Railway safety protection system

Abstract

The invention provides a railway safety protection system, which comprises a laser correlation detection module formed by a laser transmitting end and a laser receiving end symmetrically arranged on two sides of a railway opening and used for generating a plurality of groups of laser grating networks and monitoring a light beam shielding event in real time; the intelligent analysis module is used for identifying the type of an intrusion target according to the shielding signal of the laser grating network and analyzing a target movement track to filter false triggering; the alarm linkage module is used for triggering a sound-light alarm after the invasion is confirmed, closing a railway barrier gate and sending alarm information to a control center; the monitoring integration module is used for being linked with a video monitoring system to call the high-definition image of the intrusion area and assisting in judgment through AI image recognition; and the environment self-adaption module is used for integrating a temperature and humidity sensor and a wind speed sensor to dynamically adjust a laser sensitivity threshold value to adapt to environment change. Personnel invasion at the railway crossing of the thermal power plant is monitored through a laser correlation technology, so that unauthorized personnel or vehicles are prevented from entering a railway transportation area of the thermal power plant, and the operation safety of the thermal power plant is guaranteed.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of equipment operation and maintenance technology, and in particular to railway safety protection systems. Background Technology

[0002] The railway transportation system of thermal power plants undertakes the transportation of bulk materials such as coal and fuel. The railway crossing areas pose a risk of unauthorized entry by personnel or illegal vehicle crossings, potentially leading to serious safety accidents. Existing technologies, such as conventional physical fencing, infrared sensing, or video surveillance systems, have the following problems:

[0003] Physical fences: high maintenance costs and no real-time early warning system;

[0004] Infrared sensing: susceptible to environmental interference (such as rain, snow, fog), resulting in a high false alarm rate;

[0005] Video surveillance: relies on manual inspections, has a slow response time, and has limited nighttime recognition capabilities.

[0006] Therefore, a better solution is urgently needed. Summary of the Invention

[0007] In view of this, embodiments of this specification provide a railway safety protection system to address the technical deficiencies existing in the prior art.

[0008] According to a first aspect of the embodiments of this specification, a railway safety protection system is provided, comprising:

[0009] A laser beam detection module consisting of laser emitters and receivers symmetrically arranged on both sides of the railway crossing is used to generate multiple sets of laser grating networks and monitor beam blocking events in real time.

[0010] The intelligent analysis module is used to identify the type of intrusion target based on the occlusion signal of the laser grating network and analyze the target's movement trajectory to filter out false triggers;

[0011] The alarm linkage module is used to trigger an audible and visual alarm, close the railway gate, and send alarm information to the control center after confirming an intrusion.

[0012] The monitoring integration module is used to link with the video surveillance system to retrieve high-definition images of the intrusion area and use AI image recognition to assist in the judgment.

[0013] An environment adaptive module is used to integrate temperature, humidity, and wind speed sensors to dynamically adjust the laser sensitivity threshold to adapt to environmental changes.

[0014] In one possible implementation, the laser beam detection module uses an invisible laser beam to construct a three-dimensional protective light curtain with a detection range of 500 meters, covering the entire orbital area.

[0015] In one possible implementation, the intelligent analysis module extracts target features using a CNN algorithm and combines them with motion trajectories to predict behavioral intent.

[0016] Target types include people and vehicles;

[0017] False triggers include animals and floating objects.

[0018] In one possible implementation, the alarm linkage module supports a tiered response mechanism, activating warning lights and sound columns in the corresponding area based on the location of the intrusion target.

[0019] In one possible implementation, the monitoring integration module calls up high-definition camera footage from the intrusion area, verifies the intrusion event through human detection and behavior analysis algorithms, and forms a closed-loop management with the security platform.

[0020] In one possible implementation, the environmental adaptive module eliminates environmental interference through multi-beam synchronous detection technology in a temperature range of -40°C to 70°C and under strong wind conditions.

[0021] In one possible implementation, the intelligent analysis module uses the following calculation formula to determine the target's trajectory:

[0022]

[0023] Among them, v t The overall threat coefficient of the target is determined by trajectory velocity, behavioral weights, and environmental disturbances; Δx i and Δy i Δt represents the target's coordinate displacement at the i-th sampling time, derived from the spatial analysis of the laser grating occlusion signal; i w represents the sampling time interval. j θ is the preset weight for the j-th type of behavior. j From the perspective of behavioral continuity, d j The distance at which the behavior occurs; a k Let s be the interference correction factor for the k-th environmental sensor. k Here, T represents the sensor reading, and T is the system response time threshold.

[0024] In one possible implementation, the response priority of the alarm linkage module is dynamically adjusted using the following calculation formula:

[0025]

[0026] Among them, P r For response priority; v q The threat coefficient for the q-th target is derived from the output of the intelligent analysis module; l q c is the straight-line distance between the target and the orbit. qf represents the current train density on the track. u Rate the clarity of the u-th surveillance camera; g u h represents the camera's field of view coverage. u b is the ambient light intensity; z e is the preset performance value for the z-th type of alarm device; z The device is in an available state; o z Determine the equipment deployment density.

[0027] In one possible implementation, the beam spacing of the laser beam detection module is dynamically adjusted according to the track width, with a minimum spacing of 10cm.

[0028] In one possible implementation, the AI ​​image recognition module uses the YOLOv7 model to detect intrusion targets in real time and performs spatiotemporal alignment verification with the occlusion signal of the laser beam detection module.

[0029] This application provides a railway security protection system, including: a laser beam detection module consisting of laser emitters and receivers symmetrically arranged on both sides of a railway crossing, used to generate multiple sets of laser grating networks and monitor beam obstruction events in real time; an intelligent analysis module, used to identify the type of intrusion target based on the obstruction signal of the laser grating network and analyze the target's movement trajectory to filter false triggers; an alarm linkage module, used to trigger audible and visual alarms, close the railway gate, and send alarm information to the control center after confirming an intrusion; a monitoring integration module, used to link with a video surveillance system to retrieve high-definition images of the intrusion area and use AI image recognition to assist in judgment; and an environmental adaptive module, used to integrate temperature, humidity, and wind speed sensors to dynamically adjust the laser sensitivity threshold to adapt to environmental changes. By using laser beam detection technology to monitor personnel intrusion at the railway crossing of a thermal power plant, this system aims to prevent unauthorized personnel or vehicles from entering the railway transportation area of ​​the thermal power plant, ensuring the safe operation of the plant. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of a railway safety protection system provided in one embodiment of this specification. Detailed Implementation

[0031] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0032] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0033] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0034] This specification provides a railway safety protection system, which will be described in detail in the following embodiments.

[0035] See Figure 1 , Figure 1 This diagram illustrates a railway safety protection system according to one embodiment of this specification. Specifically, it includes a laser beam detection module consisting of laser emitters and receivers symmetrically arranged on both sides of a railway crossing, used to generate multiple laser grating networks and monitor beam obstruction events in real time; an intelligent analysis module used to identify the type of intrusion target based on the obstruction signal of the laser grating network and analyze the target's movement trajectory to filter false triggers; an alarm linkage module used to trigger an audible and visual alarm, close the railway gate, and send alarm information to the control center upon confirmation of intrusion; a monitoring integration module used to link with a video surveillance system to retrieve high-definition images of the intrusion area and use AI image recognition to assist in judgment; and an environmental adaptive module used to integrate temperature, humidity, and wind speed sensors to dynamically adjust the laser sensitivity threshold to adapt to environmental changes.

[0036] The laser beam detection module refers to laser transmitting and receiving equipment symmetrically installed on both sides of the railway crossing. This equipment constructs multiple sets of parallel laser beams to form a three-dimensional grating network, detecting beam obstruction events in real time. The intelligent analysis module refers to a deep learning-based signal processor capable of distinguishing target types such as personnel and vehicles based on obstruction signal characteristics, and eliminating false alarms by combining motion trajectory analysis. The alarm linkage module refers to an execution unit integrating an audible and visual alarm with a barrier gate controller, automatically triggering a local alarm and simultaneously closing the barrier gate upon confirmation of intrusion. The monitoring integration module refers to an image processing platform linked to a video surveillance system, used to retrieve real-time footage of the intrusion area and use AI algorithms to assist in verifying target attributes. The environmental adaptive module refers to an adjustment system equipped with temperature, humidity, and wind speed sensors, dynamically optimizing laser sensitivity to cope with interference from rain, snow, strong winds, etc.

[0037] As a concrete example: A railway crossing is equipped with dual-sided laser beam detectors spaced 500 meters apart, forming a vertical grating network with a 20-centimeter spacing. When a pedestrian enters, the intelligent analysis module determines it as an intrusion by detecting three consecutive laser beams blocking the path within 0.5 seconds. The linkage module immediately activates a 105-decibel audible and visual alarm and lowers the barrier gate. Simultaneously, the control center receives alarm information including the pedestrian's location coordinates. The monitoring module simultaneously retrieves 4K camera footage from the area, and after AI identification confirms the target is a pedestrian, a red warning box is marked on the control panel. When the environmental module detects a wind speed of level 8, it automatically increases the laser sensitivity threshold by 20% to prevent accidental triggering by falling leaves.

[0038] The system achieves all-weather, precise protection through the integration of multiple technologies. The laser beam network provides millimeter-level detection accuracy, intelligent analysis significantly reduces the false alarm rate caused by animals or floating objects, the hierarchical alarm mechanism ensures timely response, the video verification function enhances the credibility of incident handling, and the environmental adaptability ensures the stability of the system under extreme weather conditions.

[0039] In one possible implementation, the laser beam detection module uses an invisible laser beam to construct a three-dimensional protective light curtain with a detection range of 500 meters, covering the entire orbital area.

[0040] Invisible laser beams refer to beams with wavelengths in the infrared or ultraviolet bands, used to construct covert detection networks to avoid human interference. A three-dimensional protective light curtain refers to a three-dimensional monitoring area formed by multiple sets of intersecting laser beams, enabling omnidirectional coverage of the track space. Detection distance refers to the physical range within which the laser beam can effectively detect targets, ensuring real-time response to intrusion events within 500 meters. The track area refers to railway crossings and their extended security protection zone, used to define the core space that the system needs to monitor.

[0041] As a concrete example: A laser transmitter is deployed 500 meters outside a high-speed railway station, emitting an invisible infrared laser beam to a receiving end, forming vertical and horizontal intersecting gratings spaced 30 centimeters apart. When a train approaches, the system automatically disables the alarm function within a 10-meter radius of the track center area, monitoring only the protected areas on both sides; if a pedestrian enters the side grating, the system triggers a location alarm within 0.2 seconds.

[0042] Invisible lasers provide covert protection, three-dimensional light curtains eliminate monitoring blind spots, long-range detection meets the large-scale security needs of railways, and intelligent area shielding functions prevent false alarms of train passage, thus comprehensively improving the safety protection level of the track area.

[0043] In one possible implementation, the intelligent analysis module extracts target features using a CNN algorithm and combines them with motion trajectories to predict behavioral intent; target types include people and vehicles; false trigger filtering objects include animals and floating objects.

[0044] Among these, CNN algorithms refer to convolutional neural network models used to extract spatial features (such as shape and size) of targets from laser obstruction signals. Target features refer to attribute parameters quantified from sensor data, capable of distinguishing the physical differences between different targets (such as human silhouettes and vehicle volume). Motion trajectory refers to the displacement path of the target in the laser grating network, used to analyze movement direction and speed patterns. Behavioral intent refers to the target's next action predicted based on trajectory data (such as crossing or walking along the track). Personnel refers to individual humans who intrude into the monitoring area, used to trigger high-risk level alarms. Vehicles refer to motorized or non-motorized targets, capable of triggering differentiated response mechanisms (such as extending the gate closing time). Animals refer to birds, stray cats and dogs, etc., used to exclude non-threatening obstruction signals. Floating objects refer to lightweight objects such as plastic bags and balloons, which can avoid false alarms caused by environmental interference.

[0045] As a specific example: when a bird flies through the laser grating, the CNN algorithm extracts its 0.3m × 0.5m feature size and irregular movement trajectory, and determines it as animal interference; at the same time, the system detects another target approaching the track with a straight trajectory, and the feature size matches the size of an adult, and immediately marks it as human intrusion and initiates an early warning.

[0046] Multi-dimensional data analysis improves target recognition accuracy, convolutional neural networks enable deep analysis of complex features, trajectory prediction enhances the ability to predict dangerous behaviors, and classification mechanisms ensure differentiated responses to targets with different threat levels, effectively reducing system malfunctions caused by environmental factors.

[0047] In one possible implementation, the alarm linkage module supports a tiered response mechanism, activating warning lights and sound columns in the corresponding area based on the location of the intrusion target.

[0048] The tiered response mechanism can refer to alarm strategies categorized by threat level, used to initiate differentiated handling procedures for different risk levels. The intrusion target location can refer to coordinate data located via a laser grating network, enabling precise determination of the protected zone where the target is located. Warning lights can refer to high-brightness LED alarm devices that convey risk levels through different color flashing frequencies. Speakers can refer to directional sound wave transmitters used to play tiered warning voice messages in specific areas.

[0049] As a specific example: when the system detects personnel intrusion in the protection zone on the east side of the track, the linkage module activates the high-frequency flashing of the red warning light in that area, while the speaker plays the "Danger, Do Not Enter" message; if a vehicle is detected approaching on the west side, the yellow warning light is activated and the "Caution, Avoid" prompt is played to prevent excessive alarms from interfering with normal passage.

[0050] The beneficial effects are reflected in achieving accurate alarms through spatial positioning, optimizing response methods in different scenarios through a hierarchical mechanism, enhancing the warning effect through sound and light linkage, avoiding noise pollution through directional broadcasting, and comprehensively improving the intelligence and humanization level of the railway protection system.

[0051] In one possible implementation, the monitoring integration module calls up high-definition camera footage from the intrusion area, verifies the intrusion event through human detection and behavior analysis algorithms, and forms a closed-loop management with the security platform.

[0052] The monitoring integration module can refer to a central processing unit that integrates multiple video sources, used to coordinate data interaction and command distribution between security devices. High-definition cameras can refer to imaging devices with a resolution of 1080P or 4K, capable of capturing detailed features of intruders (such as face and clothing). Human detection algorithms can refer to models based on YOLO or Faster R-CNN to identify human silhouettes in images and distinguish non-human targets. Behavior analysis algorithms can refer to technologies combining skeletal point detection and trajectory prediction, used to determine the target's intentions (such as climbing or loitering). A security platform can refer to a software system that integrates alarm, storage, and linkage functions, enabling end-to-end management from monitoring to response. Closed-loop management can refer to a complete security chain including early warning, response, and feedback, used to ensure the effectiveness of incident handling.

[0053] As a specific example: A bank vault deploys a monitoring integration module that accesses real-time footage from six surrounding 4K cameras. When the human detection algorithm identifies someone approaching the security perimeter, the behavior analysis module analyzes that the person's loitering behavior has exceeded thirty seconds, immediately triggering the platform's linkage mechanism to simultaneously close the nearest access control door and push the real-time footage to the security terminal.

[0054] By integrating multi-source video to improve monitoring coverage, enhancing target recognition accuracy with high-definition image quality, reducing false alarm rate with dual algorithm verification, and strengthening emergency response efficiency through closed-loop management, an intelligent and highly reliable security system is constructed as a whole.

[0055] In one possible implementation, the environmental adaptive module eliminates environmental interference through multi-beam synchronous detection technology in a temperature range of -40°C to 70°C and under strong wind conditions.

[0056] The environmental adaptive module refers to an intelligent control system with extreme environmental tolerance capabilities, ensuring stable operation of the equipment under harsh conditions. The -40℃ to 70℃ temperature range refers to the extreme temperature range the equipment can operate normally in, covering various application scenarios from extremely cold to extremely hot. Strong wind conditions refer to wind conditions exceeding level eight, verifying the equipment's reliability under airflow disturbances. Multi-beam synchronous detection technology refers to a scheme that simultaneously emits multiple sets of lasers and compares the feedback signals, used to distinguish between real intrusions and environmental noise such as flying sand and gravel.

[0057] As a specific example: The monitoring system of an oilfield pipeline is equipped with an environment adaptive module. In a blizzard with temperatures as low as -35 degrees Celsius, the module automatically increases the laser emission power by 20% and starts a multi-beam cross-verification mode. Only when six out of ten laser beams continuously detect the same coordinate anomaly for three seconds will an alarm be triggered to avoid snow interference.

[0058] The beneficial effects are reflected in the wide temperature range adaptability ensuring the feasibility of deployment in all regions, the strong wind resistance design ensuring long-term outdoor stability, the multi-beam collaborative detection significantly reducing the false alarm rate, the intelligent power adjustment extending the equipment life, and the overall improvement of the comprehensive performance of the security system in extreme environments.

[0059] In one possible implementation, the intelligent analysis module uses the following calculation formula to determine the target's trajectory:

[0060]

[0061] Among them, v t The overall threat coefficient of the target is determined by trajectory velocity, behavioral weights, and environmental disturbances; Δx i and Δy i Δt represents the target's coordinate displacement at the i-th sampling time, derived from the spatial analysis of the laser grating occlusion signal; i w represents the sampling time interval. j θ is the preset weight for the j-th type of behavior. j From the perspective of behavioral continuity, d j The distance at which the behavior occurs; a k Let s be the interference correction factor for the k-th environmental sensor. kHere, T represents the sensor reading, and T is the system response time threshold.

[0062] As a concrete example: A nuclear power plant's perimeter protection system detects a target approaching the fence at a speed of two meters per second. The intelligent analysis module calculates its v... t Values: The trajectory speed item received a high score due to straight-line movement, the behavior item triggered a wandering judgment due to continuous 90-degree turns, and the environmental item had reduced credibility due to heavy rain. Finally, an orange warning was generated and pushed to the central control station to activate enhanced monitoring of the area.

[0063] Multi-dimensional parameter fusion enhances the scientific nature of threat assessment, dynamic correction mechanisms improve environmental adaptability, hierarchical response strategies optimize resource allocation, and real-time trajectory analysis shortens decision-making delays, achieving an overall intelligent upgrade from passive alarm to proactive early warning.

[0064] In one possible implementation, the response priority of the alarm linkage module is dynamically adjusted using the following calculation formula:

[0065]

[0066] Among them, P r For response priority; v q The threat coefficient for the q-th target is derived from the output of the intelligent analysis module; l q c is the straight-line distance between the target and the orbit. q f represents the current train density on the track. u Rate the clarity of the u-th surveillance camera; g u h represents the camera's field of view coverage. u b is the ambient light intensity; z e is the preset performance value for the z-th type of alarm device; z The device is in an available state; o z Determine the equipment deployment density.

[0067] As a concrete example: The subway dispatch center receives three alarm signals, and the module calculates P. r Values: The first target was given medium priority due to its high threat level but its distance of 300 meters from the track; the second target was given the highest priority due to full coverage by platform cameras and sufficient lighting; the third target was automatically downgraded due to its high equipment density but the gate was under maintenance. Ultimately, red, yellow and blue instructions were generated and pushed to different handling teams.

[0068] Dynamic priority mechanisms enhance the rationality of emergency response, multi-dimensional parameter fusion avoids deviations from single indicators, equipment status awareness reduces the risk of system misoperation, and resource load balancing optimization ensures long-term operational stability, achieving a leapfrog upgrade from mechanical response to intelligent scheduling.

[0069] In one possible implementation, the beam spacing of the laser beam detection module is dynamically adjusted according to the track width, with a minimum spacing of 10cm.

[0070] The laser beam detection module refers to security equipment that forms a protective light curtain by emitting invisible laser beams, used for high-precision perimeter protection. Beam spacing refers to the vertical distance between adjacent laser beams, which can adaptively adjust the detection density according to the track width. Track width refers to the physical span of the platform edges on both sides of the railway track, used to determine the lateral coverage of the protected area. Dynamic adjustment refers to the real-time calculation and reconfiguration of laser beam distribution parameters, which can optimize detection sensitivity in different scenarios. Minimum spacing refers to the minimum laser beam interval threshold allowed by the system, used to ensure effective identification of small intrusive objects.

[0071] As a specific example: A laser beam detection module is deployed on a high-speed railway platform. When a 1.2-meter-wide track area is detected, the system automatically adjusts the beam spacing to 15 centimeters. When a train enters the station and reduces the effective track width to 0.8 meters, the module immediately compresses the spacing to the lower limit of 10 centimeters while maintaining a scanning frequency of 30 times per second.

[0072] The adaptive spacing design enhances compatibility with different track scenarios, the minimum spacing limit ensures the ability to capture small falling objects, and the dynamic adjustment mechanism avoids manual reconfiguration, achieving a technological breakthrough from fixed protection to intelligent adaptation.

[0073] In one possible implementation, the AI ​​image recognition module uses the YOLOv7 model to detect intrusion targets in real time and performs spatiotemporal alignment verification with the occlusion signal of the laser beam detection module.

[0074] The AI ​​image recognition module refers to a deep learning-based visual analysis system used to capture abnormal targets in surveillance footage in real time. The YOLOv7 model refers to the most advanced single-stage target detection algorithm currently available, capable of high-speed recognition at hundreds of frames per second. Real-time detection refers to frame-by-frame video stream analysis and processing technology, ensuring zero-latency response to intrusion events. Intrusion target refers to a biological or object that has entered the protected area, used to trigger a tiered alarm mechanism. The laser beam detection module refers to an electronic fence constructed using infrared laser beams, used to provide a physical trigger signal. The obstruction signal refers to the electrical pulse generated when the laser beam is blocked, capable of accurately locating the intrusion site. Spatiotemporal alignment verification refers to timestamp matching and coordinate mapping of multi-sensor data, used to eliminate false alarms and missed alarms.

[0075] As a specific example: when YOLOv7 detects a human-shaped target climbing over a fence in the monitoring screen, the system immediately retrieves the obstruction signal of the laser beam module at that moment, confirms through coordinate transformation that the two are pointing to the same location, and finally generates a red alarm and activates the audible and visual warning device.

[0076] The dual verification of visual and physical detection enhances system reliability, the spatiotemporal alignment mechanism effectively filters out interference sources such as birds and fallen leaves, and the deep learning model ensures recognition accuracy in complex environments, forming an intelligent protection system that ranges from single-point detection to multi-dimensional collaboration.

[0077] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0078] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0079] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A railway safety protection system, characterized in that, include: A laser beam detection module consisting of laser emitters and receivers symmetrically arranged on both sides of the railway crossing is used to generate multiple sets of laser grating networks and monitor beam blocking events in real time. The intelligent analysis module is used to identify the type of intrusion target based on the occlusion signal of the laser grating network and analyze the target's movement trajectory to filter out false triggers; The alarm linkage module is used to trigger an audible and visual alarm, close the railway gate, and send alarm information to the control center after confirming an intrusion. The monitoring integration module is used to link with the video surveillance system to retrieve high-definition images of the intrusion area and use AI image recognition to assist in the judgment. An environment adaptive module is used to integrate temperature, humidity, and wind speed sensors to dynamically adjust the laser sensitivity threshold to adapt to environmental changes.

2. The railway safety protection system according to claim 1, characterized in that, The laser beam detection module uses an invisible laser beam to construct a three-dimensional protective light curtain, with a detection distance of 500 meters, covering the entire orbital area.

3. The railway safety protection system according to claim 1, characterized in that, The intelligent analysis module extracts target features using a CNN algorithm and predicts behavioral intent by combining motion trajectories. The target types include personnel and vehicles; The objects that are falsely triggered for filtering include animals and floating objects.

4. The railway safety protection system according to claim 1, characterized in that, The alarm linkage module supports a tiered response mechanism, which activates warning lights and sound columns in the corresponding areas based on the location of the intrusion target.

5. The railway safety protection system according to claim 1, characterized in that, The monitoring integration module calls up high-definition camera footage from the intrusion area, verifies the intrusion event through human detection and behavior analysis algorithms, and forms a closed-loop management with the security platform.

6. The railway safety protection system according to claim 1, characterized in that, The environmental adaptive module eliminates environmental interference through multi-beam synchronous detection technology in a temperature range of -40℃ to 70℃ and under strong wind conditions.

7. The railway safety protection system according to claim 3, characterized in that, The intelligent analysis module uses the following calculation formula to determine the target's trajectory: Among them, v t The overall threat coefficient of the target is determined by trajectory velocity, behavioral weights, and environmental disturbances; Δx i and Δy i Δt represents the target's coordinate displacement at the i-th sampling time, derived from the spatial analysis of the laser grating occlusion signal; i w represents the sampling time interval. j θ is the preset weight for the j-th type of behavior. j From the perspective of behavioral continuity, d j The distance at which the behavior occurs; a k Let s be the interference correction factor for the k-th environmental sensor. k Here, T represents the sensor reading, and T is the system response time threshold.

8. The railway safety protection system according to claim 7, characterized in that, The response priority of the alarm linkage module is dynamically adjusted using the following calculation formula: Among them, P r For response priority; v q The threat coefficient of the q-th target is derived from the output of the intelligent analysis module; l q c is the straight-line distance between the target and the orbit. q f represents the current train density on the track. u Rate the clarity of the u-th surveillance camera; g u h represents the camera's field of view coverage. u b is the ambient light intensity; z e is the preset performance value for the z-th type of alarm device; z The device is in an available state; z Determine the equipment deployment density.

9. The railway safety protection system according to claim 1, characterized in that, The beam spacing of the laser beam detection module is dynamically adjusted according to the track width, with a minimum spacing of 10cm.

10. The railway safety protection system according to claim 1, characterized in that, The AI ​​image recognition module uses the YOLOv7 model to detect intrusion targets in real time and performs spatiotemporal alignment verification with the occlusion signal of the laser beam detection module.

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