images and generate AI analysis results that include contextual description text and event tags. The data management module is used to store reorganization alarm messages generated based on the AI ​​analysis results and to provide retrieval services; The client unit includes: The adaptation and display module is used to render and display dynamically adapted alarm event interaction interfaces and corresponding information based on device service status and user permissions.

9. The system according to claim 8, characterized in that, The client unit also includes: The intelligent search module is used to receive the user's search or filtering operation and call the search service of the background service unit to obtain the results; The configuration and feedback module provides a personalized service configuration interface and a user feedback entry point.

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