NB-LOT-based train proximity detection sensor

Abstract

The utility model relates to the technical field of railway transportation safety monitoring and intelligent operation and maintenance, and discloses a train approach detection sensor based on NB-LOT, which comprises a magnetic steel detection unit, a magnetic steel processing unit, an internet of things communication module and a power supply unit. According to the utility model, an intelligent alarm system covering the whole scene of a railway can be formed with a cloud server and Ethernet alarm equipment based on the Internet communication technology, stable remote communication, low-delay data transmission and multi-environment adaptability can be realized, and early warning and fault tolerance can be realized by utilizing AI analysis of a cloud platform; therefore, the safety guarantee capability of railway maintenance personnel is remarkably improved, and the accident risk of collision between a train and operating personnel is reduced.

Description

Technical Field

[0001] This utility model relates to the field of railway transportation safety monitoring and intelligent operation and maintenance technology, specifically to a train proximity detection sensor based on NB-IoT. Background Technology

[0002] As a vital national transportation infrastructure, railway transportation's safety and operational efficiency directly impact economic and social development. With the continuous expansion of the railway network and the increasing speed of trains, the demand for safety monitoring and intelligent operation and maintenance in complex areas such as railway station throats and marshalling yards is becoming increasingly urgent. Traditional railway safety monitoring mainly relies on manual inspections and localized sensor deployments, which suffer from limited coverage, insufficient real-time performance, and slow response times. In recent years, with the development of the Internet of Things, artificial intelligence, and big data technologies, the railway industry has gradually transformed towards intelligent and digital transformation, aiming to achieve comprehensive monitoring and precise management of key railway areas.

[0003] Currently, typical solutions in the field of railway safety monitoring include:

[0004] ① Sensor network monitoring: By deploying various sensors (such as vibration sensors, temperature sensors, video surveillance equipment, etc.), railway equipment and environment are monitored in real time, data is collected and uploaded to the central control system for analysis.

[0005] ② Intelligent image recognition technology: Using computer vision technology to conduct video surveillance of railway areas and automatically identify abnormal events (such as trains deviating from the track, equipment failure, etc.).

[0006] ③ Artificial intelligence data analysis: Based on machine learning algorithms, historical and real-time data are analyzed to predict potential faults and provide early warnings.

[0007] However, existing solutions still face some challenges when applied to complex areas such as railway station throats and marshalling yards: first, the cost of sensor deployment is high; second, the data integration and analysis capabilities are insufficient; and third, the adaptability and compliance with the "Railway Freight Car Operation and Maintenance Regulations" need to be improved.

[0008] In the field of railway transportation safety monitoring, existing technologies mainly focus on three aspects: sensor network monitoring, intelligent image recognition, and artificial intelligence data analysis. These existing technologies have some application value in railway station monitoring, but from... Figure 8 As can be seen, the existing technology has a relatively simple structure and lacks targeted design for complex areas and deep fusion processing of multi-source data, resulting in problems such as limited monitoring range, insufficient real-time performance, and poor compliance. In other words, it has the following drawbacks:

[0009] ① Limited coverage

[0010] Problem: The sensor modules are mainly concentrated on the track and the train itself, resulting in insufficient coverage of complex areas such as the throat area of ​​railway stations and marshalling yards, leading to many monitoring blind spots.

[0011] Impact: It is impossible to comprehensively monitor the equipment status and environmental changes in complex areas, making it difficult to meet the requirements for high-precision monitoring.

[0012] ② Poor real-time performance

[0013] Problem: The response time of the data acquisition and processing module is too long, making it difficult to meet the real-time monitoring requirements of high-speed railways.

[0014] Impact: Delays in the detection and early warning of abnormal events may result in safety hazards not being discovered and addressed in a timely manner.

[0015] ③ Insufficient compliance

[0016] Problem: The system fails to fully meet the technical requirements for monitoring systems in the "Railway Freight Car Operation and Maintenance Regulations", for example, it lacks the ability to monitor key parameters such as magnetization current and surface current density.

[0017] Impact: The system's technical specifications differ from industry standards, making it difficult to meet the compliance requirements of railway operation and maintenance.

[0018] ④ High deployment costs

[0019] Problem: The large number and dispersed nature of the sensor modules result in high system construction and maintenance costs.

[0020] Impact: It limited the widespread application of this technology in large-scale railway networks.

[0021] No effective solutions have yet been proposed to address the problems in the relevant technologies. Utility Model Content

[0022] To address the problems in related technologies, this utility model proposes a train proximity detection sensor based on NB-IoT, aiming to solve the following problems: expanding the monitoring range to achieve comprehensive coverage of complex areas such as railway station throat areas and marshalling yards; improving real-time performance and shortening the response time for data acquisition and processing; enhancing compliance to ensure that the system meets the technical requirements of the "Railway Freight Car Operation and Maintenance Regulations"; and reducing costs by optimizing the sensor deployment scheme and improving the system's economic efficiency.

[0023] Therefore, the specific technical solution adopted by this utility model is as follows:

[0024] A train proximity detection sensor based on NB-IoT includes a magnet detection unit, a magnet processing unit, an IoT communication module, and a power supply unit. The magnet detection unit, magnet processing unit, and IoT communication module can communicate with a cloud server and an Ethernet alarm device. The power supply unit is connected to the magnet processing unit and the IoT communication module.

[0025] Preferably, the magnetic steel detection unit includes resistors R10, R17, and R18, diode D2, diode LED2, capacitor C20, and optocoupler U1. The first pin of optocoupler U1 is connected to the negative terminal of diode D2 and one end of resistor R18, while the other end of resistor R18 is connected to interface P4. The positive terminal of diode D2 is connected to interface P4 and the third pin of optocoupler U1. The fourth pin of optocoupler U1 is connected to one end of capacitor C20 and grounded. The other end of capacitor C20 is connected to the negative terminal of diode LED2, one end of resistor R10, and the sixth pin of optocoupler U1. The positive terminal of diode LED2 is connected to one end of resistor R17, and the other end of resistor R17 is connected to the other end of resistor R10 and the positive terminal of the power supply. The magnetic steel detection unit also includes resistors R26, R27, and R28, diode D6, diode LED4, capacitor C49, and optocoupler U6. The first pin of optocoupler U6 is connected to the negative terminal of diode D6 and one end of resistor R28. The other end of resistor R28 is connected to interface P7. The positive terminal of diode D6 is connected to interface P7 and the third pin of optocoupler U6. The fourth pin of optocoupler U6 is connected to one end of capacitor C49 and grounded. The other end of capacitor C49 is connected to the negative terminal of diode LED4, one end of resistor R27, and the sixth pin of optocoupler U6. The positive terminal of diode LED4 is connected to one end of resistor R26. The other end of resistor R26 is connected to the other end of resistor R27 and the positive power supply terminal.

[0026] Preferably, optocoupler U1 and optocoupler U6 are both TLP127, diodes D2 and D6 are both Zener diodes, and diodes LED2 and LED4 are both light-emitting diodes.

[0027] Preferably, the magnet processing unit includes a main control chip U3, capacitors C2, C3, and C4; wherein the third, thirty-third, and thirty-fifth pins of the main control chip U3 are connected to capacitors C4, C2, and C3, respectively; the magnet processing unit also includes resistors R12, R13, and R14, diode LED1, interface P3, interface P2, inductors L1 and L2, capacitor C7, crystal oscillator Y1, and crystal oscillator Y2; wherein one end of resistor R12 is connected to one end of resistor R13, and the other end of resistor R13 is grounded; one end of resistor R14 is connected to the positive terminal of the power supply, and the other end of resistor R14 is connected to the positive terminal of diode LED1, and the negative terminal of diode LED1 is connected to the fourth pin of the main control chip U3; the second pin of interface P3 and the fourth pin of interface P2 are also connected to the fourth pin of interface P2. All pins are grounded. The first pin of interface P2 is connected to the positive terminal of the power supply. The first pin of interface P3 is connected to the twenty-sixth pin of the main control chip U3. The second and third pins of interface P2 are connected to the twelfth and eleventh pins of the main control chip U3, respectively. One end of inductor L1 is connected to one end of inductor L2, one end of capacitor C7, and the first pin of the main control chip U3. The other end of capacitor C7 is grounded. The other end of inductor L1 is connected to the second pin of the main control chip U3. The other end of inductor L2 is connected to the thirty-fifth pin of the main control chip U3. The two ends of crystal oscillator Y1 are connected to the forty-seventh and forty-eighth pins of the main control chip U3, respectively. The two ends of crystal oscillator Y2 are connected to the thirty-first and thirty-second pins of the main control chip U3, respectively. The other end of crystal oscillator Y2 is also grounded.

[0028] Preferably, the main control chip U3 is model CH582M.

[0029] Preferably, the IoT communication module includes an NB module M1, a card slot SIM1, a card slot SIM2, a capacitor C1, capacitors C5, C6, C15, C16, C19, C22, C3, and C24, resistors R1, R3, R7, R8, R9, R11, R29, and R30, a serial port J4, an antenna interface UFL-R-SMT-1, a transistor Q3, and a MOSFET Q2; wherein, pins 22, 23, 25, 26, 28, 29, and 30 of the NB module M1 are all grounded; the first pin of the card slot SIM1 is respectively... One end of capacitor C6 is connected to pin 4 of SIM1 and pin 15 of NB module M1, and the other end of capacitor C6 is grounded. Pins 3, 5, and 6 of SIM1 are connected to pins 12, 13, and 14 of NB module M1, respectively. Pins 2, 7, and 8 of SIM1 are grounded. Pin 1 of SIM2 is grounded, and pins 3, 6, 7, and 8 of SIM2 are connected to pins 14, 13, 12, and 15 of NB module M1, respectively. One end of resistor R9 is connected to pin 6 of main control chip U3, and the other end of resistor R9 is connected to pin 15 of NB module M1. Connect the tenth pin of module M1; connect one end of resistor R11 to the fifth pin of the main control chip U3; connect the other end of resistor R11 to the ninth pin of NB module M1; connect one end of resistor R1, one end of resistor R3, and one end of resistor R7 to the thirteenth, fourteenth, and twelfth pins of NB module M1 respectively; connect the other ends of resistors R1, R3, and R7 to one end of capacitor C1, one end of capacitor C15, and one end of capacitor C5 respectively; connect the other ends of capacitors C1, C15, and C5 to ground; connect the first and second pins of serial port J4 to the tenth and ninth pins of NB module M1 respectively. Connect the serial port J4 to ground; one end of capacitor C22 is connected to the 31st and 32nd pins of NB module M1, one end of capacitor C23, and one end of capacitor C24 respectively, and the other ends of capacitors C22, C23, and C24 are all grounded; the first pin of antenna interface UFL-R-SMT-1 is connected to one end of capacitor C19 and one end of resistor R8 respectively, the other end of capacitor C8 is connected to one end of capacitor C16 and the 27th pin of NB module M1 respectively, and the other ends of capacitors C16 and C19 are all grounded; the second, third, and fourth pins of antenna interface UFL-R-SMT-1 are all grounded;The emitter of transistor Q3 is grounded. The base of transistor Q3 is connected to one end of resistor R30, and the other end of resistor R30 is connected to pin 22 of the main control chip U3. The collector of transistor Q3 is connected to one end of resistor R29 and pin 1 of the MOSFET. The other end of resistor R29 is connected to pin 2 of the MOSFET. The third pin of the MOSFET is connected to pins 31 and 32 of the NB module M1.

[0030] Preferably, the NB module M1 is model MN316DLVD, the MOSFET Q2 is model SSM3J328R, and the transistor Q3 is model MMBT2222.

[0031] The beneficial effects of this utility model are as follows:

[0032] 1) This utility model constructs an intelligent alarm system covering the entire railway scenario through Internet-based communication technology (NB-IoT), cloud server integration, and redundant design. It can achieve stable remote communication, low-latency data transmission, and adaptability to multiple environments (including field scenarios). It also utilizes cloud platform AI analysis to achieve early warning and fault tolerance, thereby significantly improving the safety assurance capabilities of railway maintenance personnel, reducing the risk of collisions between trains and workers, and significantly improving the performance and application value of railway safety monitoring systems. It provides an efficient, economical, and compliant solution for the field of railway transportation safety monitoring, and provides strong support for the intelligent transformation of the railway industry, thus having important practical application value.

[0033] 2) Significantly improved communication stability and coverage: By adopting NB-IoT combined with the IoT network coverage of the three major operators, the communication distance is extended to the whole country in complex environments along the railway (such as tunnels and station obstacles), breaking through the physical limitation of 10 kilometers of traditional LoRa communication; the network interruption rate is reduced to <0.1%; it supports deployment in all scenarios, including remote fields, with a coverage rate of over 98%;

[0034] 3) Low latency and high capacity real-time data transmission: Lightweight data transmission based on the MQTT protocol, with a single frame data transmission latency of ≤100ms (90% improvement compared to the traditional LoRa's 1 second / frame), meeting the real-time early warning requirements of high-speed train scenarios, supporting concurrent communication of 1000+ devices without data blocking, and increasing system throughput to 5000 messages / second.

[0035] 4) Full-scene adaptability and deployment flexibility: The solar power module and long-endurance battery design eliminate the need for an external power source. The equipment can work continuously for ≥7 days in the absence of sunlight (such as on cloudy or rainy days), adapting to the needs of field construction and maintenance. The installation time is ≤2 hours, meeting the railway maintenance "window" restrictions and improving deployment efficiency by 60%.

[0036] 5) Intelligent early warning and fault tolerance: The cloud server AI model (LSTM neural network) and data redundancy compensation mechanism ensure that the train arrival time prediction accuracy error is ≤5 seconds, the early warning advance is 5-10 seconds, the false alarm rate is reduced to <0.5%, and when equipment fails, the cloud platform compensates through data interpolation of adjacent nodes, and the overall system reliability is ≥99.9%.

[0037] 6) Multi-channel redundant alarm mechanism: Ethernet control unit and multi-level alarm output (radio + loudspeaker), radio broadcast covers UHF band (430-440MHz), walkie-talkie reception success rate ≥99%, loudspeaker sound pressure level ≥120dB, effective coverage radius ≥500 meters, ensuring alarm reachability in complex noise environments.

[0038] 7) Low maintenance cost and long operation cycle: NB-IoT low power consumption design (≤200mW) and solar power supply, the equipment has a battery life of 3-5 years, the battery replacement frequency is reduced by 80%, no need to lay cables or conduct regular inspections, and the operation and maintenance cost is reduced by more than 50%.

[0039] 8) Social and economic benefits: Through precise early warning and redundant design, the accident rate of train collisions with maintenance personnel is expected to decrease by 90%; the loss of line downtime caused by accidents can be reduced, saving hundreds of millions of yuan in railway operation and maintenance costs annually; the system can be extended to subway, mining areas and other scenarios, and has wide applicability. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this utility model or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this utility model. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a structural block diagram of a train proximity detection sensor based on NB-IoT according to an embodiment of the present utility model;

[0042] Figure 2 This is a circuit diagram of a magnet detection unit in a train proximity detection sensor based on NB-LOT according to an embodiment of the present utility model;

[0043] Figure 3 This is a circuit diagram of the magnet processing unit in a train proximity detection sensor based on NB-LOT according to an embodiment of the present invention.

[0044] Figure 4 This is a circuit diagram of an Internet of Things (IoT) communication module in a train proximity detection sensor based on NB-IoT, according to an embodiment of the present invention.

[0045] Figure 5 This is a solar charging circuit diagram in a train proximity detection sensor based on NB-IoT according to an embodiment of the present invention;

[0046] Figure 6 This is a circuit diagram of the USB interface in a train proximity detection sensor based on NB-IoT according to an embodiment of the present invention.

[0047] Figure 7 This is a voltage regulation circuit diagram of a train proximity detection sensor based on NB-IoT according to an embodiment of the present utility model;

[0048] Figure 8 This is a block diagram of a train proximity alarm system in the existing technology. Detailed Implementation

[0049] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are usually used to represent similar components.

[0050] According to embodiments of this utility model, a train proximity detection sensor based on NB-IoT is provided, aiming to solve the following technical problems of existing train proximity alarm systems: ① Insufficient communication distance and stability: Traditional LoRa communication suffers from signal instability due to obstacles in railway stations, limiting coverage. ② Communication delay and capacity deficiencies: LoRa single-frame data transmission takes a long time (1 second / frame), easily causing communication congestion when multiple devices operate concurrently, affecting real-time performance. ③ Limited application scenarios: It is only applicable to station environments and cannot meet the needs of railway construction and maintenance in the field. ④ Low technical scalability and reliability: It relies on local detection, and equipment failure can easily lead to missed detections. Furthermore, it lacks deep integration with Internet technology, making it difficult to achieve global early warning and intelligent analysis.

[0051] By utilizing Internet-based communication technology (NB1), cloud server integration, and redundant design, an intelligent alarm system covering all railway scenarios is constructed. This system enables stable remote communication, low-latency data transmission, and adaptability to multiple environments (including field scenarios). It also leverages cloud platform AI analysis to provide early warnings and fault tolerance, thereby significantly improving the safety assurance capabilities of railway maintenance personnel and reducing the risk of collisions between trains and workers.

[0052] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figures 1-7As shown, the NB-IoT-based train proximity detection sensor according to an embodiment of the present invention includes a magnet detection unit, a magnet processing unit, an Internet of Things communication module, and a power supply unit.

[0053] Among them, the magnetic steel detection unit, the magnetic steel processing unit, and the Internet of Things communication module are connected to the cloud server and the Ethernet alarm device through the network of the telecommunications company, and the power supply unit is connected to the magnetic steel processing unit and the Internet of Things communication module respectively.

[0054] Specifically, the magnetic steel detection unit is deployed on the track side. It uses magnetic steel sensors to detect changes in the magnetic field generated when train wheels pass by, triggering a signal to detect train approach. The magnetic steel sensor uses a high-sensitivity Hall element (such as model A1324LUA-T) with a detection distance ≥1.5 meters and a response time ≤10ms.

[0055] Magnet processing unit: Receives signals from the magnet detection unit, performs filtering and digitization processing, and generates train approach information. The processing unit has a built-in CH582 domestic MCU and acquires dual-channel magnet signals via interrupts.

[0056] IoT communication module: integrates NB-IoT (Narrowband Internet of Things) to upload processed train information to the cloud server.

[0057] Specifically, the technical details of the IoT communication module are as follows:

[0058] Priority mode: In areas covered by NB network (such as the outskirts of cities), low-power NB-IoT transmission is used, with power consumption ≤200mW and battery life of 3-5 years.

[0059] Data protocol: Data packets are encapsulated based on the MQTT protocol, with frame format in JSON format, including fields such as timestamp, device ID, and train speed.

[0060] Function: To solve the problems of limited communication distance and delay, and to adapt to the complex environment along railway lines.

[0061] Power supply module: integrates solar panels and lithium batteries, supporting deployment without external power supply.

[0062] Specifically, the technical details of the power supply module are as follows:

[0063] Solar power supply: It adopts a monocrystalline silicon solar panel (power 10W, conversion efficiency ≥22%), and is equipped with a TP4056 charging management chip.

[0064] Energy storage design: 3.7V / 10000mAh lithium-ion battery, supporting continuous operation for ≥7 days in cloudy or rainy weather.

[0065] Function: To solve the deployment challenges in remote areas without power supply and to meet the requirements of the installation gap period for railway equipment.

[0066] The signal transmission and workflow of the above technical solution are as follows:

[0067] ① Train Approach Detection: A magnetic sensor detects a passing wheel signal → the processing unit digitizes the signal → a "train approach" event is generated. ② Data Transmission: Event data is uploaded to the cloud server via NB → the AI ​​model calculates the warning time → a command is generated and sent to the Ethernet control unit. ③ Alarm Trigger: Dual CAT units simultaneously activate radio broadcast and loudspeaker → maintenance personnel receive the alarm and evacuate. ④ System Self-Check: The cloud platform monitors the equipment status in real time → faulty equipment is automatically marked and a compensation mechanism is activated.

[0068] In one embodiment, the magnetic steel detection unit includes resistors R10, R17, and R18, diode D2, diode LED2, capacitor C20, and optocoupler U1. The first pin of optocoupler U1 is connected to the negative terminal of diode D2 and one end of resistor R18, the other end of resistor R18 is connected to interface P4, and the positive terminal of diode D2 is connected to interface P4 and the third pin of optocoupler U1. The fourth pin of optocoupler U1 is connected to one end of capacitor C20 and grounded, and the other end of capacitor C20 is connected to the negative terminal of diode LED2, one end of resistor R10, and the sixth pin of optocoupler U1. The positive terminal of diode LED2 is connected to one end of resistor R17, and the other end of resistor R17 is connected to the other end of resistor R10 and the positive power supply. The magnetic steel detection unit also includes resistors R26, R27, and R28, diode D6, diode LED4, capacitor C49, and optocoupler U6. The first pin of optocoupler U6 is connected to the negative terminal of diode D6 and one end of resistor R28. The other end of resistor R28 is connected to interface P7. The positive terminal of diode D6 is connected to interface P7 and the third pin of optocoupler U6. The fourth pin of optocoupler U6 is connected to one end of capacitor C49 and grounded. The other end of capacitor C49 is connected to the negative terminal of diode LED4, one end of resistor R27, and the sixth pin of optocoupler U6. The positive terminal of diode LED4 is connected to one end of resistor R26. The other end of resistor R26 is connected to the other end of resistor R27 and the positive power supply. Optocouplers U1 and U6 are both TLP127, diodes D2 and D6 are Zener diodes, and diodes LED2 and LED4 are light-emitting diodes.

[0069] Specifically, the main components and working principle of the magnet detection section are as follows:

[0070] Main components:

[0071] 1) Sensor section: It contains two identical detection channels (WKUP1 and WKUP2), each channel using a TLP127 optocoupler (optically isolated magnetic sensor);

[0072] 2) Signal processing:

[0073] R17 (10K) and R28 (470R): Pull-up / current limiting resistors;

[0074] C20 and C49 (0.1uF): Filter capacitors used to eliminate signal jitter;

[0075] 3) Status indication:

[0076] LED2 and LED4: Detection status indicator lights;

[0077] A single LED marker may indicate a power indicator;

[0078] Working principle:

[0079] 1) Magnet testing:

[0080] When the train wheel approaches the magnet, an induced current is generated, which drives the internal LED of the TLP127, turns on the phototransistor, generates a low level, and triggers the microcontroller interrupt.

[0081] The TLP127 is an optocoupler that provides electrically isolated switching signals;

[0082] 2) Signal output:

[0083] WKUP1 / WKUP2 are wake-up signal output terminals, which will generate a level change when a magnet is detected.

[0084] A 10K resistor may be used for pull-up, and a 470R resistor may be used for current limiting.

[0085] 3) Filter design:

[0086] A 0.1uF capacitor filters out high-frequency interference, ensuring signal stability;

[0087] 4) Status display:

[0088] The LED indicator light changes according to the detection status, providing visual feedback;

[0089] In addition, this embodiment may include a voltage regulator circuit after the magnet detection unit, the main components and working principle of which are as follows:

[0090] Main components:

[0091] ME6214-3V: Low dropout linear regulator (LDO), fixed output 3V, with EN (enable) control function;

[0092] Input (VCC): The specific voltage is not specified, but it is usually 3.3V to 4.2V;

[0093] Output (+3V): Stable 3V output for use by other circuits;

[0094] Filter capacitor:

[0095] C8 (10μF): Input filter to reduce power supply noise;

[0096] C17 (0.1μF): High-frequency decoupling, suppressing high-frequency interference;

[0097] C11 / C13 / C14 / C47 / C48 (100μF): Output energy storage capacitors to improve load transient response capability;

[0098] Working principle:

[0099] 1) Input voltage (VCC) enters the IN pin of ME6214-3V;

[0100] 2) The EN (enable) pin controls the chip's operation:

[0101] EN = high level → chip operates, input voltage;

[0102] EN = low level → chip shut down, no output (power saving mode);

[0103] 3) Voltage stabilization process:

[0104] The internal regulating transistor of the chip automatically adjusts to keep the OUT terminal stable at 3V;

[0105] When the load changes, the LDO dynamically adjusts to maintain a constant output voltage.

[0106] 4) Filtering and energy storage:

[0107] Input capacitors (C8, C17) filter out high-frequency noise from the input power supply;

[0108] Output capacitors (C11 / C13 / C14 / C47 / C48) provide instantaneous high current to prevent voltage drops.

[0109] In one embodiment, the magnet processing unit includes a main control chip U3, capacitors C2, C3, and C4; wherein the third, thirty-third, and thirty-fifth pins of the main control chip U3 are connected to capacitors C4, C2, and C3, respectively; the magnet processing unit also includes resistors R12, R13, and R14, diode LED1, interface P3, interface P2, inductors L1 and L2, capacitor C7, crystal oscillator Y1, and crystal oscillator Y2; wherein one end of resistor R12 is connected to one end of resistor R13, and the other end of resistor R13 is grounded; one end of resistor R14 is connected to the positive terminal of the power supply, and the other end of resistor R14 is connected to the positive terminal of diode LED1; the negative terminal of diode LED1 is connected to the fourth pin of the main control chip U3; the second pin of interface P3 and the third pin of interface P2 are connected to the fourth pin of interface P2. All four pins are grounded. The first pin of interface P2 is connected to the positive terminal of the power supply. The first pin of interface P3 is connected to the twenty-sixth pin of the main control chip U3. The second and third pins of interface P2 are connected to the twelfth and eleventh pins of the main control chip U3, respectively. One end of inductor L1 is connected to one end of inductor L2, one end of capacitor C7, and the first pin of the main control chip U3. The other end of capacitor C7 is grounded. The other end of inductor L1 is connected to the second pin of the main control chip U3. The other end of inductor L2 is connected to the thirty-fifth pin of the main control chip U3. The two ends of crystal oscillator Y1 are connected to the forty-seventh and forty-eighth pins of the main control chip U3, respectively. The two ends of crystal oscillator Y2 are connected to the thirty-first and thirty-second pins of the main control chip U3, respectively. The other end of crystal oscillator Y2 is also grounded.

[0110] In one embodiment, the main control chip U3 is model CH582M with an operating frequency of 168MHz.

[0111] In one embodiment, the IoT communication module includes an NB module M1, a card slot SIM1, a card slot SIM2, a capacitor C1, capacitors C5, C6, C15, C16, C19, C22, C3, and C24, resistors R1, R3, R7, R8, R9, R11, R29, and R30, a serial port J4, an antenna interface UFL-R-SMT-1, a transistor Q3, and a MOSFET Q2; wherein, pins 22, 23, 25, 26, 28, 29, and 30 of the NB module M1 are all grounded; the first pin of the card slot SIM1... Connect one end of capacitor C6, the fourth pin of SIM1, and the fifteenth pin of NB module M1 respectively. The other end of capacitor C6 is grounded. The third, fifth, and sixth pins of SIM1 are connected to the twelfth, thirteenth, and fourteenth pins of NB module M1 respectively. The second, seventh, and eighth pins of SIM1 are grounded. The first pin of SIM2 is grounded. The third, sixth, seventh, and eighth pins of SIM2 are connected to the fourteenth, thirteenth, twelfth, and fifteenth pins of NB module M1 respectively. One end of resistor R9 is connected to the sixth pin of the main control chip U3, and the other end of resistor R9 is connected to the NB module M1. The tenth pin of module M1 is connected to the main control chip U3. One end of resistor R11 is connected to the fifth pin of the main control chip U3, and the other end of resistor R11 is connected to the ninth pin of the NB module M1. One end of resistor R1, one end of resistor R3, and one end of resistor R7 are connected to the thirteenth, fourteenth, and twelfth pins of the NB module M1, respectively. The other ends of resistor R1, R3, and R7 are connected to one end of capacitor C1, one end of capacitor C15, and one end of capacitor C5, respectively. The other ends of capacitors C1, C15, and C5 are all grounded. The first and second pins of serial port J4 are connected to the tenth and ninth pins of the NB module M1, respectively. Connect the serial port J4 to ground; one end of capacitor C22 is connected to the 31st and 32nd pins of NB module M1, one end of capacitor C23, and one end of capacitor C24 respectively, and the other ends of capacitors C22, C23, and C24 are all grounded; the first pin of antenna interface UFL-R-SMT-1 is connected to one end of capacitor C19 and one end of resistor R8 respectively, the other end of capacitor C8 is connected to one end of capacitor C16 and the 27th pin of NB module M1 respectively, and the other ends of capacitors C16 and C19 are all grounded; the second, third, and fourth pins of antenna interface UFL-R-SMT-1 are all grounded;The emitter of transistor Q3 is grounded. The base of transistor Q3 is connected to one end of resistor R30, and the other end of resistor R30 is connected to pin 22 of the main control chip U3. The collector of transistor Q3 is connected to one end of resistor R29 and pin 1 of the MOSFET. The other end of resistor R29 is connected to pin 2 of the MOSFET. Pin 3 of the MOSFET is connected to pins 31 and 32 of the NB module M1. The NB module M1 is model MN316DLVD, the MOSFET Q2 is model SSM3J328R, and the transistor Q3 is model MMBT2222.

[0112] Specifically, the NB module uses the MN316. SIM1 is the SIM card socket for debugging; SIM2 is for use after mass production of the chip card. Three 22Ω resistors and a 22pF capacitor at the SIM card interface are used for SIM card signal anti-interference. R9 and R11 are for level matching between the module and the microcontroller's TTL serial port; J4 is reserved for serial port debugging. R8, C16, and C19 are connected to the antenna mount, forming a 50Ω antenna matching circuit. C22, C23, and C24 are for power supply filtering. Q2 and Q3 form a power switch, which can be controlled by the microcontroller to power the NB. The NB circuit power supply range is 3.3V to 4.2V. Working principle: The microcontroller powers the module and sends AT commands via the serial port to send data to the cloud server.

[0113] In addition, the power supply unit in this embodiment also includes a solar charging circuit, the specific composition and principle of which are as follows:

[0114] Main components:

[0115] 1) Solar input section: Connect the solar panel via P5 interface. YTY / Y0K / Y20 and other markings are the identifiers for the solar panel input end;

[0116] 2) Energy storage components: C12 (22uF / 50V electrolytic capacitor) is used for power supply filtering, C10 is another filter capacitor, and BAT is the battery connection terminal;

[0117] 3) Charging control: MPPT (Maximum Power Point Tracking) is the core of the solar charging system, used to optimize the power output of the solar panels; CSP is the current sensing point;

[0118] 4) Protection circuit: Multiple diodes (D2-D600+) are used to prevent reverse current and protect the solar panel and battery; LED3 is a charging status indicator.

[0119] 5) Resistor networks: R23 / R24, etc., are used for voltage division, current detection, or current limiting;

[0120] Working principle:

[0121] 1) Energy harvesting: The solar panel converts light energy into electrical energy, which is then input into the circuit via the P5 interface;

[0122] 2) MPPT control: The MPPT controller continuously adjusts the operating point to ensure that the solar panel always operates at maximum power output.

[0123] 3) Charging management: The system adjusts the charging current according to the battery voltage and charging status, and uses diodes to prevent the battery from discharging back to the solar panel at night;

[0124] 4) Status indication: LED3 displays the charging status (e.g., charging / fully charged / fault).

[0125] In summary, by utilizing the above-mentioned technical solution of this utility model, this utility model, through Internet-based communication technology (NB), can construct an intelligent alarm system covering the entire railway scenario with cloud servers and Ethernet alarm devices. It can achieve stable remote communication, low-latency data transmission, and adaptability to multiple environments (including field scenarios). Furthermore, it utilizes cloud platform AI analysis to achieve early warning and fault tolerance, thereby significantly improving the safety assurance capabilities of railway maintenance personnel, reducing the risk of collisions between trains and workers, and significantly enhancing the performance and application value of railway safety monitoring systems. It provides an efficient, economical, and compliant solution for the field of railway transportation safety monitoring, while also providing strong support for the intelligent transformation of the railway industry, and has significant practical application value.

[0126] In addition, the communication stability and coverage are significantly improved: adopting the NB-IoT design and combined with the IoT network coverage of the three major operators, the communication distance is extended to the whole country, breaking through the physical limitation of 10 kilometers of traditional LoRa communication; the network interruption rate is reduced to <0.1%; it supports deployment in all scenarios, including remote and wild areas, with a coverage rate of over 98%;

[0127] In addition, it features low latency and high capacity real-time data transmission: the lightweight data transmission based on the MQTT protocol has a single-frame data transmission latency of ≤100ms (90% improvement over the traditional LoRa's 1 second / frame), meeting the real-time early warning requirements of high-speed train scenarios, supporting concurrent communication of 1000+ devices without data blocking, and increasing the system throughput to 5000 messages / second.

[0128] In addition, it offers full-scenario adaptability and deployment flexibility: the solar power module and long-endurance battery design eliminate the need for an external power source. The equipment can work continuously for ≥7 days in the absence of sunlight (such as on cloudy or rainy days), adapting to the needs of field construction and maintenance. The installation time is ≤2 hours, meeting the railway maintenance "window period" restrictions and improving deployment efficiency by 60%.

[0129] In addition, intelligent early warning and fault tolerance capabilities: cloud server AI model (LSTM neural network) and data redundancy compensation mechanism, train arrival time prediction accuracy error ≤ 5 seconds, early warning advance 5-10 seconds, false alarm rate reduced to <0.5%, when equipment fails, cloud platform compensates through data interpolation of adjacent nodes, the overall system reliability ≥99.9%.

[0130] In addition, a multi-channel redundant alarm mechanism is provided: an Ethernet control unit and multi-level alarm output (radio + loudspeaker), radio broadcasting covers the UHF band (430-440MHz), walkie-talkie reception success rate ≥99%, loudspeaker sound pressure level ≥120dB, effective coverage radius ≥500 meters, ensuring alarm reachability in complex noise environments.

[0131] In addition, it features low maintenance costs and long operating cycles: NB-IoT's low power consumption design (≤200mW) and solar power supply allow the device to last for 3-5 years, reducing battery replacement frequency by 80%, eliminating the need for laying cables or regular inspections, and reducing maintenance costs by more than 50%.

[0132] In addition, the social and economic benefits are as follows: through precise early warning and redundant design, the accident rate of train collisions with maintenance personnel is expected to decrease by 90%; the losses caused by line downtime due to accidents can be reduced, saving hundreds of millions of yuan in railway operation and maintenance costs annually; the system can be extended to subway, mining areas and other scenarios, and has wide applicability.

[0133] The above description is only a preferred embodiment of the present utility model and is not intended to limit the present utility model. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present utility model should be included within the protection scope of the present utility model.

Claims

1. A train approach detection sensor based on NB-LOT, characterized by, It includes a magnet detection unit, a magnet processing unit, an IoT communication module, and a power supply unit; The magnet detection unit, the magnet processing unit, and the IoT communication module are connected to the cloud server and the Ethernet alarm device via a network, and the power supply unit is connected to the magnet processing unit and the IoT communication module respectively.

2. The NB-LOT based train proximity detection sensor of claim 1, wherein, The magnetic steel detection unit includes resistors R10, R17, and R18, diode D2, diode LED2, capacitor C20, and optocoupler U1. The first pin of the optocoupler U1 is connected to the negative terminal of the diode D2 and one end of the resistor R18, the other end of the resistor R18 is connected to the interface P4, and the positive terminal of the diode D2 is connected to the interface P4 and the third pin of the optocoupler U1. The fourth pin of the optocoupler U1 is connected to one end of the capacitor C20 and grounded. The other end of the capacitor C20 is connected to the negative terminal of the diode LED2, one end of the resistor R10, and the sixth pin of the optocoupler U1. The positive terminal of the diode LED2 is connected to one end of the resistor R17, and the other end of the resistor R17 is connected to the other end of the resistor R10 and the positive terminal of the power supply. The magnetic steel detection unit also includes resistors R26, R27, and R28, diode D6, diode LED4, capacitor C49, and optocoupler U6; The first pin of the optocoupler U6 is connected to the negative terminal of the diode D6 and one end of the resistor R28, the other end of the resistor R28 is connected to the interface P7, and the positive terminal of the diode D6 is connected to the interface P7 and the third pin of the optocoupler U6. The fourth pin of the optocoupler U6 is connected to one end of the capacitor C49 and grounded. The other end of the capacitor C49 is connected to the negative terminal of the diode LED4, one end of the resistor R27, and the sixth pin of the optocoupler U6. The positive terminal of the diode LED4 is connected to one end of the resistor R26, and the other end of the resistor R26 is connected to the other end of the resistor R27 and the positive terminal of the power supply.

3. The NB-LOT based train proximity detection sensor of claim 2, wherein, Both optocoupler U1 and optocoupler U6 are TLP127, both diodes D2 and D6 are Zener diodes, and both diodes LED2 and LED4 are light-emitting diodes.

4. The NB-LOT based train proximity detection sensor of claim 1, wherein, The magnet processing unit includes a main control chip U3, capacitor C2, capacitor C3 and capacitor C4; The third, thirty-third, and thirty-fifth pins of the main control chip U3 are connected to capacitor C4, capacitor C2, and capacitor C3, respectively. The magnet processing unit also includes resistors R12, R13, and R14, diode LED1, interface P3, interface P2, inductor L1, inductor L2, capacitor C7, crystal oscillator Y1, and crystal oscillator Y2. Wherein, one end of resistor R12 is connected to one end of resistor R13, and the other end of resistor R13 is grounded; One end of the resistor R14 is connected to the positive terminal of the power supply, the other end of the resistor R14 is connected to the positive terminal of the diode LED1, and the negative terminal of the diode LED1 is connected to the fourth pin of the main control chip U3. The second pin of interface P3 and the fourth pin of interface P2 are both grounded. The first pin of interface P2 is connected to the positive terminal of the power supply. The first pin of interface P3 is connected to the twenty-sixth pin of the main control chip U3. The second and third pins of interface P2 are connected to the twelfth and eleventh pins of the main control chip U3, respectively. One end of the inductor L1 is connected to one end of the inductor L2, one end of the capacitor C7, and the first pin of the main control chip U3. The other end of the capacitor C7 is grounded. The other end of the inductor L1 is connected to the second pin of the main control chip U3. The other end of the inductor L2 is connected to the thirty-fifth pin of the main control chip U3. The two ends of the crystal oscillator Y1 are connected to the 47th and 48th pins of the main control chip U3, respectively. The two ends of the crystal oscillator Y2 are connected to the 31st and 32nd pins of the main control chip U3, respectively. The other end of the crystal oscillator Y2 is also grounded.

5. The NB-LOT based train proximity detection sensor of claim 4, wherein, The main control chip U3 is model CH582M.

6. The NB-LOT based train proximity detection sensor of claim 4, wherein, The IoT communication module includes NB module M1, SIM1 and SIM2 card slots, capacitor C1, capacitors C5, C6, C15, C16, C19, C22, C3, and C24, resistors R1, R3, R7, R8, R9, R11, R29, and R30, serial port J4, antenna interface UFL-R-SMT-1, transistor Q3, and MOSFET Q2. Among them, the 22nd, 23rd, 25th, 26th, 28th, 29th and 30th pins of the NB module M1 are all grounded; The first pin of the card slot SIM1 is connected to one end of the capacitor C6, the fourth pin of the card slot SIM1, and the fifteenth pin of the NB module M1, respectively. The other end of the capacitor C6 is grounded. The third, fifth, and sixth pins of the card slot SIM1 are connected to the twelfth, thirteenth, and fourteenth pins of the NB module M1, respectively. The second, seventh, and eighth pins of the card slot SIM1 are all grounded. The first pin of the SIM card slot 2 is grounded, and the third, sixth, seventh and eighth pins of the SIM card slot 2 are connected to the fourteenth, thirteenth, twelfth and fifteenth pins of the NB module M1, respectively. One end of the resistor R9 is connected to the sixth pin of the main control chip U3, and the other end of the resistor R9 is connected to the tenth pin of the NB module M1. One end of the resistor R11 is connected to the fifth pin of the main control chip U3, and the other end of the resistor R11 is connected to the ninth pin of the NB module M1. One end of resistor R1, one end of resistor R3, and one end of resistor R7 are respectively connected to pin 13, pin 14, and pin 12 of NB module M1. The other ends of resistor R1, resistor R3, and resistor R7 are respectively connected to one end of capacitor C1, one end of capacitor C15, and one end of capacitor C5. The other ends of capacitor C1, capacitor C15, and capacitor C5 are all grounded. The first and second pins of the serial port J4 are connected to the tenth and ninth pins of the NB module M1, respectively, and the third pin of the serial port J4 is grounded. One end of capacitor C22 is connected to the 31st and 32nd pins of NB module M1, one end of capacitor C23 and one end of capacitor C24 respectively, and the other ends of capacitor C22, capacitor C23 and capacitor C24 are all grounded. The first pin of the antenna interface UFL-R-SMT-1 is connected to one end of the capacitor C19 and one end of the resistor R8, respectively. The other end of the capacitor C8 is connected to one end of the capacitor C16 and the twenty-seventh pin of the NB module M1, respectively. The other ends of the capacitor C16 and the other ends of the capacitor C19 are both grounded. The second, third and fourth pins of the antenna interface UFL-R-SMT-1 are all grounded. The emitter of transistor Q3 is grounded, the base of transistor Q3 is connected to one end of resistor R30, the other end of resistor R30 is connected to the twenty-second pin of the main control chip U3, the collector of transistor Q3 is connected to one end of resistor R29 and the first pin of the MOSFET, the other end of resistor R29 is connected to the second pin of the MOSFET, and the third pin of the MOSFET is connected to the thirty-first and thirty-second pins of the NB module M1.

7. The NB-LOT based train proximity detection sensor of claim 6, wherein, The NB module M1 is model MN316DLVD, the MOSFET Q2 is model SSM3J328R, and the transistor Q3 is model MMBT2222.

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