A data collection-based anti-treading early warning method

Abstract

This invention discloses a data acquisition-based anti-stampede warning method, comprising the following steps: Step 1: System and functional design, including designing the anti-stampede warning process; Step 2: Measure corridor dimensions and determine installation locations, including corridor measurement, design drawing, and camera installation location determination; Step 3: Program and algorithm implementation, including writing the anti-stampede warning program and algorithm to complete the construction of the anti-stampede warning system; Step 4: System debugging and optimization, including debugging and optimizing the anti-stampede warning system. This invention can efficiently and accurately provide anti-stampede warnings, offering safety assurance for schools.

Description

Technical Field

[0001] This invention relates to the field of early warning methods, and specifically to an anti-stampede early warning method based on data collection. Background Technology

[0002] Preventing stampedes on school campuses is a critical safety issue, affecting the lives of students, faculty, and staff. It's a comprehensive issue requiring a multi-pronged approach, including strengthening safety education, improving order management, enhancing hazard identification, and conducting emergency drills. Simultaneously, students themselves need to raise their safety awareness and acquire basic knowledge and skills to prevent stampedes. Only through the concerted efforts of the entire society can we effectively prevent stampede accidents on school campuses and safeguard the lives of teachers and students.

[0003] Anti-stampede warning methods are used in the process of preventing stampedes on campus.

[0004] Existing stampede warning methods rely on limited information collection and have poor warning effectiveness, leading to certain negative impacts. Therefore, this paper proposes a stampede warning method based on data collection. Summary of the Invention

[0005] The technical problem to be solved by this invention is: how to address the shortcomings of existing anti-stampede warning methods, such as limited information collection, poor warning effect, and the negative impact of using such methods. This invention provides an anti-stampede warning method based on data collection.

[0006] The present invention solves the above-mentioned technical problems through the following technical solution, and the present invention includes the following steps:

[0007] Step 1: Design the system and its functions, including the process of designing the anti-stampede warning system;

[0008] Step Two: Measuring and Determining the Dimensions of the Stairwell and the Installation Locations; This involves measuring the stairwell, drawing up design plans, and determining the camera installation locations.

[0009] Step 3: Write the program and algorithm implementation, write the anti-stampede warning program and algorithm, and complete the construction of the anti-stampede warning system;

[0010] Step 4: System debugging and optimization. Debug and optimize the anti-stampede warning system.

[0011] Furthermore, the specific process of step one is as follows:

[0012] First, define the objectives, then select the appropriate technologies, including camera monitoring, image processing, and radar identification.

[0013] Then, functional planning and implementation will be carried out to monitor real-time crowd density and detect people who have fallen.

[0014] Furthermore, the specific process of real-time crowd density monitoring is as follows:

[0015] By collecting corridor image information through cameras, and based on image processing technology, a people density calculation algorithm is designed to count the number of people in the corridor in real time and calculate the people density. Based on factors such as corridor size and passage capacity, a safety threshold is set. When the people density exceeds the threshold, the system triggers an early warning signal.

[0016] Furthermore, the specific process of monitoring fallen persons is as follows: the radar equipment monitors the dynamics of people in the corridor in real time. When it detects abnormal behavior such as falling, it triggers an alarm signal. When the radar triggers the alarm signal, the camera immediately captures the image and records the specific location and time information of the fallen person.

[0017] The system immediately sends alarm signals to managers or security personnel via sound, light, or text message.

[0018] Furthermore, the process of measuring the corridor dimensions is as follows:

[0019] Prepare measuring tools, check their accuracy and integrity, and conduct on-site measurements, including width, length, and height.

[0020] Record the measurement results and number or label each measurement point;

[0021] The specific process of drawing the design is as follows:

[0022] First, select a drawing software to draw the floor plan of the corridor. Based on the measurement results, draw the floor plan of the corridor in the drawing software.

[0023] Next, determine the installation locations of the cameras and radar. Based on the corridor floor plan, determine the installation locations of the cameras and radar. When determining the installation locations of the cameras and radar, the angle of view and focal length of the cameras must be taken into account.

[0024] The installation locations of the cameras and radars, including their height and angle, should be clearly marked on the design drawings.

[0025] The specific process for determining the camera installation location is as follows:

[0026] Analyze the stairwell structure: Analyze the structural characteristics of the stairwell, including the number of steps, the width of the landings, and the angle of the turns;

[0027] Identify key locations in the stairwell, including stairwell entrances, turns, and bottleneck areas;

[0028] Consider monitoring needs: Determine the number and type of cameras based on the usage of the corridors and monitoring requirements;

[0029] Determine the installation location: Based on the building structure and monitoring requirements, determine the installation location of the cameras;

[0030] Verify installation location: Simulate the installation location of the camera in an actual building corridor to verify its monitoring effect.

[0031] Based on the verification results, the installation location was adjusted and optimized.

[0032] Furthermore, the specific process of writing the program and implementing the algorithm is as follows:

[0033] Development environment selection: Select a programming language, use the OpenCV Python library for image processing, and introduce the YOLOv8 model for object detection and fall behavior recognition;

[0034] Programming: Write a camera monitoring program to achieve real-time image capture and display;

[0035] Develop a crowd density analysis program to calculate crowd density using image processing technology and set a safety threshold.

[0036] Write a fall detection program to detect fall behavior in real time using the YOLOv8 model;

[0037] Write an alarm program to trigger an alarm signal when the crowd density exceeds a threshold or when a fall is detected;

[0038] Algorithm optimization: The YOLOv8 model was tuned to improve the accuracy and real-time performance of fall detection, and the crowd density analysis algorithm was optimized.

[0039] Furthermore, the specific process of system debugging and optimization is as follows:

[0040] Test environment setup, simulated scenario design, and installation of camera and radar sensor devices in designated locations according to the design drawings;

[0041] Software configuration: Configure the system software in the monitoring center, including the crowd density analysis algorithm, fall detection model, and alarm program;

[0042] Safety threshold setting: First, analyze the characteristics of pedestrian flow. Based on the area, structure and pedestrian flow characteristics of the corridor, analyze the degree of congestion at different time periods and pedestrian flow directions, and set the safety threshold corresponding to the preset value as the trigger condition for system alarm.

[0043] Threshold adjustment: During the testing process, the security threshold is adjusted based on system performance and user feedback;

[0044] System testing: Select a preset number of people to participate in the test to simulate a crowded scenario when going up and down stairs;

[0045] Design different test scenarios, including normal pedestrian flow, peak pedestrian flow, and emergency evacuation;

[0046] Data recording: Records the system's performance under different crowd densities, including crowd density data, the accuracy of the fall detection system, and the time of alarm signal issuance;

[0047] Problem feedback: During the testing process, promptly identify and record any problems and shortcomings in the system;

[0048] Based on the feedback, the system underwent initial adjustments and optimizations.

[0049] Compared with existing technologies, this invention has the following advantages: This data-driven anti-stampede warning method, by monitoring pedestrian density and fall behavior in real time, can promptly detect potential stampede risks and take preventative measures to avoid accidents. The fall detection function can respond quickly, ensuring that fallen individuals receive timely assistance and reducing injury. Utilizing advanced image processing technology and target detection models (such as YOLOv8), the system can automatically and accurately analyze pedestrian density and detect fall behavior without human intervention. The intelligent management approach improves efficiency and reduces the possibility of human error. Considering different stairwell structures and monitoring needs, the installation positions of cameras and radar can be adjusted to adapt to different application scenarios. It can record data such as pedestrian density, fall detection accuracy, and alarm signal issuance time, providing a foundation for subsequent analysis and optimization, making this method more worthy of widespread application. Attached Figure Description

[0050] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0051] The embodiments of the present invention are described in detail below. These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments.

[0052] like Figure 1 As shown, this embodiment provides a technical solution: a data acquisition-based method for preventing stampedes and providing early warnings, comprising the following steps:

[0053] Step 1: Design the system and its functions, including the process for triggering alarms and warnings to prevent footsteps;

[0054] Step Two: Measuring and Determining the Dimensions of the Stairwell, including measuring the stairwell, drawing up design plans, and determining the camera installation locations;

[0055] Step 3: Write the program and algorithm implementation, write the anti-stampede warning program and algorithm, and complete the construction of the anti-stampede warning system;

[0056] Step 4: System debugging and optimization. Debug and optimize the anti-stampede warning system.

[0057] The specific process of step one is as follows:

[0058] First, define the objectives, then select the appropriate technologies, including camera monitoring, image processing, and radar identification.

[0059] Then, functional planning and implementation will be carried out to monitor real-time crowd density and detect people who have fallen.

[0060] The specific process of real-time crowd density monitoring is as follows:

[0061] By collecting corridor image information through cameras, and based on image processing technology, a people density calculation algorithm is designed to count the number of people in the corridor in real time and calculate the people density. Based on factors such as corridor size and passage capacity, a safety threshold is set. When the people density exceeds the threshold, the system triggers an early warning signal.

[0062] Furthermore, the specific process of monitoring fallen persons is as follows: the radar equipment monitors the dynamics of people in the corridor in real time. When it detects abnormal behavior such as falling, it triggers an alarm signal. When the radar triggers the alarm signal, the camera immediately captures the image and records the specific location and time information of the fallen person.

[0063] The system immediately sends alarm signals to managers or security personnel via sound, light, or text message.

[0064] Detailed process of measuring corridors:

[0065] Prepare measuring tools: Ensure you have the necessary measuring tools such as a tape measure, laser rangefinder, and level.

[0066] Check the accuracy and integrity of the tools to ensure accurate and reliable measurement results.

[0067] On-site measurement: Width measurement: Measure the width of the corridor at each entrance, turn, and each step of the stairs.

[0068] Length measurement: Measure the total length of the stairwell, including the number of steps and the length of each landing.

[0069] Height measurement: Measure the height from the top of the stairwell to the ground, as well as the height of each step on the stairs.

[0070] Special area measurement: For special locations such as bottleneck areas and narrow passages in the corridor, detailed measurements are taken to facilitate the subsequent design and installation plan.

[0071] Record measurement results: Record the measurement results in detail on paper or in electronic devices to ensure that the data is accurate.

[0072] Each measurement point should be numbered or labeled so that it can be accurately matched in subsequent design.

[0073] Detailed process of drawing design diagrams:

[0074] Choose drafting software: Select professional drafting software such as CAD (Computer-Aided Design) to ensure the accuracy and professionalism of the design drawings.

[0075] Draw a floor plan of the corridor: Based on the measurement results, draw a floor plan of the corridor in the drawing software, including key elements such as stairs, platforms, and walls.

[0076] Ensure that the dimensions of the floor plan match the actual corridor dimensions and that the proportions are accurate.

[0077] Determine the installation locations for cameras and radar: Based on the building's floor plan, determine the installation locations for cameras and radar. Cameras should be installed in key locations in the hallway, such as stairwells, turns, and bottleneck areas, to ensure that the monitoring field of view covers the entire hallway.

[0078] Consider the camera's angle of view and focal length to ensure clear capture of crowd movements. The radar installation location should cover the entire corridor area without being obstructed.

[0079] Mark the installation location: Clearly mark the installation location of the camera and radar in the design drawings, including key information such as height and angle.

[0080] Ensure that the labeling information is clear and accurate to facilitate subsequent installation and construction.

[0081] Design and installation plan: Based on the design drawings, develop a detailed installation plan, including installation steps, required materials, tools, etc.

[0082] Consider safety issues and precautions during construction to ensure a smooth installation process.

[0083] The specific process of determining the camera installation location:

[0084] Analyze the stairwell structure: Carefully analyze the structural characteristics of the stairwell, including the number of steps, the width of the landings, and the angle of the turns.

[0085] Identify key locations in the stairwell, such as stairwell entrances, turns, and bottleneck areas.

[0086] Consider monitoring needs: Determine the number and type of cameras based on the usage of the corridors and monitoring requirements.

[0087] Consider the camera's angle of view and focal length to ensure that it can clearly capture the dynamics of the crowd.

[0088] Determine the installation location: Based on the building structure and monitoring requirements, determine the installation location of the camera.

[0089] Ensure that the camera's field of view covers the entire corridor and is not obstructed.

[0090] Verify installation location: Simulate the installation location of the camera in an actual building corridor to verify its monitoring effect.

[0091] Based on the verification results, the installation location was adjusted and optimized as necessary.

[0092] Through the above specific process, you can accurately measure the corridor dimensions, draw up design drawings, and determine the installation locations of cameras and radar, providing a reliable basis for subsequent installation and construction.

[0093] Camera monitoring and image processing: Technology selection: Select high-definition cameras to ensure that the image clarity and field of view meet the monitoring requirements.

[0094] Image processing technology: Advanced image processing algorithms (such as background subtraction, target detection, etc.) are used to process the images captured by the camera in real time to calculate the crowd density.

[0095] Radar technology introduction: Technology selection: Select radar equipment that can penetrate crowds and is not affected by obstructions.

[0096] Functionality: The radar equipment is responsible for real-time monitoring of people's movements in the corridors, especially abnormal behaviors such as falls.

[0097] Functional planning and implementation

[0098] Real-time pedestrian density monitoring: Algorithm design: Based on image processing technology, a pedestrian density calculation algorithm is designed to count the number of people in the corridor in real time and calculate the pedestrian density.

[0099] Safety threshold setting: A reasonable safety threshold is set based on factors such as corridor size and passage capacity. When the pedestrian density exceeds the threshold, the system triggers an early warning signal.

[0100] Fall detection: Radar monitoring: Radar equipment monitors the movement of people in the corridors in real time. When it detects abnormal behavior such as falling, it triggers an alarm signal.

[0101] Camera linkage: When the radar triggers an alarm, the camera immediately captures relevant footage and records the specific location and time of the fall.

[0102] Alarm mechanism: The system immediately sends alarm signals to managers or security personnel via sound, light, or text message so that timely measures can be taken.

[0103] IV. System Design and Optimization

[0104] System architecture design: Design a reasonable system architecture, including the integration and connection of components such as cameras, radar, processors, and alarm devices.

[0105] Algorithm optimization: Continuous optimization of the crowd density calculation algorithm and fall detection algorithm to improve accuracy and real-time performance.

[0106] User interface design: Design an intuitive and easy-to-use user interface to facilitate administrators in viewing system status and alarm information.

[0107] Testing and Verification

[0108] Simulation testing: Conducting system tests in a laboratory or simulated environment to verify the accuracy and reliability of various functions.

[0109] On-site testing: Conduct system testing in a real building corridor environment, collect data and analyze it to further optimize system performance.

[0110] User feedback: Invite users to try out the system and collect feedback to make necessary adjustments and improvements.

[0111] Deployment and Operation

[0112] System Deployment: Based on the test results and user needs, the system will be deployed to the actual building corridor environment.

[0113] Operation and maintenance: Regularly maintain and upgrade the system to ensure stable operation and meet user needs.

[0114] Continuous improvement: Based on user feedback and technological advancements, continuously improve and optimize the system.

[0115] Through the above specific process, you can gradually build and optimize an efficient and accurate anti-stampede early warning system to provide safety guarantees for schools and other public places.

[0116] The specific process of writing the program and implementing the algorithm is as follows:

[0117] Development environment selection: Select a programming language, use the OpenCV Python library for image processing, and introduce the YOLOv8 model for object detection and fall behavior recognition;

[0118] Programming: Write a camera monitoring program to achieve real-time image capture and display;

[0119] Develop a crowd density analysis program to calculate crowd density using image processing technology and set a safety threshold.

[0120] Write a fall detection program to detect fall behavior in real time using the YOLOv8 model;

[0121] Write an alarm program to trigger an alarm signal when the crowd density exceeds a threshold or when a fall is detected;

[0122] Algorithm optimization: The YOLOv8 model was tuned to improve the accuracy and real-time performance of fall detection, and the crowd density analysis algorithm was optimized.

[0123] The specific process of system debugging and optimization is as follows:

[0124] Test environment setup:

[0125] Simulated scenario design: Simulate actual usage scenarios in a 1.2-meter-wide corridor, including stair sections, corners, and platform areas.

[0126] Ensure that the test environment is as similar as possible to the actual corridor in terms of structure, materials, and lighting conditions.

[0127] Equipment layout: Install cameras, radar and other sensor equipment in the designated locations according to the design drawings.

[0128] Check the equipment connections and power supply to ensure the equipment is functioning properly.

[0129] Software configuration: Configure the system software in the monitoring center, including the crowd density analysis algorithm, fall detection model and alarm program.

[0130] Ensure the system can receive and process data from sensors in real time.

[0131] Safety threshold setting

[0132] Analysis of pedestrian flow characteristics: Based on the area, structure and pedestrian flow characteristics of the corridor, the degree of congestion is analyzed under different time periods and pedestrian flow directions.

[0133] Set a safety threshold of 7 people per square meter as the trigger condition for system alarms.

[0134] Threshold adjustment: During the testing process, the security thresholds are adjusted as necessary based on system performance and user feedback.

[0135] Ensure the system can accurately identify and issue alarms under different crowd densities.

[0136] System testing

[0137] Test design: Invite a certain number of students to participate in the test to simulate a crowded scenario when going up and down stairs.

[0138] Design different test scenarios, including normal pedestrian flow, peak pedestrian flow, and emergency evacuation.

[0139] Data recording: Records the system's performance under different crowd densities, including crowd density data, the accuracy of the fall detection system, and the time of alarm signal issuance.

[0140] Ensure the accuracy and completeness of the data for subsequent analysis and optimization.

[0141] Problem feedback: During the testing process, promptly identify and record any problems and shortcomings in the system.

[0142] Based on the feedback, the system underwent initial adjustments and optimizations.

[0143] System debugging and optimization

[0144] Algorithm improvements: Analyze system test data to improve the crowd density analysis algorithm and fall detection model.

[0145] Improve the accuracy and stability of the system, and reduce false alarms and missed alarms.

[0146] Alarm procedure optimization: Optimize the alarm procedure to ensure that alarm signals can be transmitted to relevant personnel in a timely and accurate manner.

[0147] Different alarm levels and response mechanisms can be set up to deal with different levels of crowding and fall incidents.

[0148] Equipment adjustment: Based on the test results, adjust the camera angle, focal length, and radar sensitivity.

[0149] Ensure the equipment can accurately capture crowd movements and fall events.

[0150] System verification

[0151] Real-world scenario verification: Verify the system's performance and effectiveness in real-world usage scenarios.

[0152] Ensure the system can accurately identify and trigger alarms under varying crowd densities and fall incidents.

[0153] User feedback collection: Collect user feedback to understand the system's usage and existing problems.

[0154] Based on user feedback, the system is continuously improved and optimized.

[0155] System evaluation: Evaluate the overall performance of the system, including accuracy, stability, reliability, and user satisfaction.

[0156] Based on the evaluation results, further optimization plans will be developed.

[0157] Through the above specific process, you can perform comprehensive debugging and optimization of the system, improve its accuracy and stability, and ensure that the system can play a good role in actual use scenarios.

[0159] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0160] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0161] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for preventing stampedes and providing early warnings based on data acquisition, characterized in that, Includes the following steps: Step 1: Design the system and its functions, including the process of designing the anti-stampede warning system; Step Two: Measuring and Determining the Dimensions of the Stairwell, including measuring the stairwell, drawing up design plans, and determining the camera installation locations; Step 3: Write the program and algorithm implementation, write the anti-stampede warning program and algorithm, and complete the construction of the anti-stampede warning system; Step 4: System debugging and optimization. Debug and optimize the anti-stampede warning system.

2. The anti-stampede early warning method based on data acquisition according to claim 1, characterized in that: The specific process of step one is as follows: First, define the objectives, then select the appropriate technologies, including camera monitoring, image processing, and radar identification. Then, functional planning and implementation will be carried out to monitor real-time crowd density and detect people who have fallen.

3. The anti-stampede early warning method based on data acquisition according to claim 2, characterized in that: The specific process of real-time crowd density monitoring is as follows: By collecting corridor image information through cameras, and based on image processing technology, a people density calculation algorithm is designed to count the number of people in the corridor in real time and calculate the people density. Based on factors such as corridor size and passage capacity, a safety threshold is set. When the people density exceeds the threshold, the system triggers an early warning signal.

4. The anti-stampede early warning method based on data acquisition according to claim 2, characterized in that: The specific process of monitoring fallen persons is as follows: The radar equipment monitors the dynamics of people in the corridor in real time. When it detects abnormal behavior such as falling, it triggers an alarm signal. When the radar triggers the alarm signal, the camera immediately captures the image and records the specific location and time information of the fallen person. The system immediately sends alarm signals to managers or security personnel via sound, light, or text message.

5. The anti-stampede early warning method based on data acquisition according to claim 1, characterized in that: The process of measuring the corridor dimensions is as follows: Prepare measuring tools, check their accuracy and integrity, and conduct on-site measurements, including width, length, and height measurements. Record the measurement results and number or label each measurement point; The specific process of drawing the design is as follows: First, select a drawing software to draw the floor plan of the corridor. Based on the measurement results, draw the floor plan of the corridor in the drawing software. Next, determine the installation locations of the cameras and radar. Based on the corridor floor plan, determine the installation locations of the cameras and radar. When determining the installation locations of the cameras and radar, the angle of view and focal length of the cameras must be taken into account. The installation locations of the cameras and radars, including their height and angle, should be clearly marked on the design drawings. The specific process for determining the camera installation location is as follows: Analyze the stairwell structure: Analyze the structural characteristics of the stairwell, including the number of steps, the width of the landings, and the angle of the turns; Identify key locations in the stairwell, including stairwell entrances, turns, and bottleneck areas; Consider monitoring needs: Determine the number and type of cameras based on the usage of the corridors and monitoring requirements; Determine the installation location: Based on the building structure and monitoring requirements, determine the installation location of the cameras; Verify installation location: Simulate the installation location of the camera in an actual building corridor to verify its monitoring effect; Based on the verification results, the installation location was adjusted and optimized.

6. The anti-stampede early warning method based on data acquisition according to claim 1, characterized in that: The specific process of writing the program and implementing the algorithm is as follows: Development environment selection: Select a programming language, use the OpenCV Python library for image processing, and introduce the YOLOv8 model for object detection and fall behavior recognition; Programming: Write a camera monitoring program to achieve real-time image capture and display; Develop a crowd density analysis program to calculate crowd density using image processing technology and set a safety threshold. Write a fall detection program to detect fall behavior in real time using the YOLOv8 model; Write an alarm program to trigger an alarm signal when the crowd density exceeds a threshold or when a fall is detected; Algorithm optimization: The YOLOv8 model was tuned to improve the accuracy and real-time performance of fall detection, and the crowd density analysis algorithm was optimized.

7. The anti-stampede early warning method based on data acquisition according to claim 1, characterized in that: The specific process of system debugging and optimization is as follows: Test environment setup, simulated scenario design, and installation of camera and radar sensor devices in designated locations according to the design drawings; Software configuration: Configure the system software in the monitoring center, including the crowd density analysis algorithm, fall detection model, and alarm program; Safety threshold setting: First, analyze the characteristics of pedestrian flow. Based on the area, structure and pedestrian flow characteristics of the corridor, analyze the degree of congestion at different time periods and pedestrian flow directions, and set the safety threshold corresponding to the preset value as the trigger condition for system alarm. Threshold adjustment: During the testing process, the security threshold is adjusted based on system performance and user feedback; System testing: Select a preset number of people to participate in the test to simulate a crowded scenario when going up and down stairs; Design different test scenarios, including normal pedestrian flow, peak pedestrian flow, and emergency evacuation; Data recording: Records the system's performance under different crowd densities, including crowd density data, the accuracy of the fall detection system, and the time of alarm signal issuance; Problem feedback: During the testing process, promptly identify and record any problems and shortcomings in the system; Based on the feedback, the system underwent initial adjustments and optimizations.

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