Railway perimeter intrusion monitoring and alert system

Abstract

The present application relates to a railway intrusion monitoring and warning system, which integrates data acquisition, edge computing, data transmission, cloud processing and user interaction modules, realizing real-time monitoring of the environment along the railway. Through cameras and millimeter wave radars, the system collects video and radar data, intelligently analyzes them through the edge computing module, quickly identifies potential intruders and triggers alarms. The data is uploaded to the cloud through the transmission module, and the cloud processing module judges again and pushes the alarm information to the user. The user interaction module provides an intuitive interface for administrators to view alarms and device status, and also supports user interaction with the system. Advantages: The system has strong real-time performance and high intelligence, can quickly respond to and effectively prevent railway intrusion events, and improves the safety of railway operation. At the same time, the combination of cloud processing and user interaction modules makes the system more flexible and easy to manage.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of railway and information interaction, and particularly relates to a railway surrounding intrusion monitoring and alarming system. BACKGROUND

[0002] With the rapid development of railway transportation, railway safety has become a crucial issue. Traditional railway safety monitoring methods mainly rely on manual patrol and video monitoring, but these methods have limitations such as high labor cost, many blind spots in monitoring, slow response speed, etc. Especially in the complex and variable environment along the railway, potential intruders such as personnel, animals, and vehicles may appear at any time, which brings great hidden dangers to the safety of railway operation. SUMMARY

[0003] In view of the above situation, it is necessary to provide a railway surrounding intrusion monitoring and alarming system to solve at least one of the above problems, comprising:

[0004] A data acquisition module is configured to acquire video and radar data along the railway through a camera and a millimeter wave radar device, and to monitor the surrounding environment of the railway in real time.

[0005] An edge computing module is connected to the data acquisition module and is configured to analyze the acquired video and radar data in real time, identify potential intruders such as personnel, animals, and vehicles through intelligent algorithms, and trigger corresponding alarms.

[0006] A data transmission module is connected to the edge computing module and is configured to transmit data processed by the edge computing module to a cloud platform.

[0007] A cloud processing module is connected to the data transmission module and is configured to receive data from the data transmission module, make a secondary judgment and processing, and push alarm information to the corresponding user.

[0008] A user interaction module is connected to the cloud processing module and is configured to provide a user interface and allow an administrator to view alarm information and device status, and support user interaction with the system.

[0009] Preferably, after the edge computing module identifies a potential intruder, if it detects that the moving speed of the intruder exceeds 5 meters per second, the moving direction of the intruder points to the railway track, and the nearest distance from the intruder to the railway track is less than 50 meters, the edge computing module determines that the intruder is an intruder through a preset algorithm and triggers an alarm.

[0010] Preferably, after the cloud processing module receives data from the data transmission module, the cloud processing module verifies the timestamp and integrity check code of the data. If the timestamp differs from the current time by more than 5 minutes, or the integrity check code does not match, the cloud processing module determines that the data is incomplete or abnormal, triggers a data error alarm, and starts a data retransmission mechanism.

[0011] Preferably, the user interaction module allows the administrator to customize the alarm triggering conditions, including but not limited to setting the intruder's moving speed threshold to 3 meters per second, the moving direction range to less than 30 degrees with the railway track, and the safety distance threshold from the railway track to 100 meters, when these conditions are met, the alarm is triggered.

[0012] Preferably, the intelligent learning module automatically adjusts the logical judgment conditions for alarm triggering by analyzing historical data.

[0013] Preferably, the intelligent learning module takes into account the influence of geographic location on intruder behavior, and in mountainous areas or near wildlife reserves, increases the monitoring sensitivity value for animal intrusion and reduces the speed threshold for alarm triggering.

[0014] Preferably, the alarm level management module divides the alarm information into three levels: first-level alarm, second-level alarm, and third-level alarm, according to the urgency of the alarm information and the size of the potential risk, wherein the triggering conditions for the first-level alarm include the intruder's moving speed exceeding 5 meters per second and the distance from the railway track being less than 50 meters; the triggering conditions for the second-level alarm include the intruder's moving speed exceeding 3 meters per second and the distance from the railway track being less than 100 meters; and the third-level alarm is triggered in other cases.

[0015] Preferably, the alarm level management module also takes into account the type analysis of the intruder when dividing the alarm level, and when a vehicle intrusion is detected, a first-level alarm is triggered directly.

[0016] Preferably, the alarm history record module stores detailed information of each alarm, including alarm time, latitude and longitude coordinates, intruder type, alarm level, processing result, and the name of the processing personnel and processing time.

[0017] Preferably, the alarm history record module also supports visual analysis of alarm data. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 is a railway perimeter intrusion monitoring system according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the following further describes the railway perimeter intrusion monitoring system of the present application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0020] In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more; the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like is based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second", "third" and the like are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0021] In the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0022] Please refer to Figure 1 The railway surrounding intrusion monitoring and alarming system of the embodiment of the present application comprises: a data acquisition module for acquiring video and radar data along the railway line through a camera and a millimeter wave radar device to monitor the environment around the railway in real time; an edge computing module connected with the data acquisition module for real-time analysis of the collected video and radar data, identifying potential intruders such as personnel, animals and vehicles through intelligent algorithms, and triggering corresponding alarms; a data transmission module connected with the edge computing module for transmitting data processed by the edge computing module to a cloud platform; a cloud processing module connected with the data transmission module for receiving data from the data transmission module, making secondary judgment and processing, and pushing alarm information to the corresponding user; a user interaction module connected with the cloud processing module for providing a user interface and allowing the administrator to view alarm information and device status, and supporting user interaction with the system.

[0023] In the above embodiment, the data acquisition module is the front-end part of the system, mainly responsible for collecting video and radar data along the railway through cameras and millimeter wave radar devices. The camera can capture video images of the surrounding area of the railway, providing intuitive environmental information; while the millimeter wave radar device can detect objects around the railway, unaffected by light and weather, providing more stable detection data. The working principle of the data acquisition module is to automatically or manually control the camera and radar device for data acquisition through pre-set acquisition parameters and strategies. The camera captures images through the lens, which are converted optically and digitally to form a video data stream; the radar device transmits millimeter waves and receives echo signals to calculate the distance, speed and direction of the target object, forming a radar data stream. After preliminary processing, the two data streams are transmitted to the edge computing module for further analysis and processing. The edge computing module is the core part of the system, responsible for real-time analysis of the collected video and radar data. This module uses intelligent algorithms to detect, track and identify targets in video images, accurately identifying potential intruders such as people, animals and vehicles; at the same time, it analyzes and fuses radar data to improve the accuracy and reliability of target identification and tracking. The working principle of the edge computing module is based on high-performance computing platforms and advanced algorithm technology. It first receives video and radar data streams from the data acquisition module, then uses target detection algorithms to analyze video images frame by frame to identify target objects in the images; then, it uses target tracking algorithms to continuously track target objects to obtain their motion trajectories and speeds; finally, it identifies and classifies target objects in combination with radar data to determine whether they are potential intruders. If it is determined to be an intruder, the corresponding alarm signal is triggered, and the alarm information is transmitted to the data transmission module. The data transmission module is responsible for transmitting the data processed by the edge computing module to the cloud platform. Due to the complex and variable environment along the railway, the stability and reliability of data transmission are crucial to the performance of the system. Therefore, the data transmission module uses efficient data transmission protocols and encryption algorithms to ensure the security and real-time performance of data during transmission. The working principle of the data transmission module is to establish a stable data transmission channel to transmit the alarm information and related data streams generated by the edge computing module to the cloud platform. During transmission, the data transmission module compresses and encrypts the data to reduce the bandwidth occupancy and prevent data leakage. At the same time, the data transmission module also has a breakpoint resume and retransmission mechanism to ensure the integrity and reliability of the data during transmission. The cloud processing module is the back-end part of the system, responsible for receiving data from the data transmission module, making secondary judgments and processing, and pushing alarm information to the corresponding users. This module uses cloud computing technology, with powerful computing and storage capabilities, capable of efficiently processing and analyzing massive data.The working principle of the cloud processing module is to first receive the data stream from the data transmission module, then use big data analysis technology to mine and analyze the data, and extract valuable information and patterns. Next, according to the preset rules and algorithms, the alarm information is judged and processed again to improve the accuracy and reliability of the alarm. Finally, the processed alarm information is pushed to the corresponding user through the user interaction module for the administrator to view and handle. The user interaction module is the human-computer interaction part of the system, responsible for providing the user interface and allowing the administrator to view the alarm information and device status, and supporting user interaction with the system. This module provides convenient operation experience and rich functional support for users through graphical interface and friendly interaction design. The working principle of the user interaction module is to establish a communication channel between the user and the system to realize user interaction with the system. The administrator can view real-time video, alarm information, device status and other key information through the user interface; at the same time, the administrator can also configure parameters, control devices and other operations through the user interface. In addition, the user interaction module supports multiple interaction methods such as touch screen, mouse and keyboard to meet the needs and habits of different users. Through the cooperation of the data acquisition module, the edge computing module, the data transmission module, the cloud processing module and the user interaction module, the real-time monitoring and alarm function of the surrounding environment of the railway is realized. The system not only has the advantages of high efficiency, intelligence and reliability, but also can provide users with rich functional support and convenient operation experience.

[0024] See Figure 1 In another embodiment, after identifying a potential intruder, if the detection speed of the intruder exceeds 5 meters per second, the direction of movement points to the railway track, and the nearest distance to the railway track is less than 50 meters, the edge computing module determines that it is an intrusion through a preset algorithm and triggers an alarm.

[0025] In the above embodiments, the working principle of the edge computing module in the railway perimeter intrusion monitoring alarm system mainly involves multiple steps such as real-time data collection, target detection and tracking, verification of intrusion judgment conditions, and confirmation and triggering of alarm information. The module first receives video and radar data from the data acquisition module, after preprocessing, uses the pre-trained algorithm model to detect the target in the video image, and identifies personnel, animals, vehicles and other targets in the image. Once the target is detected, the module will start the target tracking algorithm to obtain the target's motion trajectory and speed information. After obtaining the target's motion information, the edge computing module will make a decision based on the pre-set intrusion judgment conditions. These conditions usually include the target's moving speed, moving direction, and the closest distance to the railway track. If the target's moving speed is detected to be more than 5 meters per second, and its moving direction points to the railway track, and the closest distance to the railway track is less than 50 meters, the module will judge that the target is a potential intruder. In order to ensure the accuracy and reliability of the alarm, the edge computing module will further confirm the potential intruder through the pre-set algorithm. Once the target is confirmed as an intruder, the module will immediately trigger an alarm signal, and package the alarm information into a specific data format, and send it to the cloud processing module through the data transmission module for further processing and analysis. In the cloud, the alarm information may be associated with other data for correlation analysis to provide a more comprehensive security situation awareness.

[0026] Please refer to Figure 1 In another embodiment, after receiving data from the data transmission module, the cloud processing module will verify the timestamp and integrity check code of the data. If the timestamp is more than 5 minutes away from the current time, or the integrity check code does not match, it is judged that the data is incomplete or abnormal, a data error alarm is triggered, and a data retransmission mechanism is started.

[0027] In the above embodiment, the cloud processing module receives data packets from the data transmission module. These data packets usually contain real-time information about potential intruders sent from the edge computing module, such as location, speed, timestamp, and other key data. Once the data arrives in the cloud, the processing module will immediately start the data verification process. The first step of data verification is timestamp verification. The timestamp is a key piece of information that records the time when the data is generated, and it is crucial for ensuring the timeliness and accuracy of the data. The cloud processing module will extract the timestamp from the data packet and compare it with the current time. If the timestamp is more than 5 minutes away from the current time, it means that the data may have serious delays or transmission errors, affecting the real-time nature and effectiveness of the data. In this case, the cloud processing module will determine that the data is incomplete or abnormal and immediately trigger a data error alarm. At the same time of triggering the alarm, the cloud processing module will also start the data retransmission mechanism. The purpose of this mechanism is to ensure the integrity and reliability of the data. When the cloud processing module detects that the data is abnormal, it will send a retransmission request to the data transmission module, requiring it to resend the data packet. After receiving the retransmission request, the data transmission module will repackage the data and send it to the cloud processing module. In this way, the cloud processing module can ensure the integrity and accuracy of the data and improve the reliability of the system. In addition to timestamp verification, the cloud processing module will also perform integrity check code matching. The integrity check code is an important tool for detecting whether the data has been tampered with or errors have occurred during transmission. Before data transmission, the sender will calculate the check code of the data and attach it to the data packet. When the cloud processing module receives the data packet, it will recalculate the check code of the data and compare it with the check code in the data packet. If they match, it means that the data has not been tampered with or errors have occurred during transmission; if they do not match, it means that the data may have a problem. In the case of finding that the integrity check code does not match, the cloud processing module will also trigger a data error alarm and start the data retransmission mechanism. By retransmitting the data packet, the cloud processing module can ensure the integrity and accuracy of the data and avoid false positives or false negatives caused by data errors or tampering.

[0028] See Figure 1 In another embodiment, the user interaction module allows administrators to customize alarm triggering conditions, including but not limited to setting the intruder's moving speed threshold to 3 meters per second, the moving direction range to less than 30 degrees from the railway track, and the safety distance threshold from the railway track to 100 meters, triggering an alarm when these conditions are met.

[0029] In the above embodiment, the user interaction module provides an intuitive and user-friendly interface through which the administrator can access and manage the alarm triggering conditions of the system. This interface typically includes a series of editable parameters and options that the administrator can set and adjust as needed. When customizing the alarm triggering conditions, the administrator can set multiple parameters, including but not limited to the intruder's moving speed threshold, moving direction range, and safe distance threshold from the railway track. For example, in this case, the administrator can set the moving speed threshold to 3 meters per second, which means that the system will only consider triggering an alarm when the intruder's moving speed exceeds this threshold. At the same time, the administrator can also set the moving direction range. In this example, the moving direction range is set to an angle less than 30 degrees with the railway track. This means that the system will only consider the intruder as a potential threat when its moving direction points to or approaches the railway track. Finally, the administrator can also set the safe distance threshold from the railway track. In this example, the safe distance threshold is set to 100 meters. This means that the system will only consider the intruder as a potential intruder and consider triggering an alarm when its closest distance to the railway track is less than this threshold. Once the administrator sets these alarm triggering conditions, the user interaction module will pass these conditions to the core processing module of the system. When the core processing module detects an event that meets these conditions, it will trigger the corresponding alarm according to the administrator's settings. The form of the alarm can be various, including sound, light flash, SMS notification, email reminder, etc., to ensure that the administrator can timely learn about the potential intrusion event. In addition, the user interaction module also allows the administrator to view and modify the set alarm triggering conditions at any time. In this way, the administrator can flexibly adjust the alarm triggering conditions of the system according to the changes of the actual situation, to adapt to different security needs.

[0030] See Figure 1 In another embodiment, the intelligent learning module automatically adjusts the logical judgment conditions of alarm triggering by analyzing historical data.

[0031] In the above embodiments, the intelligent learning module collects and analyzes a large amount of historical data. This data may come from various alarm records, surveillance videos, radar data, etc., generated by the railway intrusion detection system during its daily operation. Through in-depth analysis of this data, the intelligent learning module can gradually understand and master the patterns and characteristics of railway intrusion events. During the analysis of historical data, the intelligent learning module pays particular attention to intrusion events that are falsely reported or missed. For falsely reported events, the intelligent learning module attempts to identify the cause, which may be a special situation in a specific scenario or due to improper parameter settings. For missed events, the intelligent learning module analyzes why the system failed to detect the intrusion event in a timely manner—whether it was due to incomplete data collection, insufficient processing speed, or overly strict logical judgment conditions. After mastering these patterns and characteristics, the intelligent learning module automatically adjusts the logical judgment conditions for alarm triggering. This adjustment process is based on deep learning and machine learning algorithms, and through continuous iteration and optimization, the system's alarm triggering conditions become more consistent with the actual situation, reducing the possibility of false alarms and missed alarms.

[0032] Specifically, the intelligent learning module may adjust the following parameters:

[0033] 1. Movement Speed ​​Threshold: Based on the distribution of intruder movement speeds in historical data, the intelligent learning module can automatically adjust the movement speed threshold. For example, if historical data shows that most intruders' movement speeds fall within a certain range, then this range can be set as the new movement speed threshold.

[0034] 2. Movement Direction Range: Similarly, the intelligent learning module can automatically adjust the movement direction range based on the distribution of intruder movement directions in historical data. This ensures that the system only alerts to intruders who are actually heading towards the railway tracks.

[0035] 3. Safe Distance Threshold: The adjustment of the safe distance threshold is also based on historical data. The intelligent learning module analyzes the actual distance between intruders and railway tracks in historical data, as well as the relationship between these distances and alarm triggering, thereby automatically adjusting the safe distance threshold.

[0036] In addition to adjusting the parameters in the three aspects mentioned above, the intelligent learning module can also optimize other logical judgment conditions, such as increasing the ability to recognize specific scenarios or special situations, and improving the system's flexibility and adaptability.

[0037] Please see Figure 1 In another embodiment, the intelligent learning module incorporates the influence of geographical location on intruder behavior, increasing the monitoring sensitivity value for animal intrusions in mountainous areas or near wildlife reserves, and reducing the alarm triggering speed threshold.

[0038] In the above embodiments, the intelligent learning module utilizes technologies such as geographic information systems (GIS) to accurately identify the geographical characteristics of different regions along the railway, such as mountainous areas, wildlife protection zones, etc. These regions often have complex terrain, dense vegetation, and other factors that make it difficult for traditional intrusion monitoring systems to effectively respond. However, the intelligent learning module can combine the characteristics of these regions to develop more reasonable monitoring strategies. In areas near mountains or wildlife protection zones, the intelligent learning module will pay special attention to the risk of animal intrusion. It analyzes historical data on animal intrusion events using deep learning and data analysis techniques to understand animal activity patterns, movement speed, movement paths, and other key information. This information provides valuable references for the system, allowing it to more accurately predict and identify potential animal intrusion behavior. Based on an understanding of animal intrusion behavior in these regions, the intelligent learning module adjusts the monitoring strategy accordingly. It increases the monitoring sensitivity value for these regions, improves the frequency and accuracy of data collection, and more comprehensively understands the activities in these regions. At the same time, it also reduces the speed threshold for triggering alarms to ensure that an alarm can be issued quickly when an animal approaches the railway track, providing sufficient response time for railway personnel. The working principle of the intelligent learning module not only improves the accuracy and reliability of the monitoring system, but also enhances its adaptability and flexibility. It can automatically adjust the monitoring strategy and alarm triggering conditions according to the characteristics of different regions, ensuring that the system can perform optimally in various complex environments. In addition, the intelligent learning module also has the ability to learn and optimize in real time. It can continuously collect new monitoring data and update animal intrusion behavior patterns based on these data. At the same time, it also fine-tunes the alarm triggering conditions based on actual alarm triggering situations to ensure the accuracy and reliability of the system.

[0039] Please refer to Figure 1 In another embodiment, the alarm level management module divides the alarm information into three levels: first-level alarm, second-level alarm, and third-level alarm according to the urgency of the alarm information and the size of the potential risk. The triggering conditions of the first-level alarm include an intruder moving at a speed of more than 5 meters per second and being less than 50 meters away from the railway track. The triggering conditions of the second-level alarm include an intruder moving at a speed of more than 3 meters per second and being less than 100 meters away from the railway track. Other conditions trigger the third-level alarm.

[0040] In the above embodiments, according to the emergency degree of the alarm information and the size of the potential risk, the alarm information is accurately divided into different levels, so that the railway staff can respond quickly and accurately. Specifically, the alarm level management module divides the alarm information into three levels: first-level alarm, second-level alarm and third-level alarm, each level has its specific trigger condition and response strategy. The detailed working principle of the alarm level management module is as follows: The working principle of the alarm level management module is based on the in-depth analysis of the alarm information. The alarm information received by the system usually contains the moving speed of the intruder, the distance from the railway track, the intrusion time, the location and other key parameters. These parameters are important basis for evaluating the emergency degree of the alarm and the potential risk. After receiving the alarm information, the alarm level management module will immediately start the evaluation process. First, it will check the moving speed parameter in the alarm information. If the moving speed of the intruder exceeds 5 meters per second, it is a clear emergency signal, because the intruder moving at high speed may quickly approach the railway track, posing a serious threat to train operation. At the same time, the module will also check the distance parameter between the intruder and the railway track. If this distance is less than 50 meters, the intruder is already in a very close range to the railway track, which makes the emergency degree of the alarm further rise. When both conditions are met, the alarm level management module will immediately trigger a first-level alarm. First-level alarm means that the railway staff needs to take immediate action, which may include emergency stopping, evacuating passengers and other measures to ensure the safety of the train and passengers. If the moving speed of the intruder exceeds 3 meters per second but does not reach 5 meters per second, and the distance from the railway track is less than 100 meters, the alarm level management module will trigger a second-level alarm. Although the second-level alarm is not as urgent as the first-level alarm, it still needs the attention of the railway staff. The staff may need to take appropriate safety measures according to the actual situation, such as reducing speed, strengthening observation, etc., to ensure the safety of the train. In addition to the above two cases, all other alarm information will be classified as a third-level alarm. Third-level alarm may include intruders with slower moving speed, intruders farther away from the railway track, etc. These alarms, although not a direct threat, still need the attention and handling of the railway staff. The staff can monitor the scene in real time through the video monitoring system and take appropriate measures according to the actual situation.

[0041] See Figure 1 In another embodiment, the alarm level management module also adds type analysis of the intruder when dividing the alarm level, and directly triggers a first-level alarm when a vehicle intrusion is detected.

[0042] In the above embodiment, when the system detects a vehicle intrusion, the alarm level management module will immediately trigger a level one alarm. This is because vehicles, as a kind of fast-moving objects, often pose a much higher risk than pedestrians or other types of intruders. The high-speed movement of vehicles can lead to collisions with trains, posing a serious threat to the safety of trains and passengers. When a level one alarm is triggered, the alarm level management module will quickly send alarm information to relevant staff and provide detailed alarm levels and response strategies. At the same time, the system will automatically start relevant safety measures, such as emergency stopping, passenger evacuation, etc., to ensure the safety of trains and passengers.

[0043] See Figure 1 In another embodiment, the alarm history recording module will store detailed information of each alarm, including alarm time, latitude and longitude coordinates, intruder type, alarm level, processing result, and the name of the processing personnel and processing time.

[0044] In the above embodiment, the alarm history recording module will record detailed information of each alarm. First of all, the alarm time is a very important parameter, which indicates the specific time point of the alarm, and helps managers understand the frequency and trend of the alarm. Second, the latitude and longitude coordinates are the accurate identification of the alarm location, through which the specific location of the alarm can be quickly located, which is of great significance to on-site processing and subsequent investigation. The intruder type is another key information, which reveals the source of the alarm. Whether it is a pedestrian, a vehicle, or a wild animal, different types of intruders may pose different degrees of threat to railway safety. By recording the intruder type, managers can understand which areas or time periods are more prone to specific types of intrusion, and thus take appropriate preventive measures. The alarm level is another important field in the alarm history recording module. It reflects the emergency level and potential risk size of the alarm. As mentioned earlier, the alarm level management module will divide the alarm information into level one, level two, and level three alarms according to the emergency level and potential risk size of the alarm information. The alarm history recording module will store these level information for subsequent analysis and query. In addition to the above information, the alarm history recording module will also record the processing result and the name of the processing personnel and the processing time. These information is crucial for evaluating the effectiveness and efficiency of alarm processing. By querying the processing result, managers can understand whether the alarm has been handled in a timely and effective manner, and whether there are any problems in the processing process. At the same time, the name of the processing personnel and the processing time also provide the basis for responsibility tracing.

[0045] See Figure 1 In another embodiment, the alarm history recording module also supports visual analysis of alarm data.

[0046] In the above embodiments, after the data preprocessing is completed, the alarm history record module starts data analysis. It can select appropriate data dimensions and indicators for analysis according to the user's needs and interest points. For example, the user can view the number of a certain type of alarm, processing time and results within a certain time period; or view the density and trend of the alarm in a certain area. After the analysis is completed, the alarm history record module will display the analysis results to the user in a visual form. Users can freely adjust the angle and granularity of data display through interactive controls on the interface, such as sliders, selectors, zoom buttons, etc. This interactive visual display method enables users to have a more in-depth understanding of the characteristics of the alarm data.

[0047] The above merely describes preferred embodiments of the present application, and is not intended to limit the present application in any form. Although the present application has been disclosed as above with reference to the preferred embodiments, the present application is not intended to be limited thereto. Any person skilled in the art, without departing from the technical solution of the present application, can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application shall still fall within the scope of the technical solution of the present application.

Claims

1. A railway perimeter intrusion monitoring alarm system, characterized by, Comprise: a data acquisition module for collecting video and radar data along the railway through a camera and a millimeter wave radar device, and monitoring the environment around the railway in real time; an edge computing module connected with the data acquisition module, for real-time analysis of the collected video and radar data, identifying potential intruders such as personnel, animals and vehicles through intelligent algorithms, and triggering corresponding alarms; a data transmission module connected with the edge computing module, for transmitting data processed by the edge computing module to a cloud platform; a cloud processing module connected with the data transmission module, for receiving data from the data transmission module, making secondary judgments and processing, and pushing alarm information to the corresponding user; a user interaction module connected with the cloud processing module, for providing a user interface and allowing administrators to view alarm information and device status, and supporting user interaction with the system; an intelligent learning module for automatically adjusting the logical judgment conditions for triggering alarms by analyzing historical data; an alarm level management module for dividing the alarm information into three levels: first-level alarm, second-level alarm and third-level alarm according to the urgency of the alarm information and the size of the potential risk; wherein, after identifying potential intruders, if the speed of the intruder is detected to be more than 5 meters per second, and the moving direction of the intruder points to the railway track, and the nearest distance to the railway track is less than 50 meters, the edge computing module determines that an intrusion has occurred and triggers an alarm through a preset algorithm; after receiving data from the data transmission module, the cloud processing module verifies the timestamp and integrity check code of the data, if the timestamp is more than 5 minutes away from the current time, or the integrity check code does not match, the cloud processing module determines that the data is incomplete or abnormal, triggers a data error alarm, and starts a data retransmission mechanism; the intelligent learning module takes into account the influence of geographical location on intruder behavior, increases the monitoring sensitivity value for animal intrusion in mountainous areas or near wildlife protection areas, and reduces the speed threshold for triggering alarms; the triggering conditions for the first-level alarm include an intruder moving at a speed of more than 5 meters per second and being less than 50 meters away from the railway track; the triggering conditions for the second-level alarm include an intruder moving at a speed of more than 3 meters per second and being less than 100 meters away from the railway track; other conditions trigger the third-level alarm; the alarm level management module also analyzes the type of intruder when dividing the alarm level, and directly triggers a first-level alarm when a vehicle intrusion is detected.

2. The railway perimeter intrusion monitoring alarm system of claim 1, wherein, The user interaction module allows administrators to customize alarm triggering conditions, including but not limited to setting the intruder's moving speed threshold to 3 meters per second, the moving direction range to an angle of less than 30 degrees with the railway track, and the safety distance threshold to the railway track to 100 meters, and triggering an alarm when these conditions are met.

3. The railway perimeter intrusion monitoring alarm system of claim 1, wherein, It also includes an alarm history record module for storing detailed information of each alarm, including alarm time, latitude and longitude coordinates, intruder type, alarm level, processing result, and the name and processing time of the processing personnel.

4. The railway perimeter intrusion monitoring alarm system of claim 3, wherein, The alarm history record module also supports visual analysis of alarm data.

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