Intelligent pipeline support hanger monitoring system and method based on thermoelectric power generation self-power supply

Abstract

The invention discloses an intelligent pipeline support hanger monitoring system and method based on thermoelectric power generation self power supply, and relates to the technical field of pipeline monitoring and Internet of Things, and the system comprises a thermoelectric power generation module which generates a first voltage signal based on the difference value between the pipeline surface temperature and the environment temperature; the energy management module is used for storing energy and outputting a working voltage signal; the multi-parameter intelligent monitoring module is used for acquiring an original displacement signal, an original pressure signal and an original temperature signal of the support hanger, generating digital monitoring data based on the original displacement signal, the original pressure signal and the original temperature signal, and generating a state judgment signal based on a comparison result of the digital monitoring data and a preset threshold value; and the 5G Internet of Things communication module selects a transmission mode, processes the digital monitoring data to generate a data packet, transmits the data packet based on a 5G network and outputs monitoring state information. All-weather, self-powered and high-frequency intelligent monitoring of the state of the pipeline support hanger is achieved, and the safety and reliability of pipeline operation are greatly improved.

Description

Technical Field

[0001] This invention relates to the fields of pipeline monitoring and Internet of Things (IoT) technology, and in particular to an intelligent pipeline support and hanger monitoring system and method based on thermoelectric power generation. Background Technology

[0002] In industries such as petroleum, chemical, and power, pipeline systems play a crucial role in transporting high-temperature, high-pressure fluids. Pipelines, as key components supporting and constraining pipelines, have their health status directly impacting the safe operation of the entire pipeline system. Traditional methods of inspecting pipeline supports primarily rely on regular manual inspections. This approach is not only inefficient and costly but also fails to detect sudden faults in a timely manner, posing significant safety hazards. To improve the safety and reliability of pipeline systems, the industry has begun exploring the application of intelligent monitoring technologies in the health management of pipeline supports.

[0003] Currently, some intelligent pipe support monitoring devices have emerged on the market, using sensors to monitor key parameters such as displacement, stress, and temperature in real time. However, existing solutions still have the following significant drawbacks: First, power supply and wiring issues are prominent. In complex industrial sites, especially at heights, in pipe corridors, explosion-proof areas, and remote locations, deploying dedicated power and signal lines for monitoring equipment is extremely difficult and costly, and may introduce new safety hazards. While battery power can avoid wiring problems, it requires regular replacement and maintenance, resulting in a huge workload. Furthermore, batteries degrade rapidly under extreme temperatures, making long-term reliable operation difficult to guarantee. Second, the real-time and comprehensiveness of monitoring are insufficient. Due to limitations in power supply and communication wiring, existing monitoring equipment often has low deployment density and limited monitoring point coverage. It also frequently employs low-frequency sampling and local data storage, failing to achieve wide coverage, high real-time performance, and high-frequency online intelligent monitoring. Many devices monitor only a single parameter and lack efficient data feedback mechanisms, making it impossible to promptly capture transient anomalies or conduct in-depth intelligent fault diagnosis and analysis, thus failing to meet the actual needs of industrial sites for real-time monitoring and early warning of the health status of pipe supports.

[0004] Therefore, how to overcome the power supply bottleneck and realize the self-powered, high-frequency multi-parameter acquisition and real-time intelligent diagnosis of the support and hanger monitoring system has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] This invention provides an intelligent pipeline support and hanger monitoring system and method based on thermoelectric power generation to solve the above-mentioned problems in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A smart pipeline support and hanger monitoring system based on thermoelectric power generation includes:

[0008] Thermoelectric power generation module generates a first voltage signal based on the difference between the surface temperature of the pipe and the ambient temperature;

[0009] The energy management module stores energy based on the first voltage signal and outputs a working voltage signal;

[0010] The multi-parameter intelligent monitoring module is powered by the working voltage signal, collects the original displacement signal, original pressure signal and original temperature signal of the support and hanger, generates digital monitoring data based on the original displacement signal, original pressure signal and original temperature signal, and generates a status judgment signal based on the comparison result of the digital monitoring data and the preset threshold.

[0011] The 5G IoT communication module selects the transmission mode based on the status determination signal, processes the digital monitoring data to generate data packets based on the transmission mode, transmits the data packets based on the 5G network, and outputs the monitoring status information.

[0012] Furthermore, the thermoelectric power generation module includes:

[0013] The thermoelectric conversion unit has its hot end attached to the surface of the pipe to collect a first temperature, and its cold end exposed to the ambient air to collect a second temperature. Based on the difference between the first temperature and the second temperature, a first voltage signal is generated through the Seebeck effect.

[0014] A heat-conducting substrate is disposed between the surface of the pipe and the hot end to conduct heat from the surface of the pipe to the hot end;

[0015] Heat dissipation fins, connected to the cold end, dissipate heat from the cold end to the ambient air.

[0016] Furthermore, the energy management module includes:

[0017] The energy storage unit is charged based on the first voltage signal and outputs an energy storage voltage signal;

[0018] The voltage conversion circuit generates a stable working voltage signal by boosting or bucking the energy storage voltage signal.

[0019] The voltage monitoring circuit generates a working status signal based on the comparison between the energy storage voltage signal and the start-up threshold. When the energy storage voltage signal is lower than the start-up threshold, it outputs a sleep control signal.

[0020] Furthermore, the multi-parameter intelligent monitoring module includes:

[0021] The sensor group, powered by the operating voltage signal, includes a laser displacement sensor that outputs the original displacement signal, a strain gauge pressure sensor that outputs the original pressure signal, and a temperature sensor that outputs the original temperature signal.

[0022] The main control unit performs analog-to-digital conversion based on the original displacement signal, original pressure signal, and original temperature signal to generate digital monitoring data. It then compares the digital monitoring data with the stored preset thresholds to generate a status determination signal.

[0023] Furthermore, the laser displacement sensor includes:

[0024] The first laser displacement sensor illuminates the first measuring point on the pipe surface and generates a first displacement component signal based on the reflected light distance measurement.

[0025] The second laser displacement sensor illuminates the second measuring point on the pipe surface and generates a second displacement component signal based on the reflected light distance measurement.

[0026] The third laser displacement sensor illuminates the third measuring point on the pipe surface and generates a third displacement component signal based on the reflected light distance measurement.

[0027] The first, second, and third displacement components together constitute the original displacement signal.

[0028] Furthermore, the strain gauge pressure sensor includes:

[0029] The first strain gauge pressure sensor is attached to the first bearing position of the support and hanger, and generates a first pressure component signal based on the strain change at that position;

[0030] The second strain gauge pressure sensor is attached to the second bearing position of the support and generates a second pressure component signal based on the strain change at that position.

[0031] The first pressure component signal and the second pressure component signal together constitute the original pressure signal.

[0032] Furthermore, the main control unit includes:

[0033] The signal conditioning unit amplifies and filters the original displacement signal, original pressure signal, and original temperature signal to generate a conditioned signal.

[0034] The analog-to-digital conversion unit performs analog-to-digital conversion on the conditioned signal to generate digital monitoring data;

[0035] The threshold comparison unit generates an abnormal state judgment signal when any data exceeds the corresponding threshold, based on the comparison results of digital monitoring data with the corresponding preset thresholds; otherwise, it generates a normal state judgment signal.

[0036] Furthermore, the 5G IoT communication module includes:

[0037] The transmission mode selection unit selects the periodic transmission mode when the status determination signal is normal and selects the real-time transmission mode when the status determination signal is abnormal, and outputs a mode control signal.

[0038] The data encapsulation unit compresses and encapsulates digital monitoring data to generate compressed data packets when the mode control signal is in periodic transmission mode, and maintains the original format of digital monitoring data to generate original data packets when the mode control signal is in real-time transmission mode.

[0039] The wireless transmission unit sends compressed or raw data packets to the cloud platform based on the 5G network protocol, and generates monitoring status information containing the transmission status based on the transmission results.

[0040] Furthermore, a system-based intelligent pipeline support and hanger monitoring method based on thermoelectric self-powered generation includes:

[0041] S1: The thermoelectric power generation module generates a first voltage signal based on the difference between the pipe surface temperature and the ambient temperature;

[0042] S2: The energy management module stores energy based on the first voltage signal and outputs a working voltage signal;

[0043] S3: The multi-parameter intelligent monitoring module is powered by the working voltage signal, collects the original displacement signal, original pressure signal and original temperature signal of the support and hanger, generates digital monitoring data based on the collected signals, and generates a status judgment signal based on the comparison result of the digital monitoring data and the preset threshold.

[0044] S4: The 5G IoT communication module selects the transmission mode based on the status determination signal, processes the digital monitoring data to generate data packets based on the transmission mode, transmits the data packets based on the 5G network, and outputs the monitoring status information.

[0045] Furthermore, S3 includes:

[0046] S31: The sensor group is powered by the working voltage signal. The laser displacement sensor outputs the original displacement signal through distance measurement, the strain gauge pressure sensor outputs the original pressure signal through strain detection, and the temperature sensor outputs the original temperature signal through temperature detection.

[0047] S32: The main control unit amplifies and filters the original displacement signal, original pressure signal and original temperature signal to generate a conditioned signal;

[0048] S33: Perform analog-to-digital conversion based on the conditioned signal to generate digital monitoring data;

[0049] S34: Based on the comparison of digital monitoring data with corresponding preset thresholds one by one, an abnormal state judgment signal is generated when any data exceeds the threshold, otherwise a normal state judgment signal is generated.

[0050] Compared with the prior art, the present invention has the following advantages:

[0051] The present invention provides an intelligent pipeline support and hanger monitoring system and method based on thermoelectric power generation, which achieves the following technical effects:

[0052] First, it achieves true "zero wiring, zero external power" deployment. This system fundamentally eliminates reliance on fixed power supplies and wired communication networks. It utilizes the pipeline's own waste heat through a thermoelectric generator module to achieve energy self-sufficiency, and transmits monitoring data in real-time via a 5G wireless network. This allows monitoring equipment to be "mounted" anywhere on the pipeline, especially in high-altitude areas, pipe corridors, explosion-proof areas, and remote locations where traditional wiring is difficult to reach, enabling rapid and flexible installation and deployment. This technical solution greatly simplifies the engineering implementation process, reduces wiring costs and construction safety risks by more than 90%, and achieves "plug-and-play" monitoring points and rapid large-scale deployment.

[0053] Secondly, it achieves all-weather, permanent online monitoring. The thermoelectric power generation module can continuously utilize the stable temperature difference that exists during pipeline operation to provide uninterrupted energy supply 24 / 7. Combined with low-power design and high-performance energy storage unit, the system can operate autonomously and stably for a long time in extreme industrial environments with no sunlight, no wind, and no external maintenance, achieving truly permanent online monitoring, and the maintenance cost throughout the equipment's life cycle is close to zero.

[0054] Third, it achieves comprehensive monitoring and rapid response. The system adopts high-frequency, multi-parameter intelligent sensing technology, which can simultaneously monitor multiple dimensions of operating parameters of supports and hangers, such as displacement, stress, vibration, and temperature. The monitoring frequency is significantly improved, and it can promptly capture transient anomalies and subtle changing trends, providing a comprehensive and reliable data foundation for accurate assessment of the health status of supports and hangers and early warning of faults.

[0055] Fourth, the system achieves intelligent and networked collaboration. This system deeply integrates local edge intelligent judgment with cloud-based big data analysis, ensuring reliable, low-latency transmission of massive amounts of high-frequency monitoring data and real-time alarm information through 5G networks. This technical architecture not only significantly improves the accuracy of fault diagnosis and the speed of emergency response, but also provides key data node support for building a digital twin model of the entire pipeline system and a predictive maintenance platform, powerfully promoting the in-depth development of industrial pipeline monitoring technology towards intelligence and networking. Attached Figure Description

[0056] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0057] Figure 1 This is a structural diagram of an intelligent pipeline support and hanger monitoring system based on thermoelectric power generation in an embodiment of the present invention;

[0058] Figure 2 This is a flowchart illustrating the operation of an intelligent pipeline support and hanger monitoring system based on thermoelectric power generation in an embodiment of the present invention. Detailed Implementation

[0059] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0060] This invention provides an intelligent pipeline support and hanger monitoring system based on thermoelectric power generation, comprising:

[0061] Thermoelectric power generation module generates a first voltage signal based on the difference between the surface temperature of the pipe and the ambient temperature;

[0062] The energy management module stores energy based on the first voltage signal and outputs a working voltage signal;

[0063] The multi-parameter intelligent monitoring module is powered by the working voltage signal, collects the original displacement signal, original pressure signal and original temperature signal of the support and hanger, generates digital monitoring data based on the original displacement signal, original pressure signal and original temperature signal, and generates a status judgment signal based on the comparison result of the digital monitoring data and the preset threshold.

[0064] The 5G IoT communication module selects the transmission mode based on the status determination signal, processes the digital monitoring data to generate data packets based on the transmission mode, transmits the data packets based on the 5G network, and outputs the monitoring status information.

[0065] The following is a detailed description with reference to specific embodiments.

[0066] Example 1:

[0067] like Figure 1As shown, this embodiment provides an intelligent pipeline support and hanger monitoring system based on thermoelectric power generation for self-powered operation. The core hardware of this system is integrated into a waterproof and dustproof aluminum-plastic coated metal casing, which is fixed to the support and hanger body using clamps or bolts, facilitating on-site installation and subsequent maintenance. The thermoelectric power generation module consists of three parts: a hot-end contacting the pipeline surface, a thermoelectric array, and a cold-end heat dissipation fin. The working process is as follows: the hot-end contacting the pipeline surface collects heat from the pipeline; the thermoelectric array converts the heat energy into electrical energy based on the Seebeck effect; the cold-end heat dissipation fins exchange heat with the environment to maintain the necessary temperature difference; and finally, the module outputs electrical energy. The energy management module includes a maximum power point tracking (MPPT), an energy storage unit (HPC / supercapacitor), and a DC-DC voltage regulator circuit. The MPPT optimizes energy capture efficiency, and the output electrical energy is stored in the HPC / supercapacitor and converted into a stable voltage by the DC-DC voltage regulator circuit to power the entire system. The multi-parameter intelligent monitoring module includes a sensor group and a main control unit (MCU). The sensor group is responsible for collecting the physical parameters of the pipeline and supports, while the MCU processes the sensor data. The processed data is then transmitted to the 5G IoT communication module via a data reporting channel. The 5G IoT communication module contains a 5G module. It receives data from the MCU and reports the data to a remote cloud platform via a wireless network, enabling remote transmission and cloud processing of monitoring data.

[0068] Specifically, the thermoelectric power generation module is implemented as follows: The thermoelectric power generation module includes a thermoelectric conversion unit, a thermally conductive substrate, and heat dissipation fins. The thermoelectric conversion unit uses a commercially available semiconductor thermoelectric generator, with its hot end attached to the pipe surface to collect a first temperature, and its cold end exposed to ambient air to collect a second temperature. During installation, the contact surface of the thermally conductive substrate is first cleaned, and then high-temperature thermal grease is applied between the thermally conductive substrate and the pipe surface. The thermally conductive substrate is then tightly attached to the pipe surface using an aluminum alloy clamp or a special fixture. The high temperature of the pipe surface (e.g., 200°C for steam pipes) causes the hot end of the thermoelectric conversion unit to heat up to near the pipe temperature through heat conduction from the thermally conductive substrate. Aluminum heat dissipation fins are welded to the cold end of the thermoelectric conversion unit. These fins dissipate heat from the cold end to the ambient air through natural convection, maintaining a relatively low temperature at the cold end (e.g., ambient temperature 30°C). Based on the temperature difference between the first and second temperatures (approximately 170°C in this example), the thermoelectric conversion unit generates a first voltage signal across its terminals through the Seebeck effect. A single thermoelectric generator can produce several watts of electrical energy output under these temperature difference conditions. Depending on the actual power consumption requirements of the system, multiple thermoelectric generators can be connected in series to increase the output voltage or in parallel to increase the output current, in order to meet the specific voltage and current requirements of subsequent circuits.

[0069] The energy management module is composed of the following components: an energy storage unit, a voltage conversion circuit, and a voltage monitoring circuit. The energy storage unit uses a high-capacity rechargeable HPC battery, which offers a longer cycle life and a wider operating temperature range compared to ordinary lithium batteries, making it particularly suitable for extreme industrial environments. The energy storage unit receives the first voltage signal from the thermoelectric generator module for charging and outputs a storage voltage signal. The voltage conversion circuit uses a power management chip specifically designed for energy harvesting. This chip can efficiently boost or buck the low-voltage and fluctuating first voltage signal generated by the thermoelectric element. Based on the storage voltage signal, the voltage conversion circuit generates a stable operating voltage signal using DC-DC conversion technology, providing a stable 3.3V and 5V dual-channel operating voltage for the subsequent multi-parameter intelligent monitoring module and 5G IoT communication module. The voltage monitoring circuit monitors the storage voltage signal output by the energy storage unit in real time, comparing it with a preset start-up threshold to generate an operating status signal. When the energy storage voltage signal is lower than the start-up threshold (e.g., 3.7V), the voltage monitoring circuit outputs a sleep control signal to the main control unit, causing the system to enter a low-power sleep state. At this time, the thermoelectric generator continues to charge the energy storage unit. When the energy storage voltage signal reaches or exceeds the start-up threshold, the system automatically wakes up and operates normally.

[0070] The multi-parameter intelligent monitoring module consists of two main parts: a sensor group and a main control unit.

[0071] The sensor array, powered by the operating voltage signal provided by the energy management module, includes a laser displacement sensor, a strain gauge pressure sensor, and a temperature sensor. Specifically, the laser displacement sensor comprises three independent laser ranging units: the first laser displacement sensor is installed at a specific angle and illuminates a first measuring point on the pipe surface. It calculates the distance by emitting a laser beam and receiving the reflected light, generating a first displacement component signal representing the displacement change in that direction; the second laser displacement sensor illuminates a second measuring point on the pipe surface and generates a second displacement component signal based on the reflected light distance measurement; the third laser displacement sensor illuminates a third measuring point on the pipe surface and generates a third displacement component signal based on the reflected light distance measurement. The three laser displacement sensors are arranged at a certain spatial angle. Using the principle of triangulation, the three-dimensional spatial displacement vector of the pipe relative to the support reference point can be calculated. The first, second, and third displacement component signals together constitute the original displacement signal. The strain gauge pressure sensor includes two independent strain detection channels: the first strain gauge pressure sensor, using a full-bridge strain gauge configuration, is attached to the first load-bearing position of the support (e.g., the upper root of the spring hanger). The minute strain change generated at this position under stress is converted into a first pressure component signal via a Wheatstone bridge. The second strain gauge pressure sensor is attached to the second load-bearing position of the support (e.g., the connection between the support base and the structural component). The strain change at this position generates a second pressure component signal. The first and second pressure component signals together constitute the original pressure signal, used to reflect the actual load magnitude and load distribution of the support. The temperature sensor uses a high-precision digital temperature detection chip (e.g., the MAX6675 model), which detects the internal ambient temperature or the local temperature of the support via a thermistor or thermocouple, and outputs the original temperature signal.

[0072] The main control unit employs an ultra-low-power microcontroller based on the ARM Cortex-M3 core. The main control unit includes a signal conditioning unit, an analog-to-digital converter (ADC), and a threshold comparison unit. The signal conditioning unit receives the raw displacement, pressure, and temperature signals output from the sensor array. It amplifies the weak sensor signals using a preamplifier circuit and then filters out high-frequency interference and noise from the industrial environment using a multi-stage active filter circuit, generating a conditioned signal with a higher signal-to-noise ratio. The ADC incorporates a high-precision multi-channel ADC, performing synchronous analog-to-digital conversion based on the conditioned signal at a preset sampling frequency (100Hz in this embodiment), converting the analog signal into a digital quantity to generate digital monitoring data containing displacement, pressure, and temperature data. The threshold comparison unit reads preset threshold values ​​from the main control unit's non-volatile memory, including a displacement safety threshold (e.g., ±15mm), a pressure change rate threshold (e.g., 10% / second), and a temperature anomaly threshold. The threshold comparison unit compares the digital monitoring data with the corresponding preset thresholds one by one. When any of the displacement data, pressure data, or temperature data exceeds its corresponding threshold, the threshold comparison unit determines that the system is in an abnormal state and generates an abnormal state determination signal. When all monitoring data are within the safe range, a normal state determination signal is generated.

[0073] The 5G IoT communication module includes a transmission mode selection unit, a data encapsulation unit, and a wireless transmission unit.

[0074] The transmission mode selection unit receives the status determination signal output by the main control unit. When the status determination signal is a normal status determination signal, the transmission mode selection unit selects the periodic transmission mode, in which the system employs a low-power, timed reporting strategy. When the status determination signal is an abnormal status determination signal, the transmission mode selection unit immediately switches to the real-time transmission mode, in which the system initiates high-bandwidth, high-speed continuous data transmission. The transmission mode selection unit outputs the corresponding mode control signal to the data encapsulation unit.

[0075] The data encapsulation unit processes the digital monitoring data differently based on the received mode control signal. When the mode control signal indicates periodic transmission mode, the data encapsulation unit performs statistical analysis on a large amount of digital monitoring data collected within a time window (e.g., 5 minutes), calculates the average, maximum, and minimum values ​​of each parameter, and then compresses the feature data using a data compression algorithm (e.g., differential coding or Huffman coding). Finally, it encapsulates the data according to the MQTT protocol standard to generate a small compressed data packet. When the mode control signal indicates real-time transmission mode, the data encapsulation unit maintains the original sampling format and complete timing information of the digital monitoring data, does not perform compression processing, and directly encapsulates the data according to the TCP / IP protocol format to generate an original data packet containing all original high-frequency sampling points.

[0076] The wireless transmission unit employs a commercial IoT communication module supporting 5G NR or 5G NR Light standards. In periodic transmission mode, the wireless transmission unit remains in deep sleep (power consumption below 10 microamps) most of the time, only being woken up by the main control unit via a GPIO interrupt signal at set time intervals (e.g., every 5 minutes). Upon waking, the wireless transmission unit establishes a network connection with the cloud platform, transmitting compressed data packets to the cloud IoT platform server via a wireless channel based on the 5G network protocol. After data transmission is complete, it re-enters sleep mode. In real-time transmission mode, the wireless transmission unit is forced to remain in the RRC Connected state, establishing a continuous high-speed data transmission channel to continuously stream raw data packets to the cloud platform. After each data transmission, the wireless transmission unit generates monitoring status information, including data transmission status, signal strength, and packet loss rate, based on the transmission result (success or failure) and network signal quality parameters. This information is fed back to the main control unit for system self-diagnosis and communication quality assessment.

[0077] The workflow of the entire system is as follows Figure 2 As shown: After system installation, the thermoelectric generator module begins continuous operation to charge the energy storage unit. Once the energy storage voltage reaches the startup threshold, the system completes self-testing and initialization, entering the normal operating cycle. During this cycle, the sensor array synchronously collects six data streams at a high frequency of 100Hz, and the main control unit processes and judges the data status in real time. Based on the judgment result, the 5G communication module automatically switches between low-power periodic reporting mode and high-speed real-time push mode. The two operating modes are seamlessly connected through an interrupt mechanism, ensuring both long-term battery life requirements and real-time alarm capabilities in abnormal situations.

[0078] Example 2:

[0079] This embodiment provides a smart pipeline support and hanger monitoring method based on the system described in Embodiment 1. The specific steps are as follows:

[0080] Step S1: The thermoelectric power generation module generates a first voltage signal based on the temperature difference between the pipe surface and the ambient temperature. During normal operation of the pipe, the high-temperature medium being transported keeps the pipe surface at a stable high temperature. The hot end of the thermoelectric conversion unit of the thermoelectric power generation module is tightly thermally coupled to the pipe surface through a heat-conducting substrate, continuously collecting the first temperature of the pipe surface; the heat dissipation fins mounted on the cold end exchange heat with the surrounding ambient air, continuously collecting the second ambient temperature. The semiconductor thermoelectric material inside the thermoelectric conversion unit forms a temperature gradient between the hot and cold ends. According to the Seebeck effect, the temperature gradient drives the directional movement of charge carriers, generating a potential difference across the thermoelectric conversion unit, thereby outputting the first voltage signal. The magnitude of the first voltage signal is directly proportional to the difference between the first and second temperatures; the greater the temperature difference, the higher the amplitude of the output first voltage signal.

[0081] Step S2: The energy management module stores energy based on the first voltage signal and outputs a working voltage signal. The energy storage unit in the energy management module continuously receives the first voltage signal output by the thermoelectric generator module for charging. In the initial charging stage, when the energy storage voltage signal of the energy storage unit is lower than the start-up threshold, the voltage monitoring circuit outputs a sleep control signal, and the system temporarily enters standby mode, maintaining only the energy storage function. As charging progresses, the energy storage voltage signal gradually rises. When the energy storage voltage signal reaches or exceeds the start-up threshold, the voltage monitoring circuit switches its working state signal, allowing the system to start normally. The voltage conversion circuit begins to work, extracting electrical energy from the energy storage unit and converting the fluctuating energy storage voltage signal into a stable working voltage signal through a boost or buck DC-DC converter circuit. The working voltage signal includes two outputs: 3.3V and 5V, which power electronic modules of different power consumption levels, ensuring the stable operation of the entire monitoring system.

[0082] Step S3: The multi-parameter intelligent monitoring module, powered by the operating voltage signal, collects the original displacement, pressure, and temperature signals of the support and hanger. Based on the collected signals, it generates digital monitoring data and, based on the comparison of the digital monitoring data with preset thresholds, generates a status judgment signal. This step specifically includes the following sub-steps:

[0083] Step S31: The sensor group begins normal operation based on the working voltage signal. The three laser ranging units in the laser displacement sensor emit laser beams to three measuring points on the pipe surface, receive the reflected laser signals, and measure the distance change from each measuring point to the sensor by calculating the laser flight time or phase difference. The three laser displacement sensors output the first displacement component signal, the second displacement component signal, and the third displacement component signal, respectively. The three component signals combine to form the original displacement signal reflecting the three-dimensional spatial displacement of the pipe. The two strain gauge pressure sensors monitor the strain changes at different load positions of the support. When the support is under stress, the strain gauges attached to the load-bearing structure undergo slight geometric deformation, causing a change in their resistance value. This resistance change is converted into a voltage signal by a Wheatstone bridge circuit. The two strain gauge pressure sensors output the first pressure component signal and the second pressure component signal, respectively, which together constitute the original pressure signal reflecting the load state of the support. The temperature sensor continuously detects temperature changes at the monitoring points through its built-in temperature-sensitive element and outputs the original temperature signal corresponding to the temperature value through the temperature detection circuit.

[0084] Step S32: The signal conditioning unit in the main control unit receives the raw displacement signal, raw pressure signal, and raw temperature signal output from the sensor group. The signal conditioning unit first amplifies the signals from each sensor channel using a programmable gain amplifier, amplifying the weak millivolt or microvolt signals to a voltage range suitable for subsequent processing. Then, it filters the amplified signals using a multi-stage active filter circuit to remove 50Hz power frequency interference from the industrial environment, electromagnetic radiation noise, and the high-frequency noise from the sensors themselves, generating a conditioned signal with improved signal-to-noise ratio.

[0085] Step S33: The analog-to-digital conversion unit in the main control unit performs analog-to-digital conversion based on the conditioned signal. The multi-channel high-precision ADC converter within the ADC unit synchronously samples and quantizes six conditioned signals (three displacement signals, two pressure signals, and one temperature signal) according to a 100Hz sampling frequency set by the main control unit. The ADC converter converts the continuously changing analog voltage signal into discrete digital quantities, generating 100 sampling points per second for each channel. After analog-to-digital conversion, digital monitoring data containing timestamps, channel identifiers, and numerical information is generated. The digital monitoring data is stored in the buffer memory of the main control unit in the form of data frames.

[0086] Step S34: The threshold comparison unit in the main control unit reads the pre-configured preset threshold parameters from the memory. The threshold comparison unit checks each item of the digital monitoring data in the buffer memory, comparing the three component values ​​of the displacement data with the displacement threshold, comparing the two channel values ​​of the pressure data and their rate of change with the pressure threshold and the pressure rate of change threshold, and comparing the temperature data with the temperature anomaly threshold. During the comparison process, the threshold comparison unit uses a sliding window algorithm to continuously monitor the data trends of multiple sampling points. When any parameter value in the digital monitoring data exceeds its corresponding preset threshold, or when multiple consecutive sampling points (e.g., three consecutive points) are close to the threshold boundary, the threshold comparison unit determines that the current support status has an abnormal risk, generates an abnormal status judgment signal, and sets the corresponding alarm flag. When the values ​​of all monitored parameters are within their respective safety threshold ranges and the data change trend is stable, the threshold comparison unit determines that the support status is normal and generates a normal status judgment signal.

[0087] Step S4: The 5G IoT communication module selects the transmission mode based on the status determination signal, processes the digital monitoring data to generate data packets based on the transmission mode, transmits the data packets based on the 5G network, and outputs the monitoring status information.

[0088] The transmission mode selection unit receives the status judgment signal output by the main control unit. When a normal status judgment signal is received, the transmission mode selection unit selects the periodic transmission mode and outputs the corresponding mode control signal. In periodic transmission mode, the data encapsulation unit performs statistical processing on the accumulated digital monitoring data within a reporting period (e.g., 5 minutes), extracts representative feature parameters (such as average, peak, and standard deviation), and then performs data compression encoding on the feature parameters, encapsulating them according to the lightweight MQTT IoT protocol format to generate a small compressed data packet. The wireless transmission unit wakes up from sleep mode under timer triggering, establishes a connection with the cloud platform via the 5G network, uploads the compressed data packet to the cloud platform server, disconnects after transmission and returns to sleep mode to save energy to the greatest extent.

[0089] When the transmission mode selection unit receives an abnormal state judgment signal, it immediately selects the real-time transmission mode and outputs the corresponding mode control signal. In real-time transmission mode, the data encapsulation unit no longer performs data compression and statistical processing, but maintains the complete original format of the digital monitoring data, encapsulating all 100Hz sampling rate raw data points into raw data packets in chronological order. The wireless transmission unit is forcibly awakened by the main control unit and remains in an active connection state, establishing a high-bandwidth TCP streaming transmission channel through the 5G network to push the raw data packets to the cloud platform in real time as a continuous data stream. After receiving the real-time data stream, the cloud platform server immediately triggers the alarm mechanism of the monitoring center, issuing alarm information to maintenance personnel through audible and visual alarm devices, SMS notifications, or mobile APP push notifications.

[0090] The wireless transmission unit continuously monitors the network communication status during data transmission, recording communication quality parameters such as the transmission result of each data packet, network latency, RSSI signal strength, and packet loss rate. Based on these parameters, the wireless transmission unit generates detailed monitoring status information, including transmission success or failure status, network coverage quality assessment, and cumulative data transmission volume. This monitoring status information is fed back to the main control unit for system self-diagnosis and communication link optimization. When communication anomalies occur, the main control unit can control the wireless transmission unit to attempt switching to a backup network or adjust the transmission power to ensure data transmission reliability.

[0091] Through the cyclical execution of steps S1 to S4, the entire monitoring system achieves fully automated operation from energy harvesting, data acquisition, intelligent judgment to data transmission. The system operates 24 / 7 during pipeline operation, requiring no manual intervention or external power supply, truly realizing permanent online intelligent monitoring of pipeline support and hanger status.

[0092] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from the spirit and scope of this invention.

Claims

1. A smart pipeline support and hanger monitoring system based on thermoelectric power generation, characterized in that, include: Thermoelectric power generation module generates a first voltage signal based on the difference between the surface temperature of the pipe and the ambient temperature; The energy management module stores energy based on the first voltage signal and outputs a working voltage signal; The multi-parameter intelligent monitoring module is powered by the working voltage signal, collects the original displacement signal, original pressure signal and original temperature signal of the support and hanger, generates digital monitoring data based on the original displacement signal, original pressure signal and original temperature signal, and generates a status judgment signal based on the comparison result of the digital monitoring data and the preset threshold. The 5G IoT communication module selects the transmission mode based on the status determination signal, processes the digital monitoring data to generate data packets based on the transmission mode, transmits the data packets based on the 5G network, and outputs the monitoring status information.

2. The intelligent pipeline support and hanger monitoring system based on thermoelectric power generation according to claim 1, characterized in that, Thermoelectric power generation module includes: The thermoelectric conversion unit has a hot end that is attached to the surface of the pipe to collect a first temperature, and a cold end that is exposed to the ambient air to collect a second temperature. Based on the difference between the first temperature and the second temperature, a first voltage signal is generated through the Seebeck effect. A heat-conducting substrate is disposed between the surface of the pipe and the hot end to conduct heat from the surface of the pipe to the hot end; Heat dissipation fins, connected to the cold end, dissipate heat from the cold end to the ambient air.

3. The intelligent pipeline support and hanger monitoring system based on thermoelectric power generation according to claim 1, characterized in that, The energy management module includes: The energy storage unit is charged based on the first voltage signal and outputs an energy storage voltage signal; The voltage conversion circuit generates a stable working voltage signal by boosting or bucking the energy storage voltage signal. The voltage monitoring circuit generates a working status signal based on the comparison between the energy storage voltage signal and the start-up threshold. When the energy storage voltage signal is lower than the start-up threshold, it outputs a sleep control signal.

4. The intelligent pipeline support and hanger monitoring system based on thermoelectric power generation according to claim 1, characterized in that, The multi-parameter intelligent monitoring module includes: The sensor group, powered by the operating voltage signal, includes a laser displacement sensor that outputs the original displacement signal, a strain gauge pressure sensor that outputs the original pressure signal, and a temperature sensor that outputs the original temperature signal. The main control unit performs analog-to-digital conversion based on the original displacement signal, original pressure signal, and original temperature signal to generate digital monitoring data. It then compares the digital monitoring data with the stored preset thresholds to generate a status determination signal.

5. The intelligent pipeline support and hanger monitoring system based on thermoelectric power generation according to claim 4, characterized in that, Laser displacement sensors include: The first laser displacement sensor illuminates the first measuring point on the pipe surface and generates a first displacement component signal based on the reflected light distance measurement. The second laser displacement sensor illuminates the second measuring point on the pipe surface and generates a second displacement component signal based on the reflected light distance measurement. The third laser displacement sensor illuminates the third measuring point on the pipe surface and generates a third displacement component signal based on the reflected light distance measurement. The first, second, and third displacement components together constitute the original displacement signal.

6. The intelligent pipeline support and hanger monitoring system based on thermoelectric power generation according to claim 4, characterized in that, Strain gauge pressure sensors include: The first strain gauge pressure sensor is attached to the first bearing position of the support and hanger, and generates a first pressure component signal based on the strain change at that position; The second strain gauge pressure sensor is attached to the second bearing position of the support and generates a second pressure component signal based on the strain change at that position. The first pressure component signal and the second pressure component signal together constitute the original pressure signal.

7. The intelligent pipeline support and hanger monitoring system based on thermoelectric power generation according to claim 4, characterized in that, The main control unit includes: The signal conditioning unit amplifies and filters the original displacement signal, original pressure signal, and original temperature signal to generate a conditioned signal. The analog-to-digital conversion unit performs analog-to-digital conversion on the conditioned signal to generate digital monitoring data; The threshold comparison unit generates an abnormal state judgment signal when any data exceeds the corresponding threshold, based on the comparison results of digital monitoring data with the corresponding preset thresholds; otherwise, it generates a normal state judgment signal.

8. The intelligent pipeline support and hanger monitoring system based on thermoelectric power generation according to claim 1, characterized in that, The 5G IoT communication module includes: The transmission mode selection unit selects the periodic transmission mode when the status determination signal is normal and selects the real-time transmission mode when the status determination signal is abnormal, and outputs a mode control signal. The data encapsulation unit compresses and encapsulates digital monitoring data to generate compressed data packets when the mode control signal is in periodic transmission mode, and maintains the original format of digital monitoring data to generate original data packets when the mode control signal is in real-time transmission mode. The wireless transmission unit sends compressed or raw data packets to the cloud platform based on the 5G network protocol, and generates monitoring status information containing the transmission status based on the transmission results.

9. A method for monitoring intelligent pipeline supports and hangers based on thermoelectric self-powered generation according to the system of claim 1, characterized in that, include: S1: The thermoelectric power generation module generates a first voltage signal based on the difference between the pipe surface temperature and the ambient temperature; S2: The energy management module stores energy based on the first voltage signal and outputs a working voltage signal; S3: The multi-parameter intelligent monitoring module is powered by the working voltage signal, collects the original displacement signal, original pressure signal and original temperature signal of the support and hanger, generates digital monitoring data based on the collected signals, and generates a status judgment signal based on the comparison result of the digital monitoring data and the preset threshold. S4: The 5G IoT communication module selects the transmission mode based on the status determination signal, processes the digital monitoring data to generate data packets based on the transmission mode, transmits the data packets based on the 5G network, and outputs the monitoring status information.

10. The intelligent pipeline support and hanger monitoring method based on thermoelectric power generation according to claim 9, characterized in that, S3 include: S31: The sensor group is powered by the working voltage signal. The laser displacement sensor outputs the original displacement signal through distance measurement, the strain gauge pressure sensor outputs the original pressure signal through strain detection, and the temperature sensor outputs the original temperature signal through temperature detection. S32: The main control unit amplifies and filters the original displacement signal, original pressure signal and original temperature signal to generate a conditioned signal; S33: Perform analog-to-digital conversion based on the conditioned signal to generate digital monitoring data; S34: Based on the comparison of digital monitoring data with corresponding preset thresholds one by one, an abnormal state judgment signal is generated when any data exceeds the threshold, otherwise a normal state judgment signal is generated.

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