Rail transit wisdom flood prevention system based on internet of things technology

Abstract

The utility model discloses a track transportation wisdom flood control system based on internet of things technique, including sensor group, power module, control module, communication module and cloud server, the sensor group includes ultrasonic water level sensor, water immersion type sensor, battery fire detection sensor, power module is connected with sensor group, control module, communication module respectively, and power module provides power supply for sensor group, control module, communication module, control module includes MCU chip, and is connected with sensor group, is used for handling the signal of sensor group input, communication module includes 4 5G module or LoRa module or NBIoT module, and the data that sensor group gathered is handled through control module again and is uploaded to cloud server in real time through communication module, cloud server is used for receiving, storage, transmission monitoring data, the utility model discloses can improve the reliability and real -time of data, and effectively improve the working efficiency of track transportation flood control system.

Description

Technical Field

[0001] The utility model belongs to the technical field of rail transit safety, and particularly relates to a rail transit intelligent flood control system based on Internet of Things technology, which is applicable to the flood season safety monitoring and intelligent response of underground traffic facilities such as subways and light rails. Background Art

[0002] In recent years, with the progress of technologies such as sensors, wireless communication, artificial intelligence, and integrated circuits, the Internet of Things and its applications have developed rapidly and become increasingly popular. As an important part of public transportation, urban rail transit needs to ensure the safe travel of passengers at all times. Especially in the rainy season, rail transit flood control work is particularly important. However, there are problems such as data link redundancy, low equipment reliability, isolated functions, and poor communication stability in the existing technologies, specifically: 1. Data link redundancy: Traditional systems rely on manual inspections or single sensors, with high development difficulty in the cloud and lack of real-time analysis capabilities, resulting in low data collection efficiency and inability to respond to emergencies in a timely manner; 2. Low equipment reliability: The sensors in traditional systems have insufficient anti-interference capabilities and simple control logics, and are easily affected by environmental noise to generate false alarms, affecting the accuracy and reliability of the system; 3. Isolated functions: Traditional systems cannot be compatible with multiple types of monitoring devices (such as fire and smoke sensors), and emergency responses mainly rely on manual operations, making it difficult to achieve fast and intelligent linkage responses. The existence of these problems seriously affects the efficiency, reliability, and intelligent level of the rail transit flood control system, and cannot meet the requirements of modern rail transit for safety monitoring and intelligent response.

[0003] Patent CN214669651U discloses a rail transit flood control detection system, including a controller, and also including a photoelectric water immersion sensor. The signal output end of the photoelectric water immersion sensor is connected to the controller, the controller is connected to an Internet of Things intelligent switch, and the intelligent switch output interface of the Internet of Things intelligent switch is connected to a linkage output line. However, this system only uses a photoelectric water immersion sensor as the only data collection end, and still has the technical problem of single function. Summary of the Invention

[0004] In order to overcome the deficiencies of the existing technology, the utility model provides a rail transit intelligent flood control system based on Internet of Things technology with high reliability.

[0005] In order to achieve the above invention purpose, the technical solution of the utility model is as follows:

[0006] A rail transit intelligent flood control system based on Internet of Things technology includes a sensor group, a power supply module, a control module, a communication module, and a cloud server;

[0007] The sensor group includes an ultrasonic water level sensor, a water immersion sensor, and a battery fire detection sensor; wherein the ultrasonic water level sensor is used to monitor the water level in low-lying areas, the water immersion sensor is used to detect rainwater backflow, and the battery fire detection sensor is used to monitor abnormal temperature or smoke.

[0008] The power module is connected to the sensor group, the control module, and the communication module respectively, and provides power to the sensor group, the control module, and the communication module.

[0009] The control module includes an MCU chip, which is connected to the sensor group and is used to process the signals input from the sensor group.

[0010] The communication module includes a 4 / 5G module, a LoRa module, or an NBIoT module. The 4 / 5G module, LoRa module, or NBIoT module are connected to the control module through a USART interface. The data collected by the sensor group is processed by the control module and then uploaded to the cloud server in real time through the communication module.

[0011] The cloud server is used to receive, store, and transmit monitoring data.

[0012] How the system works:

[0013] By integrating ultrasonic water level sensors, water immersion sensors, and battery fire detection sensors, multi-source data acquisition such as water level, abnormal battery temperature, or smoke in rail transit is achieved, solving the problem of data link redundancy in traditional systems. Local preprocessing through the control module improves data reliability and real-time performance, and the communication module ensures that data can be uploaded to the cloud server in a timely manner.

[0014] As a further technological improvement, the IoT-based smart flood control system for rail transit also includes a virtual serial port unit and a host computer platform. The cloud server connects to the host computer platform through the virtual serial port unit, and data received by the cloud server is pushed to the host computer platform through the virtual serial port unit. The system adopts a lightweight cloud architecture, with the cloud server acting as a data relay node, receiving and forwarding data to the virtual serial port unit in a transparent transmission mode. This simplifies the communication process and system architecture with the host computer, reducing development complexity. The host computer platform uses a visual interface developed based on Visual Studio to display water levels, equipment online status, and fire alarm information in real time. It supports remote control functions. If the sensor group data, combined with the set water level prediction method, predicts that the overflow time is lower than the threshold or a fire signal is detected, the host computer platform automatically starts the water pump or cuts off the power, forming intelligent linkage between water pumps, power supplies, and other equipment, solving the problem of isolated functions in traditional systems.

[0015] As a further technological improvement, the IoT-based smart flood control system for rail transit also includes an expansion interface module; this module reserves standard protocol interfaces. New sensor types, such as smoke sensors and gas concentration sensors, can be flexibly added as needed without requiring significant hardware modifications.

[0016] As a further technological improvement, the smart flood control system for rail transit based on Internet of Things technology also includes an encapsulation shell and a substrate; the encapsulation shell is a protective shell made of encapsulation material; the substrate is a PCB board used to carry sensor groups, power modules, control modules, and communication modules.

[0017] As a further technological improvement, the sensor array also includes a soil moisture sensor, installed in low-lying areas of the rail transit system to monitor groundwater infiltration. During flood control in rail transit, monitoring points in low-lying areas of subway stations need to monitor not only water level changes but also the complex factors of groundwater infiltration. Traditional single water level sensors are insufficient to accurately reflect the overall water accumulation situation, potentially leading to misjudgments or missed reports. Based on the existing ultrasonic water level sensor in this system, a soil moisture sensor has been added to monitor groundwater infiltration, forming a multi-dimensional data acquisition network. The data from the newly added soil moisture sensor, along with the existing water level data, is transmitted to the MCU control module for local preprocessing. This allows for a comprehensive assessment of the actual water accumulation risk in low-lying areas, effectively overcoming the limitations of single water level monitoring in complex hydrological environments and further improving the system's accuracy and reliability.

[0018] As a further technological improvement, the control module of the smart flood control system for rail transit based on Internet of Things technology also includes an ADC chip; the ADC chip is a multi-channel analog-to-digital converter, and the ultrasonic water level sensor and the battery fire detection sensor are connected to the MCU chip through the ADC chip, which is used to convert the analog signals output by the ultrasonic water level sensor and the battery fire detection sensor into digital signals and transmit them to the MCU chip.

[0019] As a further technical improvement, the aforementioned sensor group adopts a dynamic heartbeat detection mechanism, sending a heartbeat command every 30 seconds; the host computer platform is set to time out after 30 seconds. This enables real-time monitoring of the online status of the data acquisition equipment. In the event of a disconnection, the host computer platform triggers an audible and visual alarm and records a log, further improving system reliability.

[0020] The advantages and beneficial effects of this utility model are as follows:

[0021] 1. This utility model uses a sensor group that integrates an ultrasonic water level sensor, a water immersion sensor, and a battery fire detection sensor to collect multi-source data such as water level, abnormal temperature, or smoke in rail transit, solving the problem of data link redundancy in traditional systems; it improves the reliability and real-time performance of data through local preprocessing by the control module, and ensures that data can be uploaded to the cloud server in a timely manner through the communication module, effectively improving the reliability and working efficiency of the rail transit flood control system.

[0022] 2. The cloud server of this utility model acts as a data relay node and does not perform complex data processing, thus greatly reducing the time that data stays in the cloud and improving the real-time performance of the system; the simple data forwarding function reduces the possibility of cloud server errors and also reduces the dependence on cloud server performance, improving the stability and reliability of the entire system; the cloud server adopts a transparent transmission mode to receive and forward data to the virtual serial port unit, which simplifies the system architecture and reduces development complexity and system operating costs.

[0023] 3. The host computer platform of this utility model can display water level, equipment online status and fire alarm information in real time, and supports remote control function, thereby realizing intelligent linkage control of flood control response or signal, and improving the intelligence level of rail transit flood control system. Attached Figure Description

[0024] Figure 1 This is a system architecture diagram of this utility model.

[0025] Figure 2 This is a partial circuit diagram of the system. Detailed Implementation

[0026] The present invention will be further described below with reference to specific embodiments.

[0027] Example 1:

[0028] A smart flood control system for rail transit based on Internet of Things (IoT) technology includes a sensor array, a power supply module, a control module, a communication module, and a cloud server.

[0029] The sensor group includes an ultrasonic water level sensor (model ZT-UL300-1), a water immersion sensor (model HSM-WT202), and a battery fire detection sensor (model MQ-2).

[0030] The power module is connected to the sensor group, the control module, and the communication module respectively, and provides power to the sensor group, the control module, and the communication module.

[0031] The control module includes an MCU chip (model STC12C5A60S2), which is connected to the sensor group and is used to process the signals input from the sensor group.

[0032] The communication module is a 4 / 5G module, a LoRa module, or an NBIoT module. The 4 / 5G module, LoRa module, or NBIoT module is connected to the control module through a USART interface. The data collected by the sensor group is processed by the control module and then uploaded to the cloud server in real time through the communication module.

[0033] The cloud server is used to receive, store, and transmit monitoring data.

[0034] The smart flood control system for rail transit based on Internet of Things technology also includes an encapsulation shell and a substrate; the encapsulation shell is a protective shell made of encapsulation material; the substrate is a PCB board used to carry sensor groups, power modules, control modules, and communication modules.

[0035] The control module also includes an ADC chip; the ADC chip is a multiplex analog-to-digital converter, and the ultrasonic water level sensor and the battery fire detection sensor are connected to the MCU chip through the ADC chip, which is used to convert the analog signals output by the ultrasonic water level sensor and the battery fire detection sensor into digital signals and transmit them to the MCU chip.

[0036] In this embodiment, a sensor group integrating ultrasonic water level sensors, water immersion sensors, and battery fire detection sensors is used to collect multi-source data such as water level, abnormal battery temperature, or smoke within the rail transit system, solving the data link redundancy problem of traditional systems. Local preprocessing by the control module improves data reliability and real-time performance, and the communication module ensures timely data upload to the cloud server. N collection points can be distributed across the monitoring area, each including a sensor group, power module, control module, and communication module. Data from all collection points is uploaded to the cloud server.

[0037] Example 2:

[0038] The difference from Embodiment 1 is that the communication module, LoRa module, or NBIoT module, is connected to the control module via a USART interface. The IoT-based smart flood control system for rail transit also includes a virtual serial port unit and a host computer platform. The cloud server connects to the host computer platform via the virtual serial port unit, and data received by the cloud server is pushed to the host computer platform through the virtual serial port unit. The system adopts a lightweight cloud architecture, with the cloud server acting as a data relay node, receiving and forwarding data to the virtual serial port unit in a transparent transmission mode. This simplifies the communication process and system architecture with the host computer, reducing development complexity. The host computer platform uses a visual interface developed based on Visual Studio to display real-time water levels, equipment online status, and fire alarm information. It supports remote control functions. If the sensor group data, combined with the set water level prediction method, predicts that the overflow time is below a threshold or a fire signal is detected, the host computer platform automatically starts the water pump or cuts off the power, forming intelligent linkage between the water pump, power supply, and other equipment, solving the problem of isolated functions in traditional systems.

[0039] The water level prediction method is as follows:

[0040] 1. Calculation of water level rise rate:

[0041] v = Δh / Δt

[0042] in:

[0043] v - Rate of water level rise (cm / s)

[0044] Δh - The change in water level between two consecutive measurements (cm)

[0045] Δt - The time interval (s) between two consecutive measurements

[0046] 2. Calculation of predicted overflow time:

[0047] T = (H_threshold - H_current) / v

[0048] in:

[0049] T - Predicted overflow time (s)

[0050] H_threshold - Preset water level threshold (e.g., 50cm)

[0051] H_current - Current water level measurement (cm)

[0052] v - Rate of water level rise (cm / s)

[0053] The sensor group adopts a dynamic heartbeat detection mechanism and sends a heartbeat instruction every 30s. The upper computer platform sets a 30s timeout judgment, which can realize real-time monitoring of the online status of the data acquisition device. When the device goes offline, the upper computer platform triggers an audible and visual alarm and records the log, further improving the reliability of the system.

[0054] Embodiment 3:

[0055] The difference from Embodiment 2 is that the communication module is an NBIoT module, and the NBIoT module is connected to the control module through the USART interface. The rail transit intelligent flood control system based on the Internet of Things technology further includes an expansion interface module, and the expansion interface module reserves a standard protocol interface. New sensor types, such as smoke sensors and gas concentration sensors, can be flexibly added according to requirements without major hardware modifications.

[0056] Embodiment 4:

[0057] The difference from Embodiment 2 is that the sensor group further includes a soil humidity sensor, which is installed in the low-lying area of the rail transit to monitor the groundwater infiltration situation. During the flood control process of rail transit, the flood control monitoring points in the low-lying areas of subway stations not only need to monitor the water level changes, but also need to consider the complex factors of groundwater infiltration. Traditional single water level sensors are difficult to accurately reflect the overall water accumulation situation, which may lead to misjudgment or missed reports. Based on the original ultrasonic water level sensor in this system, a soil humidity sensor is added to monitor the groundwater infiltration situation, forming a multi-dimensional data acquisition network. The data of the newly added soil humidity sensor and the original water level data are transmitted to the MCU control module for local preprocessing together, which can comprehensively evaluate the actual water accumulation risk in the low-lying area, effectively solve the limitation of single water level monitoring in complex hydrological environments, and further improve the accuracy and reliability of the system.

[0058] In this embodiment, through the data fusion of the ultrasonic water level sensor and the soil humidity sensor, a multi-parameter fusion water accumulation evaluation system is formed to calculate the risk index. The risk index calculation formula is as follows:

[0059] RI = α * f(W) + β * g(S_norm) + γ * h(dS / dt)

[0060] α, β, γ: are the dynamic weight coefficients of the water level, humidity, and humidity change rate respectively.

[0061] f(W): water level influence function. It is designed as a piecewise linear or non-linear function (such as Sigmoid), which maps the water level value to the [0, 1] interval. Specifically:

[0062] W < W_low (safety threshold): f(W) = 0

[0063] W_low <= W <= W_high (warning threshold): f(W) = (W - W_low) / (W_high - W_low)

[0064] W > W_high: f(W) = 1

[0065] g(S_norm): Humidity influence function. Also mapped to [0, 1]. Since the risk increases sharply after humidity saturation, a non-linear function can be used, specifically:

[0066] S_norm < S_th_low: g(S_norm) = 0

[0067] S_th_low <= S_norm <= S_th_high: g(S_norm) = k * (S_norm - S_th_low) (k is a coefficient)

[0068] S_norm > S_th_high: g(S_norm) = 1 (or rapidly rise to 1)

[0069] h(dS / dt): Humidity change rate influence function, mapped to [0, 1], specifically:

[0070] dS / dt <= Rate_low (normal slow change): h(dS / dt) = 0

[0071] Rate_low < dS / dt <= Rate_high (medium acceleration): h(dS / dt) = (dS / dt - Rate_low) / (Rate_high - Rate_low)

[0072] dS / dt > Rate_high (sharp rise): h(dS / dt) = 1 (or use a larger coefficient to amplify the influence)

[0073] The weights α, β, γ are not fixed, but are dynamically adjusted according to the flood season and non-flood season. During the flood season (April to September): Increase α (water level weight) because surface runoff is the main risk source, (α = 0.6, β = 0.2, γ = 0.2).

[0074] During the non-flood season (October to March): Increase β (humidity weight) and γ (change rate weight) because groundwater infiltration and saturation成为主要风险 (α = 0.2, β = 0.4, γ = 0.4).

[0075] Divide the risk levels according to the calculated RI (range [0, 1]).

[0076] Level 0 (Normal / Low Risk): RI < RI_low_threshold (0.3), low water level, normal and stable humidity.

[0077] Level 1 (Attention / Low to Medium Risk): RI_low_threshold <= RI < RI_med_threshold (0.3 <= RI < 0.6), the water level has risen somewhat or the humidity has increased somewhat but the change is not fast.

[0078] Level 2 (Warning / Medium to High Risk): RI_med_threshold <= RI < RI_high_threshold (0.6 <= RI < 0.8), the water level is close to the warning line, or the humidity is high and increasing rapidly.

[0079] Level 3 (Severe / High Risk): RI >= RI_high_threshold (e.g., RI >= 0.8) or h(dS / dt) == 1 (triggered by a sharp rise in humidity).

[0080] Upload the calculated RI value, the finally determined risk level, and the key parameters (W, S_norm, dS / dt, α, β, γ) to the cloud server through the communication module. The upper computer platform detects the risk level through the cloud server and activates the corresponding pre - plan.

[0081] The above are only the preferred embodiments of the present utility model. The protection scope of the present utility model is not limited to the above - mentioned embodiments. All technical solutions falling within the idea of the present utility model belong to the protection scope of the present utility model. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present utility model, several improvements and refinements should also be regarded as within the protection scope of the present utility model.

Claims

1. A smart flood control system for rail transit based on Internet of Things (IoT) technology, characterized in that: It includes a sensor array, a power module, a control module, a communication module, and a cloud server; The sensor group includes an ultrasonic water level sensor, a water immersion sensor, and a battery fire detection sensor. The power module is connected to the sensor group, the control module, and the communication module respectively, and provides power to the sensor group, the control module, and the communication module. The control module includes an MCU chip, which is connected to the sensor group and is used to process the signals input from the sensor group. The communication module includes a 4 / 5G module, a LoRa module, or an NBIoT module. The 4 / 5G module, LoRa module, or NBIoT module are connected to the control module through a USART interface. The data collected by the sensor group is processed by the control module and then uploaded to the cloud server in real time through the communication module. The cloud server is used to receive, store, and transmit monitoring data.

2. The smart flood control system for rail transit based on Internet of Things technology according to claim 1, characterized in that: It also includes a virtual serial port unit and a host computer platform; the cloud server is connected to the host computer platform through the virtual serial port unit, and the data received by the cloud server is pushed to the host computer platform through the virtual serial port unit.

3. The smart flood control system for rail transit based on Internet of Things technology according to claim 1, characterized in that: It also includes an extension interface module; the extension interface module reserves a standard protocol interface.

4. The smart flood control system for rail transit based on Internet of Things technology according to claim 1, characterized in that: It also includes a packaging shell and a substrate; the packaging shell is a protective shell made of packaging material; the substrate is a PCB board used to carry the sensor group, power module, control module and communication module.

5. The smart flood control system for rail transit based on Internet of Things technology according to claim 1, characterized in that: The sensor array also includes a soil moisture sensor, which is installed in low-lying areas of the rail transit system to monitor groundwater infiltration.

6. The smart flood control system for rail transit based on Internet of Things technology according to claim 1, characterized in that: The control module also includes an ADC chip; the ADC chip is a multiplex analog-to-digital converter, and the ultrasonic water level sensor and the battery fire detection sensor are connected to the MCU chip through the ADC chip, which is used to convert the analog signals output by the ultrasonic water level sensor and the battery fire detection sensor into digital signals and transmit them to the MCU chip.

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